Adha, Rishan and Hong, Cheng-Yih and Firmansyah, M. and Paranata, Ade (2021): Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia. Published in: Sustainable Production and Consumption , Vol. 28, No. October 2021 (22 June 2021): pp. 556-565.
Preview |
PDF
MPRA_paper_110444.pdf Download (472kB) | Preview |
Abstract
This study aims to estimate the economy-wide rebound effect using the determinants of household energy demand in Indonesia. Identifying the size of the rebound effect is essential for the government's energy efficiency and carbon emission reduction programs. The estimation of the rebound effect uses a two-stage analysis with panel data of every province in Indonesia from 2002 to 2018. We employ the Input Demand Function of the Stochastic Frontier Analysis to measure the energy efficiency of residential aggregate in Indonesia. In the second stage, we adopt the dynamic panel data model to estimate the economy-wide rebound effect. The estimated dynamic panel data model reveals that the magnitudes of the short-run and long-run rebound effects were 87.2% and -45.5%, respectively. In other words, a 1% increase in household energy efficiency results in a reduction in energy consumption of 0.13% in the short term and 1.45% in the long term. Our research also discovers that a backfire rebound effect exists in provinces with high energy efficiency. Therefore, we prove to backfire claims that improving energy efficiency will increase energy use. Henceforth, energy efficiency programs in the household sector still need to be implemented, followed by increasing technological innovation and improving housing policy.
Item Type: | MPRA Paper |
---|---|
Original Title: | Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia |
Language: | English |
Keywords: | Electricity demand, energy efficiency, rebound effect, stochastic frontier analysis |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy |
Item ID: | 110444 |
Depositing User: | Mr Rishan Adha |
Date Deposited: | 23 Nov 2021 20:27 |
Last Modified: | 23 Nov 2021 20:27 |
References: | Adetutu, M. O., Glass, A. J., & Weyman-Jones, T. G. (2016). Economy-wide Estimates of Rebound Effects: Evidence from Panel Data. The Energy Journal, 37(3), 251-269. Retrieved from http://www.jstor.org/stable/44075657 Adha, R., & Hong, C.-Y. (2021). How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption. International Journal of Energy Economics and Policy, 11(3), 354-364. doi:https://doi.org/10.32479/ijeep.9834 Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37. doi:https://doi.org/10.1016/0304-4076(77)90052-5 Amjadi, G., Lundgren, T., & Persson, L. (2018). The Rebound Effect in Swedish Heavy Industry. Energy Economics, 71, 140-148. doi:https://doi.org/10.1016/j.eneco.2018.02.001 Bentzen, J. (2004). Estimating the rebound effect in US manufacturing energy consumption. Energy Economics, 26(1), 123-134. doi:https://doi.org/10.1016/S0140-9883(03)00047-1 Berkhout, P. H. G., Muskens, J. C., & W. Velthuijsen, J. (2000). Defining the rebound effect. Energy Policy, 28(6), 425-432. doi:https://doi.org/10.1016/S0301-4215(00)00022-7 Broberg, T., Berg, C., & Samakovlis, E. (2015). The economy-wide rebound effect from improved energy efficiency in Swedish industries–A general equilibrium analysis. Energy Policy, 83, 26-37. doi:https://doi.org/10.1016/j.enpol.2015.03.026 Brookes, L. (1979). A Low Energy Strategy for the UK by G Leach et al: a Review and Reply. Atom, 269(3–8). Bruno, G. S. F. (2005). Estimation and Inference in Dynamic Unbalanced Panel-data Models with a Small Number of Individuals. The Stata Journal, 5(4), 473-500. doi:https://doi.org/10.1177/1536867X0500500401 Filippini, M. (2011). Short- and long-run time-of-use price elasticities in Swiss residential electricity demand. Energy Policy, 39(10), 5811-5817. doi:https://doi.org/10.1016/j.enpol.2011.06.002 Filippini, M., & Hunt, L. C. (2011). Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach. The Energy Journal, Volume 32(2), 59-80. doi:https://doi.org/10.5547/ISSN0195-6574-EJ-Vol32-No2-3 Filippini, M., & Hunt, L. C. (2012). US residential energy demand and energy efficiency: A stochastic demand frontier approach. Energy Economics, 34(5), 1484-1491. doi:https://doi.org/10.1016/j.eneco.2012.06.013 Filippini, M., & Hunt, L. C. (2016). Measuring persistent and transient energy efficiency in the US. Energy Efficiency, 9(3), 663-675. doi:https://doi.org/10.1007/s12053-015-9388-5 Filippini, M., & Zhang, L. (2016). Estimation of the energy efficiency in Chinese provinces. Energy Efficiency, 9(6), 1315-1328. doi:https://doi.org/10.1007/s12053-016-9425-z Freire-González, J. (2017). Evidence of direct and indirect rebound effect in households in EU-27 countries. Energy Policy, 102, 270-276. doi:http://dx.doi.org/10.1016/j.enpol.2016.12.002 Frondel, M., & Vance, C. (2013). Re-Identifying the Rebound: What About Asymmetry? The Energy Journal, Volume 34(4), 42-54. doi:https://doi.org/10.5547/01956574.34.4.3 Gillingham, K., & Palmer, K. (2014). Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence. Review of Environmental Economics and Policy, 8(1), 18-38. doi:https://doi.org/10.1093/reep/ret021 Gillingham, K., Rapson, D., & Wagner, G. (2016). The Rebound Effect and Energy Efficiency Policy. Review of Environmental Economics and Policy, 10(1), 68-88. doi:10.1093/reep/rev017 Greene, W. H. (2005). Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity Analysis, 23(1), 7-32. doi:https://doi.org/10.1007/s11123-004-8545-1 Greene, W. H. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269-303. doi:https://doi.org/10.1016/j.jeconom.2004.05.003 Greening, L. A., Greene, D. L., & Difiglio, C. (2000). Energy efficiency and consumption - the rebound effect - a survey. Energy Policy, 28(6-7), 389-401. doi:https://doi.org/10.1016/S0301-4215(00)00021-5 Hanley, N., McGregor, P. G., Swales, J. K., & Turner, K. (2009). Do increases in energy efficiency improve environmental quality and sustainability? Ecological Economics, 68(3), 692-709. doi:https://doi.org/10.1016/j.ecolecon.2008.06.004 Hausman, J. A. (1979). Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables. The Bell Journal of Economics, 10(1), 33-54. doi:https://doi.org/10.2307/3003318 IEA. (2019). World energy balances and statistics. Retrieved from https://www.iea.org/subscribe-to-data-services/world-energy-balances-and-statistics. from IEA https://www.iea.org/subscribe-to-data-services/world-energy-balances-and-statistics Jenkins, J., Nordhaus, T., & Shellenberger, M. (2011). Energy emergence: rebound and backfire as emergent phenomena. Retrieved from Oakland, CA: Jevons, W. S. (1866). The Coal Question; An Inquiry concerning the Progress of the Nation, and the Probable Exhaustion of our Coal-mines (2nd ed.). London: Macmillan and Co. Jondrow, J., Knox Lovell, C. A., Materov, I. S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19(2), 233-238. doi:https://doi.org/10.1016/0304-4076(82)90004-5 Khazzoom, J. D. (1980). Economic Implications of Mandated Efficiency in Standards for Household Appliances. The Energy Journal, Volume 1(4), 21-40. doi:https://doi.org/10.5547/ISSN0195-6574-EJ-Vol1-No4-2 Kiviet, J. F. (1995). On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), 53-78. doi:https://doi.org/10.1016/0304-4076(94)01643-E Kumbhakar, S. C., & Heshmati, A. (1995). Efficiency measurement in Swedish dairy farms: an application of rotating panel data, 1976–88. American Journal of Agricultural Economics, 77(3), 660-674. doi: https://doi.org/10.2307/1243233 Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic Frontier Analysis. Cambridge: Cambridge University Press. Kumbhakar, S. C., Wang, H.-J., & Horncastle, A. P. (2015). A Practitioner's Guide to Stochastic Frontier Analysis Using Stata. Cambridge: Cambridge University Press. Llorca, M., & Jamasb, T. (2017). Energy efficiency and rebound effect in European road freight transport. Transportation Research Part A: Policy and Practice, 101, 98-110. doi:https://doi.org/10.1016/j.tra.2017.05.002 Orea, L., Llorca, M., & Filippini, M. (2015). A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand. Energy Economics, 49, 599-609. doi:https://doi.org/10.1016/j.eneco.2015.03.016 Otsuka, A. (2017). Determinants of efficiency in residential electricity demand: stochastic frontier analysis on Japan. Energy, Sustainability and Society, 7(1), 31. doi:https://doi.org/10.1186/s13705-017-0135-y PLN. (2009). PLN Statistic 2009. Retrieved from Jakarta: https://web.pln.co.id/en/stakeholders/statistical-report PLN. (2018). PLN Statistic 2018. Retrieved from Jakarta: https://web.pln.co.id/en/stakeholders/statistical-report Saunders, H. D. (1992). The Khazzoom-Brookes Postulate and Neoclassical Growth. The Energy Journal, 13(4), 131-148. doi:www.jstor.org/stable/41322471 Saunders, H. D. (2000). A view from the macro side: rebound, backfire, and Khazzoom–Brookes. Energy Policy, 28(6), 439-449. doi:https://doi.org/10.1016/S0301-4215(00)00024-0 Saunders, H. D. (2013). Historical evidence for energy efficiency rebound in 30 US sectors and a toolkit for rebound analysts. Technological Forecasting and Social Change, 80(7), 1317-1330. doi:https://doi.org/10.1016/j.techfore.2012.12.007 Shahbaz, M., Hye, Q. M. A., Tiwari, A. K., & Leitão, N. C. (2013). Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews, 25, 109-121. doi:https://doi.org/10.1016/j.rser.2013.04.009 Sorrell, S., Dimitropoulos, J., & Sommerville, M. (2009). Empirical estimates of the direct rebound effect: A review. Energy Policy, 37(4), 1356-1371. doi:https://doi.org/10.1016/j.enpol.2008.11.026 Turner, K. (2009). Negative rebound and disinvestment effects in response to an improvement in energy efficiency in the UK economy. Energy Economics, 31(5), 648-666. doi:https://doi.org/10.1016/j.eneco.2009.01.008 van den Bergh, J. C. J. M. (2011). Energy Conservation More Effective With Rebound Policy. Environmental and Resource Economics, 48(1), 43-58. doi:https://doi.org/10.1007/s10640-010-9396-z Zhang, Y.-J., & Peng, H.-R. (2016). Measuring the Direct Rebound Effect of China's Residential Electricity Consumption. Energy Procedia, 104, 305-310. doi:https://doi.org/10.1016/j.egypro.2016.12.052 Zhang, Y.-J., Peng, H.-R., Liu, Z., & Tan, W. (2015). Direct energy rebound effect for road passenger transport in China: A dynamic panel quantile regression approach. Energy Policy, 87, 303-313. doi:https://doi.org/10.1016/j.enpol.2015.09.022 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/110444 |