Share:


Environmental performance of China's economic system: integrative perspective of efficiency and productivity

    Yingwen Chen Affiliation
    ; Rui Yang Affiliation
    ; Christina W. Y. Wong Affiliation
    ; Xin Miao Affiliation

Abstract

The high-quality development of regional economic system is inseparable from the collective efforts of multiple economic sectors. Increasing attention has been paid to the environmental performance evaluation of different administrative levels or economic sectors, but integrated research is scarce. Taking the three industries (the primary, secondary and tertiary industries) into account, this paper proposes a data envelopment analysis (DEA) model with parallel network structure to assess the environmental performance of 30 provinces in China from integrative perspective of efficiency and productivity. Then, the Tobit model is adopted to investigate the effects of external factors on the environmental performance. The results show that environmental efficiency of Chinese economy is only 0.4436 during 2010–2019 and the performance of the secondary industry is the highest, followed by the tertiary and the primary industries. Moreover, the environmental efficiency of eastern region is far higher than that of the central or western regions. Technological progress is the main driver of environmental productivity improvement for China’s economic system. Most of the external factors such as energy structure and technology innovation, have different effects on the environmental performance of different regions. Finally, several targeted policy implications are suggested for improving the environmental performance of China’s economic system.

Keyword : environmental performance, economic system, efficiency, productivity, parallel network structure, data envelopment analysis (DEA)

How to Cite
Chen, Y., Yang, R., Wong, C. W. Y., & Miao, X. (2022). Environmental performance of China’s economic system: integrative perspective of efficiency and productivity. Technological and Economic Development of Economy, 28(3), 743–774. https://doi.org/10.3846/tede.2022.16594
Published in Issue
Apr 22, 2022
Abstract Views
813
PDF Downloads
544
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

An, Q. X., Wu, Q. F., Li, J. L., Xiong, B. B., & Chen, X. H. (2019). Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index. Energy Economics, 81, 95–103. https://doi.org/10.1016/j.eneco.2019.03.022

Angulo-Meza, L., González-Araya, M., Iriarte, A., Rebolledo-Leiva, R., & Mello, J. C. (2019). A multi-objective DEA model to assess the Eco-efficiency of agricultural practices within the CF + DEA method. Computers and Electronics in Agriculture, 161, 151–161. https://doi.org/10.1016/j.compag.2018.05.037

Aparicio, J., Barbero, J., Kapelko, M., Pastor, J. T., & Zofío, J. L. (2017). Testing the consistency and feasibility of the standard Malmquist-Luenberger index: Environmental productivity in world air emissions. Journal of Environmental Management, 196, 148–160. https://doi.org/10.1016/j.jenvman.2017.03.007

Beltrán-Esteve, M., Giménez, V., & Picazo-Tadeo, A. J. (2019). Environmental productivity in the European Union: A global Luenberger-metafrontier approach. Science of The Total Environment, 692, 136–146. https://doi.org/10.1016/j.scitotenv.2019.07.182

Bi, G., Luo, Y., Ding, J., & Liang, L. (2012). Environmental performance analysis of Chinese industry from a slacks-based perspective. Annals of Operations Research, 228(1), 65–80. https://doi.org/10.1007/s10479-012-1088-3

Bi, G., Shao, Y., Song, W., Yang, F., & Luo, Y. (2018). A performance evaluation of China’s coal-fired power generation with pollutant mitigation options. Journal of Cleaner Production, 171, 867–876. https://doi.org/10.1016/j.jclepro.2017.09.271

Bian, Y., He, P., & Xu, H. (2013). Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach. Energy Policy, 63, 962–971. https://doi.org/10.1016/j.enpol.2013.08.051

Bian, Y., Hu, M., Wang, Y., & Xu, H. (2016). Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach. Renewable and Sustainable Energy Reviews, 55, 990–998. https://doi.org/10.1016/j.rser.2015.11.008

Chen, L., Huang, Y., Li, M.J., & Wang, Y.M. (2020). Meta-frontier analysis using cross-efficiency method for performance evaluation. European Journal of Operational Research, 280, 219–229. https://doi.org/10.1016/j.ejor.2019.06.053

Chen, L., Lai, F., Wang, Y. M., Huang, Y., & Wu, F. M. (2018). A two-stage network data envelopment analysis approach for measuring and decomposing environmental efficiency. Computers & Industrial Engineering, 119, 388–403. https://doi.org/10.1016/j.cie.2018.04.011

Chen, L., Wang, Y.-M., & Lai, F. (2017a). Semi-disposability of undesirable outputs in data envelopment analysis for environmental assessments. European Journal of Operational Research, 260(2), 655–664. https://doi.org/10.1016/j.ejor.2016.12.042

Chen, W., Zhou, K., & Yang, S. (2017b). Evaluation of China’s electric energy efficiency under environmental constraints: A DEA cross efficiency model based on game relationship. Journal of Cleaner Production, 164, 38–44. https://doi.org/10.1016/j.jclepro.2017.06.178

Chen, W. H., & Wang, X. W. (2019). Study on the mechanism of sustainable development of forest carbon sink supply in western China. Forestry Economics, 3, 79–86.

