Does urbanization improve energy efficiency? Empirical evidence from China
Abstract
An analysis of urbanization’s effects on energy efficiency (EE) is presented in this paper. We develop an input-oriented data envelopment analysis method to estimate EE in the presence of non-convex metafrontier, and examine how urbanization affects China’s EE using data from 251 cities for the period 2003 to 2016. The findings indicate that demographic urbanization (DU), land urbanization (LU), and economical urbanization (EU) significantly exert positive effects on EE. Specifically, estimates from a Tobit model with random effects show that a unit increase in DU, LU and EU would result in an increase in EE by 0.15, 0.15 and 0.45, respectively. These results are robust across econometric specifications, including fixed and correlated random effects Tobit models. Sensitivity analysis of quasi-DID and stochastic frontier estimations also support our findings. The policy implications suggest policymakers should steer urbanization and energy consumption towards becoming more market-oriented and take advantage of how energy market structure complements energy structure, cultivating new energy industries that can greatly improve EE.
First published online 20 May 2022
Keyword : urbanization, energy efficiency, non-convex metafrontier, Tobit model, stochastic frontier analysis
How to Cite
Yu, Y., & Luo, N. (2022). Does urbanization improve energy efficiency? Empirical evidence from China. Technological and Economic Development of Economy, 28(4), 1003–1021. https://doi.org/10.3846/tede.2022.16736
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Nunn, N., & Qian, N. (2011). The potato’s contribution to population and urbanization: Evidence from a historical experiment. The Quarterly Journal of Economics, 126(2), 593–650. https://doi.org/10.1093/qje/qjr009
Ouyang, X., Chen, J., & Du, K. (2021). Energy efficiency performance of the industrial sector: From the perspective of technological gap in different regions in China. Energy, 214, 118865. https://doi.org/10.1016/j.energy.2020.118865
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Sadorsky, P. (2013). Do urbanization and industrialization affect energy intensity in developing countries? Energy Economics, 37, 52–59. https://doi.org/10.1016/j.eneco.2013.01.009
Sheng, P., He, Y., & Guo, X. (2017). The impact of urbanization on energy consumption and efficiency. Energy & Environment, 28(7), 673–686. https://doi.org/10.1177/0958305X17723893
Tiedemann, T., Francksen, T., & Latacz-Lohmann, U. (2011). Assessing the performance of German Bundesliga football players: A non-parametric metafrontier approach. Central European Journal of Operations Research, 19(4), 571–587. https://doi.org/10.1007/s10100-010-0146-7
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
Wang, Q., Zhao, Z., Zhou, P., & Zhou, D. (2013). Energy efficiency and production technology heterogeneity in China: A metafrontier DEA approach. Economic Modelling, 35(5), 283–289. https://doi.org/10.1016/j.econmod.2013.07.017
Wooldridge, J. M. (2008). Nonlinear dynamic panel data models with unobserved effects. Invited lecture. Canadian Econometrics Study Group, Montreal.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
Yang, Z., Fan, M., Shao, S., & Yang, L. (2017). Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis. Energy Economics, 68, 271–282. https://doi.org/10.1016/j.eneco.2017.10.009
Yu, Y., Huang, J., & Zhang, N. (2018). Industrial eco-efficiency, regional disparity, and spatial convergence of China’s regions. Journal of Cleaner Production, 204, 872–887. https://doi.org/10.1016/j.jclepro.2018.09.054
Yu, Y., Zhang, N., & Kim, J. D. (2020). Impact of urbanization on energy demand: An empirical study of the Yangtze River Economic Belt in China. Energy Policy, 139, 111354. https://doi.org/10.1016/j.enpol.2020.111354
Zhang, N., Kong, F., & Yu, Y. (2015). Measuring ecological total-factor energy efficiency incorporating regional heterogeneities in China. Ecological Indicators, 51, 165–172. https://doi.org/10.1016/j.ecolind.2014.07.041
Afsharian, M. (2017). Metafrontier efficiency analysis with convex and non-convex metatechnologies by stochastic nonparametric envelopment of data. Economics Letters, 160, 1–3. https://doi.org/10.1016/j.econlet.2017.08.006
Al-Mulali, U., & Tang, C. F. (2013). Investigating the validity of pollution haven hypothesis in the gulf cooperation council (GCC) countries. Energy Policy, 60, 813–819. https://doi.org/10.1016/j.enpol.2013.05.055
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264. https://www.jstor.org/stable/2632964
Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332. https://doi.org/10.1007/BF01205442
Battese, G. E., Rao, D. S. P., & O’Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91–103. https://doi.org/10.1023/B:PROD.0000012454.06094.29
Bilgili, F., Koçak, E., Bulut, Ü., & Kuloğlu, A. (2017). The impact of urbanization on energy intensity: Panel data evidence considering cross-sectional dependence and heterogeneity. Energy, 133, 242–256. https://doi.org/10.1016/j.energy.2017.05.121
Boyd, G. A., & Lee, J. M. (2019). Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis. Energy Economics, 81, 159–174. https://doi.org/10.1016/j.eneco.2019.03.021
Elliott, R. J. R., Sun, P., & Zhu, T. (2017). The direct and indirect effect of urbanization on energy intensity: A province-level study for China. Energy, 123, 677–692. https://doi.org/10.1016/j.energy.2017.01.143
Greene, W. H. (2011). Econometric analysis (7th ed.). Prentice Hall.
