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Addition and Subtraction

These activities are part of our Primary collections , which are problems grouped by topic.

Pairs of Numbers

If you have ten counters numbered 1 to 10, how many can you put into pairs that add to 10? Which ones do you have to leave out? Why?

Butterfly Flowers

Can you find two butterflies to go on each flower so that the numbers on each pair of butterflies adds to the number on their flower?

Subtraction Slip

Can you spot the mistake in this video? How would you work out the answer to this calculation?

Number Lines

A resource to try once children are familiar with number lines, and they have begun to use them for addition. It could be a good way to talk about subtraction. Leah and Tom each have a number line. Can you work out where their counters will land?

The Add and Take-away Path

Two children made up a game as they walked along the garden paths. Can you find out their scores? Can you find some paths of your own?

Equivalent Pairs

Can you match pairs of cards which show the same amount?

Two Spinners

What two-digit numbers can you make with these two dice? What can't you make?

Cuisenaire Counting

Here are some rods that are different colours. How could I make a yellow rod using white and red rods?

Find all the numbers that can be made by adding the dots on two dice.

What Could It Be?

In this calculation, the box represents a missing digit. What could the digit be? What would the solution be in each case?

What's in a Name?

What do you notice about these squares of numbers? What is the same? What is different?

Sort Them Out (1)

Can you each work out the number on your card? What do you notice? How could you sort the cards?

Unit Differences

This challenge is about finding the difference between numbers which have the same tens digit.

Number Balance

Can you hang weights in the right place to make the the number balance balanced?

This project challenges you to work out the number of cubes hidden under a cloth. What questions would you like to ask?

Domino Sorting

Try grouping the dominoes in the ways described. Are there any left over each time? Can you explain why?

What Was in the Box?

This big box adds something to any number that goes into it. If you know the numbers that come out, what addition might be going on in the box?

One Big Triangle

Make one big triangle so the numbers that touch on the small triangles add to 10.

Arranging Additions and Sorting Subtractions

Order these four calculations from easiest to hardest. How did you decide?

Doing and Undoing

An investigation looking at doing and undoing mathematical operations focusing on doubling, halving, adding and subtracting.

How Do You See It?

Here are some short problems for you to try. Talk to your friends about how you work them out.

What could the half time scores have been in these Olympic hockey matches?

Strike it Out

Use your addition and subtraction skills, combined with some strategic thinking, to beat your partner at this game.

Poly Plug Rectangles

The computer has made a rectangle and will tell you the number of spots it uses in total. Can you find out where the rectangle is?

Dicey Addition

Who said that adding couldn't be fun?

Sitting Round the Party Tables

Sweets are given out to party-goers in a particular way. Investigate the total number of sweets received by people sitting in different positions.

Cuisenaire Environment

An environment which simulates working with Cuisenaire rods.

Secret Number

Annie and Ben are playing a game with a calculator. What was Annie's secret number?

Birthday Cakes

Jack's mum bought some candles to use on his birthday cakes and when his sister was born, she used them on her cakes too. Can you use the information to find out when Kate was born?

Find the Difference

Place the numbers 1 to 6 in the circles so that each number is the difference between the two numbers just below it.

Noah saw 12 legs walk by into the Ark. How many creatures did he see?

Heads and Feet

On a farm there were some hens and sheep. Altogether there were 8 heads and 22 feet. How many hens were there?

Jumping Squares

In this problem it is not the squares that jump, you do the jumping! The idea is to go round the track in as few jumps as possible.

Ladybirds in the Garden

In Sam and Jill's garden there are two sorts of ladybirds with 7 spots or 4 spots. What numbers of total spots can you make?

Eggs in Baskets

There are three baskets, a brown one, a red one and a pink one, holding a total of 10 eggs. How many eggs are in each basket?

The Brown Family

Use the information about Sally and her brother to find out how many children there are in the Brown family.

The Tall Tower

As you come down the ladders of the Tall Tower you collect useful spells. Which way should you go to collect the most spells?

Using the cards 2, 4, 6, 8, +, - and =, what number statements can you make?

Number Round Up

Arrange the numbers 1 to 6 in each set of circles below. The sum of each side of the triangle should equal the number in its centre.

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Years 5 & 6: Addition and subtraction

This list consists of visual resources, activities and games designed to support the new curriculum programme of study in Years Five and Six. Containing tips on using the resources  and suggestions for further use, it covers:

Year 5:  Add and subtract whole numbers with more than 4 digits, including formal written methods, add and subtract numbers mentally with increasingly large numbers, use rounding to check answers and determine levels of accuracy, solve addition and subtraction multi-step problems in contexts.

Year 6:  Perform mental calculations, including with mixed operations and large numbers, use knowledge of the order of operations to carry out calculations involving the four operations, solve addition and subtraction multi-step problems in contexts, solve problems involving addition, subtraction, multiplication and division, use estimation to check answers to calculations and determine, in the context of a problem, an appropriate degree of accuracy.

Visit the primary mathematics webpage to access all lists.

Column subtraction using place value counters

This interactive resource is a great way of helping children understand the process of column subtraction. Three digit numbers are partitioned and place value counters are used before carrying out the column subtraction. This is a useful step when moving children towards a more formal written method for subtraction. Make your own place value counters and use them in class. A great aid for children struggling with column subtraction or for the whole class, dependent on specific class needs.

There are examples of expanded subtraction and column subtraction both with and without borrowing.

Developing a Classroom Culture That Supports a Problem-solving Approach to Mathematics

This article from NRICH discusses ways in which teachers may develop children's problem solving skills. It provides ideas and links which would benefit a teacher's own practice or could be used as a basis of a staff training session.

Here are nine challenges from NRICH which support Addition and Subtraction at KS2 .

Quality Assured Category: Mathematics Publisher: SMILE

Addition pack one  contains fifteen work cards with activities on simple counting, number bonds to ten, addition using money and adding two digit numbers.

Addition pack two  contains eleven work cards with slightly more challenging activities. Students are required to add two digit numbers which require a digit to be carried, know number bonds up to a hundred, to find multiples of ten and to be able to use a calculator to solve more challenging problems.

Addition pack three  contains nine work cards in which the degree of challenge is greater. Students are required to add simple decimals, solve more challenging puzzles and add larger decimal numbers using money as the context.

nrich problem solving subtraction

Subtraction

This resource contains one pack of games, investigations, worksheets and practical activities supporting the teaching and learning of subtraction.

The six work cards provide activities covering subtracting two digit numbers using physical apparatus, and using the column method.

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Year 5 Column Subtraction Worksheets (differentiated) and Other Resources

Year 5 Column Subtraction Worksheets (differentiated) and Other Resources

Subject: Mathematics

Age range: 7-11

Resource type: Lesson (complete)

STS

Last updated

30 May 2022

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pptx, 1.03 MB

Resources on Column Subtraction for Year 5:

  • Year 5 column subtraction worksheets (differentiated to 4 levels and with the answers)
  • a link to an nrich problem-solving activity involving missing numbers in a column addition calculation (this is different to the higher level worksheet questions)
  • a PowerPoint presentation
  • a page with the steps for column subtraction
  • success criteria

There is a PDF version and an editable version of each file.

Click here to see how people have rated other resources that we have on TES.

Other Year 5 Maths resources: https://www.tes.com/teaching-resource/-12160846 - Numbers in Words https://www.tes.com/teaching-resource/-12160851 - Place Value (Whole Numbers) https://www.tes.com/teaching-resource/-12160854 - Ordering Whole Numbers https://www.tes.com/teaching-resource/-12160859 - Rounding Whole Numbers https://www.tes.com/teaching-resource/-12160861 – Place Value (Decimals) https://www.tes.com/teaching-resource/-12161134 – Complements to 1 https://www.tes.com/teaching-resource/-12163410 – Comparing Decimals and Fractions https://www.tes.com/teaching-resource/-12163418 – Decimal Sequences https://www.tes.com/teaching-resource/-12163429 – Rounding Decimals https://www.tes.com/teaching-resource/-12163439 – Comparing and Ordering Decimals https://www.tes.com/teaching-resource/-12163443 – Multiply and Divide by 10, 100 & 1,000 https://www.tes.com/teaching-resource/-12163455 – Column Addition https://www.tes.com/teaching-resource/-12163587 – Column Subtraction https://www.tes.com/teaching-resource/-12163589 – Column Addition and Subtraction https://www.tes.com/teaching-resource/-12163592 – Add and Subtract By Partitioning https://www.tes.com/teaching-resource/-12163597 – Negative Numbers https://www.tes.com/teaching-resource/-12163601 – Addition and Subtraction Word Problems https://www.tes.com/teaching-resource/-12163606 – Short Multiplication https://www.tes.com/teaching-resource/-12163614 – Square and Cubed Numbers https://www.tes.com/teaching-resource/-12163616 – Short Division

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The Joint Mathematical Council of the United Kingdom

Addressing the five ‘big questions’ in problem-solving with NRICH

nrich problem solving subtraction

The importance of ensuring learners acquire the problem-solving skills which will enable them to thrive both socially and economically in their increasingly automated world is widely recognised (Luckin et al., 2017). Nevertheless, government inspectors have reported serious concerns about the quality and quantity of problem-solving in our schools (Ofsted, 2015). This summer schools were challenged to reflect on ‘Five big questions for problem-solving’ (EEF, 2021). In this blog, we will consider each of those five questions and explore the ways that the NRICH team is supporting schools to address them.

Question one: Do teachers in your school select genuine problem-solving tasks for which pupils do not already have a ready-made method available?

Too often, learners are presented with routine word problems which merely require the application of a known algorithm. ‘Genuine’ problems enable them to make their own problem-solving decisions by choosing their own strategies and enabling them to compare their approach with those of other learners, thus developing their problem-solving efficiency and flexibility. At NRICH , our award-winning activities allow learners to develop these key skills alongside the confidence to tackle genuine problems. Moreover, our ‘ low threshold, high ceiling ‘ approach enables everyone to get started on the problem while ensuring a suitable level of challenge too, making them ideal for whole-class teaching.

Question two: Are pupils given the opportunity to see – through multiple worked examples – to use, and to compare different approaches to solving a problem?

Many problems can be explored in more than one way. Working flexibly, making connections between different areas of the curriculum and reflecting on various problem-solving approaches are key steps towards becoming a more fluent mathematician. NRICH encourages learners to develop these skills in these two ways:

Our primary , secondary and post-16 Live Problems invite learners to explore and submit their ideas to the team. We review each submission that we receive and publish a selection on our website showcasing different approaches and the reasoning behind them.

Our NRIC H online activities sometimes feature ‘hide and reveal’ buttons showcasing different starting points towards a solution for learners to explore further for themselves. This approach enables learners to widen their range of strategies for solving unfamiliar problems and develop alternative approaches to explore when they get stuck using their first-choice strategy.

Question three: Are pupils encouraged to use visual representations to support them to solve a problem?

One of the most important approaches towards solving an unfamiliar problem is drawing a good diagram. Learning to draw diagrams is a skill which we encourage learners of all ages to develop alongside their other mathematical skills and knowledge. From sketching graphs to drawing a bar model, good diagrams can help learners clarify their understanding and identify possible ways forward.

Our four steps towards problem-solving feature highlights the importance of drawing a diagram to enable young learners to get started on a problem. We often highlight a useful diagram, table or sketch graph in the solutions chosen for publication. As learners progress through their learning, the team model more specific drawing skills, such as sketching a graph to help solve a STEP problem.

Question four: Are pupils supported to monitor, reflect on, and communicate their reasoning and choice of strategies, possibly through the use of prompt questions?

NRICH  encourages learners to reflect on their learning using this approach inspired by the Strands of Mathematical Proficiency model introduced by Kilpatrick et al. (2001).

nrich problem solving subtraction

Our approach uses child-friendly language that teachers and parents can share with students five key ingredients that characterise successful mathematicians. At NRICH , we believe that learning mathematics is about much more than just learning topics and routines. Successful mathematicians understand the curriculum content and are fluent in mathematical skills and procedures, but they can also solve problems, explain their thinking and have a positive attitude about themselves as learners of mathematics.

With this in mind, we have created  this self assessment tool  to help learners recognise where their mathematical strengths and weaknesses lie. We hope learners will explore NRICH activities and then take time to reflect on their own mathematical capabilities using our model.

Question 5: Is professional development time allocated to develop teachers’ pedagogical understanding of problem-solving, with particular support for early career teachers?

NRICH supports teachers to maximise the potential of our activities by offering free, regular professional development for teachers .  Each session is delivered online, enabling teachers to access the support wherever they are based, reducing teacher travel and cover costs for schools. We also record the sessions and upload them to our website so that schools can access them for future professional development days or staff/department meetings in their settings.

The live sessions are led by NRICH team members and they link directly to our latest primary , secondary and post-16 Live Problems. This approach enables teachers to consider the possibilities of the activities with the NRICH team before exploring them the next day with their own classes. Later, they are invited to share their classwork with our team for possible publication on the NRICH website.

The five ‘big questions’ provide excellent starting points for evaluating the teaching and learning of problem-solving in different settings. I hope that this blog shares an insight into the different ways that NRICH can support schools to address the five questions for themselves by engaging with our activities, Live Problems and teacher webinars.

Dr Ems Lord FCCT

Director of NRICH

Centre for Mathematical Sciences

University of Cambridge

Selected references

EEF. (2021). EEF Blog: Integrating evidence into maths teaching – guiding problem-solving. Accessed from https://educationendowmentfoundation.org.uk/news/eef-blog-integrating-evidence-into-mathematics-guiding-problem-solving /

Kilpatrick, J. Swafford, J., & Findell, B. (2001). Adding it up: Helping children learn mathematics (Vol. 2101). J. Kilpatrick, & National research council (Eds.). Washington, DC: National Academy Press.

Luckin, R., Baines, E., Cukurova, M., Holmes, W., & Mann, M. (2017). Solved! Making the case for collaborative problem-solving. Accessed from http://oro.open.ac.uk/50105/1/solved-making-case-collaborative-problem-solving.pdf

Ofsted. (2015). Better Maths Conference Spring Keynote 2015. Accessed here https://www.slideshare.net/Ofstednews/better-mathematics-keynote-spring-2015

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nrich problem solving subtraction

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nrich problem solving subtraction

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  • Coordinates, Functions and Graphs

Geometry and measure

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Resources tagged with: Addition and subtraction

There are 227 results.

nrich problem solving subtraction

Pairs of Numbers

If you have ten counters numbered 1 to 10, how many can you put into pairs that add to 10? Which ones do you have to leave out? Why?

nrich problem solving subtraction

Follow the Numbers

What happens when you add the digits of a number then multiply the result by 2 and you keep doing this? You could try for different numbers and different rules.

nrich problem solving subtraction

How Do You Do It?

This group activity will encourage you to share calculation strategies and to think about which strategy might be the most efficient.

nrich problem solving subtraction

Even and Odd

This activity is best done with a whole class or in a large group. Can you match the cards? What happens when you add pairs of the numbers together?

nrich problem solving subtraction

Sort Them Out (1)

Can you each work out the number on your card? What do you notice? How could you sort the cards?

nrich problem solving subtraction

It Was 2010!

If the answer's 2010, what could the question be?

nrich problem solving subtraction

Which Symbol?

Choose a symbol to put into the number sentence.

nrich problem solving subtraction

Nice or Nasty

There are nasty versions of this dice game but we'll start with the nice ones...

nrich problem solving subtraction

Here is a chance to play a version of the classic Countdown Game.

nrich problem solving subtraction

Can you put the numbers 1-5 in the V shape so that both 'arms' have the same total?

nrich problem solving subtraction

Shut the Box

An old game but lots of arithmetic!

nrich problem solving subtraction

A group of children are using measuring cylinders but they lose the labels. Can you help relabel them?

nrich problem solving subtraction

How Much Did it Cost?

Use your logical-thinking skills to deduce how much Dan's crisps and ice-cream cost altogether.

nrich problem solving subtraction

The picture shows a lighthouse and some underwater creatures. Can you work out the distances between some of the different creatures?

nrich problem solving subtraction

Tug Harder!

In this game, you can add, subtract, multiply or divide the numbers on the dice. Which will you do so that you get to the end of the number line first?

nrich problem solving subtraction

Can you use the numbers on the dice to reach your end of the number line before your partner beats you?

nrich problem solving subtraction

First Connect Three

Add or subtract the two numbers on the spinners and try to complete a row of three. Are there some numbers that are good to aim for?

nrich problem solving subtraction

Caterpillars

These caterpillars have 16 parts. What different shapes do they make if each part lies in the small squares of a 4 by 4 square?

nrich problem solving subtraction

Making Longer, Making Shorter

Ahmed is making rods using different numbers of cubes. Which rod is twice the length of his first rod?

nrich problem solving subtraction

Domino Pick

Are these domino games fair? Can you explain why or why not?

nrich problem solving subtraction

The Twelve Pointed Star Game

Have a go at this game which involves throwing two dice and adding their totals. Where should you place your counters to be more likely to win?

nrich problem solving subtraction

Diagonal Sums

In this 100 square, look at the green square which contains the numbers 2, 3, 12 and 13. What is the sum of the numbers that are diagonally opposite each other? What do you notice?

nrich problem solving subtraction

Number Differences

Place the numbers from 1 to 9 in the squares below so that the difference between joined squares is odd. How many different ways can you do this?

nrich problem solving subtraction

Ring a Ring of Numbers

Choose four of the numbers from 1 to 9 to put in the squares so that the differences between joined squares are odd.

nrich problem solving subtraction

Carrying Cards

These sixteen children are standing in four lines of four, one behind the other. They are each holding a card with a number on it. Can you work out the missing numbers?

nrich problem solving subtraction

Robot Monsters

Use these head, body and leg pieces to make Robot Monsters which are different heights.

nrich problem solving subtraction

A Mixed-up Clock

There is a clock-face where the numbers have become all mixed up. Can you find out where all the numbers have got to from these ten statements?

nrich problem solving subtraction

The Deca Tree

Find out what a Deca Tree is and then work out how many leaves there will be after the woodcutter has cut off a trunk, a branch, a twig and a leaf.

nrich problem solving subtraction

A Square of Numbers

Can you put the numbers 1 to 8 into the circles so that the four calculations are correct?

1, 2, 3 Magic Square

Arrange three 1s, three 2s and three 3s in this square so that every row, column and diagonal adds to the same total.

nrich problem solving subtraction

This is an adding game for two players. Can you be the first to reach the target?

nrich problem solving subtraction

Cubes Within Cubes

We start with one yellow cube and build around it to make a 3x3x3 cube with red cubes. Then we build around that red cube with blue cubes and so on. How many cubes of each colour have we used?

nrich problem solving subtraction

Two Primes Make One Square

Can you make square numbers by adding two prime numbers together?

One Wasn't Square

Mrs Morgan, the class's teacher, pinned numbers onto the backs of three children. Use the information to find out what the three numbers were.

The Tomato and the Bean

At the beginning of May Tom put his tomato plant outside. On the same day he sowed a bean in another pot. When will the two be the same height?

nrich problem solving subtraction

Abundant Numbers

48 is called an abundant number because it is less than the sum of its factors (without itself). Can you find some more abundant numbers?

nrich problem solving subtraction

Zios and Zepts

On the planet Vuv there are two sorts of creatures. The Zios have 3 legs and the Zepts have 7 legs. The great planetary explorer Nico counted 52 legs. How many Zios and how many Zepts were there?

nrich problem solving subtraction

Watch the Clock

During the third hour after midnight the hands on a clock point in the same direction (so one hand is over the top of the other). At what time, to the nearest second, does this happen?

nrich problem solving subtraction

What's in a Name?

What do you notice about these squares of numbers? What is the same? What is different?

nrich problem solving subtraction

Prison Cells

There are 78 prisoners in a square cell block of twelve cells. The clever prison warder arranged them so there were 25 along each wall of the prison block. How did he do it?

nrich problem solving subtraction

Two and Two

How many solutions can you find to this sum? Each of the different letters stands for a different number.

nrich problem solving subtraction

In a square in which the houses are evenly spaced, numbers 3 and 10 are opposite each other. What is the smallest and what is the largest possible number of houses in the square?

nrich problem solving subtraction

The Puzzling Sweet Shop

There were chews for 2p, mini eggs for 3p, Chocko bars for 5p and lollypops for 7p in the sweet shop. What could each of the children buy with their money?

nrich problem solving subtraction

The Amazing Splitting Plant

Can you work out how many flowers there will be on the Amazing Splitting Plant after it has been growing for six weeks?

nrich problem solving subtraction

Ben has five coins in his pocket. How much money might he have?

nrich problem solving subtraction

Can you go through this maze so that the numbers you pass add to exactly 100?

nrich problem solving subtraction

Doplication

We can arrange dots in a similar way to the 5 on a dice and they usually sit quite well into a rectangular shape. How many altogether in this 3 by 5? What happens for other sizes?

nrich problem solving subtraction

Find a great variety of ways of asking questions which make 8.

Three children are going to buy some plants for their birthdays. They will plant them within circular paths. How could they do this?

nrich problem solving subtraction

Consecutive Numbers

An investigation involving adding and subtracting sets of consecutive numbers. Lots to find out, lots to explore.

addition and subtraction problem solving nrich

addition and subtraction problem solving nrich

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Consumers' perceptions of energy use and energy savings: A literature review

Vedran Lesic 1 , Wändi Bruine de Bruin 1,2 , Matthew C Davis 3 , Tamar Krishnamurti 2 and Inês M L Azevedo 2,4

Published 6 March 2018 • © 2018 The Author(s). Published by IOP Publishing Ltd Environmental Research Letters , Volume 13 , Number 3 Citation Vedran Lesic et al 2018 Environ. Res. Lett. 13 033004 DOI 10.1088/1748-9326/aaab92

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Author affiliations

1 Centre for Decision Research, Leeds University Business School, University of Leeds, Leeds, LS2 9JT, United Kingdom

2 Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States of America

3 Socio-Technical Centre,Leeds University Business School, Leeds, LS2 9JT, United Kingdom

4 Author to whom any correspondence should be addressed.

Wändi Bruine de Bruin https://orcid.org/0000-0002-1601-789X

Inês M L Azevedo https://orcid.org/0000-0002-4755-8656

  • Received 25 November 2016
  • Accepted 30 January 2018
  • Published 6 March 2018

Peer review information

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Background . Policy makers and program managers need to better understand consumers' perceptions of their energy use and savings to design effective strategies for promoting energy savings. Methods . We reviewed 14 studies from the emerging interdisciplinary literature examining consumers' perceptions electricity use by specific appliances, and potential savings. Results . We find that: (1) electricity use is often overestimated for low-energy consuming appliances, and underestimated for high-energy consuming appliances; (2) curtailment strategies are typically preferred over energy efficiency strategies; (3) consumers lack information about how much electricity can be saved through specific strategies; (4) consumers use heuristics for assessing the electricity use of specific appliances, with some indication that more accurate judgments are made among consumers with higher numeracy and stronger pro-environmental attitudes. However, design differences between studies, such as variations in reference points, reporting units and assessed time periods, may affect consumers' reported perceptions. Moreover, studies differ with regard to whether accuracy of perceptions was evaluated through comparisons with general estimates of actual use, self-reported use, household-level meter readings, or real-time smart meter readings. Conclusion . Although emerging findings are promising, systematic variations in the measurement of perceived and actual electricity use are potential cause for concern. We propose avenues for future research, so as to better understand, and possibly inform, consumers' perceptions of their electricity use. Ultimately, this literature will have implications for the design of effective electricity feedback for consumers, and related policies.

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1. Introduction

The use of fossil fuels in electricity generation is one of the major contributors to greenhouse gas emissions (GHG) worldwide (Intergovernmental Panel on Climate Change 2014 ). A large de-carbonization of the energy system is necessary to reduce and stabilize carbon dioxide (CO 2 ) and other GHG emissions in the atmosphere (IPCC 2014 ). A portfolio of de-carbonization strategies and technologies will likely include curtailment (which is also called 'energy conservation' in much of the energy literature) and energy efficiency strategies targeting the reduction of residential energy use (IPCC 2014 , Pacala and Socolow 2004 ). Curtailment strategies and pertain to actions consumers can pursue to reduce the energy use of existing appliances by using them less or not at all (Azevedo 2014 , Rubin et al 1992 ). Energy efficiency strategies involve the implementation of more efficient appliances (Karlin et al 2014 ). If people misjudge the relative energy use or savings of one appliance or action over another, their efforts to save electricity may end up being misdirected.

Consumers with more accurate perceptions of energy use and savings may be better able to identify the actions that save the most energy, as a first potential step towards behavior change and reduced GHG emissions. Providing consumers with better information about their energy use and potential savings brings the promise of promoting the implementation of more curtailment and energy efficiency strategies and reducing residential greenhouse gas emissions (Bin and Dowlatabadi 2005 , Vassileva et al 2012 , Attari et al 2010 , Attari 2014 , Baird and Brier 1981 , Chen et al 2015 , Frederick et al 2011 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 ). Many consumers want better information, and hope that smart meters will help them to understand how much electricity is used by specific appliances (Krishnamurti et al 2012 ). Without information, consumers may develop folk theories and associated misconceptions about their energy use (Kempton 1986 , Kempton and Montgomery 1982 , Krishnamurti et al 2013 ).

