The economic valuation of green electricity

SPRINGER BRIEFS IN ENVIRONMENTAL SCIENCE Simona Bigerna · Paolo Polinori The Economic Valuation of Green Electricity 123 SpringerBriefs in Environmental Science SpringerBriefs in Environmental Science present concise summaries of cutting-edge research and practical applications across a wide spectrum of environmental fields, with fast turnaround time to publication Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic Monographs of new material are considered for the SpringerBriefs in Environmental Science series Typical topics might include: a timely report of state-of-the-art analytical techniques, a bridge between new research results, as published in journal articles and a contextual literature review, a snapshot of a hot or emerging topic, an in-depth case study or technical example, a presentation of core concepts that students must understand in order to make independent contributions, best practices or protocols to be followed, a series of short case studies/debates highlighting a specific angle SpringerBriefs in Environmental Science allow authors to present their ideas and readers to absorb them with minimal time investment Both solicited and unsolicited manuscripts are considered for publication More information about this series at http://www.springer.com/series/8868 Simona Bigerna • Paolo Polinori The Economic Valuation of Green Electricity Simona Bigerna Università di Perugia Perugia, Italy Paolo Polinori Università di Perugia Perugia, Italy ISSN 2191-5547 ISSN 2191-5555 (electronic) SpringerBriefs in Environmental Science ISBN 978-94-024-1572-8 ISBN 978-94-024-1574-2 (eBook) https://doi.org/10.1007/978-94-024-1574-2 Library of Congress Control Number: 2018952352 © The Author(s), under exclusive licence to Springer Nature B.V 2019 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of 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jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature B.V The registered company address is: Van Godewijckstraat 30, 3311 GX Dordrecht, The Netherlands Introduction The aim of the book is to analyze the relationship between renewable (or green) electricity and citizens, focusing on both the demand side and the supply side Today the consequences of the use of fossil energy are seen from a different perspective because issues related to climate change are evident worldwide Thus, climate change and resource depletion are real problems to be addressed for the welfare of society Renewable energy sources are essential to reduce polluting emissions, but they can produce a range of environmental effects, which might be detrimental to human activities, as attested to by several types of “Not in My Back Yard” (NIMBY) reactions This is because renewable energy infrastructure siting usually implies several pros and cons to the local stakeholders involved in the projects For example, in Italy, according to the last report available in 2016 (Nimbyforum 2017), there are 359 contested facility projects and, among these, 45% involve renewable energy Nevertheless, empirical evidence (from the Eurobarometer survey, among other sources) shows that in Italy, as in several European countries, citizens are willing to pay a significant amount to produce renewable electricity Renewable electricity production involves citizens from two opposite points of view Indeed, they are involved both as end users and as stakeholders in the construction of the facilities and in the local production process In this book we analyze this dual role played by citizens in order to evaluate the actual and global public acceptance of renewable electricity generation in Italy We address a specific and important area of the economic analysis—the stated preferences method—focusing on two welfare measures: willingness to pay and willingness to accept Consequently, the research evaluates the attitudes of citizens toward the end use of renewable electricity and the likelihood of acceptance of new infrastructure related to renewable electricity generation Our aim is not to consider all technologies; we focus only on site-specific cases that involve siting controversies To this