Haider, S., & Mishra, P. P. (2021). Does innovative capability enhance the energy efficiency of Indian Iron and Steel firms? A Bayesian stochastic frontier analysis. Energy Economics, 95, 105128. https://doi.org/10.1016/j.eneco.2021.105128
He, Y., Liao, N., & Zhou, Y. (2018). Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN. Energy, 142, 79–89. https://doi.org/10.1016/j.energy.2017.10.011
Huang, C. W., Ting, C. T., Lin, C. H., & Lin, C. T. (2013). Measuring non-convex metafrontier efficiency in international tourist hotels. Journal of the Operational Research Society, 64(2), 250–259. https://doi.org/10.1057/jors.2012.52
Huang, J., & Hua, Y. (2018). Eco-efficiency convergence and green urban growth in China. International Regional Science Review, 42(3–4), 307–334. https://doi.org/10.1177/0160017618790032
Huang, J., Yu, Y., & Ma, C. (2018). Energy efficiency convergence in China: Catch-up, lock-in and regulatory uniformity. Environmental and Resource Economics, 70(1), 107–130. https://link.springer.com/article/10.1007/s10640-017-0112-0
Jorgenson, D., Gollop, F. M., & Fraumeni, B. (1987). Productivity and U.S. economic growth. Harvard University Press.
Kou, Z., & Liu, X. (2017). FIND report on city and industrial innovation in China. Fudan Institute of Industrial Development, School of Economics, Fudan University.
Li, K., Fang, L., & He, L. (2018). How urbanization affects China’s energy efficiency: A spatial econometric analysis. Journal of Cleaner Production, 200, 1130–1141. https://doi.org/10.1016/j.jclepro.2018.07.234
Lv, Y., Chen, W., & Cheng, J. (2020). Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models. Energy Policy, 147, 111858. https://doi.org/10.1016/j.enpol.2020.111858
Mardani, A., Zavadskas, E., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable and Sustainable Energy Reviews, 70, 1298–1322. https://doi.org/10.1016/j.rser.2016.12.030
Markandya, A., Pedroso-Galinato, S., & Streimikiene, D. (2006). Energy intensity in transition economies: Is there convergence towards the EU average? Energy Economics, 28(1), 121–145. https://doi.org/10.1016/j.eneco.2005.10.005
Nunn, N., & Qian, N. (2011). The potato’s contribution to population and urbanization: Evidence from a historical experiment. The Quarterly Journal of Economics, 126(2), 593–650. https://doi.org/10.1093/qje/qjr009
Ouyang, X., Chen, J., & Du, K. (2021). Energy efficiency performance of the industrial sector: From the perspective of technological gap in different regions in China. Energy, 214, 118865. https://doi.org/10.1016/j.energy.2020.118865
Rafiq, S., Salim, R., & Nielsen, I. (2016). Urbanization, openness, emissions, and energy intensity: A study of increasingly urbanized emerging economies. Energy Economics, 56, 20–28. https://doi.org/10.1016/j.eneco.2016.02.007
Sadorsky, P. (2013). Do urbanization and industrialization affect energy intensity in developing countries? Energy Economics, 37, 52–59. https://doi.org/10.1016/j.eneco.2013.01.009
Sheng, P., He, Y., & Guo, X. (2017). The impact of urbanization on energy consumption and efficiency. Energy & Environment, 28(7), 673–686. https://doi.org/10.1177/0958305X17723893
Tiedemann, T., Francksen, T., & Latacz-Lohmann, U. (2011). Assessing the performance of German Bundesliga football players: A non-parametric metafrontier approach. Central European Journal of Operations Research, 19(4), 571–587. https://doi.org/10.1007/s10100-010-0146-7
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
Wang, Q., Zhao, Z., Zhou, P., & Zhou, D. (2013). Energy efficiency and production technology heterogeneity in China: A metafrontier DEA approach. Economic Modelling, 35(5), 283–289. https://doi.org/10.1016/j.econmod.2013.07.017
Wooldridge, J. M. (2008). Nonlinear dynamic panel data models with unobserved effects. Invited lecture. Canadian Econometrics Study Group, Montreal.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
Yang, Z., Fan, M., Shao, S., & Yang, L. (2017). Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis. Energy Economics, 68, 271–282. https://doi.org/10.1016/j.eneco.2017.10.009
Yu, Y., Huang, J., & Zhang, N. (2018). Industrial eco-efficiency, regional disparity, and spatial convergence of China’s regions. Journal of Cleaner Production, 204, 872–887. https://doi.org/10.1016/j.jclepro.2018.09.054
Yu, Y., Zhang, N., & Kim, J. D. (2020). Impact of urbanization on energy demand: An empirical study of the Yangtze River Economic Belt in China. Energy Policy, 139, 111354. https://doi.org/10.1016/j.enpol.2020.111354
Zhang, N., Kong, F., & Yu, Y. (2015). Measuring ecological total-factor energy efficiency incorporating regional heterogeneities in China. Ecological Indicators, 51, 165–172. https://doi.org/10.1016/j.ecolind.2014.07.041