This paper aims to understand how well consumers can assess the electricity used by different household appliances, and how much can be saved by implementing different curtailment or energy efficiency strategies. We provide a systematic overview of the empirical studies that have focused on the accuracy of consumers' perceptions of energy consumption and energy savings for specific appliances and actions. The paper is organized as follows. First, we briefly describe how we selected the studies that are included in this paper. Second, we discuss the key empirical findings reported in these studies. Third, we describe methodological differences in terms of how studies have measured consumers' perceptions of energy use. Fourth, we discuss the different ways in which actual energy consumption has been measured across studies, so as to evaluate the accuracy of consumers' perceptions. Finally, we conclude with recommendations for future studies and implications for developing effective feedback design and programs.

2. Methods and data

We performed a search for studies that used all possible combinations of the following keywords: 'consumer perceptions', 'consumer awareness', 'energy consumption', 'energy use', and 'energy savings'. We searched the following online databases: ScienceDirect, EBSCO, general library catalogues of Carnegie Mellon University and University of Leeds, limiting our search to articles published after 1980. From this initial search, we only retained peer-reviewed articles that reported the direct results of experimental, survey, or interview research with human participants. We also searched for studies in Google Scholar (where we focused solely on the first 25 pages of results). We read the abstract of each of the papers (and when it was unclear from the abstract, we also read the full paper to assess if a study would remain in our final dataset). We focused on identifying the papers that specifically reported perceptions or awareness of energy use and savings. Our initial search identified 32 peer-reviewed papers. We also identified six additional peer-reviewed papers in the references of these 32 papers. We included one additional paper on the basis of a reviewer's recommendation. In appendix table A1 we present the resulting 39 papers. We then read each of the 39 papers to identify those papers that met the inclusion criteria of: (1) focusing.... (2) presenting and (3) measuring actual use without necessarily making a comparison of actual use with perceptions (see table 1 ). Our review covers the resulting 14 studies that meet the inclusion criteria. For example, Allcott's ( 2011 ) paper on fuel energy consumption or Becken's ( 2013 ) paper on perceptions of energy use and actual saving opportunities for tourism accommodation made it into the initial selection of 32 papers but did not made it to final review because they are not in the domain of residential energy use. Of the 14 studies we reviewed, ten papers specifically presented comparisons of assessed perceptions and actual use (see table 1 ).

Table 1.  Summary of the studies reviewed.

3. Main empirical findings

We identify four main empirical findings across the 14 studies in our review:

  • 1.   Consumers have systematic misperceptions of energy use, such that electricity use is often overestimated for low-energy consuming appliances, and underestimated for high-energy consuming appliances (Attari et al 2010 , Baird and Brier 1981 , Chen et al 2015 , Frederick et al 2011 , Gatersleben et al 2002 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 );
  • 2.   Consumers tend to prefer curtailment over energy efficiency strategies (Attari et al 2010 , Becker et al 1979 , Kempton et al 1985 , Mettler-Meibom and Wichmann 1982 );
  • 3.   Consumers lack information about the electricity savings associated with specific strategies (Attari et al 2010 , Easton and Smith 2010 );
  • 4.   Consumers use heuristics for assessing the electricity use of specific appliances (Baird and Brier 1981 , Schley and DeKay 2015 ), with some indication that more accurate judgments are made among consumers with higher numeracy and stronger pro-environmental attitudes (Attari et al 2010 , Schley and DeKay 2015 ).

We discuss each of these findings in turn in the sections below.

Table 2.  Key methodological features across studies.

3.1. Systematic misperceptions of energy use

Consumers tend to systematically overestimate the electricity use of low-energy consuming appliances and activities, while underestimating the electricity use of high-energy consuming appliances and activities (Attari et al 2010 , Chen et al 2015 , Frederick et al 2011 , Gatersleben et al 2002 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 ). In one study, participants reported their perceived energy use for nine appliances, in terms of their hourly electricity use in kWh (Attari et al 2010 ). Participants received a reference point of a 100 W incandescent light bulb when making their assessments. The accuracy of perceptions was evaluated by comparing perceptions to actual energy use, as estimated from the literature and government agencies. According to the authors, participants underestimated the energy use of the nine appliances by a factor of 2.8 on average, while also overestimating the electricity use of low-energy consuming appliances (Attari et al 2010 ). A follow-up study asked participants to consider the same nine appliances, while providing either a 3 W LED, a 100 W incandescent light bulb or a 9000 W electric furnace as the single reference point (Frederick et al 2011 ). Frederick et al ( 2011 ) used the same estimates for actual energy use and savings as Attari et al ( 2010 ). Participants reported higher perceptions of electricity use across the nine appliances when they were presented with a higher rather than a lower reference point, with perceptions being highest when no reference point was provided at all (Frederick et al 2011 ). Moreover, overestimations were larger when questions were asked in terms of kWh versus Wh (Frederick et al 2011 ). Although Frederick et al ( 2011 ) found that the findings of Attari et al ( 2010 ) depended on reference points and reporting units, the overall pattern of underestimating the electricity use for high-consuming appliances and overestimating it for low-consuming appliances remained (Attari et al 2011 ).

Other studies revealed that same pattern (Chen et al 2015 , Gatersleben et al 2002 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 ) despite measuring perceptions and actual use in different ways (table 1 ) and varying reference points and reporting units (table 2 ). Regression towards the mean may have contributed to electricity use being overestimated for low-energy consuming appliances and underestimated for high-energy consuming appliances, because perceptions and actual use are imperfectly correlated (Attari et al ( 2010 ). However, regression towards the mean does not 'explain' why the correlation is imperfect, or why reported perceptions depend on how they are assessed. Similar patterns of findings have also been reported with regards fuel consumption (Allcott 2011 , Larrick and Soll 2008 ) and water use (Attari 2014 ).

3.2. Tendency to prefer curtailment strategies over energy efficiency strategies

Several studies in the literature note that consumers tend to choose curtailment strategies over energy efficiency strategies, even though the latter are potentially more effective for saving energy (Attari et al 2010 , Becker et al 1979 , Kempton et al 1985 , Mettler-Meibom and Wichmann 1982 ). For example, open-ended interviews with Michigan residents revealed that they tended to talk more about curtailment actions such as turning off the lights and lowering the winter thermostat, rather than on energy efficiency actions, such as better house insulation (Kempton et al 1985 ). A similar pattern was found in other open-ended interviews (Mettler-Meibom and Wichmann 1982 ) and in a national survey that asked participants for strategies to reduce energy use (Attari et al 2010 ). Another study found that most participants overestimated the savings that could be derived from curtailment by lowering the thermostat, as compared to implementing more energy-efficient devices (Becker et al 1979 ). Possible reasons for this preference for curtailment over energy efficiency are (i) that that curtailment is likely to have no financial costs in most circumstances, whereas efficiency will likely involve some form of investment or additional financial cost, e.g. investment in insulation or LED lighting; (ii) curtailment behaviors come to mind more easily than energy efficiency strategies, due to the former being implemented more frequently than the latter.

3.3. Lack of information about energy savings

In the absence of information, consumers may use their own experience to create folk theories about how different appliances or behaviors might consume or save energy (Kempton 1986 , Kempton and Montgomery 1982 ). Perhaps as a result, consumers misjudge how much electricity is used by specific appliances and behaviors (Attari et al 2010 , Easton and Smith 2010 ). The same pattern of misperceptions is seen in perceptions of energy use and energy savings (Attari et al 2010 ). Indeed, participants tend to overestimate low-consuming actions and underestimate high-consuming ones (Attari et al 2010 ).

Easton and Smith ( 2010 ) asked questions related to consumers' perceptions of energy consumption, energy-related behavior, and energy savings over a year, and then combined the responses to those questions with direct monitoring of metered energy, water, and temperatures provided by four community based retrofit organizations. Notably, they show that households underestimate the extent of repairs and maintenance that is required on their dwellings to save energy.

3.4. Heuristics and individual differences

When reporting their perceptions, participants also seemed to use heuristics or decision rules to simplify the task at hand (Tversky and Kahneman 1974 ). The commonly used 'availability heuristic' reflects the tendency to judge the likelihood of an event by the ease with which an example comes to mind (Schwarz et al 1991 ). Individuals who use the availability heuristic tend to systematically overestimate events that come to mind more easily, and underestimate events that come to mind less easily (Tversky and Kahneman 1973 ). Consumers may also use such heuristics when generating strategies for saving energy (Wilson and Dowlatabadi 2007 ) and assessing the electricity use of their appliances (Baird and Brier 1981 , Schley and DeKay 2015 ). Specifically, participants judge electricity use to be higher for appliances that are frequently used or thought of (Schley and DeKay 2015 ) as well as those that are larger in size (Baird and Brier 1981 ). Such heuristics will lead to predictable inaccuracies, such as for infrequently used appliances that use relatively more electricity or frequently used appliances that use relatively little (Baird and Brier 1981 ). Similarly, curtailment actions may come to mind more easily than energy-efficiency actions due to being implemented more frequently—leading to overestimations of the associated energy savings.

Moreover, the accuracy of perceptions may systematically vary across participants. Two studies find that more numerate participants have more accurate perceptions of energy use for specific appliances (Attari et al 2010 , Schley and DeKay 2015 ). One study reports that participants with stronger pro-environmental attitudes have more accurate perceptions of energy use and potential savings (Attari et al 2010 ), while another reports that they do not (Schley and DeKay 2015 ).

4. Methodological differences between studies

The studies we reviewed differ in their research method, including qualitative interviews (Easton and Smith 2010 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 ), and surveys (Abrahamse et al 2007 , Abrahamse and Steg 2009 , Becker et al 1979 , Gatersleben et al 2002 , Kempton et al 1985 , Attari et al 2010 , Baird and Brier 1981 , Chen et al 2015 , Frederick et al 2011 ). Across these research methods, we identify three methodological features that may affect consumers' reported perceptions of electricity use:

  • the presence or absence of a reference point, with reference points varying in size from a 3 W LED (Frederick et al 2011 ), to a 100 W incandescent light bulb (Attari et al 2010 , Frederick et al 2011 ), and even a 9000 W electric furnace (Frederick et al 2011 );
  • the units in which consumers report their perceptions of electricity use, such as in kWh (Attari et al 2010 , Baird and Brier 1981 ) or in dollars (Karjalainen 2011 );
  • the time periods in which consumers report their perceptions of electricity use, such as per hour (Attari et al 2010 , Baird and Brier 1981 , Frederick et al 2011 ), per month (e.g. Mettler-Meibom and Wichmann 1982 ) or per year (Easton and Smith 2010 : Schley and DeKay 2015 ).

4.1. Reference point

Behavioral decision researchers have long suggested that the provision of a reference point, or comparison information, affects people's reported perceptions (Hammond et al 1998 , Sunstein 2002 ). That is, people tend to adjust their perceptions towards the reference point that is provided (Chapman and Johnson 2002 , Attari et al 2010 ). Some studies in our review provided reference points to participants with the aim of helping them generate their perceptions (table 2 ). For example, studies have presented information about the electricity use of a 3 W LED (Frederick et al 2011 ), a 100 W incandescent light bulb (Attari et al 2010 , Frederick et al 2011 ), a 100 W washing machine (Baird and Brier 1981 ), and a 9000 W electric furnace (Frederick et al 2011 ). Perhaps not surprisingly, participants report higher perceptions of electricity use when being presented with a higher rather than a lower reference point, with perceptions being highest when no reference point is provided at all (Frederick et al 2011 ). Future studies should test whether the provision of multiple reference points provides information about the feasible range, without biasing judgments upwards or downwards, as compared to when no reference point is provided.

4.2. Reporting unit

Some studies asked participants to report the electricity use of their appliances in different units of consumption (table 2 ), such as kWh (Attari et al 2010 , Baird and Brier 1981 ) or dollars (Becker et al 1979 , Easton and Smith 2010 ). When describing the energy consumption associated with their home heating, most people tend to refer to monetary values (Kempton and Montgomery 1982 ). Indeed, consumers may be more familiar with monetary units than with energy units because of the salience of paying electricity or heating fuel bills (Darby 2006 ). As a result, they may want to see feedback about their electricity use displayed in terms of monetary units rather than energy units (Karjalainen 2011 ). However, simple feedback provided in energy units may be the most effective way to increase knowledge about energy use (Krishnamurti et al 2013 ). Behavioral decision studies in other domains suggest that consumers may overestimate prices as compared to other units (Bruine de Bruin et al 2011 , Vohs et al 2006 ). Because of the small sample sizes and variability in study designs, it is unclear at this stage whether monetary units or energy units might be better at helping consumers to judge their electricity use. Future research should systematically test the effect of reporting units on consumers' perceptions of how much electricity is used by their appliances.

4.3. Time period

Studies vary in terms of the time period participants have considered when reporting their perceptions of appliance's electricity use (table 2 ). For example, participants have been asked to assess how much electricity an appliance uses over the course of an hour (Attari et al 2010 , Frederick et al 2011 ), a month (e.g. Mettler-Meibom and Wichmann 1982 ), or a year (Easton and Smith 2010 , Schley and DeKay 2015 ). The time period may also be left unspecified (Chen et al 2015 ). One drawback of asking consumers about their perceived energy use over the course of an hour is that comparisons with actual use may not be realistic (i.e. it may not make sense to ask how much energy a coffee machine or a toaster uses if it is running for a full hour, since that does not reflect usual usage patterns). Instead, the researcher may ask participants for the frequency of use of an appliance and the energy use over that period. Additionally, the time period consumers are asked to consider may affect their reported perceptions. Monthly periods may be more familiar to people given that historically most utilities would send monthly utility bills. Yet, technology that enables consumers to receive more frequent electricity use information is available (Anderson and White 2009 ) and some work has shown that consumers are interested in seeing information such as daily load curves (Ueno et al 2006 ). In other research that does not focus on energy use, researchers have found that self-reported hours of TV watching depend on the time period used in the survey, with more accurate responses being provided when time periods match people's natural experiences (Schwarz 1999 ).

Although none of the reviewed studies examined whether assessed time periods used affects perceptions, there is reason to believe that they might. Especially when considering longer time periods, participants may assume the appliance is running for the full duration of that time period, or they may assume what is a 'typical' usage of the appliance for them. If participants make different assumptions about how to respond to such questions as the time period increases, their reported perceptions will likely show a larger variability. If perceptions are to be reported for typical use over a time period, it is important to note that people often misestimate the amount of time they spend on tasks (Fasolo et al 2009 ). They may overestimate the electricity use of appliances they tend to use longer (Yeung and Soman 2007 ). In addition, behavioral economics research on magnitude effects suggests that people display a larger subjective temporal discount rate for small magnitudes than for large ones (Chapman and Winquist 1998 ). Thus, it may be easier to think of specific appliances in terms of their relative time periods of use.

Table 3.  Approaches to measure actual energy use.

Note: Ratings include very low, low, medium, high and very high. The values shown in the table reflect the authors' own subjective assessment of these criteria.

5. Measures of actual energy use

This section focuses on the methods for measuring actual energy use and energy savings, so as to assess the accuracy of consumers' reported perceptions. The 14 studies identified in our review that include a measure of actual energy use can be divided into four categories with regards how they measured actual energy use:

  • 1.   General estimates from the existing literature and other sources (these include Attari et al 2010 , Becker et al 1979 , Baird and Brier 1981 , Frederick et al 2011 , Mettler-Meibom and Wichmann 1982 , Kempton et al 1985 , Schley and DeKay 2015 );
  • 2.   Estimates based on self-reported energy use (these include Gatersleben et al 2002 , Abrahamse et al 2007 , Abrahamse and Steg 2009 );
  • 3.   Estimates based on household-level meter readings (this includes Kempton and Montgomery 1982 , Easton and Smith 2010 );
  • 4.   Measures of real-time energy usage from smart meters (Chen et al 2015 ).

Each of these approaches has its own set of advantages and disadvantages, as summarized in table 3 . In table 3 , we provide our assessment of these four approaches on five criteria, on a scale ranging from very low to very high: (1) data accessibility, which refers to the ease of obtaining the data, (2) cost of measurement, which refers to how costly it might be to gather the data, (3) data accuracy, which refers to the extent to which the data reflect actual energy consumption rather than an estimate, (4) data complexity, which refers to the level of analysis needed to prepare, store, and compute the data, and (5) third-party involvement, which refers to the need to involve other organizations in obtaining the data.

5.1. General estimates from the existing literature and other sources

Many of the reviewed studies used general estimates of energy use or energy savings of specific appliances and behaviors, so as to evaluate the accuracy of participants' reported perceptions (table 1 ). Some studies used publicly available estimates from existing publications including expert reports (Becker et al 1979 , Mettler-Meibom and Wichmann 1982 , Kempton et al 1985 ), energy statistics from for example governmental agencies (Attari et al 2010 , Frederick et al 2011 , Schley and DeKay 2015 ), or information from local stores (Baird and Brier 1981 ). Using these sources is convenient because they are readily available. However, this approach comes with the severe limitation of not capturing individual heterogeneity in consumption. As a result, it is impossible to know whether any differences between perceived and actual consumption are due to misperceptions by the consumer or due to average energy use being a poor proxy for the actual energy consumption of a specific household.

5.2. Estimates based on self-reported energy use

It is also possible to estimate an individual's actual energy use for specific appliances from self-reports (Abrahamse et al 2007 , Abrahamse and Steg 2009 , Gatersleben et al 2002 ). Gatersleben et al ( 2002 ) developed a model to calculate actual energy consumption based on participants' self-reported behavior. The authors asked participants to report which appliances they own. For each appliance, the total number of appliances of that type in the household was multiplied by the average annual energy use of the appliance as estimated for an average Dutch household.

Estimates of actual energy use by appliance were then computed for individual participants and compared to their reported perceptions of energy use. The benefit of this approach is that individuals' perceptions are compared to their own usage patterns and appliances. However, one limitation is that participants may not know the required information, or provide inaccurate reports due to imperfect memory or response biases (Baumeister et al 2007 ). Another drawback of self-reports is that they may be labor-intensive for participants to complete, especially if the study includes a large number of appliances.

5.3. Estimates based on household-level meter readings

Another approach is to estimate an individual's energy use for specific appliances after obtaining a household-level meter reading from the utility company. Since the late 1970s, many studies have evaluated the accuracy of consumers' perceptions of electricity, gas, or water use on the basis of meter readings provided by utility companies (e.g. Heberlein and Warriner 1983 , Hirst et al 1982 , Kempton and Montgomery 1982 , Midden et al 1983 , Seligman et al 1978 , Verhallen and van Raaij 1981 ). The benefit of this approach is that it provides household-specific information, allowing comparisons of individuals' perceptions with their own electricity use (Schley and DeKay 2015 ). Various intervention studies (Battalio et al 1979 , King 2010 , Kline 2007 ) have also used household-level energy data to provide feedback to households and to test the resulting effects on residential energy use. However, household-level readings too come with potential limitations. First, they do not provide information regarding the energy consumption of specific appliances. Second, many studies have relied on monthly assessments from utilities which only conduct actual meter readings a few times per year, and make estimates for the rest of the year.

5.4. Measures of actual energy use from smart meters

The deployment of smart meters has enabled the measurement of households' real-time energy consumption (Asensio and Delmas 2015 , Chen et al 2015 ). These measurements may include (i) single load monitoring combined with algorithms to estimate the consumption of different appliances, or (ii) multi-modal sensing. Single-load monitoring through smart meters is a non-intrusive method for measuring real-time household-level electricity use and can be combined with specifically designed algorithms to identify when specific appliances are being used (Berges et al 2008 ). Even with advanced algorithms, this approach will involve underlying uncertainty. Instead, multi-modal sensing overcomes that uncertainty through the installation of special sub-meters to capture usage for each appliance (Froehlich et al 2011 ). Sub-meter data facilitate direct comparisons between consumers' perceived and actual use of appliance-level energy use. Using sub-meter data also allows for better tests of the effectiveness of interventions. This approach has been implemented in the Pecan Street community located at the University of Austin in Texas (Pecan Street 2017 , Smith 2009 ). However, sub-meters are more intrusive and costly to implement, limiting the feasibility of using them with a large or nationally representative sample.

6. Conclusions and recommendations for future studies

Our review of the literature covers 14 peer-reviewed studies that empirically assessed consumer perceptions of electricity use that has been published over the past 35 years. An even smaller number of studies (N=10) compared consumers' perceptions to actual energy use or savings. The main findings from the reviewed studies include: (1) electricity use is typically overestimated for low-energy consuming appliances, and underestimated for high-energy consuming appliances; (2) curtailment strategies are typically preferred over energy efficiency strategies; (3) consumers lack information about how much electricity can be saved through specific strategies; (4) consumers use heuristics for assessing the electricity use of specific appliances, with some indication that more accurate judgments are made among consumers with higher numeracy and stronger pro-environmental attitudes.

However, we note that methodological differences between studies may affect consumers' reported perceptions, including the provision of reference points, as well as the units and time periods used in the existing studies. Moreover, studies vary in terms of whether the accuracy of perceptions has been evaluated in terms of general estimates of actual use, self-reported use, house-level meter readings, or real-time smart meter readings.

We suggest several avenues for future research. First, there is a need to systematically examine the effect of reference points, units, and time periods on reported perceptions. Second, to better compare consumers' perceptions to their actual appliance energy use, measures of households' actual energy consumption should be taken at the individual households' appliance level. Ideally, such studies would be conducted with large representative samples. Moreover, it remains unclear whether consumers with more accurate perceptions of their energy use by appliance, or of the savings they could obtain, do indeed make more informed decisions about their energy use and savings. It also remains to be seen whether informed decisions lead to behavior change and reductions of residential GHG emissions.

Understanding consumers' perceptions (and misperceptions) of energy use and savings may help to inform the design of curtailment and energy efficiency policies. The use of smart technology and associated services, such as in-home displays, mobile apps, and other information and communication technology related services could facilitate improved measurement as well as improved feedback to consumers (Krishnamurti et al 2012 ). However, care should be taken to present feedback in a way that consumers can use and understand (Davis et al 2014 ). For example, tailored feedback may be provided to consumers to explain their misperceptions, while using reference points, units, and time periods that make the most sense to them. Research should also be developed to then test whether correcting misperceptions through feedback does indeed help consumers to make more informed decisions about curtailment and energy efficiency. In the domain of health, researchers have shown that correcting misperceptions of risk can foster behavior change (Avis et al 1989 , Kreuter and Strecher 1995 , Lindan et al 1991 ). Thus, continued research on the topic of how well consumers can assess appliance energy use brings some promise of informing consumers' decisions to implement curtailment and energy efficiency behaviors.

Appendix.  Table A1.

Acknowledgments

We acknowledge support from by the Consumer Data Research Centre at University of Leeds, Economic and Social Research Council [grant number ES/L011891/1], Centre for Decision Research at Leeds University Business School. This work was supported by the center for Climate and Energy Decision Making (SES-1463492), through a cooperative agreement between the National Science Foundation and Carnegie Mellon University, as well as the Swedish Risksbanken Jubileumsfond Program on Science and Proven Experience.

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Systematic review article, renewable energy consumption and economic growth nexus—a systematic literature review.

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  • 1 School of Economics, Guangdong University of Finance and Economics, Guangzhou, China
  • 2 School of Technology, Management and Engineering, NMIMS, Indore, India
  • 3 Department of Banking and Financial Markets, Financial University Under the Government of the Russian Federation, Moscow, Russia
  • 4 University Center for Circular Economy, University of Pannonia, Nagykanizsa, Hungary

An efficient use of energy is the pre-condition for economic development. But excessive use of fossil fuel harms the environment. As renewable energy emits no or low greenhouse gases, more countries are trying to increase the use of energies from renewable sources. At the same time, no matter developed or developing, nations have to maintain economic growth. By collecting SCI/SSCI indexed peer-reviewed journal articles, this article systematically reviews the consumption nexus of renewable energy and economic growth. A total of 46 articles have been reviewed following the PRISMA guidelines from 2010 to 2021. Our review research shows that renewable energy does not hinder economic growth for both developing and developed countries, whereas, there is little significance of consuming renewable energy (threshold level) on economic growth for developed countries.

Introduction

Consuming non-renewable energy may produce output and foster economic development, but undoubtedly it is a significant source of carbon emission and environmental degradation ( Awodumi and Adewuyi 2020 ). Using non-renewable energy sources put countries in a dilemma in policy priority between pollution reduction and economic growth. Thus, whether renewable or non-renewable, the energy should be used carefully and efficiently as its sources are limited. In addition, due to climate change and global warming situation, renewable energy could be the most attractive alternative to fossil fuel, reducing the CO 2 emission process. However, introducing new renewable energy technologies, consuming, and making them available for the citizens, is very time-consuming and costly. On the other side, countries struggle to maintain economic growth and development. Due to the COVID-19 crisis, the situation has been worsening. The governments of both developing and developed nations have to balance spending for climate change mitigation and economic growth.