we focus on empirical results in Europe, including Italian case studies, comparing them with our contingent valuation field experiments Furthermore, in our empirical v vi Introduction analysis we explicitly take into account the uncertainty associated with the respondents in order to obtain more robust results The book, therefore, is not about how to reconcile consumers’ and citizens’ behavior regarding renewable electricity consumption and production; rather, it explores the main determinants of people’s behavior, on the two sides of the market, for better understanding of this phenomenon, to obtain useful information for public and private decision makers The structure of the book is as follows In Chap we use a meta-analysis to collect and analyze related literature about renewable electricity adoption, taking into account the double role played by the citizens The main determinants of citizens’ behavior are analyzed for better understanding of the renewable electricity adoption process In Chap we investigate whether existing wind farms affect respondents’ attitudes and perceptions toward potential enlargement of wind farms, using a contingent valuation method We explicitly take into account the existence of respondents’ heterogeneity in perceiving the new project externalities referring to potential land use conflicts and local opposition To this, we use both willingness to pay and willingness to accept measures, and we also appraise the impact of uncertainty, taking into account several degrees of uncertainty, using the numerical scale method In Chap we estimate Italian households’ willingness to pay for renewable electricity, comparing our results with those of other similar studies conducted worldwide Furthermore, we use two approaches to treat uncertainty, appraising consumers’ willingness to pay for renewable electricity, to provide more robust results Contents Citizens’ Versus Consumers’ Attitudes Toward Renewable Electricity: What the Literature Tells Us in a Contingent Valuation Framework 1.1 Introduction 1.2 Methods 1.2.1 Meta-analysis Approach 1.2.2 Review of Related Literature 1.2.3 Topics of Interest 1.2.4 What the Literature Tells Us 1.2.5 Quantitative Analysis: Metaregressions 1.2.6 Qualitative Analysis: Local Survey 1.3 Results and Discussion 1.3.1 Willingness to Pay for Renewable Electricity on the Demand Side 1.3.2 Willingness to Accept and Willingness to Pay for Renewable Electricity on the Supply Side 1.3.3 Local Survey Results 1.4 Conclusions References Evaluating an Onshore Wind Farm Enlargement Project: A Contingent Valuation Study in Central Italy 2.1 Introduction 2.2 Related Literature 2.3 Method for Valuing Wind Farm Enlargement 2.3.1 The Scenario: Wind Power Generation in Italy 2.3.2 Case Study: The Monte Cucco Regional Park 2.3.3 Survey Method and Questionnaire 1 3 7 10 12 13 13 15 17 21 22 27 27 29 31 31 31 33 vii viii Contents 2.4 Theoretical and Econometric Framework 2.4.1 Theoretical Model and Elicitation Format 2.4.2 Econometric Model 2.5 Results and Discussion 2.5.1 Descriptive Results 2.5.2 Econometric Analysis 2.5.3 Welfare Measures 2.6 Conclusions References 34 35 36 37 37 41 47 49 50 Consumers’ Willingness to Pay for Renewable Electricity in Italy: A Comparative Analysis 3.1 Introduction 3.2 Related Literature 3.3 Renewable Electricity in Italy 3.3.1 Cost of Renewable Electricity in Italy 3.4 Methods and Data 3.4.1 Theoretical Model 3.4.2 Survey Design 3.4.3 Questionnaire 3.4.4 Elicitation Format and Econometric Model 3.5 Empirical Findings 3.5.1 Estimation Results 3.6 Comparative Analysis 3.7 Conclusions References 53 53 55 62 63 64 64 66 67 68 72 73 81 85 85 Appendix 91 Acknowledgments 97 Index 99 Abbreviations ANEV CEx CV DBDC DETR DK DN DY EU HB LB LHS MBDC NAP NCS NIMBY NSM OE OLS PC PN PY REn REnS RE RHS SBDC SPC SUR WTP WTA National Wind Energy Association Choice experiment Contingent valuation Double bound dichotomous choice Department of Environment, Transport and the Regions Do not know Definitely no Definitely yes European Union Higher bound Lower bound Left hand side Multiple bound dichotomous choice National Action Plan Numerical certainty scale “Not in My Back Yard” Numerical