Chen, Y. B., Yin, G. W., & Liu, K. (2021). Regional differences in the industrial water use efficiency of China: The spatial spillover effect and relevant factors. Resources, Conservation & Recycling, 167, 105239. https://doi.org/10.1016/j.resconrec.2020.105239

Cheng, Z., Liu, J., Li, L., & Gu, X. (2020). Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces. Energy Economics, 86, 104702. https://doi.org/10.1016/j.eneco.2020.104702

China Statistical Yearbook. (2020). http://data-cnki-net-s.ivpn.hit.edu.cn:1080/yearbook/Single/N2020100004

Chodakowska, E., & Nazarko, J. (2017). Environmental DEA method for assessing productivity of European countries. Technological and Economic Development of Economy, 23(4), 589–607. https://doi.org/10.3846/20294913.2016.1272069

Chu, J. F., & Zhu, J. (2021). Production scale-based two-stage network data envelopment analysis. European Journal of Operational Research, 294, 283–294. https://doi.org/10.1016/j.ejor.2021.01.020

Desimone, L. D., & Popoff, F. (2000). Eco-efficiency: The business link to sustainable development. MIT Press. https://doi.org/10.1108/ijshe.2000.1.3.305.5

Ding, L., Lei, L., Wang, L., Zhang, L., & Calin, A. C. (2020). A novel cooperative game network DEA model for marine circular economy performance evaluation of China. Journal of Cleaner Production, 253, 120071. https://doi.org/10.1016/j.jclepro.2020.120071

Du, J., Chen, Y., & Huang, Y. (2018). A modified Malmquist-Luenberger productivity index: Assessing environmental productivity performance in China. European Journal of Operational Research, 269(1), 171–187. https://doi.org/10.1016/j.ejor.2017.01.006

Du, J., Duan, Y., & Xu, J. (2017). The infeasible problem of Malmquist–Luenberger index and its application on China’s environmental total factor productivity. Annals of Operations Research, 278, 235–253. https://doi.org/10.1007/s10479-017-2603-3

Ebrahimi, B., Tavana, M., Toloo, M., & Charles, V. (2020). A novel mixed binary linear DEA model for ranking decision-making units with preference information. Computers & Industrial Engineering, 149, 106720. https://doi.org/10.1016/j.cie.2020.106720

Fang, L. (2020). Opening the “black box” of environmental production technology in a nonparametric analysis. European Journal of Operational Research, 286, 769–780. https://doi.org/10.1016/j.ejor.2020.03.043

Feng, C., Wang, M., Liu, G. C., & Huang, J. B. (2017). Sources of economic growth in China from 2000–2013 and its further sustainable growth path: A three-hierarchy meta-frontier data envelopment analysis. Economic Modelling, 64, 334–348. https://doi.org/10.1016/j.econmod.2017.04.007

Feng, S. L., Wu, H. Y., Li, G. X., Li, L. P., & Zhou, W. T. (2020). Convergence analysis of environmental efficiency from the perspective of environmental regulation: Evidence from China. Technological and Economic Development of Economy, 26(5), 1074–1097. https://doi.org/10.3846/tede.2020.13233

Feng, Y. Q., Zhang, H. L., Chiu, Y. H., & Chang, T. H. (2021). Innovation efficiency and the impact of the institutional quality: A cross country analysis using the two stage meta frontier dynamic network DEA model. Scientometrics, 126, 3091–3129. https://doi.org/10.1007/s11192-020-03829-3

Gao, Y., Zhang, M., & Zheng, J. (2021). Accounting and determinants analysis of China’s provincial total factor productivity considering carbon emissions. China Economic Review, 65, 101576. https://doi.org/10.1016/j.chieco.2020.101576

Gazheli, A., van den Bergh, J., & Antal, M. (2016). How realistic is green growth? Sectoral-level carbon intensity versus productivity. Journal of Cleaner Production, 129, 449–467. https://doi.org/10.1016/j.jclepro.2016.04.032