Moreover, there is still limited information regarding all the perceived critical factors in moving toward fully renewable energy sources. This article shows a comprehensive assessment of how renewable energy systems affect the country’s economic growth. In this article, assessment is carried out based on G7 and Next-11 countries. France, Germany, Italy, Japan, the United States, the United Kingdom, and Canada make up the Group of Seven (G7) intergovernmental organization. Government officials from these nations meet regularly to discuss world economic and monetary matters, with each member alternating through the chairmanship.

Along with the BRICs, the Next-11 (or N-11) are eleven countries identified by Goldman Sachs as having a high potential to become the world’s largest economies in the twenty-first century, namely, Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam. Figure 1 shows the name of G7 and Next-11 countries.

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FIGURE 1 . (Group Seven) G7 and (Next-11) N-11 countries.

Energy resource has been the fundamental element for an economy or economic development ( Xiong et al., 2014 ). It is clear that economic growth mainly depends on energy consumption, which is highly responsible for greenhouse gas (GHG) emissions, particularly CO 2 , as stated by Gabr and Mohamed (2020) . CO 2 emissions are a by-product generated by primary consumption sources of non-renewable energy, such as fossil fuels ( Thollander et al., 2007 ). Starting from this general environmental framework due to non-renewable sources, several national economies, after having experienced several disasters, have tried to bring about a structural change in production methods and energy use. Some countries have mainly switched to renewable sources, leaving fossil fuels to no longer be based on non-renewable energy sources ( Irfan et al., 2021 ). According to the EY Company’s Renewable Energy Country Attractiveness Index (RECAI), which integrates new global trends, the countries with the most significant opportunities for investments in renewables are the United States, China, and India, three large economies that have been competing for these positions for several years now ( RECAI, 2020 ). Implementing renewable energy sources (RES) is essential but still faces some challenges in some European countries. Perception and awareness toward RES are the main challenges in countries such as Montenegro ( Djurisic et al., 2020 ).

One of the world’s major power resource user countries, China, has put forward the “double carbon” target to reduce emissions ( Jiang et al., 2022 ). China’s domestic market has shown some resilience despite the end of domestic subsidies in December 2020 and the COVID-19 crisis, which affected 10% of new capacity additions. Chinese solar panel production grew by 15.7% compared to 2019 ( RECAI, 2020 ). Australia represents the third, this country has experienced exponential growth in residential photovoltaics, distributing over 10 GW of solar energy to civilian homes and adopting necessary plans to export hydrogen to Asia ( RECAI, 2020 ). India follows, from 7th to 4th place, and thanks to the growth of photovoltaic capacity to meet the ambitious national green goals for 2030 ( RECAI, 2020 ). In addition to G7 and N-11 countries, Table 1 shows the general information and technology-specific scores of the top 10 countries that invest in renewable energy sources, and Figure 2 shows the data visualization of the dataset in Table 1 .

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TABLE 1 . Top 10 countries that invest in renewable sources.

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FIGURE 2 . Comparison of technology-specific score of top 10 countries.

Some studies tried to relate the consumption of renewable energy and economic growth. But most of the studies concern EU countries and other factors. For example Tutak and Brodny . (2022 ) have tried to analyze the impact of renewable energy on economics, environmental, and conventional energy sources. In addition, ( Smolović et al., 2020 ), by using the pooled mean group (PMG) estimator in a dynamic panel setting (an ARDL model) has carried out a nexus between renewable energy consumption and economic growth in the traditional and new member states of the EU. Furthermore, the panel vector autoregression (PVAR) model ( Koengkan, Fuinhas, and Marques 2019 ) has examined the relationship between financial openness, renewable and non-renewable energy consumption, CO2 emissions, and economic growth in 12 Latin American countries. Furthermore Lorente et al. ( 2022 ) found that there is an association between economic complexity and CO 2 emissions is inverted-U and further N-shaped relationship for Portugal, Italy, Ireland, Greece, and Spain.

We have noticed a research gap of systematic review analysis regarding economic growth and renewable energy consumption in recent years by analyzing other existing research work. From this point of view, our study tried to fill the research gap and make it a collection of systematic reviews in this field. Moreover, there were no such systematic reviews (including developing, developed, and underdeveloped countries) in this field of study.

Due to the higher cost of implementing and maintaining, cost-benefit analysis, and other external–internal factors, renewable energy is still under consideration to entirely depend on the energy source. Thus, this is a burning question for the researchers, policy makers, and related organizations whether introducing the renewable energy source would hinder or slow down the economic growth. Many researchers are trying to answer for their respective country or region of interest. No such review work tried to find the nexus between RE and EG for G7 and N-11 countries. This study attempted to gather the related research outcomes and give a broader picture of introducing and using the renewable energy and economic growth relationship.

Basic Interpretation With Renewable Energy and Economic Growth

Introducing renewable energy and economic growth is a widespread debate among researchers. From this point of view, by executing the panel data (1970–2017) ( Konuk et al., 2021 , 11), examined the relationship between economic growth and biomass energy consumption for N-11 countries. According to their research work, economic development and biomass energy consumption act together in the long run. In addition, Jenniches (2018 ) tried to assess the regional economic impacts of a transition to renewable energy generation in his review article. He believes clearly that defining technologies and assessment periods is very significant. Doytch and Narayan (2021) estimated the effects of non-renewable and renewable energy consumption on manufacturing and services growth. They have found that renewable energy enhances growth in high-growth sectors, that is, the services sector in high-income economies and the manufacturing sector in middle-income economies. Acheampong et al. (2021) investigated the causal relationship between renewable energy, CO 2 emission, and economic growth for 45 African (sub-Saharan) countries over 57 years (1960–2017). Using the GMM-PVAR method, they have concluded that a bidirectional causal relationship exists between economic growth and renewable energy ( Acheampong, Dzator, and Savage 2021 ). Another old study (comparatively) in 2003 by Ugur and Sari examined the causality relationship between the two series in the top 10 emerging economies and G7 countries. They have discovered bi-directional causality for Argentina, GDP to energy consumption causality for Korea and Italy, energy and consumption to GDP for Turkey, France, Germany, and Japan. Additionally, it was found that countries such as Argentina, Brazil, Paraguay, Uruguay, and Venezuela have low renewable energy participation in their energy mix. An effect between renewable energy consumption and fossil fuels, as a possible response to periods of scarcity in reservoirs, was detected for these countries ( Koengkan et al., 2020b ).

In contrast, economic growth may slow down due to energy conservation in the case of the rest four nations ( Soytas and Sari, 2003 ). Another estimation suggested that non-renewable energy consumption has a significant and positive impact on economic activities and development across a large number of Organization for Economic Co-operation and Development (OECD) countries ( Ivanovski, Hailemariam, and Smyth 2021 ). A review of hybrid renewable energy systems (HRES) in developing countries has been conducted by Zebra et al. (2021) . They believe Asian developing countries perform better than African nations for renewable and non-renewable mini-grids maintenance and productivity. They also believe that, in general, the costs of mini-grids will continue to decline, making renewable sources even more competitive at the utility scale. Some researchers also tried to find the opposite relationship between economic growth (barriers) and renewable energy development. Seetharaman et al. (2019 ) believe technological, social, and regulatory barriers hinder the development of RE development, but economic constraints do not directly impact the outcome of renewable energy.

In some countries, renewable energy and consumption do not hinder economic development, and on the other side, it plays a vital role in hindering economic development. So, according to Islam et al. (2022) , income growth shows positive and negative effects on renewable and non-renewable energy consumption. Consider that domestic and foreign investments positively affect renewable and non-renewable energy consumption. Furthermore, institutional quality has a positive impact on renewable energy consumption. Instead, the urbanization process has a negative impact on the consumption of renewable energy because it has a positive influence on the consumption of non-renewable energy ( Islam et al., 2022 ).

Unfortunately, despite the revolutionary attempt to adopt renewable energy technologies, some industrial countries are still firm on the consumption of fossil fuels energies with the aim of recording faster and more impressive economic growth ( Shrinkhal, 2019 ; Islam et al., 2021 ). Contrary to the positive effects on the environment generated with renewable energy sources, the economic serenity that can be reached using non-renewable enriches the coffers of different economies and the lifestyles of their people, but not those of the environment ( Doytch and Narayan, 2016 ). In some cases, renewable energy consumption (threshold level) does not significantly affect economic growth for developed countries. Renewable energy (RE) and economic development indicators may not correlate in selected EU countries. Despite some debate and unstable economic conditions, the share of RE in total energy consumption in EU countries has been systematically growing and was not much dependent on economic factors ( Ogonowski 2021 ). The economic value of solely replacing renewable energy with nuclear power and fossil energy could be very high and infeasible. They consider that electricity and power generation based on only renewable energy would cost an additional 35 trillion KRW/year for South Korea ( Park et al., 2016 ). This method is infeasible, and customer willingness to pay will be low. Lema et al. (2021) by taking in-depth analysis, tried to measure to what extent direct and indirect economic benefits are created when Chinese investments in RE projects in sub-Saharan Africa. Their research revealed that the FDI and investments on RE projects might have “bounded economic benefits” for the region by creating new job opportunities, production and training activities, linkage with local systems, and so on. In addition, economic awareness, public opinion, and mass participation are essential for the use of RE in the region. Citizens of Kenya (73%) (both urban and rural) strongly approved the development of RE sources technologies and (91%) believe that RE technologies will reduce the cost of electricity and power generation ( Oluoch et al., 2020 ).

Methodology Used in Review Assessment

We have considered Group seven (G7: Canada, France, Germany, Italy, Japan, UK, and the United States), countries (as developed nations and the Next-11 (N- 11: Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam) countries (exclude South Korea) as developing countries.

To maintain the whole process, we have followed the PRISMA flowchart explained in Figure 3 :

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FIGURE 3 . PRISMA flow diagram.

The PRISMA method—Preferred Reporting Items for Systematic Reviews and Meta-Analyzes—built a set of minimum elements based on the references highlighted in the systematic reviews and meta-analysis. The primary purpose of PRISMA is to focus primarily on studies that evaluate the effects of certain interventions. However, they can also be used to report systematic reviews that present with different objectives (e.g., from the evaluation of interventions) ( Prisma, 2021 ).

For this purpose, PRISMA was used because it is helpful for the critical evaluation of the published systematic reviews of this study, although it is not a tool for assessing the quality of a systematic review. For the main results of the literature review according to the PRISMA guidelines, we have considered the available online date for the “Year” column. We have followed the MLA style for the author’s name. The applied and references related theories are in the “Theories” column. Authors’ article methodologies are considered in the “Methods” column. The author’s near-future predictions or consequences are listed in the “Predictors” column. The results, conclusions, or outcomes are in the “Outcomes” column, followed by article keywords in the “Keywords” section. We have used google scholar citation for the citation column until the last week of December 2021. The citation number may vary as the citations are increasing every day. The last column is “Journal,” which denotes the respective article published journal name.

We have used Google Scholar, Scopus, Science Direct, and PubMed for research articles. Initially, we searched the articles using the keywords “Renewable energy” and “Economic growth.” We have 553 articles related to good governance and sustainable tourism mentioned in the article’s title. There were 17 duplicate articles that we had to remove. We deducted the articles unrelated to the topic content from this initial screening. After removing the irrelevant articles, we had 97 full-text eligible articles. From these 97 articles, we have selected 46 closely matched full-text articles for review ( Figure 3 ).

Effect of Renewable Energy in Economic Growth G7 Countries

While presenting economic prosperity, the G7 countries can still not guarantee environmental well-being. In fact, using the annual frequency data from 1980 to 2016, the impact on the environment of some variables was ascertained using panel data. The results show that financial globalization and eco-innovation reduce the ecological footprint. On the contrary, urbanization stimulates environmental degradation by increasing the ecological footprint values ( Ahmad et al., 2021 ).

Amri (2017) , using the dynamic simultaneous-equation panel data approach, investigated, over the period 1990–2012, the relationship between three indicators (economic growth, renewable energy, and trade) in different income groups of countries and underlined the interdependence of these variables. Notably, the main findings reveal a bidirectional nexus between renewable energy consumption and GDP in all groups of nations; a persistent bidirectional relationship among foreign trade and renewable energies in all groups of countries; finally, a bidirectional nexus between trade and economic growth in developed, developing, and others developed countries. In addition, a team of researchers investigated the dynamic effect of RE consumption, biocapacity, and economic growth in the United States from 1985 to 2014. Using the ARDL model, the authors claim that a decline in environmental degradation can attribute to an increase in RE consumption through its negative effect on the ecological footprint. Their study revealed that biocapacity and economic growth would exert more pressure on the ecological footprint. Furthermore, a causal relationship was built between ecological footprint and economic growth and economic growth and biocapacity ( Usman, Alola, and Sarkodie 2020 ).

Armeanu et al. (2021) , investigated, using several statistical methods, the interrelationships, over the period 1990–2014, among renewable energy, types of energy, economic growth, CO 2 emissions, and urbanization in different income groups of countries, and highlighted that “In the case of the group of countries with a high level of income, the presence of the co-integration of the renewable energy use with the carbon releases, renewable and nuclear energy, electric power consumption, and the urban population was observed” and the relationship was satisfied, due to the interest of this group of countries to preserve the environment. Furthermore, through the Granger causality test, the authors find a single-bidirectional causal relationship between economic growth and energy intensity in the low-income countries, whereas many bidirectional relations among the variables in high-income countries, particularly between energy intensity and CO 2 emissions.

Another study was conducted by Hao et al. (2021) to investigate the effects of green growth on CO 2 emissions for G7 countries over the past twenty-five years, using second-generation panel data methods, for example, the distributive self-regressive-augmented transversal lag model (CSARDL). The results revealed that both short- and long-term GDP growth impact environmental impoverishment. Thus, the thesis that green growth supports the quality of the environment is confirmed. The authors highlighted that any changes in CO 2 , GDP, green growth (GG), environmental taxes (ET), renewable energy consumption (REC), and human capital (HC) in one of the G7 countries would have consequences in other G7 countries in an interconnected nexus between G7 countries.

However, at the regional level, total energy consumption positively affects growth, while renewable sources negatively affect development in some regions in low- and middle-income countries ( Namahoro et al., 2021a ). Instead of testing the relationships among variables with appropriate and feasible econometrics modeling techniques, using panel data methodologies, Li and Leung (2021) evaluated the relationship between energy prices, economic growth, and renewable energy consumption. The results of Li and Lung’s study (2021) highlighted the importance of economic growth in supporting renewable energy consumption, especially in G7 countries with developed economies. However, factors that are affected through renewable energy systems are listed in Table 2 . By focusing on R&D spending and uniform policies, the G7 countries have transformed their economies from copying countries to a community of dynamic economies. As a result, and in tandem with the economy’s digitalization. This study examines the relationship between energy, financial, environmental sustainability, and social performance of G7 countries using a data envelopment analysis (DEA)-like composite score. The foundation of this study is formed over the reconstruction and modification of regional emissions and examining aspects such as energy, efficiency, and usage, in addition to the prospect of having a regional development outline. Most prior research used certain essential methodologies to examine emission levels and variance depending on actors connected to energy efficiency, energy structure, financial development, production, industry, technological development openness, and population.

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TABLE 2 . Factor that effected through the renewable energy system.

Namahoro et al. (2021b) underlined that renewable energy consumption affects economic growth, using an asymmetric analysis with a non-linear autoregressive-distributed lagged model (NARDL) and causality test. In contrast, Wang and Wang (2020) reveal that in the G7 countries, renewable energy consumption positively affects economic growth. The threshold value changes influence in this positive relationship. Thus, the role of growing renewable energy use to stimulate economic growth is non-linear. For example, if the EU countries increase their renewable energy over a threshold value, the position of renewable energy in supporting economic development is more significant. In the same line, in 2020, Chen et al. (2020) studied the causal link between renewable energy consumption and economic growth using a threshold model. The reference period is 1995–2015, and they confirm that renewable energies positively and significantly affect the economic growth in the OECD countries, whereas no significant effect is in the developed countries. The authors underlined that in developing and non-OECD countries, renewable energies significantly affect economic growth over a certain threshold of their consumption. In addition, Yang et al. (2021) found feed-in-tarrif (FIT) have higher expected output and profit, and lower market prices. The risks of production and gain is of relatively more significant. By contrast, the production and profit of renewable portfolio standard (RPS) remain relatively more stable. In the same year, Sharma et al. (2021) examined the interrelationships between sustainability indicators and financial growth performance, using Arellano–Bond dynamic panel data estimation, system dynamic panel data estimation, and the augmented mean group model. The results highlighted that the transition toward renewable energy is economically in the long run, positively impacting economic growth in line with the environment. From this point of view, total investment in RE and descriptive statistics with technological specific scores by G7 countries are listed in Tables 3 , 4 , respectively. Table 3 shows the Renewable Energy Country Attractive Index of different countries, and according to the score it is found out in the USA the growth or electricity generation through the renewable energy in the wonderful way. Overall data also shows the growth rate of the onshore wind energy systems, solar PV, solar CSP, geothermal systems are better in the United States; on the other hand, the offshore wind energy system and biomass systems are popular in the United Kingdom. The Renewable Energy Country Attractiveness Index (RECAI) rates the attractiveness of renewable energy investment and deployment prospects in the world’s top 40 markets. The rankings reflect our evaluations of market attractiveness and worldwide market trends. Table 4 describes the different statistical parameters with central tendency in terms of mean, mode, and median of renewable energy sources. It also finds most of the energy sources are minimum RECAI for Canada and maximum for the United States.

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TABLE 3 . G 7 countries that invest in renewable sources.

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TABLE 4 . Descriptive statistics with technological specific scores of G7 countries.

In Figure 4 , we have listed the comparative technology-specific scores in various factors among G7 countries.

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FIGURE 4 . Comparison of technology-specific score of G7 countries. Data source: author elaboration.

There are also different phenomena in energy sector resources, capacity, and different level scales may have different outcomes. There is a possibility of reducing energy and resource consumption and to advance degrowth-related ideals of energy local production at local and small-scale energy systems in Spain and Greece ( Tsagkari, Roca, and Kallis 2021 ). The authors summarize that despite the degrowth potential of these local energy projects, their prospects are limited to revitalizing local economies and empowering local communities. The summary results of the literature review regarding G7 countries are listed in Table 5 .

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TABLE 5 . Main results of literature review according to PRISMA guidelines of G7 countries.

Effect of Renewable Energy in Economic Growth Next-11 Countries

Rural people in impoverished and developing nations lack access to electricity that is dependable, economical, and long-lasting. Even though these countries have limited renewable energy sources, many urban and rural people rely on kerosene, diesel, and other fossil fuels to meet their energy needs. The renewable energy capacity in the Next-11 nations is shown in Table 6 .

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TABLE 6 . Renewable energy capacity in 11 countries.

The Bangladesh’s energy sector remains deficient, impeding the country’s smooth economic activity, and progress. For greening growth and meeting sustainable development goals (SDGs), increasing the amount of renewable energy in the energy resources mix and reducing and reducing the material consumption utilized for energy generation is critical ( Baniya, Giurco, and Kelly 2021 ). The government attempts to close the gap between supply and demand for electricity by installing short-term power plants, coal-fired power plants, and importing from neighboring nations. However, the country still has a long way to produce and supply enough power. Furthermore, increased FDI inflows connected to energy limit the country’s extensive usage of renewable energy. At the same time, increased economic growth and CO 2 emissions in the area, particularly in Bangladesh, stimulate the use of renewable energy ( Murshed 2021 ). Another renewable energy source, tidal power, may play an essential part in the nation’s electrical supply by adding to it ( Ahmad and Hasan 2021 , 25). This will very certainly stimulate the industry and commercial activity along the shore. The answer may be alternatives to current energy sources, such as renewable energy resources. More renewable energy sources will be introduced and consumed, reducing energy scarcity, and promoting economic activity and growth ( Bhuiyan, Mamur, and Begum 2021 ). Researchers such as Alam et al. (2017) proposed a one-way causal relationship between economic growth and overall energy demand (renewable and non-renewable). They claim that even a cautious approach to energy sources would not affect the country’s economy, but that because economic success leads to increased energy consumption, Bangladesh must pursue renewable energy and demand-side management ( Alam, Ahmed, and Begum 2017 ). Nigeria, one of the NEXT-11 countries, is one of the Africa’s largest fossil fuel exporters. However, this country has recently experienced a significant energy problem. Biofuel has been identified as renewable energy (bioethanol and biodiesel) in recent years. Waste materials and feedstocks are widely available and accessible, potentially fueling Nigeria’s socio-economic progress ( Adewuyi 2020 ). Islam et al. describe the economic effect of renewable and non-renewable energy systems. The dynamic simulations approach looks at the influence of income growth, foreign direct investment, domestic investment, urbanization, physical infrastructure, and institutional quality on renewable and non-renewable energy consumption in Bangladesh from 1990 to 2019. According to empirical evidence, income growth positively and negatively impact renewable and non-renewable energy usage. Domestic investment has a favorable influence on renewable and non-renewable energy usage. It has been observed that foreign direct investment has a beneficial effect on renewable energy use. Although urbanization has a negative impact on renewable energy consumption, it positively impacts non-renewable energy consumption. Physical infrastructure has a positive and negative influence on renewable and non-renewable energy usage. Factor that effected through the renewable energy system on N-11 countries is listed in Table 7 .

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TABLE 7 . Factor that effected through the renewable energy system on N-11 countries.

Ramadan et al. discuss the economic evaluation of new regulatory tariffs for renewables in Egypt. After 25 years of operation, the results show that adding a CAES system will increase the profitability of the Egyptian government’s new tariff for wind installations, with an NPV of $306 million compared to $207 million for a stand-alone wind system. Furthermore, the economic advantages rise if the government incentives for new renewable energy system installations or decreases financing rates. Ghouchani et al. investigate Iran’s renewable energy development potential. Three potential possibilities for the Iran’s renewable energy sector are examined in this report “long-term technology acquisition programs,” “policy stabilization,” and “attraction of international investment.” The findings indicated that renewable energy policy planning and implementation success is determined by selecting the most adaptive policies to national goals, technological capabilities, and economy. To swiftly and successfully develop and implement a comprehensive renewable energy plan, a thorough analysis of limits, impediments, available facilities and technologies, international sanctions, and foreign investment is essential. Sovacool et al. investigated and provided remedies to the likelihood of corruption in the Mexico’s renewable energy sector. The report then examines particular corruption risks in four nations (Mexico, Malaysia, Kenya, and South Africa) before offering five recommendations and solutions to help combat corruption. These approaches include corruption risk mapping, subsidy registries, sunset clauses, transparency initiatives, anti-corruption regulations, and shared ownership models. In the Economic Community of West African States’ renewable energy plan framework, Ozoegwe et al. examined Nigeria’s solar energy policy goals and tactics. This initiative is advised since the national solar energy strategy document lacks policies on encouraging the solar technology company in Nigeria. The proposals emphasized the requirements of the Renewable Energy Policy of the Economic Community of the West African States, which are currently in place. Case studies supported the recommendations for a community-shared business model for home end users and clusters of small companies in physical market places and an energy management contract business model for large organizations.

Ajayi et al. (2022) examined the influence of sustainable energy on national climate change, food security, and job opportunities in implications for Nigeria. It looked at international data on the links between energy and renewable energy adoption, national development, population growth, job creation, rural–urban integration, and the inherent benefits of renewable energy resources in mitigating climate change and global warming incidents. If Nigeria wants to continue economic growth, particularly in agriculture and food security, renewable energy for power generation must be included in the country’s rural development policy. It also shows that renewable energy can minimize its anthropogenic climate change contribution. From this point of view, total investment in RE and descriptive statistics with technological specific scores by N-11 countries are listed in Tables 8 , 9 , respectively. According to Table 8 , RECAI of Egypt is maximum, and the growth rate of renewable energy in Egypt is also maximum. Table 8 also shows that the RECAI score of some of the countries in the offshore wind, such as Vietnam and geothermal in Egypt is minimal. The World Bank is putting out a long-term offshore wind roadmap for Turkey to issue a tender in the next 2 to 3 years. Following the cancellation of a 1.2 GW offshore wind auction in mid-2018, the World Bank is now in charge of disbursing EU money to support the feasibility and environmental studies in preparation for a second sale. Table 9 describes the different statistical parameters with central tendency in terms of mean, mode, and median of renewable energy sources. The 57th edition of our Renewable Energy Country Attractiveness Index (RECAI) demonstrates that there is a room for further renewable energy investment and strong demand for it. Institutional investors, in particular, have the ability and desire to offer massive, long-term capital injections required to support the fast-growing global renewable energy sector.

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TABLE 8 . Next-11 countries that invest in renewable sources.

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TABLE 9 . Descriptive statistics with technological specific scores of N-11 countries.

In Figure 5 , we have listed the comparative technology-specific scores in various factors among N-11 countries.

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FIGURE 5 . Comparison of technology-specific score of N-11 countries.