scale method Open ended Ordinary least squares Payment card Probably no Probably yes Renewable energy Renewable energy sources Renewable electricity Right hand side Single bound dichotomous choice Stochastic payment card Seemingly Unrelated Regression Willingness to pay Willingness to accept ix 84 Consumers’ Willingness to Pay for Renewable Electricity in Regardless of the energy mix used in RE production, uncertainty plays a crucial role in WTP, as shown in a few studies that consider this issue Vossler et al (2003) find that in a pricing program, certainty correction accounts for between 9.5% and 75% of uncorrected WTP in the DC model, while in the MBDC approach a less conservative model provides a WTP value four times greater than the conservative one In Bollino (2009) uncertainty accounts for between 8.5% and 45.5% of the Italian target cost Similar results are obtained by Bigerna and Polinori (2012, 2014) controlling also for different elicitation formats Another crucial aspect concerns the impact of the economic crisis on consumers’ WTP This is especially true in this chapter because our aim is to assess if the EU climate change policy (20–20–20 target) can be achieved by market forces in Italy This is a long-run policy target that needs to be confronted with stable and actual households’ WTP This is so because policy analysis should be based on reliable and constant structural preference parameters, and should not be affected by exceptional events In this respect, our survey fits this objective quite well because it was administrated at the end of November 2007 The financial crises that began at the end of 2007 led to a deep and prolonged global economic downturn, which afterward significantly altered consumers’ long-run perceptions This inevitably changed citizens’ spending decisions This means that our data refer to a period of macroeconomic stability; thus, they have the necessary characteristics for a long-term policy evaluation According to the literature it is doubtful whether an economic downturn reduces WTP for environmental goods Loureiro et al (2009) highlight that in the period 2006–2009, WTP estimates for environmental damage (the Prestige oil spill in Spain) were down by half even though this reduction did not affect regions more directly involved in the environmental disaster They explain this fact by the economic crisis: in 2009 the Spanish yearly GDP dropped by 4% and negative figures were expected for 2010 Conversely, in 2009, Hanemann et al (2011) estimated a very high WTP to support RE in Spain They used the forthcoming Copenhagen summit to justify this result Controversial results have also been obtained by Metcalfe and Baker (2012) They compare two identical surveys conducted in 2008–2010 for an environmental improvement in water sector The results highlight that the economic downturn consistently drops payment card WTP but does not reduce WTP elicited by CV The few studies on the impact of the economic downturn on WTP provide conflicting results Nevertheless, the results reported by Metcalfe and Baker (2012) show some sensitivity of the PC method to economic crises Considering that we utilize a variant of the PC method, we think it is crucial to use precrisis data in a longrun framework of analysis On the basis of these considerations we deem it appropriate to use our data to elicit a stable WTP structure References 3.7 85 Conclusions In Europe the current energy policy aims to increase the share of renewables in the electricity generation mix In Italy the current and binding constraint is to attain 26.4% of electricity production from renewable energy sources This chapter has investigated Italian households’ willingness to pay (WTP) to implement the European Union climate change policy in a comparative perspective, also focusing on the impact of methodological heterogeneity Our survey highlights an appreciable knowledge of REnS and broad consensus regarding the development of renewable electricity (RE) The annual median WTP is estimated at between EUR 305.3 million and EUR 1.0508 billion, which is a share between 8.7% and 30% of the national target cost Comparing annual households’ WTP, at