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Regional persistence of the energy efficiency gap: Evidence from England and Wales

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  • Huaccha, Gissell

Abstract

The pursuit of reducing imported energy dependence via energy efficiency measures has become crucial to achieving sustainability goals, reducing greenhouse gas emissions, and minimising reliance on imported energy. Despite the significant heterogeneity of energy dependence across regions, heavy reliance on energy imports can expose countries to energy security risks that impact wholesale market energy prices and global energy security, especially in periods of geopolitical conflict. Recent geopolitical conflicts and pent-up demand from post-pandemic recovery have caused global energy prices to rise, leading to high inflation and a severe cost-of-living crisis worldwide. The existence of persistent patterns of energy efficiency gap can quickly exacerbate the associated environmental and economic losses caused by energy price shocks. This paper aims to provide robust empirical evidence of the existence of patterns of persistence of the energy efficiency gap and analyses cross-sectional heterogeneity in such persistence. In a large sample of 18,361,088 domestic dwellings across England and Wales, this study incorporates observable cross-sectional heterogeneous factors, such as socioeconomic conditions, regional characteristics, and structural constraints, to understand the potential barriers preventing residents from adjusting their energy efficiency ratings and their energy efficiency gaps. Notably, the study finds that the energy efficiency gap exhibits an average high degree of persistence of almost 50%, a finding that is statistically and economically significant across all of the LSOAs in England and Wales. The study also finds significant evidence of cross-sectional heterogeneity. This analysis is unique, both in terms of methodology and the subject of investigation, as it is the first empirical analysis that investigates regional patterns of persistence of the energy efficiency gap across England and Wales with such a large degree of granularity. The findings of this study contribute to the scarce academic literature in the field and provide valuable information for designing effective policies that can help achieve energy security and climate change goals while tackling growing socioeconomic inequalities.

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  • Huaccha, Gissell, 2023. "Regional persistence of the energy efficiency gap: Evidence from England and Wales," Energy Economics, Elsevier, vol. 127(PA).
  • Handle: RePEc:eee:eneeco:v:127:y:2023:i:pa:s0140988323005406
    DOI: 10.1016/j.eneco.2023.107042
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    References listed on IDEAS