The impact of renewable energy use on Nigeria’s environmental quality in several sectors was studied by Maji and his colleagues. The influence of renewable energy consumption on sectoral environmental quality is being examined in Nigeria as part of the government’s effectiveness. A regression analysis was used to estimate a dataset from 1989 to 2019. The per capita indicator, environmental quality indicators, and sectoral output from the agricultural, manufacturing, construction sectors, transportation, oil, residential, commercial, and public services sectors, and other sectors were examined. Adelaja et al. discussed the several barriers to national renewable energy adoption in Nigeria. Despite the privatization of Nigeria’s largest power utility company, the Power Holding Company of Nigeria (PHCN), the country’s electrical demand is rarely met. Nigeria’s electricity output has lately been reduced, despite a massive increase in demand.

To fill the hole, polluting electric generators, inefficient energy sources including candles, kerosene lamps, paraffin devices, and entire energy abstention have all been employed. These problems lead to missed commercial and economic prospects, low quality of life, and missed long-term development potential. Lin et al. looked at how Nigeria’s renewable energy program affected the country’s total output. Based on Nigeria’s Renewable Energy Program aims, this research asks three main questions, Is it possible for Nigeria’s economy to be built entirely on renewable energy? Is it feasible to replace non-renewable energy with renewable energy? What is renewable energy’s economic impact? This study focuses on the growth of renewable energy in Nigeria. We calculate, among other things, the economic effect, production elasticity, and substitution possibilities of renewable and non-renewable energy sources. Our findings, based on a dataset from 1980 to 2015 and analyzed using the translog production function, demonstrate that capital and labor are the key drivers of output in Nigeria; however, although being positive, the economic effect of renewable and non-renewable energy sources is negligible. Wang and Wang. (2020) studied the non-linear behavior of aggregated and disaggregated renewable and non-renewable energy consumption on GDP per capita in Pakistan. This research looked at how diverse forms of energy, such as renewables, fossil fuels, oil-based electrical generating, and hydroelectric power, impact Pakistan’s output. While using fossil fuels to boost economic growth may be beneficial in the early stages of production, it is not helpful in the later stages of production. According to the study, using clean energy, while not beneficial in the early stages of production in expanding production activities in Pakistan, is useful in the later stages of production, not only for production but also for the environment.

Mohamed et al. (2021) in Pakistan discussed the role of renewable energy in combating terrorism. This study looks at the relationship between terrorism, renewable energy, and fossil fuel consumption in Pakistan, taking into account several variables such as economic development and income disparity. Using the autoregressive-distributed lag testing technique, this study evaluated the long-term connection between the examined variables throughout the yearly period of 1980–2015. Their variables have long-term relationships, as shown by the Wald test. The summary results of the literature review regarding the Next-11 (N-11) countries are listed in Table 10 .

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TABLE 10 . Main results of literature review according to PRISMA guidelines of Next-11 Countries.

Granger causality identifies the long-term bi-directional causal links between all variables. The research demonstrates short-term unidirectional causes between terrorism and fossil energy, GDP and renewable energy, and wealth disparity and fossil energy, even though there are bidirectional causal links between renewable energy and fossil energy in the near run. In reality, long-term statistics demonstrate that fossil fuels decrease terrorism while renewable energy increases it.

Wang and Wang (2020) studied renewable energy use, economic growth, and the human development index in Pakistan. This study examines the link between renewable energy consumption, economic growth, and the human development index in Pakistan from 1990 to 2014 using the two-stage least square approach. According to empirical research, using renewable energy does not improve Pakistan’s human development. Surprisingly, the lesser a country’s degree, the higher its income will be. CO 2 emissions also contribute to the enhancement of the human development index. Furthermore, trade liberalization stifles Pakistan’s progress in terms of human development. Again, the long-term feedback idea between environmental influences and human development is supported by causality analysis.

Islam et al. (2022) demonstrate how renewable energy helps Pakistan prosper economically. The research aims to look at the link between renewable energy consumption and economic growth in Pakistan, taking into account capital and labor as possible production function variables. In this work, the autoregressive-distributed lag (ARDL) model and the rolling window approach (RWA) were used to integrate data in a Pakistani scenario. Quarterly data from 1972Q1 to 2011Q4 were used in the study. Bertheau and his colleagues looked into it. A geospatial and techno–economic study for the Philippines was based on 100% renewable energy micro-grids. As a result, this study recommends a hybrid approach that combines geospatial analysis, cluster analysis, and energy system modeling: To begin, they identify islands that are not connected to the power grid. Second, cluster analysis is used to identify trends. Third, we perform simulations of energy systems employing solar, wind, and battery storage to generate 100% renewable energy systems. Our research will focus on 649 non-electrified islands with 650,000 people. These islands are grouped into four groups based on population and renewable resource availability. They determined that cost-optimized 100 percent renewable energy systems rely on solar and storage capacity for each cluster, with additional wind capacity. According to Doytch and Narayan (2021) , renewable energy boosts economic growth. This study examines the influence of non-renewable and renewable energy consumption on economic development, distinguishing between manufacturing and service growth. Our empirical model is based on an endogenous growth framework with an increasing number of intermediate capital goods that comprise non-renewable and renewable energy inputs. We examine the impacts of non-renewable and renewable energy consumption on manufacturing and service growth, broken down by the type of usage (industrial, residential, and total final energy consumption) while accounting for well-known growth variables. Park and his colleagues looked at the procedures used by South Korean renewable energy cooperatives. This research focuses on citizen participatory RE co-ops as a vital niche in the community-led energy route. This study did a narrative analysis based on the RE co-ops’ present state and in-depth interviews. We examined key changes and inertia in the conventional energy system at the national, regional, and local levels by comparing within and across scales. Each scale was made up of a tangle of sub-regimes such as market, policy, and culture. We believe a niche may play a creative role in changing sub-regimes of various sizes based on resources that can be handled, such as money resources, rules, and connections. Sim J et al. looked at the economic and environmental benefits of R&D investment in the renewable energy sector in South Korea. The South Korean government has announced a strategy to invest in renewable energy to shift the country’s economy away from fossil fuels and toward renewables. This study assesses R&D investment in six types of renewable energy sources: biomass, waste, solar thermal energy, photovoltaic energy, marine energy, and wind power energy while taking into account several uncertainty factors such as the amount of renewable energy produced, R&D investment, unit price, and risk-free interest rate. According to Yurtkuran et al., agriculture, renewable energy generation, and globalization all influence CO 2 emissions in Turkey. This study investigates the impact of agriculture, renewable energy production, and globalization on CO 2 emissions in Turkey between 1970 and 2017. It uses the Gregory–Hansen integration test, bootstrap autoregressive-distributed lag (ARDL) approach, fully modified ordinary least squares, dynamic ordinary least squares, and long run estimators. The KOF indices for politics, society, and economics are explanatory variables. The Gregory–Hansen test and the bootstrap ARDL approach imply co-integration variables. In Turkey, Shan et al. investigated the role of green technology innovation and renewable energy in achieving carbon neutrality. A Granger causality test determines the causal relationship between green technology innovation, energy consumption, renewable energy, population, per capita income, and carbon dioxide emissions. Green technology innovation, renewable energy, energy consumption, population, per capita income, and carbon dioxide emissions are all co-integrated in the long run. Furthermore, while green technology innovation and renewable energy reduce carbon dioxide emissions, energy consumption, population, and per capita carbon emissions increase. Kul et al. evaluated the renewable energy investment risk factors for Turkey’s long-term development. This study uses a three-stage decision framework based on the multi-criteria decision methodology (MCDM) to assess and examine the risk factors of REIs in Turkey. The Delphi approach identifies REI risk factors in the first stage. The analytical hierarchy process is used in the second stage to examine the discovered REI risk factors (AHP). The third stage involves applying fuzzy weighted aggregated sum product assessment to evaluate and prioritize methods for overcoming risk issues in REI projects (FWASPAS). The Delphi technique discovered six primary risk variables and 23 sub-risk factors. Economic and commercial risks emerged as prominent risk factors in AHP research. The energy plan for a new era of economic development in Vietnam was examined by Nong et al. (2020) . The prospective implications of such a new power strategy in Vietnam are examined in this research by extending an economic electricity-detailed model. We found that, under a 2030 target scenario, the policy will lower the prices of both fossil- and renewable-based power by 40–78%, benefiting all sectors of the economy by allowing them to replace fossil fuels. Households benefit the most, as indicated by improvements in the per capita utility of 5.64–19.19%. Overall, the Vietnamese economy benefits greatly from the various scenarios, with real GDP increasing by 5.44–24.83%, significantly greater than the results in other countries. Nguyen et al. describe the economic potential of renewable energy in the Vietnam’s electrical industry. In a baseline scenario without renewables, coal provides 44% of total electricity generation from 2010 to 2030. Renewable energy has the potential to reduce that amount to 39%, as well as the sector’s overall CO 2 emissions by 8%, SO 2 by 3%, and NOx by 4%. Furthermore, renewables have the potential to avoid the construction of 4.4 GW of fossil fuel generating capacity, save local coal, and minimize coal and gas imports, therefore boosting energy independence and security. Omri et al. demonstrate how renewable energy helps offset the adverse effects of environmental issues on socio-economic well-being. The findings of this article demonstrate that 1) CO 2 emissions have unconditionally adverse effects on human development and economic growth; 2) the net impact on human growth of the economy from the interaction among renewable power and carbon intensity are positive, that is, renewable energy reduces the impacts of per capita CO 2 emissions on human development and economic growth; and 3) sustainable energy interacts with CO 2 frequency and carbon intensity from liquid fuels.

Conclusion and Policy Implications

Global warming, environmental pollution, and other related issues are no more country-specific problems now. For power generation and carbon dioxide sequestration, the clean development mechanism involves the massive deployment of renewable energy technologies to promote the concept of sustainable development ( Latake et al., 2015 ). In addition to the (greenhouse gas) GHG mitigating potential of renewable energy resources, the energy security guarantee is swiftly becoming a reality with the exploitation of different renewable energy resources. The clean development mechanism is a fundamental idea of the Kyoto Protocol under the canopy of the United Nations Framework on Convention on Climate Change (UNFCCC). However, it was envisaged that the industrialized nations would finance emission reduction mechanisms whereby the fund will be given to developing countries as sponsorship for renewable energy programs. To mitigate this problem, introducing more green technologies and renewable energy sources can be a solution. But, uncertainty, input–output cost analysis, higher production and maintenance cost, skill workforce, enough financial strengths, awareness etc ., are only a few challenges toward mass sustainable energy development. Thus, in comparison of the effects of feed-in tariff (FIT) with a renewable portfolio standard (RPS) in the developing renewable energy industry uncertainty, FIT has higher expected output and profit and lower market prices. On the other hand, the production and profit of RPS remain relatively more stable. If the cost of renewable energy is high, the incentive effect of the policy under FIT seems better. As the price goes down, the incentive effect under RPS probably continues to rise. According to the aforementioned research, it is found out that the renewable energy sector plays a very vital role in the overall growth of the country. Developing a more renewable energy system is necessary for Pakistan, Bangladesh, and Nigeria.

Renewable energy and natural resources significantly reduce emissions ( Usman and Lorente, 2022 ). Consequently, the environmental impact of CO 2 emissions requires widespread monitoring worldwide to analyze the effects on climate change (eg., floods, landslides, droughts, and increase in global average temperature). All these effects weigh under the economic conditions of each country ( Halldó rsson and Kovács, 2010 ). As Hao et al. (2021) , green growth and eco-innovation revolutionize the industrial structure. The G7 countries must focus on a green growth strategy to achieve the SDGs.

In the renewable energy capacity in Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam, it is found that Indonesia plays a vital role using the renewable energy system in the country’s economic growth. The installed capacity of the renewable energy system in Indonesia is 14,690,000 MW. On the other hand, the Pakistan study looked at how different types of energy, such as renewables, fossil fuels, oil-based electrical generation, and hydroelectric power, can affect the output level in Pakistan. Our study concludes that while using fossil fuels to boost economic growth may be beneficial in the early stages of production, it is not helpful in the later stages of production. Whereas using clean energy may not be beneficial in the early stages of production in expanding production activities in developing countries, it is beneficial in the later stages of production not only for production but also for the environment. Policy makers should speed up the deep reforms regarding renewable energy to mitigate environmental degradation ( Koengkan et al., 2020b ). It has been proven that globalization can stimulate renewable energy sources for Latin American countries ( Koengkan et al., 2020a ). This will be beneficial in the region and at the world stage, developing green energy technologies. Thus, it is suggested that policy makers take advantage of globalization to reduce the costs of RE technologies and develop policies encouraging the access of these technologies by households with low income.

This is to note that the study has some limitations. For example, in this article, we have considered mainly G7 and N-11 countries which reflect primarily developed and developing countries. Meanwhile, many underdeveloped countries were not considered in the study. In addition, we have taken the last 10 years (2010–2021) of published articles for this systematic review. But the world economic conditions have been changing rapidly among nations. If we would consider the recent 5 years, the outcome of the review process may vary.

Furthermore, we have only analyzed English language articles. But there may be other critically related articles published in local languages such as Mandarin Chinese, Russians, and Spanish. Thus, we believe there is scope for more research on this topic area.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

MB: conceptualization, methodology, resources and software, writing—original draft, and supervision. VK: original draft. AM: investigation, methodology, writing—original draft, supervision, and formal analysis. GP: data curation, validation, writing—original draft, and writing—review and editing. QZ: Revise, Proofread. XH: Proofread.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We thank the financial support of Széchenyi 2020 under the “EFOP-3.6.1-16-2016-00015.”

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AHP analytical hierarchy process

ARDL autoregressive-distributed lag

Brics Brazil, Russia, India, China, South Africa

CAES computer-assisted execution system

CO 2 carbon dioxide

COVID-19 coronavirus disease variant

CSARDL cross sectionally augmented autoregressive distributed lag

CSP concentrated solar power

DEA data envelopment analysis

EDI economic development indicators

ET environmental taxes

FDI foreign direct investment

FIT feed-in tariff

FWASPAS fuzzy weighted aggregated sum product assessment

G7 Group of Seven

GDP gross domestic product

GG green growth

GHG greenhouse gas

GMM generalized method of moments

HC human capital

HRES hybrid renewable energy systems renewable energy

KOF Konjunkturforschungsstelle

MCDM multi-criteria decision methodology

MLA Modern Language Association

N-11 Next-11

NARDL non-linear autoregressive-distributed lagged model

NOX nitric oxide

NPV net present value

OECD Organization for Economic Co-Operation and Development

PHCN Power Holding Company of Nigeria

PMG Pooled Mean Group

PRISMA preferred reporting items for systematic reviews and meta-analyses

PV photovoltaic

PVAR panel vector autoregression

R&D research and development

RE renewable energy

REC renewble energy consumption

RECAI Company’s Renewable Energy Country Attractiveness Index

REI renewble energy investment

RES renewable energy sources

RPS renewable portfolio standard

RWA rolling window approach

SCI/SSCI science citation index/social sciences citation index

SDGs sustainable development goals

SO 2 sulfur dioxide

UNFCCC United Nations Framework Convention on Climate Change

Keywords: renewable energy, economic growth, consumption, Next-11 countries, Group 7

Citation: Bhuiyan MA, Zhang Q, Khare V, Mikhaylov A, Pinter G and Huang X (2022) Renewable Energy Consumption and Economic Growth Nexus—A Systematic Literature Review. Front. Environ. Sci. 10:878394. doi: 10.3389/fenvs.2022.878394

Received: 21 February 2022; Accepted: 28 March 2022; Published: 29 April 2022.

Reviewed by:

Copyright © 2022 Bhuiyan, Zhang, Khare, Mikhaylov, Pinter and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gabor Pinter, [email protected]

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Energy Production and Consumption

Explore data on how energy production and use varies across the world..

The availability of energy has transformed the course of humanity over the last few centuries. Not only have new sources of energy been unlocked – first fossil fuels, followed by diversification to nuclear, hydropower, and now other renewable technologies – but also in the quantity we can produce and consume.

This article focuses on the quantity of energy we consume – looking at total energy and electricity consumption; how countries compare when we look at this per person; and how energy consumption is changing over time.

In our pages on the Energy Mix and Electricity Mix , we look in more detail at what sources provide this energy.

Global energy consumption

How much energy does the world consume.

The energy system has transformed dramatically since the Industrial Revolution. We see this transformation of the global energy supply in the interactive chart shown here. It graphs global energy consumption from 1800 onwards.

It is based on historical estimates of primary energy consumption from Vaclav Smil, combined with updated figures from BP's Statistical Review of World Energy . 1

Note that this data presents primary energy consumption via the 'substitution method'. The 'substitution method' – in comparison to the 'direct method' – attempts to correct for the inefficiencies (energy wasted as heat during combustion) in fossil fuel and biomass conversion. It does this by correcting nuclear and modern renewable technologies to their 'primary input equivalents' if the same quantity of energy were to be produced from fossil fuels.

How is global energy consumption changing year-to-year?

Demand for energy is growing across many countries in the world, as people get richer and populations increase.

If this increased demand is not offset by improvements in energy efficiency elsewhere, then our global energy consumption will continue to grow year-on-year. Growing energy consumption makes the challenge of transitioning our energy systems away from fossil fuels towards low-carbon sources of energy more difficult: new low-carbon energy has to meet this additional demand and try to displace existing fossil fuels in the energy mix.

This interactive chart shows how global energy consumption has been changing from year to year. The change is given as a percentage of consumption in the previous year.

We see that global energy consumption has increased nearly every year for more than half a century. The exceptions to this are in the early 1980s, and 2009 following the financial crisis.

Global energy consumption continues to grow, but it does seem to be slowing – averaging around 1% to 2% per year.

Primary energy consumption

Total energy consumption.

How much energy do countries across the world consume?

This interactive chart shows primary energy consumption country-by-country. It is the sum of total energy consumption, including electricity, transport and heating. We look at electricity consumption individually later in this article.

Note, again, that this is based on primary energy via the 'substitution method': this means nuclear and renewable energy technologies have been converted into their 'primary input equivalents' if they had the same levels of inefficiency as fossil fuel conversion.

Per capita: where do people consume the most energy ?

When we look at total energy consumption, differences across countries often reflect differences in population size: countries with lots of people inevitably consume more energy than tiny countries.

How do countries compare when we look at energy consumption per person ?

This interactive chart shows per capita energy consumption. We see vast differences across the world.

The largest energy consumers include Iceland, Norway, Canada, the United States, and wealthy nations in the Middle East such as Oman, Saudi Arabia, and Qatar. The average person in these countries consumes as much as 100 times more than the average person in some of the poorest countries.

In fact, the true differences between the richest and poorest might be even greater. We do not have high-quality data on energy consumption for many of the world's poorest countries. This is because they often use very few commercially traded energy sources (such as coal, oil, gas, or grid electricity) and instead rely on traditional biomass – crop residues, wood, and other organic matter that is difficult to quantify. This means we often lack good data on energy consumption for the world's poorest.

Where is energy consumption growing or falling?

Year-on-year change in primary energy consumption.

Globally, primary energy consumption has increased nearly every year for at least half a century. But this is not the case everywhere in the world.

Energy consumption is rising in many countries where incomes are rising quickly and the population is growing. But in many countries – particularly richer countries trying to improve energy efficiency – energy consumption is actually falling.

This interactive chart shows the annual growth rate of energy consumption. Positive values indicate a country's energy consumption was higher than the previous year. Negative values indicate its energy consumption was lower than the previous year.

Electricity generation

Total electricity generation: how much electricity does each country generate.

We previously looked at total energy consumption. This is the sum of energy used for electricity, transport, and heating.

Although the terms 'electricity and 'energy' are often used interchangeably, it's important to understand that electricity is just one component of total energy consumption.

Let's take a look at electricity data. This interactive chart shows the amount of electricity generated by a country each year.

Per capita: which countries generate the most electricity ?

Just as with total energy, comparisons of levels of electricity generation often reflect population size. It tells us nothing about how much electricity the average person in a given country consumes relative to another.

This interactive chart shows per capita electricity generation per person. Again we see vast differences in electricity per person across the world. The largest producers – Iceland, Norway, Sweden, and Canada – generate 100s of times as much electricity as the smallest.

In many of the poorest countries in the world, people consume very little electricity, which is estimated lower than 100 kilowatt-hours per person in some places.

Energy production and consumption by source

This page focuses on total energy and electricity consumption, without digging into the details of where this energy comes from, and how sources are changing over time.

In our pages on the Energy Mix and Electricity Mix , we look at full breakdowns of the energy system; how much of our energy comes from fossil fuels versus low-carbon sources; and whether we're making progress on decarbonization.

Vaclav Smil (2017). Energy Transitions: Global and National Perspectives .

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The effect of energy consumption on the environment in the OECD countries: economic policy uncertainty perspectives

  • Research Article
  • Published: 18 May 2021
  • Volume 28 , pages 52295–52305, ( 2021 )
  • Abdulrasheed Zakari 1 , 2 ,
  • Festus Fatai Adedoyin 3 &
  • Festus Victor Bekun   ORCID: orcid.org/0000-0002-0464-4677 4  

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In this paper, we investigate the impact of energy use and economic policy uncertainties on the environment. To achieve this objective, we use the pooled mean group-autoregressive distributed lag methodology (PMG-ARDL) and Dumitrescu and Hurlin causality test on 22 Organisation for Economic Co-operation and Development (OECD) countries between 1985 and 2017. The PMG-ARDL estimation shows that energy use and economic policy uncertainties have a positive relationship with carbon dioxide emission (CO 2 ) emission, while a negative relationship is confirmed between renewable and CO 2 emissions in the long run. The short-run estimation shows a positive relationship between energy use, real gross domestic product, and per capita on CO 2 emissions. The Dumitrescu and Hurlin causality results highlight a unidirectional running from real GDP and GDP per capita square to CO 2 emissions. Furthermore, one-way causality exists between CO 2 emissions to economic policy uncertainties. These results have policy implications on the macroeconomy which are discussed in detail in the concluding section.

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Carbon emissions and the aftermath of non-renewable energy consumption have been on the increase since the beginning of the twentieth century at the global level. This is evidenced by emission figures that are 1.6 times the 1990 level leading to an excess of 36 billion tons in the year 2014 (Yao et al. 2019 ; Ozcan and Ozturk 2019 ; Rafindadi and Ozturk 2017 ). The share of fossil fuel energy of over 80% of the total energy supply (IEA 2016 ) and a considerably lesser than 20% renewable energy consumption rate all points to and corroborates the previous stance that rising carbon emission or energy consumption as the case may be is a direct result of economic growth. This, according to Grossman and Krueger ( 1995 ), would initially lead to an initial phase of environmental degradation, which is subsequently followed by the improvement phase as the average income increases on the environmental Kuznets curve.

Furthermore, Panayotou ( 1993 ) posited earlier that carbon emission would have three resultant effects: scale, structural, and technical effects stemming from economic growth, thus demonstrating and attesting to the inverted U-shaped Kuznets curve. Previous studies regarding carbon emissions, a resulting consequence of non-renewable energy consumption, and the environmental Kuznets curve hypothesis revolved mostly around international trade, technical progress, foreign direct investments, and incomes (Kaika and Zervas 2013 ; Yao et al. 2019 ; Sarkodie and Strezov 2019 ; Asongu and Nwachukwu 2018a & Asongu and Nwachukwu 2018b ; Asongu and Odhiambo 2019b ; Rjoub et al. 2021 ).

More recently, the addition to the group of information on the energy literature has been on renewable energy consumption and cleaner energies going by the outcry of the effects of an earth-wide temperature boost brought about via carbon outflow (Yao et al. 2019 ; Bekun et al. 2019a ). This has led to the renewable energy environmental Kuznets curve (RKC) proposition as a hypothesis that supersedes the conventional environmental Kuznets curve in that it accounts for renewable energy to show the U-shaped relationship that exists between the renewable energy consumption rate and per capita GDP. This unconventional phenomenon, the RKC, asserts that more renewable energy consumption can help accelerate the conventional EKC to arrive faster at its turning point. This lends credence to the fact that the consumption of renewable and non-renewable energies will lead to a renewable environmental Kuznets curve that arrives faster at its turning point than an environmental Kuznets curve designated to non-renewable energy consumption. This has led to the selection of a renewable energy consumption rate, an index to uncover the differences of renewable energy consumption while examining the environmental Kuznets curve hypothesis side by side with the renewable energy environmental Kuznets curve hypothesis.

Economic policy uncertainty shows the relative frequency of specific news media references dealing with occurrences as they pertain to economy, policy, and uncertainty; government charge code arrangements that are due to expire; and the rate of forecaster disagreement. This uncertainty measurement ranges from the Global Economic Policy Uncertainty index value to the National Economic Policy Uncertainty index value Baker et al. ( 2016 ). It merely indicates the risk that comes with an uncertain policy response on the part of the government as an economic agent to put regulatory measures in place. This ultimately leads individuals and firms to become irresolute, thus delaying consumption and investment until the uncertainty is resolved. EPU has been on the increase since the 2007 to 2009 recession due to the observed uncertainty by households and businesses on fiscal policies, future taxes, spending, health care, monetary policies, and other measures in place to regulate the economy.