constant prices, we notice that a very wide range of values exist in Europe Our results are close to those of similar countries In particular, our WTP is reasonably comparable to the German amounts This is a comfortable result, given the similarity of the Italian and German electricity markets In addition, we estimate that monetary value uncertainty amounts to 9% of the target cost if the Broberg and Brännlund approach is used This is a low figure compared with previous results, and it depends on the more conservative method (Broberg and Brännlund 2008) and the more conservative average index used in this chapter Indeed, using the Welsh and Poe approach, uncertainty increases to 21% of the national target cost This result also confirms that methodological heterogeneity is important to explain the variability of the results that exist in the literature Finally, the analysis of the A3 component burden shows that in Italy the actual additional cost to consumers due to support for RE is less than the WTP estimates obtained in our models This means that a further margin could exist for additional policy action to implement appropriate education campaigns aimed at providing information 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commodity of all public goods with unit prices and value equal to the tax charged to the household Households maximize U subject to their budget constraints—that is: M ẳ X P PP ỵ X G A:2ị where M is the nominal income and PP is a price vector of private goods Each household spends all of its disposable income by purchasing private goods: Md ¼ M À X G ðA:3Þ A maximization framework provides a set of conditional demand functions: d i ∗ ¼ dðPP ; PR ; X G ; Md Þ ðA:4Þ By substituting di* into U, we obtain a conditional indirect utility function: V ¼ V ðPP ; PR ; X G ; Md Þ © The Author(s), under exclusive licence to Springer Nature B.V 2019 S Bigerna, P Polinori, The Economic Valuation of Green Electricity, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-94-024-1574-2 ðA:5Þ 91 92 Appendix Inverting V for Md, we obtain the conditional expenditure function: E∗ ¼ Md ¼ E ∗ ðPP ; PR ; X G ; U Þ ðA:6Þ Minimizing expenditures on both private and public goods subject to the utility level, we obtain the restricted expenditure function: E ¼ EðPP ; PR ; X G ; U Þ ðA:7Þ The conditional expenditure function and restricted expenditure function are related as follows: E ðPP ; PR ; X G ; U Þ ¼ E ∗ ðPP ; PR ; X G ; U ị ỵ X G A:8ị We assume that the consumer does not observe PR and choose R; rather, the consumer is offered R and can choose to pay for it or not Therefore, PR is replaced with R, and we can rewrite the relationship as follows: EðPP ; R; X G ; U ị ẳ E PP ; R; X G ; U ị ỵ X G A:9ị By changing the scenario, we assume that the restricted expenditure function varies according to R: R0 ¼ scenario without renewable energy sources (REnS) in electricity production; R1 ¼ scenario with REnS in the energy portfolio By holding M constant, we find that the CS gives the willingness to pay (WTP) for the use of REnS in electricity production: À Á À Á CS ¼ E PP ; R0 ; X G ; U À E PP ; R1 ; X G ; U Á Á  À  À CS ¼ E ∗ PP ; R0 ; X G ; U ỵ X G À E∗ PP ; R1 ; X G ; U ỵ X G À Á CS ¼ E ∗ PP ; R0 ; X G ; U À E ∗ PP ; R1 ; X G ; U ðA:10Þ ðA:11Þ ðA:12Þ where U0 is the utility level of the household without the RE program This estimate of compensating surplus is a measure of the WTP for the “RE use” service Econometric Model Following Cameron and Huppert (1989), the WTP probability associated with the choice of the respondent is: Pðt i ị ẳ Pt li < WTPi t ui ị ðA:13Þ Appendix 93 Because WTP is non-negative and its distribution is skewed, we use a lognormal conditional distribution: log WTPi ẳ xi T ỵ i A:14ị where i is distributed normally, with mean zero and standard deviation σ The probability of choosing ti can be written: ÀÀ Á Á Pt i ị ẳ log t ui À xi T β =σ À Φ log t li À xi T β =σ ðA:15Þ where Φ is the standard normal cumulative density function The corresponding log likelihood function can be written: log L ¼ X i  ÀÀ Á Á ÀÀ Á Áà log Φ log t ui À xi T β =σ À Φ log t li À xi T β =σ ðA:16Þ We estimate the optimal values of β and