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How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach

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  • Zhang, Zibin
  • Cai, Wenxin
  • Feng, Xiangzhao

Abstract

China is the largest electricity consumption country after it has passed the United States in 2011. Residential electricity consumption in China grew by 381.35% (12.85% per annum) between 2000 and 2013. In order to deal with rapid growth in residential electricity consumption, an increasing block pricing policy was introduced for residential electricity consumers in China on July 1st, 2012. Using difference-in-differences models with a fuzzy regression discontinuity design, we estimate a causal effect of price on electricity consumption for urban households during the introduction of increasing block pricing policy in Guangdong province of China. We find that consumers do not respond to a smaller (approximately 8%) increase in marginal price. However, consumers do respond to a larger increase in marginal price. An approximately 40% increase in marginal price induces an approximately 35% decrease in electricity use (284kWh per month). Our results suggest that although the increasing block pricing could affect the behavior of households with higher electricity use, there is only a limit potential to overall energy conservation.

Suggested Citation

  • Zhang, Zibin & Cai, Wenxin & Feng, Xiangzhao, 2017. "How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach," Energy Policy, Elsevier, vol. 105(C), pages 161-172.
    • Handle: RePEc:eee:enepol:v:105:y:2017:i:c:p:161-172
    DOI: 10.1016/j.enpol.2017.02.025
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    as
    1. Sun, Chuanwang & Lin, Boqiang, 2013. "Reforming residential electricity tariff in China: Block tariffs pricing approach," Energy Policy, Elsevier, vol. 60(C), pages 741-752.
    2. Herriges, Joseph A & King, Kathleen Kuester, 1994. "Residential Demand for Electricity under Inverted Block Rates: Evidence from a Controlled Experiment," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 419-430, October.
    3. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    4. Herriges, Joseph A. & King, K.A., 1994. "Residential Demand for Electricity Under Block Rate Structures: Evidence from a Controlled Experiment," Staff General Research Papers Archive 1498, Iowa State University, Department of Economics.
    5. Beenstock, Michael & Goldin, Ephraim & Nabot, Dan, 1999. "The demand for electricity in Israel," Energy Economics, Elsevier, vol. 21(2), pages 168-183, April.
    6. Baerenklau, Kenneth A. & Schwabe, Kurt & Dinar, Ariel, 2014. "Do Increasing Block Rate Water Budgets Reduce Residential Water Demand? A Case Study in Southern California," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170019, Agricultural and Applied Economics Association.
    7. Koji Miyawaki & Yasuhiro Omori & Akira Hibiki, 2011. "Panel Data Analysis Of Japanese Residential Water Demand Using A Discrete/Continuous Choice Approach," The Japanese Economic Review, Japanese Economic Association, vol. 62(3), pages 365-386, September.
    8. H. Allen Klaiber & V. Kerry Smith & Michael Kaminsky & Aaron Strong, 2014. "Measuring Price Elasticities for Residential Water Demand with Limited Information," Land Economics, University of Wisconsin Press, vol. 90(1), pages 100-113.
    9. Wichman, Casey J., 2014. "Perceived price in residential water demand: Evidence from a natural experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 308-323.
    10. Cai, Jing & Jiang, Zhigang, 2008. "Changing of energy consumption patterns from rural households to urban households in China: An example from Shaanxi Province, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1667-1680, August.
    11. Olmstead, Sheila M., 2009. "Reduced-Form Versus Structural Models of Water Demand Under Nonlinear Prices," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 84-94.
    12. Hausman, Jerry A., 1979. "The econometrics of labor supply on convex budget sets," Economics Letters, Elsevier, vol. 3(2), pages 171-174.
    13. Liu, Ming-Hua & Margaritis, Dimitris & Zhang, Yang, 2013. "Market-driven coal prices and state-administered electricity prices in China," Energy Economics, Elsevier, vol. 40(C), pages 167-175.
    14. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    15. Katrina Jessoe & David Rapson, 2014. "Knowledge Is (Less) Power: Experimental Evidence from Residential Energy Use," American Economic Review, American Economic Association, vol. 104(4), pages 1417-1438, April.