An increase in the EPU index often leads to the postponement and reduction of business and economic activities such as recruitment, investment, and other forms of spending. It was also discovered that policy uncertainty in news articles revolved around taxes, spending, and monetary and regulatory policies. This study harnesses to paint a picture of the linkage effect between policy uncertainty energy emission nexus and the environmental Kuznets curve hypothesis. While previous studies appeared to have neglected the link between carbon emissions, Jiang et al. ( 2019 ) posited that EPU most certainly affects the external business environment, which ultimately affects the decision-making process of economic agents. This trickles down on the carbon emissions as it is closely linked to the output decisions of microeconomic agents. As an OECD country, the USA is said to maintain a fairly stable and consistent EPU index. At peak periods of the US EPU index, for example, the total carbon emission is observed to replicate the local peak, and when the EPU index falls, the total carbon emission falls as well. The effect is a shift in the priority of the governments from environmental governance to the root cause of events that led to an increased financial policy uncertainty index in the first place. For example, the USA’s withdrawal from the Paris Agreement will increase the EPU index, leading to a lower prioritization of carbon emission reduction as a goal Jiang et al. ( 2019 ). Going by this analogy, one can infer that the EPU will have a corresponding effect on the postulation of the environmental Kuznets curve hypothesis because the EPU will readily affect production and consumption activities associated with renewable and non-renewable energies (Jiang et al. 2019 ). This leads to decreased investments or consumption at periods of high uncertainty and increased investments or consumption at periods of low uncertainty. Therefore, this attributes a low EPU to a quick arrival at the turning point of the environmental Kuznets curve and a high EPU to points farther away from the turning point of the environmental Kuznets curve.

Research conclusions from previous studies lend credence to the fact that EPU is relevant to understanding the behavior of emissions in energy consumption even at the global level. This is because the EPU impacts macroeconomic activities, which has a ripple effect on societal carbon emissions across countries. Observing the USA’s economy, an OECD country that happens to be the second-largest carbon emission country in the globe, it is important to study the linkage effect of the economic policy uncertainty in relation to energy emissions to understand the appropriate actions to be taken for periods of high economic policy uncertainties especially as they relate to the environmental Kuznets curve hypothesis which is the objective of this research work. In summary, this study draws strength from the carbon-income function and EKC framework for OECD countries that have received little or no documentation in the energy-environment literature while accounting for economic policy uncertainty on the environment. The next segment presents a survey of the literature review of related studies. This is followed by a description of data, variables, and methodology in the “Data and methodology” section. The “Results and discussion” section presents the empirical results from this study and discusses the implications of the research findings. This study concludes in the “Conclusion and policy implications” section with vital energy and macroeconomic policy recommendations.

Literature review

A lot of studies have investigated the relationship between energy consumption and economic growth across countries and across regions (Al-Mulali et al. 2016 ; Ozturk and Bicimveren 2018 ; Udemba et al. 2020 ; Adedoyin et al. 2020a , 2020b ; Kirikkaleli et al. 2020 ; Udi et al. 2020 ; Tchamyou et al. 2019 ; Asongu and Odhiambo 2019a ). Some of these studies examined variants of growth-energy nexus, energy-growth nexus, and the two-way causal effect between them. Starting with an earlier trajectory by the OECD in 2011, it was predicted that the share of energy consumption allotted to the OECD group from the world consumption was set to reduce from 35% in 1995 to about 32% by 2020. Prior to this point, the literature on economic growth and energy consumption dates as far back as 1978, following a seminal work by Kraft and Kraft on the relationship between energy and Gross National Product. A handful of studies have concentrated on the relationship between economic growth and energy consumption in OECD countries (see Wong et al. 2013 ; Coers and Sanders 2013 ; Bella et al. 2014 ; Mercan and Karakaya 2015 ). For instance, Asongu et al. ( 2017 ) explored the determinants of environmental degradation in selected 44 sub-Saharan African countries using generalized method of moments techniques to explore the role of ICT modulates the effect of CO 2 emissions on inclusive development. The study found that ICT can be used to reduce the negative effect of CO 2 emission on inclusive development. That is, ICT modulating policy thresholds should be established for environmental sustainability targets in the selected African bloc.

Using autoregressive distributed lag model (ARDL) in conjunction with (FMOLS) and dynamic ordinary least squares (DOLS) for robustness, Adebayo et al. ( 2021 ) explored the nexus between environmental quality and economic growth while accounting for financial development and globalization for the case of South Africa. The study revealed that a 1% increase in energy (coal) consumption increases environmental degradation by 1.077%, while a 1% increase in financial development decreases the environmental degradation by 0.973%. The study submits that policymakers and administrators in South Africa should advance policies that encourage energy consumers to shift toward renewable energy. Furthermore, financial reforms should be implemented to reduce environmental degradation. This study is in line with Adebayo and Odugbesan ( 2021 ) study that financial development, economic growth, and urbanization contribute to the pollution level in South Africa.

Zhang et al. ( 2021 ) explored the anthropogenic effect of human activities on CO 2 emissions using the STIRPAT framework. The study explored the determinant of CO 2 emissions in Malaysia using ARDL, fully modified OLS (FMOLS), dynamic ordinary least square (DOLS), and wavelet coherence and gradual shift causality. The study regression shows that economic growth, gross capital formation, and urbanization positively impact CO 2 emissions. The direction of causality reveals a one-way causality from urbanization to CO 2 emissions, unidirectional causality from economic growth to CO 2 emissions, and unidirectional causality from gross capital formation to CO 2 emissions as reported by causality analysis. This outcome resonates with the study of Kirikkaleli et al. ( 2021 ) for the case of Turkey.

He et al. ( 2021 ) investigated the role of consumption-based carbon emissions in Mexico while accounting for the role of economic growth trajectory admits global trade flow, energy consumption using an autoregressive distributed lag approach, and a causality analysis frequency domain causality tests. The study’s key findings highlight that globalization and financial innovation improve environmental quality. Also, energy consumption and economic growth dampen environmental quality. Finally, trade openness exerts no significant impact on environmental quality. The study further illustrates the need for Mexican government officials to carefully craft energy environmental policies aimed at increasing economic growth without compromise for environmental quality.

Furthermore, regarding consumption-based carbon emissions determinants, Kirikkaleli and Adebayo ( 2021 ) for Indian identify that public-private partnership investment in energy and energy consumption also significantly causes consumption-based carbon dioxide emissions at different frequency levels in the Indian economy. While a causal relationship is said to be theoretically possible and already established as a stylized fact from these studies, discrepancies in these previous studies were traced to differences across countries, time skylines, informational collections, and factual techniques employed to determine the relationship between energy consumption and economic growth. These studies presented inconclusive results that were not fit for policy actions in OECD countries. Methods that were used by these studies ranged from vector error correction model, PVAR, autoregressive distributed lag model, DOLS, and FMOLS to explore the relationship in an attempt to explain the energy consumption-economic growth nexus, although some studies have used the panel data approach.

More recently, attempts have been made to advance the knowledge horizon of the energy literature as it pertains to OECD countries to understand and assert the direction and magnitude of the causal relationship between economic growth and energy consumption. Gozgor et al. ( 2018 ) examined the impact of renewable and non-renewable energy consumptions on growth using 29 OECD countries from 1990 to 2013. The study theoretically built a growth model to capture economic complexities and as a yardstick of capabilities amongst the countries in question. The study employed the panel autoregressive distributed lag due to the mixed nature of the sets of joining of the factors in question and the panel quantile regression methods for estimation. The study concluded that the positive effect of both renewable and non-renewable energy consumption components on economic growth was valid when checked against the growth hypothesis that the study adopted. The study, therefore, adopts the stands on the fact that energy consumption positively affects growth and that both renewable and non-renewable energy consumption is vital and important for the furtherance of economic growth. Additional studies like Jebli et al. ( 2016 ) investigated the relationships between monetary development, inexhaustible and non-sustainable power source utilization, carbon emissions, and international trade amongst 25 OECD countries over the 1980 to 2010 timeline. The study employed the granger causality tests, fully modified ordinary least squares, and the dynamic ordinary least squares. The study found that bidirectional causality existed between renewable and non-renewable energy consumption. The results also verified the inverted U-shaped environmental Kuznets curve hypothesis for the OECD countries in view. The study concluded that increased non-renewable energy consumption led to increased carbon emissions and that increased trade through renewable energy consumption measures to be considered to reduce environmental degradation.

A study by Kahouli ( 2019 ) assessed the relationship between the consumption of energy and growth of the economy across 34 OECD countries over the 1990 to 2015 timeline. The study employed an extensive and more recent panel data econometric method by using the static and dynamic techniques simultaneously and separately to look at the relationship between economic growth and energy consumption. The study found a unidirectional relationship between energy consumption and economic growth. Also, there was a one-way causal relationship running from economic growth to energy consumption under the dynamic estimation technique. This study was in line with earlier results by Salahuddin and Gow ( 2014 ), Omri and Kahouli ( 2014 ), Raza et al. ( 2015 ), and Kasman and Duman ( 2015 ), stressing the importance of the bidirectional relationship between energy consumption and economic growth.

More recently, the empirical study of Ozcan and Ozturk ( 2019 ) investigated the different linkages that exist between the use of energy and economic growth by taking a sample of 35 OECD countries over the 2000 to 2014 period. The study used three empirical models to capture the relationship between energy consumption, economic growth, and environmental degradation using the generalized method of moments and the panel vector autoregressive regression method. The study’s key contribution to the body of knowledge was a more encompassing proxy to capture environmental degradation. It employed two composite indices and the CO 2 emission proxy used by earlier studies. In addition to the components of what appears to be an encompassing proxy than earlier studies, Ozcan and Ozturk ( 2019 ) collectively adopted the ecological footprint and environmental performance index to reflect the different forms of environmental pollution. The study found a significant positive relationship between economic growth (GDP) and energy consumption on all the environmental degradation indicators used as a proxy in the model. The study further indicated that increment in industrial economic activities of the countries in view contributed more to environmental pressure and CO 2 emissions, which appeared to follow the general consensus.

Nevertheless, while there appears to be a paucity of literature in this variable combination, especially in the case of OECD countries, quite a few papers have provided a base knowledge of how the economic policy uncertainty impacts key sectors typical economy. However, one thing that comes to the fore is that corporations and economies are known to act conservatively at times of uncertainty, which slows investment activities and employment rates down. This ultimately affects the energy consumption variable of such an economy, which ultimately trickles down to other countries due to the interconnectedness that the world operates with.

Complexity is one factor responsible for the degree of uncertainty. An advancement that appeared to have eased this complexity is the innovation propounded by Baker et al. ( 2016 ), which established the economic policy uncertainty index. Prior to this point, an initial publication by Kenneth Galbraith in 1977 titled “The Age of Uncertainty” paved the way for what has now become a transformed research area. Overall, the conclusion from previous studies lent credence to the fact that more conservative policies were best at times of high economic policy uncertainty. This is because the cost of borrowing increases, making firms spend less on capital, leading to an economic downturn (Al-Thaqeb and Algharabali 2019 ).

Jens ( 2017 ) sought to understand the US gubernatorial decisions as a plausible source for exogenous variety in an attempt to investigate the link between political vulnerability and firm speculation. The study employed term confines as an instrumental variable for political decision closeness in addition to summary statistical methods to present the results. The study found that investment declined 5% before elections and rose as much as 15% for firms directly related and susceptible to this type of uncertainty. Also, because close elections are tantamount to periods of economic downturns, close election effect on investment was understated by more than half going by the ordinary least square method, and post-election rebounds to investment or consumption depended on the re-election of an incumbent administration. The implication for energy consumption is that high periods of political uncertainty will come with low energy consumption and the alternative sources of energy being considered at this period to determine the rate of environmental degradation for any typical OECD economy.

Canh et al. ( 2019 ) investigated the role of two forms of uncertainties: internal (domestic) economic policy uncertainty and the world uncertainty played on the net inflow of outside direct speculation in 21 countries the 2003 to 2013 timeline. The study adopted a sequential two stages linear panel data model technique to carry out its analysis. The study found that the domestic growth rate of the economic policy uncertainty index affected the inflow of foreign direct investments adversely. When this domestic growth rate was placed side by side with the growth rate of the World Uncertainty Index, a measure that accounts for 143 countries, the ensuing result was a positive impact on the net inflow of foreign direct investment to the host country in question. The study, therefore, concluded that while an increase in national economic policy uncertainty might present an adverse effect on FDI inflows, an increase in the world global economic policy uncertainty could lead to increased inflow of foreign direct investment, and this was explained as the behavioral bias that could averse an investor based on the investor’s sensitivity to factor in uncertainty when making an investment decision.

Zhang et al. ( 2019 ) investigated the influence of two key countries, the USA and the Republic of China, on several markets across the globe. The markets considered under this study were, namely, commodity, energy, credit, and financial markets. The study was borne out of the uncertainty that ensued from the US-China trade conflict and, thus, sought to provide answers to research questions around the rationale behind the conflict, the supposed threat that a rising Chinese economy could possibly be imposing on the US economy. The paper employed the economic policy uncertainty index of these two global players as a measure of their policy positions to build a time series that could estimate the degree of influence of the two countries on the global markets. The study found that while China’s realm of influence has increased in recent years, it has not been sufficient to oust the USA to control global world affairs. In addition, the study concluded that China’s competition with the USA in shaping the world is more politically driven rather than economically driven.

Liu et al. ( 2020 ) investigated the differential impact between investments in non-renewable and renewable energy enterprises. The study was comparative based on regulatory effects such as ownership concentration, external demand, financing constraints, growth opportunities, and how it related to investment and economic policy uncertainty. The study used data from 52 non-renewable energy enterprises and 116 renewable energy enterprises in China over the 2007Q1 to 2017Q4 timeline. The study employed a panel regression model for estimation. The study found that non-renewable energy enterprise investments were significantly inhibited by economic policy uncertainty.

On the other hand, renewable energy investments were not significant even though they were inhibited by economic policy uncertainty. The study also found that economic policy uncertainty specifically inhibited investment in the petroleum and coal enterprises, whereas economic policy uncertainty promoted investments in renewable energy enterprises like geothermal energy, solar energy, and other forms of renewable energy. The study concluded that growth opportunities could offset the inhibitory effect associated with the economic policy uncertainty and that a strengthened financial constraint brings with it an uncertainty associated with economic policy in non-renewable energy enterprise, which would not be as significant as the renewable energy enterprise.

Conclusively, the reviewed literature appears to have established a negative relationship between economic political uncertainty and energy consumption in that higher values of uncertainty reduce consumption and investment generally, but this sometimes leads to the consumption of cheaper and more traditional sources of energy which might, in turn, lead to increased carbon emissions thus increasing environmental degradation and extending the turning point of the environmental Kuznets curve.

Main gap and research contribution

One of the issues that commanded attention in the literature on economic uncertainty was the increased EPU value that came with the USA’s withdrawal from the Paris Agreement of 2015 to mitigate climate change. The importance attributed to environmental governance by the US government was reduced and reprioritized following this withdrawal, which negatively affected the implementation of a significant portion of previous environmental protection policies. This then became the testament on which the government’s determination to reduce carbon emissions as a goal became compromised. The ultimate implication of this move by the US government was that the Environmental Protection Agency’s budget had to be cut down in 2017. Secondly, the EPU was assessed to have been a possible threat to the US economy as a whole.

On the one hand, energy consumption by the US economy was cut down, making way for a decrease in carbon emission. On the flip side, a bad economic scenario for firms and the citizenry may opt for traditional cheaper sources of energy such as coal, which would result in more carbon emissions. Finally, facing high EPU, firms relaxed their effort to deliver an economy with reduced carbon emissions. This was due to the premonition that governmental departments would relax their requirements on environmental governance.

Another issue that suffices as a case for economic policy uncertainty is the decision by the UK to leave the European Union. While policies that are likely to be adopted by the European Union membership are uncertain, speculations about this uncertainty, especially in this transition period and with world events like the Coronavirus pandemic, have further increased uncertainty in the UK economy. A study by Steinberg ( 2019 ) sought to explore the macroeconomic impact of the trade policy uncertainty resulting from the Brexit movement. The study employed the dynamic stochastic general equilibrium (DSGE) model on the UK, the European Union, and the rest of the globe to address quantitative questions on the consequences of Britain exiting the European Union. Questions surrounding the uncertainty of the trade policies that were likely to replace the EU agreement post-Brexit and what the future held for the UK economy, as well as the lag periods that the turn of events as was to last for, were investigated by this study. The study found that uncertainty about Brexit will have little impact and that the welfare cost about Brexit is insignificant as households would sacrifice little to avoid this uncertainty. The study also found that the cost of Brexit, when compared with some other macroeconomic uncertainties, had a sizeable impact than other uncertainties meaning that a one-time Brexit uncertainty is the same as other unpredictable policy uncertainty in economic activity that occurs in the UK in an atypical year.

In summary, global and national issues have been identified as inflexion points that determine the degree of economic policy uncertainty. This is because the EPU index has its major components built on disagreements by forecasters, news references, and tax provisions, all of which are channels of speculation for economic agents, based on the highlighted literature and motivation in the “Introduction” section. The present study is further motivated by the United Nations Sustainable Development Goals (UN-SDGs 7, 8, and 13) crusade, which informed the choice of the variables for the econometric modeling, and subsequently, the following hypotheses have been constructed:

H1: Do conventional energy consumption (fossil fuel induced) engenders sustainability in the environment in the OECD countries in line with (UN-SDGs 7 and 8). Conventionally, energy use has been identified as a key driver for increased economic growth over the years. This proposition has been validated by several studies empirically, the first by Kraft and Kraft ( 1978 ) and more recently by several other studies affirming the pivotal role of the energy-induced growth hypothesis (Zakari et al. 2021 ; Emir and Bekun 2019 ; Bekun et al. 2019b ; Asongu et al. 2017 ). This leads to the formation of the next hypothesis

H2: Is there a positive or negative nexus between CO 2 emissions and economic growth in the study areas (OECD countries). There has been extensive literature on the economic growth-pollution connection. This is a result of increased dirty economic activities that will increase pollution emissions. This is in accordance with the fight of the UN-SDG 13 in mitigating climate change/pollution-related issues.

H3: Given the cointegration relationship establish between real income (GDP, CO 2 emissions, and energy use). What is the connection between EPU in the mix for OECD countries over the sampled period?

Data and methodology

The data are collected for 22 OECD countries spanning the period from 1985 to 2017. The selections of these countries are motivated by the amount of data available for all the variables under consideration. Data were extracted from the World Bank Development Indicator (WDI) and British Petroleum Database, which is given as CO 2 emissions (CO 2 ) measured in million tonnes of carbon dioxide (source: BP Statistical Review of World Energy June 2019); primary energy consumption (ENU) measured in million tonnes oil equivalent (source: BP Statistical Review of World Energy June 2019); real gross domestic product (RGDP), measured in constant 2010 US$ (source: WDI); and economic policy uncertainty Footnote 1 (EPU), proxy: world uncertainty index (WUI) (source: Ahir et al. 2018 , http://www.policyuncertainty.com ).

Model and methods

This paper examines the role of economic policy uncertainties in the energy emissions consumption nexus in OECD countries. Hence, our energy emission function is set to include economic policy uncertainties. Methods like Pesaran’s test of cross-sectional independence, results of Pedroni and Kao cointegration tests, PMG-ARDL, and Dumitrescu and Hurlin panel causality were adopted.

where CO 2 represents carbon dioxide emission, ENU measures the level of energy use, RGDP is a real gross domestic product, RGDP2 is GDP per capita, and EPU measure economic policy uncertainty, i, subscripts e i refers to each country’s fixed effects, that is, the countries and the time, as shown by the subscripts i (i = 1, −  − N) t (t = 1, −  − T), respectively.

Results and discussion

Table 1 provides a summary of the results for 22 OECD countries for the period 1985–2017. The emission of energy consumption, real GDP, and GDP per capita indices exhibit an increasing effect between 1985 and 2017, with real GDP having the highest increasing value of 11.7159% and energy use contributing to the lowest at 1.8260%. However, the economic policy uncertainty and economic policy uncertainty vs energy use indices have negative values, showing a decline of −1.4513% and −2.6249.

Table 2 reports unconditional correlations on the selected variables for the 22 OECD countries. The correlation results show that carbon dioxide emission (CO 2 ) is positively trending with the real gross domestic product (RDGP), economic policy uncertainty (EPU), and energy use (ENU). At the same time, it is negatively related to real domestic product per capita (RGDP2). These correlations suggest that carbon dioxide emission (CO 2 ) is highly associated with the real gross domestic product, economic policy uncertainty, energy use, and real gross domestic product per capita. Every one of these estimations is measurably critical at 1%, 5%, and 10% levels, respectively. However, we further confirm their association in the following empirical investigation.

Pesaran’s test of cross-sectional independence

In most of the empirical literature, panel data are often not tested for cross-sectional reliance among the series. While neglecting this fact posed severe implications to the analysis, the results obtained often remained unrealistic. Given this fact, it is essential to check the data set if they are cross-sectional reliance or independent. To do this, we applied the Pesaran ( 2004 ) cross-sectional dependence (CD) test on the 22-panel data. The results of the cross-sectional dependence (CD) test are reported in Table 3 . The discoveries over the arrangement and economies propose that the invalid speculation of cross-sectional autonomy is dismissed at the 5% noteworthiness level, in this manner tolerating the elective theory. Consequently, these outcomes show that the chose information arrangement is a cross-sectional ward during the investigation time frame, 1985–2017.

Stationary and cointegration tests

According to Baltagi et al. ( 2005 ), a panel data approach provides superior, robust findings, helping to increase the power of the unit root and cointegration test, given that it combines both time series and cross-sectional dimension (Brambor et al. 2006 ; Tchamyou and Asongu 2017 ; Boateng et al. 2018 ; Tchamyou 2019 . The results in Table 3 above confirmed the presence of cross-sectional dependence across the series; hence, we apply a CIPS panel unit root test that considers cross-sectional dependence in the estimation. Specifically, we use the Bailey et al. ( 2016 ) cross-sectional augmented IPS (CIPS) test. The estimated results from the CIPS test are displayed in Table 4 . The CIPS test the discoveries on level information arrangement over the factors, and economies propose the proof of a unit root. Be that as it may, the evaluations on the primary request distinction information arrangement affirmed the dismissal of the invalid theory at a 1% level of noteworthiness for the entirety of the examples and acknowledged elective speculations. This proof infers that the chose factors are not stationary at the level yet stationary at their first-request contrast.

Having confirmed that the series is stationary, we further proceed to check if the variables have a long-run relationship. To do so, we applied the Pedroni and Kao cointegration test and the result in Table 5 . The results confirmed the rejection of the null hypothesis, which says there is no cointegration. Therefore, we accept an alternate hypothesis which says the series are cointegrated at a 1% significant level. This enables us to perform the PMG-ARDL analysis.

Results of PMG-ARDL

Having established the series to be cointegrated in the long run, we further analyzed the PMG-ARDL test, as shown in Table 6 . The long-run estimation confirmed that energy use and economic policy uncertainty has a positive relationship with CO 2 emission value at the 1% and 5% significance level, respectively. This relationship means that only the energy use and economic policy uncertainty rise can lead to an increase in CO 2 emissions with an average value of 1.1843% and 0.0199%, respectively. On the contrary, real GDP and GDP per capita improve CO 2 emissions in these countries, with an average of 0.2023% and 0.3640, respectively. This is possible as more income is allotted to the individual, and such clean energy technologies became affordable. Therefore, renewable energy or clean energy technology consumption increases and reduces the level of CO 2 emissions.

The error correction term (ECM) coefficient that presents the speed of adjustment for the case of disequilibrium in the present study case is negative as expected and low (0.1137) at the 1% significance level. The ECM suggests that over 11% of the equation fit system is corrected for on an annual basis with the contribution of the study explanatory variables. The short-run estimation indicated that the values of energy use, real GDP, and GDP per capita positively influence CO 2 emissions because they increase this variable by 0.7277%, 0.2482%, and 0.2368%, respectively. However, economic policy and the interrelated economic policy and energy use do not show any connecting relationship with CO 2 emissions. Overall, energy use and economic policy positively affect CO 2 emissions, while real GDP and GDP per capita reduce the increases in the 22 OECD countries.

The FMOLS (Pedroni 2004 ; Kao et al. 1999 ); this method accounts for heterogeneity in the model; *** and * show the level of significance at 1% and 10%, respectively

Dumitrescu and Hurlin panel causality

Dumitrescu and Hurlin ( 2012 ) panel causality estimation was used to further confirm the nexus among the variables. It will interest you to know that energy use and GDP per capita all signified feedback relationships with CO 2 emissions, while a unidirectional link found running from real GDP and CO 2 emissions. Similarly, CO 2 radiation caused economic policy uncertainty; energy use; real GDP; and GDP per capita caused economic uncertainty, while feedback relationship is confirmed between real GDP and energy use.