Geng, Z. Q., Dong, J. G., Han, Y. M., & Zhu, Q. X. (2017). Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical process. Applied Energy, 205, 465–476. https://doi.org/10.1016/j.apenergy.2017.07.132

Guo, Y., Tong, L., & Mei, L. (2020). The effect of industrial agglomeration on green development efficiency in Northeast China since the revitalization. Journal of Cleaner Production, 258, 120584. https://doi.org/10.1016/j.jclepro.2020.120584

Halkos, G., & Petrou, K. N. (2019). Assessing 28 EU Member States’ environmental efficiency in national waste generation with DEA. Journal of Cleaner Production, 208, 509–521. https://doi.org/10.1016/j.jclepro.2018.10.145

He, Q., Han, J., Guan, D., Mi, Z., Zhao, H., & Zhang, Q. (2018). The comprehensive environmental efficiency of socioeconomic sectors in China: An analysis based on a non-separable bad output SBM. Journal of Cleaner Production, 176, 1091–1110. https://doi.org/10.1016/j.jclepro.2017.11.220

Huang, X., Jin, H., & Bai, H. (2019). Vulnerability assessment of China’s coastal cities based on DEA cross-efficiency model. International Journal of Disaster Risk Reduction, 101091. https://doi.org/10.1016/j.ijdrr.2019.101091

Huo, T., Tang, M., Cai, W., Ren, H., Liu, B., & Hu, X. (2020). Provincial total-factor energy efficiency considering floor space under construction: An empirical analysis of China’s construction industry. Journal of Cleaner Production, 244, 118749. https://doi.org/10.1016/j.jclepro.2019.118749

Ji, Y. H., Lei, Y. L., Li, L., Zhang, A., Wu, S. M., & Li, Q. (2021). Evaluation of the implementation effects and the influencing factors of resource tax in China. Resources Policy, 72, 102126. https://doi.org/10.1016/j.resourpol.2021.102126

Jin, F. J., & Ma, L. (2021). The direction and path of green rise of central region of China in the new era. Reform, 7, 14–23.

Jin, J., Zhou, D., & Zhou, P. (2014). Measuring environmental performance with stochastic environmental DEA: The case of APEC economies. Economic Modelling, 38, 80–86. https://doi.org/10.1016/j.econmod.2013.12.017

Jin, W., Zhang, H., Liu, S., & Zhang, H. (2019). Technological innovation, environmental regulation, and green total factor efficiency of industrial water resources. Journal of Cleaner Production, 211, 61–69. https://doi.org/10.1016/j.jclepro.2018.11.172

Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429. https://doi.org/10.1016/j.ejor.2006.11.041

Kao, C., & Liu, S. T. (2019). Cross efficiency measurement and decomposition in two basic network systems. Omega-International Journal of Management Science, 83, 73–79. https://doi.org/10.1016/j.omega.2018.02.004

Kapelko, M., Horta, I. M., Camanho, A. S., & Oude Lansink, A. (2015). Measurement of input-specific productivity growth with an application to the construction industry in Spain and Portugal. International Journal of Production Economics, 166, 64–71. https://doi.org/10.1016/j.ijpe.2015.03.030

Kong, Y. D., & Liu, J. G. (2021). Sustainable port cities with coupling coordination and environmental efficiency. Ocean and Coastal Management, 205, 105534. https://doi.org/10.1016/j.ocecoaman.2021.105534

Li, D. S., Zhao, Y. W., & Li, L. L. (2021). Change of environmental efficiency and environmental productivity of coal cities. Journal of Natural Resources, 36, 618–633. https://doi.org/10.31497/zrzyxb.20210307

Li, G., Fang, C., & He, S. (2020). The influence of environmental efficiency on PM2.5 pollution: Evidence from 283 Chinese prefecture-level cities. Science of the Total Environment, 141549. https://doi.org/10.1016/j.scitotenv.2020.141549

Li, N., Jiang, Y., Mu, H., & Yu, Z. (2018). Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA). Energy, 164, 1145–1160. https://doi.org/10.1016/j.energy.2018.08.150

Lin, B., & Chen, X. (2020). Environmental regulation and energy-environmental performance – Empirical evidence from China’s non-ferrous metals industry. Journal of Environmental Management, 269, 110722. https://doi.org/10.1016/j.jenvman.2020.110722

Lin, B., & Wang, M. (2021). What drives energy intensity fall in China? Evidence from a meta-frontier approach. Applied Energy, 281, 116034. https://doi.org/10.1016/j.apenergy.2020.116034