σ and the mean and median WTP: À Á Median WTP ¼ exp xi T β À Á À Á Mean WTP ¼ exp xi T β =exp σ =2 ðA:17Þ ðA:18Þ and we compute the confidence interval by bootstrap methods with 3000 replications Statistical Details Table A.1 provides the sample characteristics and shows that the sample is highly representative of the Italian population in terms of the male to female ratio, geographical and urban locations, demographic characteristics, education, and income distribution Table A.1 Survey respondents’ and country residents’ characteristics Variable Gender Male Female Macro region Northwest Northeast Central South (including Sicily and Sardinia) Survey respondents Country residents 47.78% 52.22% 48.40% 51.60% 26.11% 19.69% 19.64% 34.55% 26.21% 18.66% 19.14% 36.00% (continued) 94 Appendix Table A.1 (continued) Variable Municipality size 5000 people 5001–10,000 people 10,001–30,000 people 30,001–100,000 people 100,001–500,000 people >500,000 people Age 15–17 years 18–24 years 25–34 years 35–44 years 45–54 years 55–64 years >64 years Marital status Single Divorced Separated Married or cohabiting Widowed Status not stated Highest education None or primary school Lower secondary school or professional training Upper secondary school University and/or higher degree Income (EUR) Mean Quantile 10% 25% 50% 75% 90% Professional status Entrepreneur Professional class Cooperative member Self-employed Civil servant or earning employee Unemployed worker Survey respondents Country residents 17.47% 13.67% 23.69% 21.96% 11.65% 11.55% 18.58% 14.11% 22.81% 21.29% 10.98% 12.23% 3.55% 9.92% 16.78% 18.85% 16.68% 14.36% 19.84% 3.54% 9.53% 17.98% 17.77% 15.52% 13.89% 21.77% 27.99% 1.14% 1.58% 61.75% 6.71% 0.84% 27.76% 1.23% 1.92% 61.19% 7.90% – 33.50% 35.60% 23.90% 7.00% 31.16% 32.50% 29.30% 7.04% 28,658.80 24,893.70 9822.22 14,801.18 24,682.57 34,088.30 47,981.99 8918.90 13,175.46 20,152.32 30,998.86 44,049.82 6.32% 1.36% 1.83% 1.36% 6.92% 31.45% 5.62% 5.70% 33.27% 4.05% (continued) Appendix 95 Table A.1 (continued) Variable Student Housewife Pensioner Other Household size member members members members members !6 members Survey respondents 12.44% 13.38% 23.89% 0.96% Country residents 11.34% 15.30% 20.64% 4.17% 10.71% 23.20% 23.74% 32.03% 8.49% 1.83% 24.89% 27.08% 21.58% 18.96% 5.80% 1.69% Acknowledgments The authors are thankful to Carlo Andrea Bollino and to the participants in the 2007 Research Projects of National Relevance (PRIN) seminar (in Milan on June 27, 2011), especially Paolo Bruno Bosco, Lucia Visconti Parisio, and Matteo Pelagatti, for their helpful suggestions We also thank the participants in the 27th US Association for Energy Economics/International Association for Energy Economics (USAEE/IAEE) North American Conference (in Houston on September 16–19, 2007), the 30th USAEE/IAEE North American Conference (in Washington, DC, on October 9–12, 2011), and the Italian Economic Association (SIE) 52nd Annual Conference (in Rome on October 14–15, 2011) Finally, we want to express our gratitude for editorial team assistance in polishing this manuscript © The Author(s), under exclusive licence to Springer Nature B.V 2019 S Bigerna, P Polinori, The Economic Valuation of Green Electricity, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-94-024-1574-2 97 Index A Age and sex, 79, 80 B Broberg and Brännlund (B&B), 72 C Choice experiment (CEx), Climate and energy package, 53 Climate and energy policies, 28 Comparative analysis, 84 Computer-aided web interviewing method, 68 Contingent valuation (CV), 2, 55 analysis, 66 method, 28, 33 Controversial results, 84 D Demographic variables, 46, 47 Descriptive results, 38, 40 Diagonal response patterns, 75 Double bounded dichotomous choice (DBDC), 55 E Econometric analysis, 44 Econometric model, 36, 92–93 Economic downturn, 81, 84 Electricity bill, 44, 66 Electricity market, 55 Elicitation format, 71 and econometric model, 70 Empirical findings, 76 Estimation results, 79 European and Italian wind power sectors, 36 European Council, 54 European scenario, 49 F Fuel electricity mix, 63 G Government intervention, 63 Green electricity policy, 60, 61 Green energy funding, 60 The Green Heart of Italy, 33 Greenhouse