    16. Kenneth A. Baerenklau & Kurt A. Schwabe & Ariel Dinar, 2014. "The Residential Water Demand Effect of Increasing Block Rate Water Budgets," Land Economics, University of Wisconsin Press, vol. 90(4), pages 683-699.
    17. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, vol. 104(2), pages 537-563, February.
    18. Steven Sexton, 2015. "Automatic Bill Payment and Salience Effects: Evidence from Electricity Consumption," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 229-241, May.
    19. Piet Rietveld & Jan Rouwendal & Bert Zwart, 2000. "Block Rate Pricing of Water in Indonesia: An Analysis of Welfare Effects," Bulletin of Indonesian Economic Studies, Taylor & Francis Journals, vol. 36(3), pages 73-92.
    20. Bolduc, Denis & Khalaf, Lynda & Moyneur, Érick, 2008. "Identification-robust simulation-based inference in joint discrete/continuous models for energy markets," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3148-3161, February.
    21. Lin, Boqiang & Jiang, Zhujun, 2012. "Designation and influence of household increasing block electricity tariffs in China," Energy Policy, Elsevier, vol. 42(C), pages 164-173.
    22. Olmstead, Sheila M. & Michael Hanemann, W. & Stavins, Robert N., 2007. "Water demand under alternative price structures," Journal of Environmental Economics and Management, Elsevier, vol. 54(2), pages 181-198, September.
    23. Nataraj, Shanthi & Hanemann, W. Michael, 2011. "Does marginal price matter? A regression discontinuity approach to estimating water demand," Journal of Environmental Economics and Management, Elsevier, vol. 61(2), pages 198-212, March.
    24. Yu, Yihua & Zheng, Xinye & Han, Yi, 2014. "On the demand for natural gas in urban China," Energy Policy, Elsevier, vol. 70(C), pages 57-63.
    25. Burtless, Gary & Hausman, Jerry A, 1978. "The Effect of Taxation on Labor Supply: Evaluating the Gary Negative Income Tax Experiments," Journal of Political Economy, University of Chicago Press, vol. 86(6), pages 1103-1130, December.
    26. Holtedahl, Pernille & Joutz, Frederick L., 2004. "Residential electricity demand in Taiwan," Energy Economics, Elsevier, vol. 26(2), pages 201-224, March.
    27. Jingchao, Zhang & Kotani, Koji, 2012. "The determinants of household energy demand in rural Beijing: Can environmentally friendly technologies be effective?," Energy Economics, Elsevier, vol. 34(2), pages 381-388.
    28. Halicioglu, Ferda, 2007. "Residential electricity demand dynamics in Turkey," Energy Economics, Elsevier, vol. 29(2), pages 199-210, March.
    29. Julie A. Hewitt & W. Michael Hanemann, 1995. "A Discrete/Continuous Choice Approach to Residential Water Demand under Block Rate Pricing," Land Economics, University of Wisconsin Press, vol. 71(2), pages 173-192.
    30. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
    31. Wang, Qiang, 2014. "Effects of urbanisation on energy consumption in China," Energy Policy, Elsevier, vol. 65(C), pages 332-339.
    32. Berkhout, Peter H. G. & Ferrer-i-Carbonell, Ada & Muskens, Jos C., 2004. "The ex post impact of an energy tax on household energy demand," Energy Economics, Elsevier, vol. 26(3), pages 297-317, May.
    33. Krinsky, Itzhak & Robb, A Leslie, 1986. "On Approximating the Statistical Properties of Elasticities," The Review of Economics and Statistics, MIT Press, vol. 68(4), pages 715-719, November.
    34. 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.
    35. Donatos, George S. & Mergos, George J., 1991. "Residential demand for electricity: The case of Greece," Energy Economics, Elsevier, vol. 13(1), pages 41-47, January.
    36. Ellen M. Pint, 1999. "Household Responses to Increased Water Rates during the California Drought," Land Economics, University of Wisconsin Press, vol. 75(2), pages 246-266.
    37. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
    38. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
    39. Krishnamurthy, Chandra Kiran B. & Kriström, Bengt, 2015. "A cross-country analysis of residential electricity demand in 11 OECD-countries," Resource and Energy Economics, Elsevier, vol. 39(C), pages 68-88.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    C54; D12; L94; Q41; Difference-in-differences; Electricity demand; Increasing block pricing; Price elasticity; Fuzzy regression discontinuity;
    All these keywords.

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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