    as
    1. Kenneth Gillingham & Karen Palmer, 2014. "Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 8(1), pages 18-38, January.
    2. Alberini, Anna & Filippini, Massimo, 2011. "Response of residential electricity demand to price: The effect of measurement error," Energy Economics, Elsevier, vol. 33(5), pages 889-895, September.
    3. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    4. Jia, Mengda & Srinivasan, Ravi S. & Raheem, Adeeba A., 2017. "From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 525-540.
    5. Robert W. Hahn & Robert D. Metcalfe, 2021. "Efficiency and Equity Impacts of Energy Subsidies," American Economic Review, American Economic Association, vol. 111(5), pages 1658-1688, May.
    6. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    7. Ryu, Jun-Yeol & Kim, Dae-Wook & Kim, Man-Keun, 2021. "Household differentiation and residential electricity demand in Korea," Energy Economics, Elsevier, vol. 95(C).
    8. Hahn, Jinyong & Hausman, Jerry & Kuersteiner, Guido, 2007. "Long difference instrumental variables estimation for dynamic panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 574-617, October.
    9. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    10. Taruttis, Lisa & Weber, Christoph, 2022. "Estimating the impact of energy efficiency on housing prices in Germany: Does regional disparity matter?," Energy Economics, Elsevier, vol. 105(C).
    11. Xin Chang & Sudipto Dasgupta, 2009. "Target Behavior and Financing: How Conclusive Is the Evidence?," Journal of Finance, American Finance Association, vol. 64(4), pages 1767-1796, August.
    12. Haitao Yin & Hui Zhou & Kai Zhu, 2016. "Long- and short-run elasticities of residential electricity consumption in China: a partial adjustment model with panel data," Applied Economics, Taylor & Francis Journals, vol. 48(28), pages 2587-2599, June.
    13. Cook, Douglas O. & Tang, Tian, 2010. "Macroeconomic conditions and capital structure adjustment speed," Journal of Corporate Finance, Elsevier, vol. 16(1), pages 73-87, February.
    14. Hunt Allcott & Richard L. Sweeney, 2017. "The Role of Sales Agents in Information Disclosure: Evidence from a Field Experiment," Management Science, INFORMS, vol. 63(1), pages 21-39, January.
    15. Paul, Anthony & Myers, Erica & Palmer, Karen, 2009. "A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector," RFF Working Paper Series dp-08-50, Resources for the Future.
    16. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    17. Cheng, Fenfen & Yang, Shanlin & Zhou, Kaile, 2020. "Quantile partial adjustment model with application to predicting energy demand in China," Energy, Elsevier, vol. 191(C).
    18. Casu, Barbara & Girardone, Claudia, 2010. "Integration and efficiency convergence in EU banking markets," Omega, Elsevier, vol. 38(5), pages 260-267, October.
    19. Comerford, David A. & Lange, Ian & Moro, Mirko, 2018. "Proof of concept that requiring energy labels for dwellings can induce retrofitting," Energy Economics, Elsevier, vol. 69(C), pages 204-212.
    20. Judson Boomhower & Lucas Davis, 2020. "Do Energy Efficiency Investments Deliver at the Right Time?," American Economic Journal: Applied Economics, American Economic Association, vol. 12(1), pages 115-139, January.
    21. Todd D. Gerarden & Richard G. Newell & Robert N. Stavins, 2017. "Assessing the Energy-Efficiency Gap," Journal of Economic Literature, American Economic Association, vol. 55(4), pages 1486-1525, December.
    22. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    23. Ross Levine & Norman Loayza & Thorsten Beck, 2002. "Financial Intermediation and Growth: Causality and Causes," Central Banking, Analysis, and Economic Policies Book Series, in: Leonardo Hernández & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Se (ed.),Banking, Financial Integration, and International Crises, edition 1, volume 3, chapter 2, pages 031-084, Central Bank of Chile.
    24. Elsas, Ralf & Florysiak, David, 2015. "Dynamic Capital Structure Adjustment and the Impact of Fractional Dependent Variables," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(5), pages 1105-1133, October.
    25. Cave, Joshua & Chaudhuri, Kausik & Kumbhakar, Subal C., 2023. "Dynamic firm performance and estimator choice: A comparison of dynamic panel data estimators," European Journal of Operational Research, Elsevier, vol. 307(1), pages 447-467.
    26. Gillingham, Kenneth & Tsvetanov, Tsvetan, 2018. "Nudging energy efficiency audits: Evidence from a field experiment," Journal of Environmental Economics and Management, Elsevier, vol. 90(C), pages 303-316.
    27. zu Ermgassen, Sophus & Drewniok, Michal & Bull, Joseph & Walker, Christine Corlet & Mancini, Mattia & Ryan-Collins, Josh & Serrenho, André Cabrera, 2022. "A home for all within planetary boundaries: pathways for meeting England’s housing needs without transgressing national climate and biodiversity goals," OSF Preprints 5kxce, Center for Open Science.
    28. Cohen, François & Glachant, Matthieu & Söderberg, Magnus, 2017. "Consumer myopia, imperfect competition and the energy efficiency gap: Evidence from the UK refrigerator market," European Economic Review, Elsevier, vol. 93(C), pages 1-23.
    29. Ralf Elsas & David Florysiak, 2011. "Heterogeneity in the Speed of Adjustment toward Target Leverage," International Review of Finance, International Review of Finance Ltd., vol. 11(2), pages 181-211, June.