Panel fully modified least squares (FMOLS) with weighted estimation

For robustness, as reported in Table 7 , we used robust panel econometric techniques to deal with the issues of heterogeneity in the estimation (Pedroni 2004 ; Kao et al. 1999 ). In particular, this methodology utilizes since quite a while ago run covariances from the cross-segment gauges and reweights the information to represent heterogeneity in the estimation. Given the importance of this methodology, we apply the Group-FMOLS technique to evaluate the since quite a while ago run patterns among the parameters. The results from the Group-FMOLS are shown in Table 8 . The results of Group-FMOLS show that the increase in energy use and economic policy uncertainty leads to a rise in carbon emissions, while real GDP and GDP per capita help reduce the growth of CO 2 emissions. In conclusion, our robust analysis is not different from the findings from the PMG-ARDL result.

Conclusion and policy implications

There are a considerable number of studies on the determinants of environmental quality. However, previous studies have not taken into account the influence of economic policy uncertainties, especially in OECD countries. For these reasons, we use annual data for a panel of 22 OECD countries between 1985 and 2017 to test the impact of energy use and economic policy uncertainties while accounting for other macroeconomic indicators. We applied robust econometrics techniques such as PMG-ARDL and Dumitrescu and Hurlin panel causality.

Empirical results support the argument that in the long run, energy use and economic policy uncertainties further deteriorate the quality of the environment. In contrast, renewable energy improves the quality of the environment. Similarly, energy use, real GDP, and GDP per capita to environmental degradation within the region in the short run. We also found a causal relationship between real GDP and GDP per capita to CO 2 emissions, energy use to real GDP, CO 2 emissions, energy use, real GDP, GDP per capita to economic policy uncertainties.

Given our findings, we will understand that energy use, real GDP, GDP per capita square, and economic policy uncertainties posed problematic to the environment since it leads to an increase in the CO 2 emissions. Therefore, it has become a point of priority for the policymakers and government administrators to trade with caution in implementing policies on improving the quality of the environment. In addition, our study revealed that renewable energy source enhances the quality of the environment. Hence, the government of the OECD countries should adopt the use of renewable energy sources in their activities as commercial or home use. The outcome of energy-induced and economic policy uncertainty to pollution emission calls for a paradigm shift to renewables such as photovoltaic energy, hydroenergy, and wind energy, and for a promotion of renewable energy sources of electricity, grants, and taxes—holiday should be granted to investors. More so, FDI inflows should be cautiously directed to the investment in the renewable source of electricity, which are reputed to be cleaner and ecosystem friendly. Thus, there is a need for more efficient, modern, and cleaner energy technologies in the energy portfolio as a prerequisite for a successful transition from fossil fuel consumption while achieving a decarbonized economy that is in line with sustainable development goals (SDGs 8 and 13). Furthermore, to sustain the current momentum in OECD for sustainability target, there is a need to tighten commitment on environmental treaties like Kyoto Protocol and the Paris Agreement.

In conclusion, our study has revealed new findings, but not without limitation. In this present study, we were constrained to expand our study beyond the OECD countries due to the lack of data. Therefore, we will encourage future studies to consider broadening the scope of the survey beyond the OCED countries.

Data availability

The data for this present study are sourced from the World Development Indicators ( https://data.worldbank.org/ ). The current data can be made available upon request, but are all available and downloadable at the earlier mentioned database and weblink.

Note. WDI is connotation for data from World Bank Development Indicator of the World Bank database sourced from https://data.worldbank.org/ . WUI = This tab contains the beta version of the historical World Uncertainty Index (WUI) for 82 countries from 1952Q1 to 2019Q3. The tab contains a moving average index. The 3-quarter weighted moving average is computed as follows: 1996Q4= (1996Q4*0.6) + (1996Q3*0.3) + (1996Q2*0.1)/3.

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Zakari, A., Adedoyin, F.F. & Bekun, F.V. The effect of energy consumption on the environment in the OECD countries: economic policy uncertainty perspectives. Environ Sci Pollut Res 28 , 52295–52305 (2021). https://doi.org/10.1007/s11356-021-14463-8

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January 8, 2024

The U.S. Energy Transition Explained in 8 Numbers

Solar and natural gas surged last year in the U.S., while wind stumbled

By Benjamin Storrow & E&E News

Solar panels in flat landscape with mountains in background

Solar farm in the Appalachian Mountains with a distant view of the small town of Nesquehoning, Poconos region, Pennsylvania.

Alex Potemkin/Getty Images

CLIMATEWIRE |  The power sector is key to U.S. efforts to cut planet-warming pollution this decade.

Technologies for generating wind and solar energy are expected to green the economy faster than electric cars and heat pumps, according to  deep decarbonization studies . That was evident in 2023 as large solar projects catapulted toward levels never seen before in the U.S.

But there were also indications that the transition to clean energy had not gone as smoothly as some analysts predicted. Wind projects stumbled, for instance, and natural gas continued to soar.

E&E News dug into data collected by the U.S. Energy Information Administration to get a sense of what happened in the U.S. power sector last year. Here are eight numbers that tell the story.

148 terawatt-hours: The amount of electricity generated by utility scale solar

It was a boom year for solar. The amount of energy produced in 2023 by large solar projects was 130 percent more than the U.S. generated five years ago, and 16 percent more than in 2022, according to preliminary EIA data. It was enough to power almost 14 million homes and amounted to 4 percent of total power generation.

20.8 gigawatts: The amount of utility-scale solar installed in 2023

Why is solar generation growing so fast? Because the U.S. is installing a lot more of it.

The country added 10.7 gigawatts of solar in 2020, 13.6 GW in 2021 and 11.1 GW in 2022. Those numbers will likely be blown away when the 2023 figures are finalized.

Through November, power companies had installed almost 12 GW of new solar capacity. They were scheduled to bring another 8.8 GW online in December, though it remains to be seen how much of that power actually came online. Still, if just a fraction of those facilities were turned on last month, solar will outpace natural gas, which ranked second in terms of new capacity additions, with 8.7 GW brought online last year.

What does that mean for U.S. efforts to cut carbon emissions in half by 2030? It's not enough. Many modeling groups calculate the trajectory of emissions based on annual clean energy installations. The Rhodium Group, for instance, found that U.S. emissions would fall 42 percent if the country installed an average of 37 GW of solar every year between 2023 and 2025.

“Solar has been a clear winner from a renewable standpoint this year. It has seen really impressive growth year on year,” said Ben King, a power sector analyst at the Rhodium Group. “We need to be adding even more solar on an annual basis, starting this year and every year moving forward. But this is a step in the right direction.”

386 million tons: The amount of coal consumed by power companies last year

Richard Nixon was president the last time utilities used so little coal. The precipitous drop owes to a steady drumbeat of coal plant retirements. A decade ago, the U.S. coal fleet  had a combined capacity of 302 GW . As of October, that figure had fallen to 181 GW. The amount of power generated by the industry has followed suit. Coal generated 690 terawatt-hours of power last year, down from 902 TWh in 2021. Coal plants also produced less power than nuclear facilities for the first time last year.

“That’s my biggest takeaway for the power sector this year,” King said. “We have definitively shown that absent a hiccup here or there we expect continued declines in the coal fleet."

41 percent: The percentage of U.S. power coming from gas

Natural gas generation continued to rise in the U.S., fueled by cheap prices and a big hole left by coal. In 2023, gas generated 1,659,503 TWh of electricity. Forty-one percent of power generation represents a high water mark for natural gas, which stood at 37 percent of electricity production in 2019. No other fuel is even close. Nuclear is second at 19 percent.

5 GW: The amount of energy storage installed through November

The U.S. installed more storage in 11 months of 2023 than it did in all of 2022, when it broke its annual record for storage additions with 4.1 GW of new capacity. Another 2.4 GW of storage capacity was slated to come online in the last month of 2023.

The vast majority of development is occurring in California and Texas. The country’s most populous states saw 3.5 GW of installations through November, and 10.5 GW of the 14 GW of storage installed nationally to date, according to EIA. Storage developers are drawn to electricity markets with lots of solar. That makes it cheap to charge batteries during the day and profitable to discharge them in the evening, when the sun goes down and electricity prices climb.

Minus 4 percent: The amount wind generation fell in 2023

Wind hit the doldrums last year.  Lower wind speeds  throughout much of the summer (August was the exception) meant wind generation fell from 436 TWh in 2022 to 419 TWh last year.

Compounding wind’s woes was a slowdown in installations. The 6.9 GW of new onshore wind built in 2023 was the lowest annual total since 2018. By comparison, the industry built more than 14 GW in each of 2020 and 2021, respectively. The trend doesn’t look like it will be reversed anytime soon. Right now, developers have around 5 GW of new onshore projects slated annually 2024, 2025 and 2026.

“I would expect that number to go up from the [Inflation Reduction Act],” said Robbie Orvis, an analyst at Energy Innovation, a think tank that supports a transition to green energy. But he said the outlook for wind will also rely on the country’s ability to address noneconomic issues, like building more transmission and untying interconnection bottlenecks.

“I will be very curious to see what shakes out next year, whether or not we get permitting reform or interconnection reform, and how that unlocks, or doesn’t, the wind development that we need to see,” Orvis said.

39 percent: The amount of power generation from zero-carbon sources

This figure is unchanged from 2019. Wind and solar have grown from 8 percent to 14 percent of power generation over the last five years, but nuclear and hydro generation have fallen.

The reasons for those decreases differ. Hydro output often varies by the year and meteorological conditions. The nuclear industry, by contrast, has seen its output fall as plants have closed. That changed slightly last year with a new reactor coming online at the Vogtle plant in Georgia, pushing nuclear generation up slightly. But here's the bottom line: Zero-emission electricity has remained flat in recent years.

“It all points to the need to maintain the clean energy we have, while adding as much as we can, as quickly as we can,” Orvis said. “Otherwise it is going to be hard to keep pace with our climate and clean energy targets that we have.”

4.8 billion tons: U.S. energy-related carbon dioxide emissions in 2023

That’s a 3 percent drop compared to 2022 levels, and a deeper cut than the  roughly 1 percent annual decrease  averaged by the country between 2012 and 2021. The drop comes down to falling coal consumption, as oil and gas emissions were roughly flat. EIA  thinks emissions  from coal were 774 million tons last year, down from 939 million tons in 2022. The U.S. needs to cut emissions annually by 6 percent to achieve its climate targets under the Paris Agreement.

“A decline is a step in the right direction,” King said. “It absolutely is not enough to meet Paris.”

Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2023. E&E News provides essential news for energy and environment professionals.

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Consumers’ perceptions of energy use and energy savings: A literature review

Vedran lesic.

Wändi Bruine de Bruin

Matthew c davis, tamar krishnamurti.

Inês m l azevedo.

Background.

Policy makers and program managers need to better understand consumers’ perceptions of their energy use and savings to design effective strategies for promoting energy savings.

We reviewed 14 studies from the emerging interdisciplinary literature examining consumers’ perceptions electricity use by specific appliances, and potential savings.

We find that: (1) electricity use is often overestimated for low-energy consuming appliances, and underestimated for high-energy consuming appliances; (2) curtailment strategies are typically preferred over energy efficiency strategies; (3) consumers lack information about how much electricity can be saved through specific strategies; (4) consumers use heuristics for assessing the electricity use of specific appliances, with some indication that more accurate judgments are made among consumers with higher numeracy and stronger pro-environmental attitudes. However, design differences between studies, such as variations in reference points, reporting units and assessed time periods, may affect consumers’ reported perceptions. Moreover, studies differ with regard to whether accuracy of perceptions was evaluated through comparisons with general estimates of actual use, self-reported use, household-level meter readings, or real-time smart meter readings.

Conclusion.

Although emerging findings are promising, systematic variations in the measurement of perceived and actual electricity use are potential cause for concern. We propose avenues for future research, so as to better understand, and possibly inform, consumers’ perceptions of their electricity use. Ultimately, this literature will have implications for the design of effective electricity feedback for consumers, and related policies.

1. Introduction

The use of fossil fuels in electricity generation is one of the major contributors to greenhouse gas emissions (GHG) worldwide ( Intergovernmental Panel on Climate Change 2014 ). A large de-carbonization of the energy system is necessary to reduce and stabilize carbon dioxide (CO 2 ) and other GHG emissions in the atmosphere ( IPCC 2014 ). A portfolio of de-carbonization strategies and technologies will likely include curtailment (which is also called ‘energy conservation’ in much of the energy literature) and energy efficiency strategies targeting the reduction of residential energy use ( IPCC 2014 , Pacala and Socolow 2004 ). Curtailment strategies and pertain to actions consumers can pursue to reduce the energy use of existing appliances by using them less or not at all ( Azevedo 2014 , Rubin et al 1992 ). Energy efficiency strategies involve the implementation of more efficient appliances ( Karlin et al 2014 ). If people misjudge the relative energy use or savings of one appliance or action over another, their efforts to save electricity may end up being misdirected.

Consumers with more accurate perceptions of energy use and savings may be better able to identify the actions that save the most energy, as a first potential step towards behavior change and reduced GHG emissions. Providing consumers with better information about their energy use and potential savings brings the promise of promoting the implementation of more curtailment and energy efficiency strategies and reducing residential greenhouse gas emissions ( Bin and Dowlatabadi 2005 , Vassileva et al 2012 , Attari et al 2010 , Attari 2014 , Baird and Brier 1981 , Chen et al 2015 , Frederick et al 2011 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 ). Many consumers want better information, and hope that smart meters will help them to understand how much electricity is used by specific appliances ( Krishnamurti et al 2012 ). Without information, consumers may develop folk theories and associated misconceptions about their energy use ( Kempton 1986 , Kempton and Montgomery 1982 , Krishnamurti et al 2013 ).

This paper aims to understand how well consumers can assess the electricity used by different household appliances, and how much can be saved by implementing different curtailment or energy efficiency strategies. We provide a systematic overview of the empirical studies that have focused on the accuracy of consumers’ perceptions of energy consumption and energy savings for specific appliances and actions. The paper is organized as follows. First, we briefly describe how we selected the studies that are included in this paper. Second, we discuss the key empirical findings reported in these studies. Third, we describe methodological differences in terms of how studies have measured consumers’ perceptions of energy use. Fourth, we discuss the different ways in which actual energy consumption has been measured across studies, so as to evaluate the accuracy of consumers’ perceptions. Finally, we conclude with recommendations for future studies and implications for developing effective feedback design and programs.

2. Methods and data

We performed a search for studies that used all possible combinations of the following keywords: ‘consumer perceptions’, ‘consumer awareness’, ‘energy consumption’, ‘energy use’, and ‘energy savings’. We searched the following online databases: ScienceDirect, EBSCO, general library catalogues of Carnegie Mellon University and University of Leeds, limiting our search to articles published after 1980. From this initial search, we only retained peer-reviewed articles that reported the direct results of experimental, survey, or interview research with human participants. We also searched for studies in Google Scholar (where we focused solely on the first 25 pages of results). We read the abstract of each of the papers (and when it was unclear from the abstract, we also read the full paper to assess if a study would remain in our final dataset). We focused on identifying the papers that specifically reported perceptions or awareness of energy use and savings. Our initial search identified 32 peer-reviewed papers. We also identified six additional peer-reviewed papers in the references of these 32 papers. We included one additional paper on the basis of a reviewer’s recommendation. In appendix table A1 we present the resulting 39 papers. We then read each of the 39 papers to identify those papers that met the inclusion criteria of: (1) focusing…. (2) presenting and (3) measuring actual use without necessarily making a comparison of actual use with perceptions (see table 1 ). Our review covers the resulting 14 studies that meet the inclusion criteria. For example, Allcott’s (2011) paper on fuel energy consumption or Becken’s (2013) paper on perceptions of energy use and actual saving opportunities for tourism accommodation made it into the initial selection of 32 papers but did not made it to final review because they are not in the domain of residential energy use. Of the 14 studies we reviewed, ten papers specifically presented comparisons of assessed perceptions and actual use (see table 1 ).

Summary of the studies reviewed.

3. Main empirical findings

  • Consumers have systematic misperceptions of energy use, such that electricity use is often overestimated for low-energy consuming appliances, and underestimated for high-energy consuming appliances ( Attari et al 2010 , Baird and Brier 1981 , Chen et al 2015 , Frederick et al 2011 , Gatersleben et al 2002 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 );
  • Consumers tend to prefer curtailment over energy efficiency strategies ( Attari et al 2010 , Becker et al 1979 , Kempton et al 1985 , Mettler-Meibom and Wichmann 1982 );
  • Consumers lack information about the electricity savings associated with specific strategies ( Attari et al 2010 , Easton and Smith 2010 );
  • Consumers use heuristics for assessing the electricity use of specific appliances ( Baird and Brier 1981 , Schley and DeKay 2015 ), with some indication that more accurate judgments are made among consumers with higher numeracy and stronger pro-environmental attitudes ( Attari et al 2010 , Schley and DeKay 2015 ).

3.1. Systematic misperceptions of energy use

Consumers tend to systematically overestimate the electricity use of low-energy consuming appliances and activities, while underestimating the electricity use of high-energy consuming appliances and activities ( Attari et al 2010 , Chen et al 2015 , Frederick et al 2011 , Gatersleben et al 2002 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 ). In one study, participants reported their perceived energy use for nine appliances, in terms of their hourly electricity use in kWh ( Attari et al 2010 ). Participants received a reference point of a 100 W incandescent light bulb when making their assessments. The accuracy of perceptions was evaluated by comparing perceptions to actual energy use, as estimated from the literature and government agencies. According to the authors, participants underestimated the energy use of the nine appliances by a factor of 2.8 on average, while also overestimating the electricity use of low-energy consuming appliances ( Attari et al 2010 ). A follow-up study asked participants to consider the same nine appliances, while providing either a 3 W LED, a 100 W incandescent light bulb or a 9000 W electric furnace as the single reference point ( Frederick et al 2011 ). Frederick et al (2011) used the same estimates for actual energy use and savings as Attari et al (2010) . Participants reported higher perceptions of electricity use across the nine appliances when they were presented with a higher rather than a lower reference point, with perceptions being highest when no reference point was provided at all ( Frederick et al 2011 ). Moreover, overestimations were larger when questions were asked in terms of kWh versus Wh ( Frederick et al 2011 ). Although Frederick et al (2011) found that the findings of Attari et al (2010) depended on reference points and reporting units, the overall pattern of underestimating the electricity use for high-consuming appliances and overestimating it for low-consuming appliances remained ( Attari et al 2011 ).

Other studies revealed that same pattern ( Chen et al 2015 , Gatersleben et al 2002 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 , Schley and DeKay 2015 ) despite measuring perceptions and actual use in different ways ( table 1 ) and varying reference points and reporting units ( table 2 ). Regression towards the mean may have contributed to electricity use being overestimated for low-energy consuming appliances and underestimated for high-energy consuming appliances, because perceptions and actual use are imperfectly correlated ( Attari et al (2010) . However, regression towards the mean does not ‘explain’ why the correlation is imperfect, or why reported perceptions depend on how they are assessed. Similar patterns of findings have also been reported with regards fuel consumption ( Allcott 2011 , Larrick and Soll 2008 ) and water use ( Attari 2014 ).

Key methodological features across studies.

3.2. Tendency to prefer curtailment strategies over energy efficiency strategies

Several studies in the literature note that consumers tend to choose curtailment strategies over energy efficiency strategies, even though the latter are potentially more effective for saving energy ( Attari et al 2010 , Becker et al 1979 , Kempton et al 1985 , Mettler-Meibom and Wichmann 1982 ). For example, open-ended interviews with Michigan residents revealed that they tended to talk more about curtailment actions such as turning off the lights and lowering the winter thermostat, rather than on energy efficiency actions, such as better house insulation ( Kempton et al 1985 ). A similar pattern was found in other open-ended interviews ( Mettler-Meibom and Wichmann 1982 ) and in a national survey that asked participants for strategies to reduce energy use ( Attari et al 2010 ). Another study found that most participants overestimated the savings that could be derived from curtailment by lowering the thermostat, as compared to implementing more energy-efficient devices ( Becker et al 1979 ). Possible reasons for this preference for curtailment over energy efficiency are (i) that that curtailment is likely to have no financial costs in most circumstances, whereas efficiency will likely involve some form of investment or additional financial cost, e.g. investment in insulation or LED lighting; (ii) curtailment behaviors come to mind more easily than energy efficiency strategies, due to the former being implemented more frequently than the latter.

3.3. Lack of information about energy savings

In the absence of information, consumers may use their own experience to create folk theories about how different appliances or behaviors might consume or save energy ( Kempton 1986 , Kempton and Montgomery 1982 ). Perhaps as a result, consumers misjudge how much electricity is used by specific appliances and behaviors ( Attari et al 2010 , Easton and Smith 2010 ). The same pattern of misperceptions is seen in perceptions of energy use and energy savings ( Attari et al 2010 ). Indeed, participants tend to overestimate low-consuming actions and underestimate high-consuming ones ( Attari et al 2010 ).

Easton and Smith (2010) asked questions related to consumers’ perceptions of energy consumption, energy-related behavior, and energy savings over a year, and then combined the responses to those questions with direct monitoring of metered energy, water, and temperatures provided by four community based retrofit organizations. Notably, they show that households underestimate the extent of repairs and maintenance that is required on their dwellings to save energy.

3.4. Heuristics and individual differences

When reporting their perceptions, participants also seemed to use heuristics or decision rules to simplify the task at hand ( Tversky and Kahneman 1974 ). The commonly used ‘availability heuristic’ reflects the tendency to judge the likelihood of an event by the ease with which an example comes to mind ( Schwarz et al 1991 ). Individuals who use the availability heuristic tend to systematically overestimate events that come to mind more easily, and underestimate events that come to mind less easily ( Tversky and Kahneman 1973 ). Consumers may also use such heuristics when generating strategies for saving energy ( Wilson and Dowlatabadi 2007 ) and assessing the electricity use of their appliances ( Baird and Brier 1981 , Schley and DeKay 2015 ). Specifically, participants judge electricity use to be higher for appliances that are frequently used or thought of ( Schley and DeKay 2015 ) as well as those that are larger in size ( Baird and Brier 1981 ). Such heuristics will lead to predictable inaccuracies, such as for infrequently used appliances that use relatively more electricity or frequently used appliances that use relatively little ( Baird and Brier 1981 ). Similarly, curtailment actions may come to mind more easily than energy-efficiency actions due to being implemented more frequently—leading to overestimations of the associated energy savings.

Moreover, the accuracy of perceptions may systematically vary across participants. Two studies find that more numerate participants have more accurate perceptions of energy use for specific appliances ( Attari et al 2010 , Schley and DeKay 2015 ). One study reports that participants with stronger pro-environmental attitudes have more accurate perceptions of energy use and potential savings ( Attari et al 2010 ), while another reports that they do not ( Schley and DeKay 2015 ).

4. Methodological differences between studies

The studies we reviewed differ in their research method, including qualitative interviews ( Easton and Smith 2010 , Kempton and Montgomery 1982 , Mettler-Meibom and Wichmann 1982 ), and surveys ( Abrahamse et al 2007 , Abrahamse and Steg 2009 , Becker et al 1979 , Gatersleben et al 2002 , Kempton et al 1985 , Attari et al 2010 , Baird and Brier 1981 , Chen et al 2015 , Frederick et al 2011 ). Across these research methods, we identify three methodological features that may affect consumers’ reported perceptions of electricity use:

  • the presence or absence of a reference point, with reference points varying in size from a 3 W LED ( Frederick et al 2011 ), to a 100 W incandescent light bulb ( Attari et al 2010 , Frederick et al 2011 ), and even a 9000 W electric furnace ( Frederick et al 2011 );
  • the units in which consumers report their perceptions of electricity use, such as in kWh ( Attari et al 2010 , Baird and Brier 1981 ) or in dollars ( Karjalainen 2011 );
  • the time periods in which consumers report their perceptionsof electricity use, suchasperhour ( Attari et al 2010 , Baird and Brier 1981 , Frederick et al 2011 ), per month (e.g. Mettler-Meibom and Wichmann 1982 ) or per year ( Easton and Smith 2010 : Schley and DeKay 2015 ).

4.1. Reference point

Behavioral decision researchers have long suggested that the provision of a reference point, or comparison information, affects people’s reported perceptions ( Hammond et al 1998 , Sunstein 2002 ). That is, people tend to adjust their perceptions towards the reference point that is provided ( Chapman and Johnson 2002 , Attari et al 2010 ). Some studies in our review provided reference points to participants with the aim of helping them generate their perceptions ( table 2 ). For example, studies have presented information about the electricity use of a 3 W LED ( Frederick et al 2011 ), a 100 W incandescent light bulb ( Attari et al 2010 , Frederick et al 2011 ), a 100 W washing machine ( Baird and Brier 1981 ), and a 9000 W electric furnace ( Frederick et al 2011 ). Perhaps not surprisingly, participants report higher perceptions of electricity use when being presented with a higher rather than a lower reference point, with perceptions being highest when no reference point is provided at all ( Frederick et al 2011 ). Future studies should test whether the provision of multiple reference points provides information about the feasible range, without biasing judgments upwards or downwards, as compared to when no reference point is provided.