Lin, G., Fu, J., Jiang, D., Hu, W., Dong, D., Huang, Y., & Zhao, M. (2013). Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China. International Journal of Environmental Research and Public Health, 11, 173–186. https://doi.org/10.3390/ijerph110100173

Liu, C. C., Cheng, A. C., & Chen, S. H. (2017). A study for sustainable development in optoelectronics industry using multiple criteria decision making methods. Technological and Economic and Economic Development of Economy, 23(2), 221–242. https://doi.org/10.3846/20294913.2015.1072747

Liu, H., Yang, R., Wang, Y., & Zhu, Q. (2020). Measuring performance of road transportation industry in China in terms of integrated environmental efficiency in view of Streaming Data. Science of the Total Environment, 727, 138675. https://doi.org/10.1016/j.scitotenv.2020.138675

Liu, J., & Diamond, J. (2005). China’s environment in a globalizing world. Nature, 435(7046), 1179–1186. https://doi.org/10.1038/4351179a

Liu, J. G., Wang, X. Y., & Guo, J. Y. (2021). Port efficiency and its influencing factors in the context of Pilot Free Trades Zones. Transport Policy, 105, 67–79. https://doi.org/10.1016/j.tranpol.2021.02.011

Liu, W. B., Meng, W., Li, X. X., & Zhang, D. Q. (2010). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173(1), 177–194. https://doi.org/10.1007/s10479-009-0587-3

Long, X., Wu, C., Zhang, J., & Zhang, J. (2018). Environmental efficiency for 192 thermal power plants in the Yangtze River Delta considering heterogeneity: A meta-frontier directional slacks-based measure approach. Renewable and Sustainable Energy Reviews, 82, 3962–3971. https://doi.org/10.1016/j.rser.2017.10.077

Lundgren, T., & Zhou, W. (2017). Firm performance and the role of environmental management. Journal of Environmental Management, 203, 330–341. https://doi.org/10.1016/j.jenvman.2017.07.053

Mahlberg, B., & Luptacik, M. (2014). Eco-efficiency and eco-productivity change over time in a multi-sectoral economic system. European Journal of Operational Research, 234, 885–897. https://doi.org/10.1016/j.ejor.2013.11.017

Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4(2), 209–242. https://doi.org/10.1007/BF03006863

Mavi, R. K., Fathi, A., Saen, F. R., & Mavi, K. N. (2019). Eco-innovation in transportation industry: A double frontier common weights analysis with ideal point method for Malmquist productivity index. Resources, Conservation and Recycling, 147, 39–48. https://doi.org/10.1016/j.resconrec.2019.04.017

Mavi, R. K., & Mavi, R. K. (2019). Energy and environmental efficiency of OECD countries in the context of the circular economy: Common weight analysis for malmquist productivity index. Journal of Environmental Management, 247, 651–661. https://doi.org/10.1016/j.jenvman.2019.06.069

Meng, M., Shang, W., Zhao, X., Niu, D., & Li, W. (2015). Decomposition and forecasting analysis of China’s energy efficiency: An application of three-dimensional decomposition and small-sample hybrid models. Energy, 89, 283–293. https://doi.org/10.1016/j.energy.2015.05.132

Mi, Z. F., Meng, J., Guan, D., Shan, Y., Song, M., Wei, Y. M., Liu, Z., & Hubacek, K. (2017). Chinese CO2 emission flows have reversed since the global financial crisis. Nature Communications, 8, 1712. https://doi.org/10.1038/s41467-017-01820-w

Miao, Z., Balezentis, T., Tian, Z. H., Shao, S., Geng, Y., & Wu, R. (2019). Environmental performance and regulation effect of China’s atmospheric pollutant emissions: Evidence from “Three Regions and Ten Urban Agglomerations”. Environmental and Resource Economics, 74, 211–242. https://doi.org/10.1007/s10640-018-00315-6

Miao, Z., Chen, X. D., & Balezentis, T. (2021). Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition. Applied Energy, 283, 116296. https://doi.org/10.1016/j.apenergy.2020.116296

Oh, D., & Heshmati, A. (2010). A sequential Malmquist–Luenberger productivity index: Environmentally sensitive productivity growth considering the progressive nature of technology. Energy Economics, 32(6), 1345–1355. https://doi.org/10.1016/j.eneco.2010.09.003

Omrani, H., Amini, M., & Alizadeh, A. (2020). An integrated group best-worst method – Data envelopment analysis approach for evaluating road safety: A case of Iran. Measurement, 107330. https://doi.org/10.1016/j.measurement.2019.107330