gas emissions, 54 H Household’s direct utility function, 91 I Istituto Piepoli, marketing & consulting company, 68 Italian electricity market, 64 Italian electricity production, 64 Italy incentive mechanisms, 63 © The Author(s), under exclusive licence to Springer Nature B.V 2019 S Bigerna, P Polinori, The Economic Valuation of Green Electricity, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-94-024-1574-2 99 100 L Lancaster theory, Local community, 2, 3, 6, 9, 12, 17, 21, 22 M Market sustainability, 81, 84 Meta-analysis, 2–4, 20, 21 Monte Cucco Regional Park, 32 Multiple bounded dichotomous choice (MBDC), 71 N National Action Plan (NAP), 32 National Wind Energy Association, 32 Negative externalities, 29 Not In My Back Yard (NIMBY) syndrome, 2, 31 Numerical Certainty Scale (NCS) approach, 35 Numerical Scale Method (NSM), 29 P Payment card (PC), 55 method, 61 Payment responses, 76 Policy evaluation, 84 Positive externalities, 29 Q Questionnaire, 70 R Regulatory Authority for Electricity and Gas (RAEG), 64 Renewable electricity (RE), AUD, 60 in Beijing, 61 climate change, CO2 emissions, 21 CO2 reduction, consumers, 21 and citizens, DBDC, 60 empirical methods, 13 environmental and energy scenario, framework, government provision, 61 green energy, heterogeneous, Index homeownership, individual characteristics, 7, installations to power, 63 institutional communication, 22 Italy, 63 literature, 4–6 local communities, 2, local survey, 17–21 meta-analysis approach, 3, meta-analysis regression, in mountain view, non-hydro RE capacity, 63 OECD countries, 62 place attachment, pricing program, 62 production, 54 psychological aspects, 22 qualitative analysis, 12 quantitative analysis, 10–12 REnS, SBDC, 60 in Umbria region, USD, 60 wind energy project, 10 windmill, WTA and WTP, 15–17 WTP, 2, 13, 15, 56, 61, 62 Renewable energy sources (REnS), 27, 54 advantages, 28 characteristics, 67 cost, 55 EUR, 66 fossil fuels, 28 greenhouse gases emissions, 54 increasing share, 54 Italy, 54 MBDC, 62 mitigation, 60 PC method, 61 policy intervention, 28 willing to pay, 54, 55, 61 Reviewing literature, 29 S Seemingly Unrelated Regression (SUR), 36 Single bounded dichotomous choice (SBDC), 55 Social variables, 49 Spanish households, 60 Statistical index, 84 Stochastic Payment Card (SPC), 71 Survey design, 67 Index Survey method and questionnaire, 33 Survey questionnaire, 68 Survey respondent and Country resident characteristics, 93 Survivor function, 79 T Theoretical model, 66 Theoretical model and elicitation format, 36 Typical family income, 73 U Umbria, 32, 33 Uncertainty, 41 Uncertainty preferences model, 71, 73 V Variables and descriptive statistics, 73 W Welfare measurement, 38 Welfare measures distribution functions, 41 Welsh and Poe (W&P) approaches, 72, 74 Willingness to accept (WTA), 4, 8, 10–13, 28 factual variables, 15–17 101 meta-regression, 16 methodological variables, 17 partial correlations, 20 WTP, 17, 19 Willingness to pay (WTP), 2, 4, 8–13, 15, 28 factual variables, 14–15 meta-regression, 14 methodological variables, 15 WTA (see Willingness to accept (WTA)) Wind energy generation, 28 Wind farm development, 29 Wind farm enlargement project behavioral and perceptual features, 37 economic values, 36 SUR approach, 37 WTP and WTA, 36, 37 CV method, 33 Italian, 31 preliminary analysis, 34 questionnaire, 35 theoretical model and elicitation format, 36 in Umbria, 32 uncertainty, 34 wind power sector, 31 WTP and WTA, 34 Wind power, 28 Wind power development process, 30 Wind power generation, 31 Wisconsin Renewable Energy Programs, 60 ... including the production of RG, the purchase price P, and a vector of other variables We must therefore define welfare measures as the ratio of the coefficient of the production of RG, βRG, and the purchase... explain the main determinants of the acceptance (or of the opposition), while other papers simply consider the economic constraint of the RE development, especially on the demand side Other important... given that the consequences of the use of fossil energy are © The Author(s), under exclusive licence to Springer Nature B.V 2019 S Bigerna, P Polinori, The Economic Valuation of Green Electricity,