4.2. Reporting unit

Some studies asked participants to report the electricity use of their appliances in different units of consumption ( table 2 ), such as kWh ( Attari et al 2010 , Baird and Brier 1981 ) or dollars ( Becker et al 1979 , Easton and Smith 2010 ). When describing the energy consumption associated with their home heating, most people tend to refer to monetary values ( Kempton and Montgomery 1982 ). Indeed, consumers may be more familiar with monetary units than with energy units because of the salience of paying electricity or heating fuel bills ( Darby 2006 ). As a result, they may want to see feedback about their electricity use displayed in terms of monetary units rather than energy units ( Karjalainen 2011 ). However, simple feedback provided in energy units may be the most effective way to increase knowledge about energy use ( Krishnamurti et al 2013 ). Behavioral decision studies in other domains suggest that consumers may overestimate prices as compared to other units ( Bruine de Bruin et al 2011 , Vohs et al 2006 ). Because of the small sample sizes and variability in study designs, it is unclear at this stage whether monetary units or energy units might be better at helping consumers to judge their electricity use. Future research should systematically test the effect of reporting units on consumers’ perceptions of how much electricity is used by their appliances.

4.3. Time period

Studies vary in terms of the time period participants have considered when reporting their perceptions of appliance’s electricity use ( table 2 ). For example, participants have been asked to assess how much electricity an appliance uses over the course of an hour ( Attari et al 2010 , Frederick et al 2011 ), a month (e.g. Mettler-Meibom and Wichmann 1982 ), or a year ( Easton and Smith 2010 , Schley and DeKay 2015 ). The time period may also be left unspecified ( Chen et al 2015 ). One drawback of asking consumers about their perceived energy use over the course of an hour is that comparisons with actual use may not be realistic (i.e. it may not make sense to ask how much energy a coffee machine or a toaster uses if it is running for a full hour, sincethatdoesnotreflectusualusagepatterns). Instead, the researcher may ask participants for the frequency of use of an appliance and the energy use over that period. Additionally, the time period consumers are asked to consider may affect their reported perceptions. Monthly periods may be more familiar to people given that historically most utilities would send monthly utility bills. Yet, technology that enables consumers to receive more frequent electricity use information is available ( Anderson and White 2009 ) and some work has shown that consumers are interested in seeing information such as daily load curves ( Ueno et al 2006 ). In other research that does not focus on energy use, researchers have found that self-reported hours of TV watching depend on the time period used in the survey, with more accurate responses being provided when time periods match people’s natural experiences ( Schwarz 1999 ).

Although none of the reviewed studies examined whether assessed time periods used affects perceptions, there is reason to believe that they might. Especially when considering longer time periods, participants may assume the appliance is running for the full duration of that time period, or they may assume what is a ‘typical’ usage of the appliance for them. If participants make different assumptions about how to respond to such questions as the time period increases, their reported perceptions will likely show a larger variability. If perceptions are to be reported for typical use over a time period, it is important to note that people often misestimate the amount of time they spend on tasks ( Fasolo et al 2009 ). They may overestimate the electricity use of appliances they tend to use longer ( Yeung and Soman 2007 ). In addition, behavioral economics research on magnitude effects suggests that people display a larger subjective temporal discount rate for small magnitudes than for large ones ( Chapman and Winquist 1998 ). Thus, it may be easier to think of specific appliances in terms of their relative time periods of use.

5. Measures of actual energy use

This section focuses on the methods for measuring actual energy use and energy savings, so as to assess the accuracy of consumers’ reported perceptions. The 14 studies identified in our review that include a measure of actual energy use can be divided into four categories with regards how they measured actual energy use:

  • General estimates from the existing literature and other sources (these include Attari et al 2010 , Becker et al 1979 , Baird and Brier 1981 , Frederick et al 2011 , Mettler-Meibom and Wichmann 1982 , Kempton et al 1985 , Schley and DeKay 2015 );
  • Estimates based on self-reported energy use (these include Gatersleben et al 2002 , Abrahamse et al 2007 , Abrahamse and Steg 2009 );
  • Estimates based on household-level meter readings (thisincludes KemptonandMontgomery1982 , Easton and Smith 2010 );
  • Measures of real-time energy usage from smart meters ( Chen et al 2015 ).

Approaches to measure actual energy use.

Note: Ratings include very low, low, medium, high and very high. The values shown in the table reflect the authors’ own subjective assessment of these criteria.

5.1. General estimates from the existing literature and other sources

Many of the reviewed studies used general estimates of energy use or energy savings of specific appliances and behaviors, so as to evaluate the accuracy of participants’ reported perceptions ( table 1 ). Some studies used publicly available estimates from existing publications including expert reports ( Becker et al 1979 , Mettler-MeibomandWichmann1982 , Kempton et al 1985 ), energy statistics from for example governmental agencies ( Attari et al 2010 , Frederick et al 2011 , Schley and DeKay 2015 ), or information from local stores ( Baird and Brier 1981 ). Using these sources is convenient because they are readily available. However, this approach comes with the severe limitation of not capturing individual heterogeneity in consumption. As a result, it is impossible to know whether any differences between perceived and actual consumption are due to misperceptions by the consumer or due to average energy use being a poor proxy for the actual energy consumption of a specific household.

5.2. Estimates based on self-reported energy use

It is also possible to estimate an individual’s actual energy use for specific appliances from self-reports ( Abrahamse et al 2007 , Abrahamse and Steg 2009 , Gatersleben et al 2002 ). Gatersleben et al (2002) developed a model to calculate actual energy consumption based on participants’ self-reported behavior. The authors asked participants to report which appliances they own. For each appliance, the total number of appliances of that type in the household was multiplied by the average annual energy use of the appliance as estimated for an average Dutch household.

Estimates of actual energy use by appliance were then computed for individual participants and compared to their reported perceptions of energy use. The benefit of this approach is that individuals’ perceptions are compared to their own usage patterns and appliances. However, one limitation is that participants may not know the required information, or provide inaccurate reports due to imperfect memory or response biases ( Baumeister et al 2007 ). Another drawback of self-reports is that they may be labor-intensive for participants to complete, especially if the study includes a large number of appliances.

5.3. Estimates based on household-level meter readings

Another approach is to estimate an individual’s energy use for specific appliances after obtaining a household-level meter reading from the utility company. Since the late 1970s, many studies have evaluated the accuracy of consumers’ perceptions of electricity, gas, or water use on the basis of meter readings provided by utility companies (e.g. Heberlein and Warriner 1983 , Hirst et al 1982 , Kempton and Montgomery 1982 , Midden et al 1983 , Seligman et al 1978 , Verhallen and van Raaij 1981 ). The benefit of this approach is that it provides household-specific information, allowing comparisons of individuals’ perceptions with their own electricity use ( Schley and DeKay 2015 ). Various intervention studies ( Battalio et al 1979 , King 2010 , Kline 2007 ) have also used household-level energy data to provide feedback to households and to test the resulting effects on residential energy use. However, household-level readings too come with potential limitations. First, they do not provide information regarding the energy consumption of specific appliances. Second, many studies have relied on monthly assessments from utilities which only conduct actual meter readings a few times per year, and make estimates for the rest of the year.

5.4. Measures of actual energyusefromsmart meters

The deployment of smart meters has enabled the measurement of households’ real-time energy consumption ( Asensio and Delmas 2015 , Chen et al 2015 ). These measurements may include (i) single load monitoring combined with algorithms to estimate the consumption of different appliances, or (ii) multi-modal sensing. Single-load monitoring through smart meters is a non-intrusive method for measuring real-time household-level electricity use and can be combined with specifically designed algorithms to identify when specific appliances are being used ( Berges et al 2008 ). Even with advanced algorithms, this approach will involve underlying uncertainty. Instead, multi-modal sensing overcomes that uncertainty through the installation of special sub-meters to capture usage for each appliance ( Froehlich et al 2011 ). Sub-meter data facilitate direct comparisons between consumers’ perceived and actual use of appliance-level energy use. Using sub-meter data also allows for better testsof theeffectivenessof interventions. Thisapproach has been implemented in the Pecan Street community located at the University of Austin in Texas ( Pecan Street 2017 , Smith 2009 ). However, sub-meters are more intrusive and costly to implement, limiting the feasibility of using them with a large or nationally representative sample.

6. Conclusions and recommendations for future studies

Our review of the literature covers 14 peer-reviewed studies that empirically assessed consumer perceptions of electricity use that has been published over the past 35 years. An even smaller number of studies (N=10) compared consumers’ perceptions to actual energy use or savings. The main findings from the reviewed studies include: (1) electricity use is typically overestimated for low-energy consuming appliances, and underestimated for high-energy consuming appliances; (2) curtailment strategies are typically preferred over energy efficiency strategies; (3) consumers lack information about how much electricity can be saved throughspecificstrategies; (4) consumersuseheuristics for assessing the electricity use of specific appliances, with some indication that more accurate judgments are made among consumers with higher numeracy and stronger pro-environmental attitudes.

However, we note that methodological differences between studies may affect consumers’ reported perceptions, including the provision of reference points, as well as the units and time periods used in the existing studies. Moreover, studies vary in terms of whether the accuracy of perceptions has been evaluated in terms of general estimates of actual use, self-reported use, house-level meter readings, or real-time smart meter readings.

We suggest several avenues for future research. First, there is a need to systematically examine the effect of reference points, units, and time periods on reported perceptions. Second, to better compare consumers’ perceptions to their actual appliance energy use, measures of households’ actual energy consumption should be taken at the individual households’ appliance level. Ideally, such studies would be conducted with large representative samples. Moreover, it remains unclear whether consumers with more accurate perceptions of their energy use by appliance, or of the savings they could obtain, do indeed make more informed decisions about their energy use and savings. It also remains to be seen whether informed decisions lead to behavior change and reductions of residential GHG emissions.

Understanding consumers’ perceptions (and misperceptions) of energy use and savings may help to inform the design of curtailment and energy efficiency policies. The use of smart technology and associated services, such as in-home displays, mobile apps, and other information and communication technology related services could facilitate improved measurement as well as improved feedback to consumers ( Krishnamurti et al 2012 ). However, care should be taken to present feedback in a way that consumers can use and understand ( Davis et al 2014 ). For example, tailored feedback may be provided to consumers to explain their misperceptions, while using reference points, units, and time periods that make the most sense to them. Research should also be developed to then test whether correcting misperceptions through feedback does indeed help consumers to make more informed decisions about curtailment and energy efficiency. In the domain of health, researchers have shown that correcting misperceptions of risk can foster behavior change ( Avis et al 1989 , Kreuter and Strecher 1995 , Lindan et al 1991 ). Thus, continued research on the topic of how well consumers can assess appliance energy use brings some promise of informing consumers’ decisions to implement curtailment and energy efficiency behaviors.

We acknowledge support from by the Consumer Data Research Centre at University of Leeds, Economic and Social Research Council [grant number ES/L011891/1], Centre for Decision Research at Leeds University Business School. This work was supported by the center for Climate and Energy Decision Making (SES-1463492), through a cooperative agreement betweentheNationalScience Foundation and Carnegie Mellon University, as well as the Swedish Risks-banken Jubileumsfond Programon Science and Proven Experience.

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To support the energy transition, inexpensive grid-scale energy storage is needed to counteract intermittency of renewable energy sources. Redox flow batteries (RFBs) offer the potential to supply such storage, however high capital costs have hampered market penetration. To reduce costs, single-flow configurations have been explored to eliminate expensive battery components and reduce balance of plant. Here, we report on a membraneless single-flow zinc-bromine battery leveraging a unique multiphase electrolyte. The use of such emulsive electrolytes, containing a bromine-poor aqueous phase and bromine-rich polybromide phase, have allowed for effective reactant separation in single-flow architectures, although at the cost of low cycling coulombic efficiency (CE). In this study, we show that significant improvements in CEs are possible when using strong-binding bromine complexing agents (BCAs) to form the polybromide phase. We compare battery performance when using widespread but relatively weak-binding BCA N-ethyl-N-methylpyrrolidinium bromide (MEP) or novel, stronger-binding 1-butyl-3-methylpyridinium bromide (3-MBPy). We characterize for the first time the ex-situ viscosity, ionic conductivity and aqueous phase bromine concentration for such emulsive electrolytes, towards building a library of emulsive electrolyte properties. We show that use of 3-MBPy reduced significantly zinc corrosion during cycling due to a reduced aqueous phase bromine concentration, enabling an up to 23% increase in CE when cycling at 30 mA/cm2.

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  • Published: 09 January 2024

Research and analysis of energy consumption and energy saving in buildings based on photovoltaic photothermal integration

  • Yahan Cui 1 &
  • Xinyan Zhang 2  

Scientific Reports volume  14 , Article number:  923 ( 2024 ) Cite this article

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In order to reduce the energy consumption of buildings, an air source heat pump assisted rooftop photovoltaic-thermal integration system is designed. The installation area of photovoltaic modules and collectors will not only affect the power side, but also affect the thermal side. Therefore, the basic architecture of the photovoltaic photothermal integration system is first established, and then the improved whale algorithm is used to optimize the photovoltaic photothermal integration system with the daily operating cost as the optimization goal. At the same time, the influence of the installation area of the photovoltaic photothermal module on the comprehensive performance of the system is analyzed, and the environmental and economic benefits of the photovoltaic photothermal system are analyzed. The results of the example show that the roof of the building has significant benefits in environmental protection and investment recovery period when the photovoltaic photothermal system with the optimal area ratio is installed on the roof of the building. The solar photovoltaic power generation system can reduce carbon dioxide emissions by 147.11 t within 25 years, and the solar collector system can save 170.5 thousand yuan in 1 year. It has achieved the purpose of saving energy, reducing carbon dioxide emissions and protecting the environment.

The energy crisis and environmental pollution are becoming more and more serious, and solar energy is getting attention because it is clean, non-polluting and widely distributed 1 , 2 , 3 . With the continuous improvement of photovoltaic power generation technology, photovoltaic solar-thermal integrated system has begun to be combined with building roofs 4 . The system does not take up additional space, and can be self-generated and self-consumed, and the surplus power can be fed into the Internet 5 . In the generation of electricity at the same time, can also use solar heating, near the user to provide hot water, energy-saving benefits are particularly obvious 6 . In high-rise buildings, the energy saving rate of building energy consumption is 16–58% 5 , 7 , 8 . Therefore, there is a great potential for energy saving in high-rise buildings. The value of energy consumption of people living in high-rise buildings is four times that of the average person in society. At the same time, some high-rise apartments have high density and long energy use time, which has become the main body of building energy consumption 9 . Combined with the characteristics of high-rise buildings, the introduction of roof photovoltaic photo-voltaic heat integration system into the energy-saving construction of high-rise buildings is of great significance in reducing energy consumption, promoting the application of green new energy and constructing green low-carbon buildings 10 .

Current research related to the utilization of solar energy mainly focuses on the integration with buildings. Alessandro et al. 11 proposed to integrate solar photovoltaic photothermal integration with buildings organically. And use the external structure of the building to maximize the rational use of resources 12 . With the continuous development of photovoltaic photothermal technology, there are more and more forms of photovoltaic photothermal components combined with buildings 13 . Such as photovoltaic curtain walls, photovoltaic windows, photovoltaic roofs and so on. All of the above studies are combined with building materials that directly form part of the building 14 . However, for completed buildings, combining photovoltaic solar thermal modules with the building will destroy the existing structure of the original building and increase the investment cost. Therefore, for completed buildings, photovoltaic solar thermal modules can be installed separately on the roof. Power generation is realized on the basis of not changing the building structure and appearance 15 . In addition to photovoltaic solar thermal technology, solar collector technology in China has been more mature, solar thermal technology will be directly converted into heat energy 16 . However, the collector mainly depends on the amount of irradiation, in order to improve the collector in cloudy and rainy days, low irradiation limitations of heat production. Wei et al. 17 proposed a new solar-assisted heat pump system, the solar heat pump unit and air source heat pump unit complement each other to run in tandem. It effectively solves the intermittency problem of traditional solar collector system and improves the utilization efficiency of solar energy and air energy. Hossein et al. 18 proposed that the photovoltaic photothermal integration system can realize photovoltaic utilization and photothermal utilization at the same time, so as to improve the comprehensive utilization efficiency of solar energy. However, the system has high requirements for component materials and is difficult to maintain in the later stage. Shui 19 optimized the photovoltaic power generation and solar hot water system for university buildings through the analysis of actual monitoring data. It is concluded that the carbon dioxide emission reduction in the system life cycle is about 3.8 kt 20 . However, the above system photovoltaic and photo-thermal systems are installed separately on different buildings on the campus, which cannot independently satisfy the electricity and hot water demand of a building.

On the basis of not changing the original building, the photovoltaic photo-voltaic heat integration system is now combined with the air source heat pump-assisted solar collector system. The photovoltaic photothermal integration system with solar energy as the main energy source is designed on the roof of the building. Simultaneously realizing the power supply and heating demand. An optimisation analysis of the installation ratio of the system based on the improved whale algorithm, where the installation area of the PV panels and collectors is used as an optimisation variable in order to maximise the economic efficiency of the system. Taking a high-rise building dormitory building as an example, a photovoltaic photo-voltaic heat integration system is installed on the roof to analyze the influence of the installation area ratio of photovoltaic photo-voltaic heat modules on the comprehensive performance of the system. As well as the economic and environmental benefits of the system, in order to provide a theoretical basis for building energy efficiency.

Photovoltaic solar thermal integration system design

System structure.

The integrated photovoltaic-photothermal system consists of several parts, including a photovoltaic generator set, a collector and an air source heat pump. The input energy includes solar power generation, public grid electricity and collector heat collection. The operation principle of the system is mainly to generate electricity to meet the electrical load demand of the building through solar power generation equipment. The solar collector collects heat to realize the domestic hot water supply of the building 21 . The collector provides thermal energy ( Q th ), which is output to the system in the form of hot water. If the heat generated cannot meet the system demand, the air source heat pump is activated to supplement the supply (Q a ). The photovoltaic power generation provides the system's electrical energy ( P pv ). When there is insufficient light intensity or weather conditions to meet the user's demand for power generation, the power is purchased from the grid ( P buy ), to make up for the difference in supply 22 . The user's electric load P load and the energy consumption Pa of the air source heat pump are supplied by the PV and the grid. The thermal load Q load of the user is supplied by the collector and the air source heat pump.

System model

The photovoltaic solar thermal integrated system mainly uses solar energy as the main energy source, and the secondary energy source is the large power grid. The consumption of secondary energy is minimized as much as possible. The equipment of the system mainly consists of photovoltaic modules, collectors, and air source heat pumps. The mathematical model of each device is as follows 23 .

Solar irradiance at inclined surfaces

The amount of heat and power generated by the system is mainly determined by the amount of solar irradiation absorbed by the PV panels and collectors. The amount of solar irradiation at the tilted surface is mainly determined by Eqs. ( 1 )–( 4 ).

where I T is the solar irradiation on the tilted surface, I b, T , I d, T , I g, T are the direct, scattered and reflected irradiation, I b and I d are the direct and scattered irradiation on the horizontal surface, θ and θ z are the incidence angle and zenith angle of the sun, respectively 24 . ρ is the ground albedo, β is the tilt angle of the module, F 1 and F 2 are the number of the orbiting solar coefficients and the horizontal brightness coefficients, respectively. a , b are the correction coefficients of the solar incidence angle.

Photovoltaic power generation system

The power generated by the PV system is:

where: S pv is the effective light-gathering area of the PV module. η pv is the power generation efficiency. I pv is the amount of solar irradiation obtained by the PV module. T a is the ambient temperature. T NOCT and T pv are the nominal operating temperature and the operating temperature of the cell, respectively.

Vacuum tube heat collecting system

The heat collection capacity of the vacuum tube collector system is 25 :

where: S th is the effective light-gathering area of the collector. η th is the collector efficiency. i th is the amount of solar irradiation obtained by the collector. For a given type of collector, A and B are constants. t w is the collector inlet water temperature. t a is the ambient temperature. i is the unit solar irradiation.

Air source heat pump systems

The heat pump unit has a heating capacity of:

In Eq. ( 10 ): Q ah is the hourly heating capacity. g is the daily water consumption. k is the safety factor. T is the daily working time. q r is the design daily water consumption for hot water. ρ r is the density of water. t r is the temperature of hot water. t l is the temperature of cold water. C is the specific heat of water.

Methods of solving for different area ratios

System control strategy.

Based on the installation area of the PV panels and collectors 26 , the power generation and heat collection capacity of the system are calculated. When the heat generated by the collector cannot meet the demand of the building, the air source heat pump is switched on to supplement the supply 27 . When the PV power generation cannot meet the demand of the electric load, the power is purchased from the grid to supplement the supply difference, to achieve a balance between the supply and demand of the electric and heat loads. If the PV module and collector installation ratio is not reasonable system will produce a large amount of electricity and heat waste, resulting in economic losses. So, the main factor affecting the power generation and heat collection of the system is the installation area ratio of the modules. The solar photovoltaic photothermal system studied maximizes the use of solar energy resources with the help of photovoltaic and photothermal equipment under the premise of ensuring the safe operation of the system. For completed buildings, the available area of the roof is fixed, so the installation area of the modules is limited. To take into account, the principles of green and energy saving, it is necessary to rationally allocate the installation area of the modules.

Solution methods

Optimisation algorithms are used in the solution process. The algorithms are transformed from the most basic particle swarm algorithms, genetic algorithms, etc. to newly developed algorithms. For example, ant colony algorithm, whale algorithm, etc. provide fast optimisation methods for finding the optimum for the objective. Two optimisation variables, Spv and Sth, are involved in the optimisation search process, and the whale optimisation algorithm has obvious advantages in terms of solution accuracy and convergence speed compared to meta-heuristic algorithms such as particle swarm and genetic algorithms 28 . And it has the advantages of fewer parameter settings and better optimisation seeking ability. However, it is unable to balance the local and global search ability, which will cause the loss of diversity in the late iteration, resulting in insufficient convergence ability. Therefore, the improved whale algorithm is adopted to optimise the calculation. When selecting individuals, the way of expanding the filter subset can be used to take the daily operating cost as the objective function. As much solar energy as possible is utilised for power generation and heat collection, and the size of power generation and heat collection is controlled by adjusting the installation area of PV modules and collectors. In order to meet the electrical and thermal load demand of the building, as little power as possible is purchased from the grid, so that the daily operating cost of the system is minimised.

Algorithmic models

Objective function.

Let C pv and C th be the selling price per unit of electricity and hot water respectively, then the formula for calculating the final return is:

where C pv is the selling price per unit of electricity. C th is the selling price per unit of hot water. P pv,t is the amount of electricity produced by the photovoltaic system at time t . Q th,t is the amount of heat produced by the collector system at time t . P buy is the amount of electricity purchased, and C buy is the price of utility electricity. Therefore, to maximise the economic benefits, as much solar energy as possible should be utilised.

Constraint condition

Power balance is the premise of stable operation of microgrid system, and its power constraint condition is:

In Eq. ( 12 ), P buy,t is the amount of power purchased at the moment t . P load, t is the power of the user load at the moment t . P ah, t is the power consumed by the air source heat pump at the moment t .

where Q ah, t is the heat generated by the air source heat pump at time t . Q load, t is the heat required by the user at time t . The photovoltaic modules and collectors are mounted on the roof of the building and their area is constrained by the area of the building roof.

where S pv_min is the actual minimum usable area of the PV module. S pv_max is the actual maximum usable area of the PV module. S th_min is the actual minimum usable area of the collector. S th_min is the actual available minimum area of the collector. S th_max is the actual available maximum area of the collector. S eff_min is the actual available minimum area of the roof.

Example analysis

Calculation conditions.

In order to verify the correctness of the proposed model and to find out the optimal setting of the system. A high-rise dormitory building is selected for the study, where the PV genset and solar collector are mounted on the roof of the building. There is no shading from tall buildings around the roof of this building and the roof surface is flat. Considering the shading problem, the actual usable effective area of the modules is 956 m 2 . Combining the geographic information and meteorological data of a high-rise building and analyzing and calculating with the help of the pvssyst software, the mounting tilt angle of the PV modules is set to be 36°, and that of the collectors is set to be 35.2. The solar irradiation is 5. 8 × 105J/(cm 2 a). The heat transfer coefficient is 0.35–0.45 W/(m K). The shape factor is 0.7. Sunrise in Xinjiang is between 6 and 7 a.m. in the summer months, and sunset is between 9 and 10 p.m. The sunrise is between 6 and 7 a.m. in the summer months, and the sunset is between 9 and 10 p.m. in the summer months.

The power of the lamps in the public activity room, duty room, distribution room and other rooms is about 6 kw, and the power of the emergency equipment lighting and evacuation indicator light is 10 kw in total, so the load of the whole dormitory building is about 210 kw. Comprehensive home appliances are normally used at the same time and the use of coefficients, and the load of the whole dormitory building is 126–147 kw.

