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