Pastor, J .T., & Lovell, C. K. (2005). A global Malmquist productivity index. Economics Letters, 88(2), 266–271. https://doi.org/10.1016/j.econlet.2005.02.013

Piao, S. R., Li, J., & Ting, C. J. (2019). Assessing regional environmental efficiency in China with distinguishing weak disposability of undesirable outputs. Journal of Cleaner Production, 227, 748–759. https://doi.org/10.1016/j.jclepro.2019.04.207

Ralević, P., Dobrodolac, M., Švadlenka, L., Šarac, D., & Ðurić, D. (2020). Efficiency and productivity analysis of universal service obligation: a case of 29 designated operators in the European countries. Technological and Economic Development of Economy, 26(4), 785–807. https://doi.org/10.3846/tede.2020.12062

Sarkhosh-Sara, A., Tavassoli, M., & Heshmati, A. (2020). Assessing the sustainability of high-, middle-, and low-income countries: A network DEA model in the presence of both zero data and undesirable outputs. Sustainable Production and Consumption, 21, 252–268. https://doi.org/10.1016/j.spc.2019.08.009

Shao, L., Yu, X., & Feng, C. (2019). Evaluating the eco-efficiency of China’s industrial sectors: A two-stage network data envelopment analysis. Journal of Environmental Management, 247, 551–560. https://doi.org/10.1016/j.jenvman.2019.06.099

Shen, N., Liao, H., Deng, R., & Wang, Q. (2019). Different types of environmental regulations and the heterogeneous influence on the environmental total factor productivity: Empirical analysis of China’s industry. Journal of Cleaner Production, 211, 171–184. https://doi.org/10.1016/j.jclepro.2018.11.170

Shuai, S., & Fan, Z. (2020). Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency. Journal of Environmental Management, 261, 110227. https://doi.org/10.1016/j.jenvman.2020.110227

Singpai, B., & Wu, D. (2021). An integrative approach for evaluating the environmental economic efficiency. Energy, 215, 118940. https://doi.org/10.1016/j.energy.2020.118940

Song, M., Peng, J., Wang, J., & Zhao, J. (2018a). Environmental efficiency and economic growth of China: A Ray slack-based model analysis. European Journal of Operational Research, 269(1), 51–63. https://doi.org/10.1016/j.ejor.2017.03.073

Song, M., Song, Y., An, Q., & Yu, H. (2013). Review of environmental efficiency and its influencing factors in China: 1998–2009. Renewable and Sustainable Energy Reviews, 20, 8–14. https://doi.org/10.1016/j.rser.2012.11.075

Song, M., Wang, R., & Zeng, X. (2018b). Water resources utilization efficiency and influence factors under environmental restrictions. Journal of Cleaner Production, 184, 611–621. https://doi.org/10.1016/j.jclepro.2018.02.259

Song, M., Wang, S., Lei, L., & Zhou, L. (2019). Environmental efficiency and policy change in China: A new meta-frontier non-radial angle efficiency evaluation approach. Process Safety and Environmental Protection, 121, 281–289. https://doi.org/10.1016/j.psep.2018.10.023

Song, M., Zhang, G., Fang, K., & Zhang, J. (2016). Regional operational and environmental performance evaluation in China: Non-radial DEA methodology under natural and managerial disposability. Natural Hazards, 84(S1), 243–265. https://doi.org/10.1007/s11069-015-1933-1

Stefaniec, A., Hosseini, K., Xie, J., & Li, Y. (2020). Sustainability assessment of inland transportation in China: A triple bottom line-based network DEA approach. Transportation Research Part D: Transport and Environment, 80, 102258. https://doi.org/10.1016/j.trd.2020.102258

Sueyoshi, T., & Yuan, Y. (2015). China’s regional sustainability and diversified resource allocation: DEA environmental assessment on economic development and air pollution. Energy Economics, 49, 239–256. https://doi.org/10.1016/j.eneco.2015.01.024

Sueyoshi, T., Yuan, Y., & Goto, M. (2017). A literature study for DEA applied to energy and environment. Energy Economics, 62, 104–124. https://doi.org/10.1016/j.eneco.2016.11.006

Sun, H., Mohsin, M., Alharthi, M., & Abbas, Q. (2020a). Measuring environmental sustainability performance of South Asia. Journal of Cleaner Production, 251, 119519. https://doi.org/10.1016/j.jclepro.2019.119519

Sun, X., Zhou, X., Chen, Z., & Yang, Y. (2020b). Environmental efficiency of electric power industry, market segmentation and technological innovation: Empirical evidence from China. Science of the Total Environment, 706, 135749. https://doi.org/10.1016/j.scitotenv.2019.135749