The building has 6 floors, and the interior of the dormitory is composed of 120 student dormitories (4 persons per dormitory), public activity rooms, duty rooms, public corridors, stairwells, and so on. The fixed hot water use time is 8:00–22:00, totaling 14 h. According to the hot water quota of the Design Code for Water Supply and Drainage in Buildings, the hourly heat consumption of the centralized hot water supply system is:

where Q load for the hourly heat consumption. q for sanitary appliances hot water hourly water quota. n for the number of sanitary appliances, a total of 120. b for the same time the percentage of water, 70–100%. t r for the hot water temperature, take the value of 55 °C; t I for the cold-water temperature, take the value of 8 °C; C for the specific heat of water. The calculated hourly heating capacity of the system is 3471.36 MJ/h.

Results at different area ratios

The solar photovoltaic solar thermal system is applied to the building and the optimisation results are obtained from the above data and equations. The main factors that constrain the power generation and heat collection of the system are the PV module and collector mounting area, and the building's electrical and thermal loads and the maximum mounting area will have an impact on the proportion of the modules to be installed.

The analysis of the results of the calculation example reveals that the optimal installation area of the PV panels is 500 m 2 with a daily power generation of 351.69 kW. The optimal installation area of the collector is 456 m 2 with a daily heat collection of 3502.72 MJ. The daily operating benefits of the PV modules and collector with different installation areas are shown in Fig.  1 . With the optimal ratio, the system can gain up to $82.44 per day of operation.

figure 1

Daily operating revenues for different sizes of PV modules and collectors.

The optimization process curves for both loads are shown in Fig.  2 . From Fig.  2 , the power generation of PV modules and the heat collection of collectors increase with the increase of solar irradiation. During the hours of 1:00am–7:00 am and 20:00 am–24:00 pm, there is no sunlight, so the light intensity is extremely low and cannot be relied on to provide electricity and heat from the sun, relying heavily on the grid and air source heat pumps. In addition, the amount of purchased electricity and heat appears negative, indicating that the energy provided by the system at this time is greater than the user's demand. However, the time of occurrence is short, the amount of electricity and heat is small and negligible. The sum of the heat provided by the collector and the heat provided by the air source heat pump is just equal to the user's heat consumption. The power provided by the photovoltaic and the power purchased from the grid is just enough to meet the user's load throughout the day as well as the power consumed by the heat pump to supplement the supply of hot water.

figure 2

Variation curve of daily electricity and heat consumption.

Analysis of benefits

Benefit analysis of photovoltaic systems.

The system cost consists of two main parts: system investment cost and system operation and maintenance cost, as shown in Table 1 . The life cycle of the photovoltaic system is set to be 25a and the life cycle of the collector system is set to be 15a.

The average annual power generation capacity of the PV system is 128.4MWh, and the annual power generation capacity and income are calculated since the attenuation of the PV system will not be more than 20% during the whole life cycle of the PV system. At present, the national subsidy for self-generation and self-consumption in a high-level area, and the subsidy for residual power on-grid mode is RMB 0.42/kWh, and the income from self-consumption of electricity is RMB 0.9/kWh (the price of electricity consumption is calculated at RMB 0.48/kWh).

The economic benefits of the system 25a are shown in Table 2 . According to Table 3 , it can be concluded that the total power generation capacity of the solar PV power generation system in the whole life cycle is 2,834.5 MWh, the total revenue is 2,551,100 yuan, the total cost is 816,000 yuan, and the net benefit is calculated to be 1,735,100 yuan, and the cost of the system can be recovered in about 7.29 a. The system can be used to generate electricity for a period of 25 years.

PV power generation process does not produce greenhouse gases and harmful gases, the environmental benefits are obvious. Each unit of electricity generated by the PV building is equivalent to 519g of carbon dioxide emission reduction, so the solar PV power generation system can reduce carbon dioxide emission by 1471.11 t in 25a.

Benefit analysis of solar water heating systems

Solar collector systems can save 1.3 × 109 kJ of energy a year, while 1 kW/h of electricity is converted into heat energy of 3600 kJ, and the price of electricity per kWh is 0.48 yuan. The solar collector system can save 170,500 yuan a year, and the total cost of the solar collector system is 777,400 yuan, saving 2,557,500 yuan in the whole life cycle. Solar water heating systems not only save conventional energy, but also reduce the emission of pollutants (mainly carbon dioxide).

where Q CO2 is the carbon dioxide reduction over the full life cycle of the system. W is the standard heat medium value − 29.308 MJ/kg. E ff is the efficiency of the conventional energy water heating unit. n is the system lifetime. F 0 is the carbon emission factor as shown in Table 3 .

The above system adds air source heat pump assistance to the solar water heating system. Therefore, the initial investment is larger than the conventional system, but the later operating cost is significantly lower than the simple solar water heating system. The auxiliary energy source for the hot water system in this project is electricity. From Eq. ( 18 ), the carbon dioxide emission reduction in the system life cycle is 2597.18 t, and the payback period of the collector system is 4.56 a. The system is designed to reduce the carbon dioxide emission in the life cycle of the system.

Conclusions

In this paper, a rooftop solar photovoltaic (PV) photovoltaic integrated utilization system coupled with an air source heat pump is constructed. Based on the user's thermoelectric load characteristics, an optimization model is established with the daily operating cost as the optimization objective, and the installation area of photovoltaic modules and collectors is optimized. A high-rise dormitory building is taken as an example to build the proposed system, and the benefits are analyzed from the perspectives of environment and economy. The following conclusions are obtained.

In this paper, the improved whale algorithm is used to optimise the integrated photovoltaic solar thermal system. The PV power generation process does not produce greenhouse gases and harmful gases, and the environmental benefits are obvious. Each unit of electricity generated by the PV building is equivalent to 519g of carbon dioxide emission reduction, so the solar PV power generation system can reduce carbon dioxide emission by 1471.11t in 25 a.

On the basis of solar water heating system added air source heat pump auxiliary. Therefore, the initial investment is larger than the conventional system, but the later operating costs are significantly lower than the simple solar water heating system.

All data generated or analysed during this study are included in this published article.

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School of Architecture, The University of Sheffield, Sheffield, S10 2TN, UK

National Engineering Laboratory for Reducing Emissions From Coal Combustion, Engineering Research Center of Environmental Thermal Technology of Ministry of Education, School of Energy and Power Engineering, Shandong University, Jinan, 250061, China

Xinyan Zhang

Y.C. wrote the draft of the text. X.Z. revised the text. All authors reviewed the manuscript.

Correspondence to Xinyan Zhang .

Competing interests.

Publisher's note.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Cui, Y., Zhang, X. Research and analysis of energy consumption and energy saving in buildings based on photovoltaic photothermal integration. Sci Rep 14 , 923 (2024). https://doi.org/10.1038/s41598-024-51209-1

Received : 30 October 2023

Accepted : 02 January 2024

Published : 09 January 2024

DOI : https://doi.org/10.1038/s41598-024-51209-1

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Generative AI in operations: Capturing the value

In this episode of McKinsey Talks Operations , host Christian Johnson sits down with senior partner Nicolai Müller and partner Marie El Hoyek from McKinsey’s Operations Practice. Together, they discuss the game-changing potential of generative AI. From automating complex processes to unprecedented opportunities across industries, discover insights on productivity boosts, system considerations, and the vital capabilities organizations need for successful integration.

Their conversation has been edited for clarity.

Christian Johnson: Your company’s future demands agile, flexible, and resilient operations. I’m your host, Christian Johnson, and you’re listening to McKinsey Talks Operations , a podcast where the world’s C-suite leaders and McKinsey experts cut through the noise and uncover how to create a new operational reality. As we’re recording this episode in late 2023, it’s clear that generative AI, or gen AI, has become the topic in conversations about digital, analytics, and operations. This new deep learning technology is already making ripples with applications across the value chain.

For today’s episode, I’m delighted to be joined by Marie El Hoyek, a partner based in London, and Nicolai Müller, a senior partner based in Cologne. Together, we’ll be exploring what generative AI in operations is, how it’s different from digital twins and other AI technologies, its potential, and its risks. We’ll also look at what it takes to get started with these tools. Nicolai, great to have you here today. Welcome.

Nicolai Müller: Thank you. It’s a pleasure to be here, Christian.

Christian Johnson: Marie, so pleased you’re able to share your thoughts with us today. Thanks for joining.

Marie El Hoyek: Pleasure being here, Christian.

Christian Johnson: Great. So, Nicolai, can you tell us a bit about why you believe generative AI is worthy of discussion for operations leaders, especially now?

Nicolai Müller: In the past decades, there was this mantra of being faster, being more efficient, and pushing productivity. Tools we all know, such as lean, offshoring, reviewing make-or-buy decisions, and also through technology—but we see nowadays that this productivity improvement gets more complex.

In this scenario, we now have a new technology coming in: generative AI. It promises to automate processes that, in the past, were hard to automate—areas that are more in management collaboration, which currently humans are operating, and also in complex data that you have to manage. So, in this context, there’s the question: how much will generative AI help in the search for productivity?

The McKinsey Global Institute has looked into this, and we discovered that, particularly in the areas of collaboration and management, around 50 percent of typical activities can now be automated by generative AI. Also, when it comes to handling complex data and synthesizing the essence of that, we believe there’s a huge jump in automation. This may lead to value creation across industries and functions—from pharmaceuticals to automotive, to machinery and functions from engineering, procurement, and supply chain to customer operations—that can unleash tremendous value. We talk about $3.5 [trillion] to $4 trillion, which is approximately the GDP of the UK.

Christian Johnson: Nicolai, what are some of the more specific opportunities that your clients are focusing on, and that you’re focusing on right now?

Nicolai Müller: Where I see our clients acting fast is in product development. And if you look deeper into product development, especially in software coding, we see up to a 50 percent productivity increase by having a machine produce code from the simple instruction, “Please give me the code for a program doing XY,” and by using tools like ChatGPT and others, a solution is generated. This is one application area where we see generative AI becoming a copilot for humans, aiding in tasks ranging from program management to procurement, and assisting supply chain managers in performing their roles more effectively.

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Christian Johnson: Thanks, Nicolai. That has given us a great idea of the why and some of the opportunities. Now, let’s go into a little bit more detail about what generative AI is. Marie, what can you describe here for us?

Marie El Hoyek: Generative AI is a fascinating field, and just like the name suggests, it exists at the intersection of artificial intelligence and natural language processing. Essentially, it involves a machine that can analyze something, and this something can now be unstructured, like language or pictures. Similar to a person, generative AI is all about teaching machines to understand and generate text or content.

Now, to add a bit more flavor, let’s discuss the different generations of large language models—LLMs. These models are the driving force behind what we refer to as generative AI. One of the first ones we commonly heard about is GPT-3, which stands for generative pretrained transformer 3. When it was introduced, it had 175 billion parameters. Think of parameters as the amount of information it had learned, allowing it to generate text ranging from writing letters to answering questions, primarily text-based. Soon after, GPT-4 was released, and we saw a leap from 175 billion to 170 trillion parameters. Consider how much more it had learned, making it more fluent and accurate, and now it could also be used for images and video.

This is the transformative possibility with generative AI. You can now generate new content in many different types of spaces. Now, that being said, generative AI comes with its own set of risks and challenges. If you imagine that it’s based on logic or probabilities, very similar to the human brain, the answers come from what you’ve learned and your sources. Because of this fact, any generative AI can give you a convincingly wrong answer—and this is what we call hallucination .

Christian Johnson: I love that term. But what do you do about it? How do you mitigate?

Marie El Hoyek: If you had a person answering you based on wrong information, you would tell them, “I want your answer from this specific book.” Similarly, you can prompt generative AI better by telling it, “I want you to answer me from this data set or to tell me where you’re guessing.”

Another risk is model bias. Imagine that the model or the person has learned from the internet as its source, which is not the most respectful or kindest place. So, whenever you use a model, you need to be able to counter these biases and instruct it not to use inappropriate or flawed sources, or things you don’t trust. Another risk that is top of mind is IP [intellectual property] risk. Now, if you imagine generative AI generating code for you, who owns the code? Is it the gen AI that generated it or the requester who wanted it? These details are something we will need to iron out soon.

Christian Johnson: What I’m appreciating here is the discussion of the very limits of the data sources. That’s really critical, right?

Marie El Hoyek: It’s critical. Additionally, the fact that you need to guide your own data means you have to take care of your data and ensure its safety. Otherwise, that is also an added risk. That being said, all of these risks can be mitigated. However, we need to be aware of them, plan for them, or approach them in a way that limits them so we can control them. By the way, we’re witnessing regulations and offerings that are starting to adapt to these risks, and I expect we’re going to see quite a few changes in the near future.

Christian Johnson: Just the evolution here—the rapid expansion from 100 billion with a “B” to 170 trillion with a “T” is really dramatic. I think one thing we would now like to turn to is how this is being used and where we are seeing use cases come to life in businesses today. What are some really good examples of that?

Nicolai Müller: I think it’s a question that clients have to ask themselves: what impact do I want to achieve? In the end, we have to solve one big question and challenge: how to increase productivity, which involves efficiency and effectiveness.

When we look into use cases, we try to explore different angles. One is the question of automation. Things that currently take hours can be done in seconds. But it’s also about augmentation, where a human may only be able to work with a certain set of data. Imagine being able to access all the data in the world that exist. This was one of the big revolutions; the internet gave us access to all data. Now, with machines, we can use and synthesize that data. So we talk about augmentation. And then we see innovation.

Innovation is the capacity to come up with completely new solutions. Not just making an existing product cheaper or achieving faster product development, but now generating completely new ideas for features and services. So what have we seen? Automation. I talked about how I’m fascinated by what we can now do in software coding and the whole field of engineering. You also heard, for example, the CEO of Nvidia saying, “Hey, the era of software is over. I think we told all our kids to learn software; now you figure out software can be done by a machine.” It’s a huge evolution that we see, but not only in software.

Parts and hardware development. Synthesizing a huge amount of requirements that your customer gives to you, asking generative AI to understand what the requirements are and how the requirements differ from the last product. How do the requirements vary between products? Are they similar or different? It will help to come to a better synthesis, better understanding of the requirement, and develop faster and better products.

In augmentation in pharma and research, I think we’ll see a humongous increase in effectiveness, output, and research. We have cases in pharma where you can imagine understanding each little molecule, what kind of effect it has, and how it reacts with other molecules. It’s something that is instrumental. So we see vaccines or other pharma products being developed faster than traditionally was expected by using generative AI. This augmentation leads to a better kind of solution.

As for innovation, you may have also seen one famous German OEM in the US that has integrated ChatGPT into its products. So you can interact and speak with your car. This is innovation. But, Marie, you have also worked with me in this space. What have you seen?

Marie El Hoyek: My background is in industrials, very much deep in operations. Personally, I love all the copilot applications, especially in procurement. The idea that you can ask a friend who knows all your contracts and can answer any question by heart and in plain English is just mind-blowing to me. So, instead of analyzing old contracts, price history, and external trends, I can simply ask the questions. I’m sure there are many more cool applications in terms of content generation, et cetera, but this one, in particular, blew my mind.

Nicolai Müller: And Marie, what I observed are these humongous opportunities out there and the numerous use cases. I mean, we have been in workshops where we were sitting with our clients, and easily after an hour or two, we didn’t end up with just five or six potential use cases across a whole different function, but rather 150 or more. I see here a huge opportunity, but the challenge that we’re facing is, where do you start? What I call “happy generative AI,” where a copilot can help you in your daily job, may become a commodity that everybody can do. Where is the truly transformative generative AI? Is it leading to a differentiating factor for your business? Is it really adding value and creating value for your customers?

I think this is the challenge we face. It’s like what we say in Germany, you don’t see the woods because of the amount of trees in front of you. So where do you start and where do you end?

Christian Johnson: Can’t see the forest for the trees. That’s exactly it. When I hear all of this excitement, I also think of the classic chart that we’ve seen for technologies in general, where you have this initial sharp upward curve as everybody gets very excited about it. Then it sounds like where you’re moving is, we need to anticipate when organizations either find, as you’ve put it, that it’s commoditized or that it’s hard. And that gets us down then to value. How do companies think about long-term value and not just a set of very exciting use cases that may not build forward very much?

Nicolai Müller: This is a challenging question. If you look into the Google search index, which gives you a bit of a feeling of where we are on the curve, you’ll find out that it’s now googled more than any traditional operational questions you have. You have seen all the digital manufacturing terms out there. We have cloud computing and the Internet of Things that we’ve now seen over the years, and it’s a constant discussion.

Generative AI in operations has just started to pick up, I would say, in the first quarter of this year. And it has, in terms of the amount of searches people are doing, overtaken everything you can imagine. This may give you an indication that there is a huge hype out there. But has this hype and all the dreams come true yet? Indeed, people are now starting to recognize that things are easy, like the low-hanging fruits, but actually, the real core is still challenging to implement and also to make your company adaptive to changes. So we are still on the verge of answering one important question when it comes to generative AI: is it now just another tool kit in your operations, like lean or digital or any other artificial intelligence—that is, predictive maintenance—and enables levers you can pull? Or is it a disruption on its own? Is it changing the way you operate? I think these are two scenarios I can imagine.

I tend to believe that in the next two to three years, we’ll see these two questions answered. And it may differ completely by player or by industry what the outcome is. Let’s talk about disruption. Imagine that coding is now easy. Often, you have, for example, an automotive OEM defining requirements, and then you have a supplier more or less programming the code. If now that code can be programmed by machine, do you need a supplier anymore? It can be disruptive and threatening to say that the raison d’être, or the reason for the supplier to exist, is actually gone. So this is an extreme of a disruption.

For example, for a very research-heavy company, suddenly, if you tap into completely new sources of data, you come to a completely new set of products. And finding the language model that suits you by adopting generative AI in ways that are differentiating may help you to move faster and with better products. I think this is the most pressing question that clients have to answer.

Would you like to learn more about our Operations Practice ?

Christian Johnson: I think one of the things we’re struggling with and organizations seem to always struggle with when it comes to a new technology or a new methodology is how do you scale? We talked years ago about pilot purgatory—this idea that you try a bunch of ideas, but then they’re never really cohering in a way that creates lasting value. So how can organizations think about this in a way that they can minimize or even avoid that kind of stagnation with this idea?

Marie El Hoyek: This is a good question, Christian. Generative AI might be relatively new, but we have years of experience in scaling digital transformations. To your point, one of the biggest challenges is the pilot trap. Building a pilot or innovating with the technology is great, but transforming an organization is a whole different playing field.

Nicolai talked about the business-led mindset to prioritize applications that are useful with real business ROI. Beyond that, getting a real impact out of any digital change, and for generative AI in particular, will always be both a human and systems question. The way I’d summarize it is, without people, the best technology has no impact. We need to take our people on a real change journey to build the capabilities to use this technology, develop this technology, but also just to know what you can ask of this technology. And by the way, in terms of developing it, there are new skills that are needed here.

Christian Johnson: So what sort of capabilities do organizations really need now?

Marie El Hoyek: I’m thinking about prompt engineering, for example, which is the ability to ask a question really, really well. Now, number two is in terms of systems. There are fundamental questions that businesses should consider early to ensure that whatever they decide leads to capable, consistent, and safe technology usage. You don’t want to end up with ten different decisions on the technology because pilots are going left and right.

So you’re going to be wondering, do we build our own language models? Do we work with partners? Do we get off-the-shelf solutions? Where do we put our data? How do we process it? These questions are better learned early, and you need to make a conscious decision about them, to ensure that later on, as you use generative AI more and more, your solution is safe, scalable, and consistent. So, yes, for me, it’s both the people element and the systems element that will enable us to go through to the finish line.

Christian Johnson: Excellent. Thank you very much. We’re now nearing the end of our discussion. But before you go, I’d like to ask one final question, which is, what should our audience be doing now to bring generative AI to their organizations? There’s so much noise out there. We’ve got a strong idea of that with the Google searches. So how do you start to cut through and make a solid start?

Nicolai Müller: I would recommend two things. First is to start with a pilot, and I would even use the term “play” with generative AI. The cost of doing nothing is just too high because everybody has this at the top of their agenda. I think it’s the one topic that every management board has looked into, that every CEO has explored across all regions and industries. So it’s important that you start and see what generative AI can do.

In parallel, you need to really think about your strategy. When I talk about strategy, it includes a couple of elements. It’s a question of how will this impact my business? Where will it lead to improvements? Where will it not lead to improvement? Should I go fast ? Should I not go fast? Do I have solutions out there? Do I need partners? Can I rely on existing LLMs out there, or should I build my own? I think this is the whole question of truly understanding what generative AI in three to five years means for us.

Then there’s a layer in the strategy, which is about getting the data technology right. It’s understanding how you want to put governance and organization in place, which can build solutions. And there’s the question, where do the competencies in my company actually come from? Can I build them? Do I need to acquire them? So you need to be thoughtful about the whole question of competencies needed.

And then there’s the question of actually making the change. We often hear that this is the most important thing. You need to make people work with generative AI. You need to capture the early wins, but also things that are more challenging.

Christian Johnson: Excellent. And Marie, anything you’d like to add?

Marie El Hoyek: Yes, Christian. Nicolai, last time we spoke, you talked about this fresh breath of innovation in our companies, and I love to repeat this. You can see it in our discussion even. This gives us the ability to dream again, to come up with new things, and to hope for more impact. And I think, to some extent, we just need to learn, and start doing it, and start capturing it.

Christian Johnson: That’s a lovely ending, Marie. Thank you both, Nicolai and Marie, for sharing your expertise and experiences of generative AI with us today. It’s a topic that we don’t see going away anytime soon. So, your advice on diving in, but with both eyes open to risk mitigation and value creation, is a great note to end on.

Marie El Hoyek is a partner in McKinsey’s London office, and Nicolai Müller is a senior partner in the Cologne office. Christian Johnson is an executive editor and is based in McKinsey’s Washington, DC, office.

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IMAGES

  1. Nrich Addition And Subtraction Problems Ks2

    nrich problem solving subtraction

  2. Problem solving using subtraction

    nrich problem solving subtraction

  3. Subtraction Problem Solving

    nrich problem solving subtraction

  4. NRICH Maths Subtraction Surprise (3 dig) Try out some calculations. Are

    nrich problem solving subtraction

  5. Solving subtraction problems

    nrich problem solving subtraction

  6. Problem Solving: Subtraction Task Cards by Kathy Law

    nrich problem solving subtraction

VIDEO

  1. Math Episode 80 (Create and use problems that involve addition and subtraction.)

  2. 6-3 Solving Subtraction Equations

  3. Subtraction in the easy method. (Without Regrouping)

  4. simple tricks to solve problems on number system #shorts #shortvideo

  5. 3 6 Video 1 Solving 1 Step Subtraction Equations Why It Works

  6. Home Learning Y4

COMMENTS

  1. Addition and Subtraction KS2

    Challenge Level. A game for two people, or play online. Given a target number, say 23, and a range of numbers to choose from, say 1-4, players take it in turns to add to the running total to hit their target.

  2. Resources tagged with: Addition and subtraction

    We have found 288 NRICH Mathematical resources connected to Addition and subtraction, you may find related items under Calculations and numerical methods Resources tagged with: Addition and subtraction ... Cinema Problem. A cinema has 100 seats. Show how it is possible to sell exactly 100 tickets and take exactly £100 if the prices are £10 ...

  3. Addition and Subtraction

    The NRICH Project aims to enrich the mathematical experiences of all learners. To support this aim, members of the NRICH team work in a wide range of capacities, including providing professional development for teachers wishing to embed rich mathematical tasks into everyday classroom practice.

  4. NRICH topics: Calculations and numerical methods Addition and subtraction

    Problem-solving Schools; About NRICH expand_more. About us; Impact stories; Support us; Our funders; ... We have found 85 NRICH Mathematical resources connected to Addition and subtraction, you may find related items under Calculations and numerical methods ... Addition and subtraction. Types. Age range. Challenge level. There are 85 results ...

  5. Your Solutions

    The Nrich Maths Project Cambridge,England. Mathematics resources for children,parents and teachers to enrich learning. Problems,children's solutions,interactivities,games,articles.

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    This article from NRICH discusses ways in which teachers may develop children's problem solving skills. It provides ideas and links which would benefit a teacher's own practice or could be used as a basis of a staff training session. Here are nine challenges from NRICH which support Addition and Subtraction at KS2.

  7. Year 5 Column Subtraction Worksheets (differentiated) and Other

    File previews. Year 5 column subtraction worksheets (differentiated to 4 levels and with the answers) a link to an nrich problem-solving activity involving missing numbers in a column addition calculation (this is different to the higher level worksheet questions) There is a PDF version and an editable version of each file.

  8. NRICH launches new Problem-Solving Schools initiative

    30 Nov 2023. Our NRICH programme has launched a new initiative to help schools prioritise problem-solving in maths. The NRICH Problem-Solving Schools programme will offer free resources, advice and teacher professional development training. Problem-solving is a critical skill when it comes to empowering students for the future. It opens up a ...

  9. Part 1: Problem solving with NRICH

    The second will explore how you can structure the problem-solving process, and embed problem solving into every school day. Becoming a confident and competent problem solver is a complex process that requires a range of skills and experience. As a teacher, you can support this process in three key ways: Choosing appropriate tasks

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