    30. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-247, February.
    31. Huang, Rongbing & Ritter, Jay R., 2009. "Testing Theories of Capital Structure and Estimating the Speed of Adjustment," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 237-271, April.
    32. Schleich, Joachim & Gassmann, Xavier & Faure, Corinne & Meissner, Thomas, 2016. "Making the implicit explicit: A look inside the implicit discount rate," Energy Policy, Elsevier, vol. 97(C), pages 321-331.
    33. Goeschl, Timo, 2019. "Cold Case: The forensic economics of energy efficiency labels for domestic refrigeration appliances," Energy Economics, Elsevier, vol. 84(S1).
    34. Park, Jiyong & Son, WooJin & Moon, HyungBin & Woo, JongRoul, 2023. "Nudging energy efficiency behavior: The effect of message framing on implicit discount rate," Energy Economics, Elsevier, vol. 117(C).
    35. Lin, Winston T. & Chen, Yueh H. & Chatov, Robert, 1987. "The demand for natural gas, electricity and heating oil in the United States," Resources and Energy, Elsevier, vol. 9(3), pages 233-258, October.
    36. Flannery, Mark J. & Rangan, Kasturi P., 2006. "Partial adjustment toward target capital structures," Journal of Financial Economics, Elsevier, vol. 79(3), pages 469-506, March.
    37. Lin, Winston T. & Kao, Ta-Wei (Daniel), 2014. "The partial adjustment valuation approach with dynamic and variable speeds of adjustment to evaluating and measuring the business value of information technology," European Journal of Operational Research, Elsevier, vol. 238(1), pages 208-220.
    38. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    39. zu Ermgassen, Sophus O.S.E. & Drewniok, Michal P. & Bull, Joseph W. & Corlet Walker, Christine M. & Mancini, Mattia & Ryan-Collins, Josh & Cabrera Serrenho, André, 2022. "A home for all within planetary boundaries: Pathways for meeting England's housing needs without transgressing national climate and biodiversity goals," Ecological Economics, Elsevier, vol. 201(C).
    40. Morgan, J. & Chu, C.M. & Haines-Doran, T., 2023. "Competent retrofitting policy and inflation resilience: The cheapest energy is that which you don't use," Energy Economics, Elsevier, vol. 121(C).
    41. Hamilton, Ian G. & Steadman, Philip J. & Bruhns, Harry & Summerfield, Alex J. & Lowe, Robert, 2013. "Energy efficiency in the British housing stock: Energy demand and the Homes Energy Efficiency Database," Energy Policy, Elsevier, vol. 60(C), pages 462-480.
    42. Lars Leszczensky & Tobias Wolbring, 2022. "How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study," Sociological Methods & Research, , vol. 51(2), pages 837-865, May.
    43. Silvi, Mariateresa & Padilla Rosa, Emilio, 2021. "Reversing impatience: Framing mechanisms to increase the purchase of energy-saving appliances," Energy Economics, Elsevier, vol. 103(C).
    44. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    45. Holtedahl, Pernille & Joutz, Frederick L., 2004. "Residential electricity demand in Taiwan," Energy Economics, Elsevier, vol. 26(2), pages 201-224, March.
    46. Arik Levinson, 2016. "How Much Energy Do Building Energy Codes Save? Evidence from California Houses," American Economic Review, American Economic Association, vol. 106(10), pages 2867-2894, October.
    47. Jerry A. Hausman, 1979. "Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables," Bell Journal of Economics, The RAND Corporation, vol. 10(1), pages 33-54, Spring.
    48. Silk, Julian I. & Joutz, Frederick L., 1997. "Short and long-run elasticities in US residential electricity demand: a co-integration approach," Energy Economics, Elsevier, vol. 19(4), pages 493-513, October.
    49. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    50. Fuerst, Franz & McAllister, Patrick & Nanda, Anupam & Wyatt, Peter, 2015. "Does energy efficiency matter to home-buyers? An investigation of EPC ratings and transaction prices in England," Energy Economics, Elsevier, vol. 48(C), pages 145-156.
    51. Loudermilk, Margaret S., 2007. "Estimation of Fractional Dependent Variables in Dynamic Panel Data Models With an Application to Firm Dividend Policy," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 462-472, October.
    52. Hunt Allcott & Todd Rogers, 2014. "The Short-Run and Long-Run Effects of Behavioral Interventions: Experimental Evidence from Energy Conservation," American Economic Review, American Economic Association, vol. 104(10), pages 3003-3037, October.
    53. Pedro J. García†Teruel & Pedro Martínez†Solano, 2010. "A Dynamic Approach to Accounts Receivable: a Study of Spanish SMEs," European Financial Management, European Financial Management Association, vol. 16(3), pages 400-421, June.
    54. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    55. Todd Gerarden & Richard G. Newell & Robert N. Stavins, 2015. "Deconstructing the Energy-Efficiency Gap: Conceptual Frameworks and Evidence," American Economic Review, American Economic Association, vol. 105(5), pages 183-186, May.
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    More about this item

    Keywords

    Energy efficiency gap; Climate change; Retrofit; dynamic panel data models; Partial adjustment model;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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