Tan, J. L., & Wang, R. (2021). Research on evaluation and influencing factors of regional ecological efficiency from the perspective of carbon neutrality. Journal of Environmental Management, 294, 113030. https://doi.org/10.1016/j.jenvman.2021.113030

Tang, Y., Chen, Y., Yang, R., & Miao, X. (2020a). The unified efficiency evaluation of China’s industrial waste gas considering pollution prevention and end-of-pipe treatment. International Journal of Environmental Research and Public Health, 17(16), 5724. https://doi.org/10.3390/ijerph17165724

Tang, J., Wang, Q., & Choi, G. (2020b). Efficiency assessment of industrial solid waste generation and treatment processes with carry-over in China. Science of the Total Environment, 726, 138274. https://doi.org/10.1016/j.scitotenv.2020.138274

Tian, X. L., Guo, Q. G., Han, C., & Ahmad, N. (2016). Different extent of environmental information disclosure across Chinese cities: Contributing factors and correlation with local pollution. Global Environmental Change, 39, 244–257. https://doi.org/10.1016/j.gloenvcha.2016.05.014

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26, 24–36. https://doi.org/10.2307/1907382

Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3–4), 145–156. https://doi.org/10.1016/j.omega.2009.07.003

Wang, B., Wang, Q., Wei, Y.-M., & Li, Z.-P. (2018a). Role of renewable energy in China’s energy security and climate change mitigation: An index decomposition analysis. Renewable and Sustainable Energy Reviews, 90, 187–194. https://doi.org/10.1016/j.rser.2018.03.012

Wang, H., Ang, B. W., & Zhou, P. (2018b). Decomposing aggregate CO2 emission changes with heterogeneity: An extended production-theoretical approach. Energy Journal, 39, 59–79. https://doi.org/10.5547/01956574.39.1.hwan

Wang, H., Zhou, P., & Zhou, D. Q. (2013a). Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis. Energy Economics, 40, 795–803. https://doi.org/10.1016/j.eneco.2013.09.030

Wang, K., Lu, B., & Wei, Y. M. (2013b). China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis. Applied Energy, 112, 1403–1415. https://doi.org/10.1016/j.apenergy.2013.04.021

Wang, K., & Wei, Y. M. (2016). Sources of energy productivity change in China during 1997–2012: A decomposition analysis based on the Luenberger productivity indicator. Energy Economics, 54, 50–59. https://doi.org/10.1016/j.eneco.2015.11.013

Wang, M., & Feng, C. (2020). Regional total-factor productivity and environmental governance efficiency of China’s industrial sectors: A two-stage network-based super DEA approach. Journal of Cleaner Production, 273, 123110. https://doi.org/10.1016/j.jclepro.2020.123110

Wang, Q., Wang, Y., Hang, Y., & Zhou, P. (2019a). An improved production-theoretical approach to decomposing carbon dioxide emissions. Journal of Environmental Management, 252, 109577. https://doi.org/10.1016/j.jenvman.2019.109577

Wang, R., Wang, Q. Z., & Yao, S. L. (2021). Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets: Insights from DEA and Theil models. Journal of Environmental Management, 293, 112958. https://doi.org/10.1016/j.jenvman.2021.112958

Wang, X., Ding, H., & Liu, L. (2019b). Eco-efficiency measurement of industrial sectors in China: A hybrid super-efficiency DEA analysis. Journal of Cleaner Production, 229, 53–64. https://doi.org/10.1016/j.jclepro.2019.05.014

Wang, Y., Pan, J., Pei, R., Yi, B. W., & Yang, G. (2020). Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Economic Planning Sciences, 71, 100810. https://doi.org/10.1016/j.seps.2020.100810

Wei, F. Q., Zhang, X. Q., Chu, J. F., Yang, F., & Yuan, Z. (2021). Energy and environmental efficiency of China’s transportation sectors considering CO2 emission uncertainty. Transportation Research Part D: Transport and Environment, 97, 102955. https://doi.org/10.1016/j.trd.2021.102955

Wen, J., Deng, P. D., Zhang, Q. X., & Chang, C. P. (2021). Is higher government efficiency bring about higher innovation? Technological and Economic Development of Economy, 27, 625–655. https://doi.org/10.3846/tede.2021.14269

Wu, D., Li, S. W., Liu, L., Lin, J. Y., & Zhang, S. Q. (2021). Dynamics of pollutants’ shadow price and its driving forces: An analysis on China’s two major pollutants at provincial level. Journal of Cleaner Production, 283, 124625. https://doi.org/10.1016/j.jclepro.2020.124625

Wu, H., Hao, Y., & Ren, S. (2020). How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China. Energy Economics, 91, 104880. https://doi.org/10.1016/j.eneco.2020.104880

Wu, H., Shi, Y., Xia, Q., & Zhu, W. (2014). Effectiveness of the policy of circular economy in China: A DEA-based analysis for the period of 11th five-year-plan. Resources, Conservation and Recycling, 83, 163–175. https://doi.org/10.1016/j.resconrec.2013.10.003

Wu, J., Li, M., Zhu, Q., Zhou, Z., & Liang, L. (2019). Energy and environmental efficiency measurement of China’s industrial sectors: A DEA model with non-homogeneous inputs and outputs. Energy Economics, 78, 468–480. https://doi.org/10.1016/j.eneco.2018.11.036

Wu, J., Yin, P., Sun, J., Chu, J., & Liang, L. (2016a). Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspective. European Journal of Operational Research, 254(3), 1047–1062. https://doi.org/10.1016/j.ejor.2016.04.034

Wu, J., Zhu, Q., Chu, J., Liu, H., & Liang, L. (2016b). Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48, 460–472. https://doi.org/10.1016/j.trd.2015.08.001

Xiao, H., Shan, Y., Zhang, N., Zhou, Y., Wang, D., & Duan, Z. (2019). Comparisons of CO2 emission performance between secondary and service industries in Yangtze River Delta cities. Journal of Environmental Management, 252, 109667. https://doi.org/10.1016/j.jenvman.2019.109667

Xie, J., Zhou, S., & Chen, Y. (2019). Integrated data envelopment analysis methods for measuring technical, environmental, and eco-efficiencies. Journal of Cleaner Production, 238, 117939. https://doi.org/10.1016/j.jclepro.2019.117939

Xue, L. M., Zhang, W. J., Zheng, Z. X., Liu, Z., Meng, S., Li, H. Q., & Du, Y. L. (2021). Measurement and influencing factors of the efficiency of coal resources of China’s provinces: Based on Bootstrap-DEA and Tobit. Energy, 221, 119763. https://doi.org/10.1016/j.energy.2021.119763

Yang, G., Fukuyama, H., & Chen, K. (2019a). Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach. Omega – International Journal of Management Science, 84, 141–159. https://doi.org/10.1016/j.omega.2018.04.009

Yang, L., Ouyang, H., Fang, K., Ye, L., & Zhang, J. (2015). Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecological Indicators, 51, 13–19. https://doi.org/10.1016/j.ecolind.2014.08.040

Yang, L. S., Zhu, J. P., & Jia, Z. J. (2019b). Influencing factors and current challenges of CO2 emission reduction in China: A perspective based on technological progress. Economic Research Journal, 54, 118–132.

Yang, Z., & Wei, X. (2019). The measurement and influences of China’s urban total factor energy efficiency under environmental pollution: based on the game cross-efficiency DEA. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.10.271

Yu, S. W., Liu, J., & Li, L. X. (2019). Evaluating provincial eco-efficiency in China: An improved network data envelopment analysis model with undesirable output. Environmental Science and Pollution Research, 27, 6886–6903. https://doi.org/10.1007/s11356-019-06958-2

Yuan, H., Feng, Y., Lee, C. C., & Cen, Y. (2020). How does manufacturing agglomeration affect green economic efficiency? Energy Economics, 92, 104944. https://doi.org/10.1016/j.eneco.2020.104944

Zaim, O., & Taskin, F. (2000). Environmental efficiency in carbon dioxide emissions in the OECD: A non-parametric approach. Journal of Environmental Management, 58(2), 95–107. https://doi.org/10.1006/jema.1999.0312

Zha, Y., Zhao, L. L., & Bian, Y. W. (2016). Measuring regional efficiency of energy and carbon dioxide emissions in China: A chance constrained DEA approach. Computers & Operations Research, 66, 351–361. https://doi.org/10.1016/j.cor.2015.07.021

Zhang, G., & Lin, B. (2018). Impact of structure on unified efficiency for Chinese service sector – A two-stage analysis. Applied Energy, 231, 876–886. https://doi.org/10.1016/j.apenergy.2018.09.033

Zhang, K., & Feng, J. C. (2016). Agricultural environmental efficiency and its dynamic evolution of China from the perspective of pollution’s strong disposability. China Population, Resources and Environment, 26, 140–149.

Zhang, Y., Li, X., Jiang, F., Song, Y., & Xu, M. (2020a). Industrial policy, energy and environment efficiency: Evidence from Chinese firm-level data. Journal of Environmental Management, 260, 110123. https://doi.org/10.1016/j.jenvman.2020.110123

Zhang, Y., Wang, W., Liang, L., Wang, D., Cui, X., & Wei, W. (2020b). Spatial-temporal pattern evolution and driving factors of China’s energy efficiency under low-carbon economy. Science of the Total Environment, 739, 140197. https://doi.org/10.1016/j.scitotenv.2020.140197

Zhang, Y. J., Liu, J. Y., & Su, B. (2020c). Carbon congestion effects in China’s industry: Evidence from provincial and sectoral levels. Energy Economics, 86, 104635. https://doi.org/10.1016/j.eneco.2019.104635

Zhang, H., Song, Y., & Zhang, L. (2020d). Pollution control in urban China: A multi-level analysis on household and industrial pollution. Science of the Total Environment, 749, 141478. https://doi.org/10.1016/j.scitotenv.2020.141478

Zhang, X., Geng, Y., Shao, S., Song, X., Fan, M., Yang, L., & Song, J. (2020e). Decoupling PM2.5 emissions and economic growth in China over 1998–2016: A regional investment perspective. Science of the Total Environment, 714, 136841. https://doi.org/10.1016/j.scitotenv.2020.136841

Zhang, J. J., Patwary, A. K., Sun, H. P., Raza, M., Taghizadeh-Hesary, F., & Iram, R. (2021a). Measuring energy and environmental efficiency interactions towards CO2 emissions reduction without slowing economic growth in central and western Europe. Journal of Environmental Management, 279, 111704. https://doi.org/10.1016/j.jenvman.2020.111704

Zhang, J., Ouyang, Y., Ballesteros-Pérez, P., Li, H., Philbin, S. P., Li, Z., & Skitmore, M. (2021b). Understanding the impact of environmental regulations on green technology innovation efficiency in the construction industry. Sustainable Cities and Society, 65, 102647. https://doi.org/10.1016/j.scs.2020.102647

Zhang, J., Zeng, W., & Shi, H. (2016). Regional environmental efficiency in China: Analysis based on a regional slack-based measure with environmental undesirable outputs. Ecological Indicators, 71, 218–228. https://doi.org/10.1016/j.ecolind.2016.04.040

Zhong, S., Li, J., & Zhao, R. L. (2021). Does environmental information disclosure promote sulfur dioxide (SO2) remove? New evidence from 113 cities in China. Journal of Cleaner Production, 299, 126906. https://doi.org/10.1016/j.jclepro.2021.126906

Zhou, P., Ang, B. W., & Poh, K. L. (2006). Slacks-based efficiency measures for modeling environmental performance. Ecological Economics, 60(1), 111–118. https://doi.org/10.1016/j.ecolecon.2005.12.001

Zhou, P., Delmas, M. A., & Kohli, A. (2017). Constructing meaningful environmental indices: A nonparametric frontier approach. Journal of Environmental Economics and Management, 85, 21–34. https://doi.org/10.1016/j.jeem.2017.04.003

Zhou, P., Poh, K. L., & Ang, B. W. (2007). A non-radial DEA approach to measuring environmental performance. European Journal of Operational Research, 178(1), 1–9. https://doi.org/10.1016/j.ejor.2006.04.038

Zhou, Q., Zhang, X., Shao, Q., & Wang, X. (2019). The non-linear effect of environmental regulation on haze pollution: Empirical evidence for 277 Chinese cities during 2002–2010. Journal of Environmental Management, 248, 109274. https://doi.org/10.1016/j.jenvman.2019.109274

Zhou, X., Xu, Z., Yao, L., Tu, Y., Lev, B., & Pedrycz, W. (2018). A novel Data Envelopment Analysis model for evaluating industrial production and environmental management system. Journal of Cleaner Production, 170, 773–788. https://doi.org/10.1016/j.jclepro.2017.09.160

Zhu, Q., Li, X., Li, F., & Zhou, D. (2020a). The potential for energy saving and carbon emission reduction in China’s regional industrial sectors. Science of the Total Environment, 716, 135009. https://doi.org/10.1016/j.scitotenv.2019.135009

Zhu, Q., Li, X., Li, F., Wu, J., & Zhou, D. (2020b). Energy and environmental efficiency of China’s transportation sectors under the constraints of energy consumption and environmental pollutions. Energy Economics, 89, 104817. https://doi.org/10.1016/j.eneco.2020.104817