The recent ecological efficiency development in China: interactive systems of economy, society and environment
Abstract
Ecological efficiency (EE) provides much reference for formulating appropriate regional economic, social and environmental policies to promote sustainable development. Interactive subsystems of economy, society and environment within EE system have been considered in this paper. By innovatively integrating the merits of two advanced economic research methods (global super efficiency network data envelopment analysis (GSE-NDEA) and panel vector autoregression (PVAR) and updating the EE evaluation indicator system by following the new features of sustainable development in the recent China, this paper comprehensively evaluates EE by drawing evidence from 3 regions in China during the period of 2011–2020, and further reveals how the three subsystems within EE system interact to achieve EE enhancement. The findings show EE and its three subsystems’ trend, the major constrains of EE development, the regional discrepancies in EE progress, and the interactions among the subsystems of economy-society-environment within the EE system in different regions of China. The policy implications are proposed accordingly.
First published online 15 November 2022
Keyword : ecological efficiency (EE), economy-society-environment, interactive subsystems, regional development, global super efficiency network data envelopment analysis (GSE-NDEA), panel vector autoregression (PVAR)
How to Cite
Yang, R., Wu, S., Wong, C. W. Y., Liu, K., Miao, X., Chen, Y., Wang, S., & Tang, Y. (2023). The recent ecological efficiency development in China: interactive systems of economy, society and environment. Technological and Economic Development of Economy, 29(1), 217–252. https://doi.org/10.3846/tede.2022.17913
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in stata. Stata Journal, 16(3), 778–804. https://doi.org/10.1177/1536867x1601600314
Acheampong, A. O. (2018). Economic growth, CO2 emissions and energy consumption: What causes what and where? Energy Economics, 74, 677–692. https://doi.org/10.1016/j.eneco.2018.07.022
Adler, N., & Volta, N. (2016). Accounting for externalities and disposability: A directional economic environmental distance function. European Journal of Operational Research, 250(1), 314–327. https://doi.org/10.1016/j.ejor.2015.10.064
Alsaedi, Y. H., & Tularam, G. A. (2020). The relationship between electricity consumption, peak load and GDP in Saudi Arabia: A VAR analysis. Mathematics and Computers in Simulation, 175, 164–178. https://doi.org/10.1016/j.matcom.2019.06.012
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264. https://doi.org/10.1287/mnsc.39.10.1261
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Beltrán-Esteve, M., Reig-Martínez, E., & Estruch-Guitart, V. (2017) Assessing eco-efficiency: A metafrontier directional distance function approach using life cycle analysis. Environmental Impact Assessment Review, 63, 116–127. https://doi.org/110.1016/j.eiar.2017.01.001
Berdiev, A. N., & Saunoris, J. W. (2016). Financial development and the shadow economy: A panel VAR analysis. Economic Modelling, 57, 197–207. https://doi.org/10.1016/j.econmod.2016.03.028
Bing, Z., Bi, J., Fan, Z., Yuan, Z., & Ge, J. (2008). Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological Economics, 68(1–2), 306–316. https://doi.org/10.1016/j.ecolecon.2008.03.009
Bostian, M., Färe, R., Grosskopf, S., Lundgren, T., & Weber, W. L. (2018). Time substitution for environmental performance: The case of Swedish manufacturing. Empirical Economics, 54(1), 129–152. https://doi.org/10.1007/s00181-016-1180-7
Boussemart, J.-P., Leleu, H., Shen, Z., & Valdmanis, V. (2020). Performance analysis for three pillars of sustainability. Journal of Productivity Analysis, 53, 305–320. https://doi.org/10.1007/s11123-020-00575-9
BP. (2021). Statistical review of world energy. http://www.bo.com/statisticalreview
Carrasco-Gutierrez, C. E., Souza, R. C., & Guillén, O. (2009). Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features. Brazilian Review of Econometrics, 29(1), 1–14. https://doi.org/10.12660/bre.v29n12009.2696
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen, C.-M., & Delmas, M. A. (2012). Measuring eco-inefficiency: A new frontier approach. Operations Research, 60(5), 1064–1079. https://doi.org/10.1287/opre.1120.1094
Chen, H. B., Dong, K., Wang, F. F., & Emmanuel, C. A. (2020). The spatial effect of tourism economic development on regional ecological efficiency. Environmental Science and Pollution Research, 27, 38241–38258. https://doi.org/10.1007/s11356-020-09004-8
Chen, X., Liu, X., Gong, Z., & Xie, J. (2021a). Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156, 107234. https://doi.org/10.1016/j.cie.2021.107234
Chen, Y. W., Wong, C. W. Y., Yang, R., & Miao, X. (2021b). Optimal structure adjustment strategy, emission reduction potential and utilization efficiency of fossil energies in China. Energy, 237, 121623. https://doi.org/10.1016/j.energy.2021.121623
Chen, Y. W., Yang, R., Wong, C. W., Ji, J., & Miao, X. (2022). Efficiency and productivity of air pollution control in Chinese cities. Sustainable Cities and Society, 76, 103423. https://doi.org/10.1016/j.scs.2021.103423
Cheng, X., Long, R., Chen, H., & Li Q. (2019). Coupling coordination degree and spatial dynamic evolution of a regional green competitiveness system: A case study from China. Ecological Indicators, 104, 489–500. https://doi.org/10.1016/j.ecolind.2019.04.003
China Civil Affairs’ Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2017110010
China Energy Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022060061
China Industry Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022010304
China Statistical Yearbook on Science and Technology. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022010277
China Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2021110004
China Statistics Yearbook on Environment. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022030234
Choi, Y., Zhang, N., & Zhou, P. (2012). Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure. Applied Energy, 98, 198–208. https://doi.org/10.1016/j.apenergy.2012.03.024
Cui, Q., & Li, Y. (2018). Airline dynamic efficiency measures with a Dynamic RAM with unified natural & managerial disposability. Energy Economics, 75, 534–546. https://doi.org/10.1016/j.eneco.2018.09.016
De Simone, L., & Popoff, F. with the WBCSD. (1997). Eco-efficiency: The business link to sustainable development. The MIT Press.
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
Duan, X., Dai, S., Yang, R., Duan, Z., & Tang, Y. (2020). Environmental collaborative governance degree of government, corporation and public. Sustainability, 12(3), 1138. https://doi.org/10.3390/su12031138
Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132(2), 312–325. https://doi.org/10.1016/s0377-2217(00)00154-5
Fan, Y., Bai, B., Qiao, Q., Kang, P., Zhang, Y., & Guo, J. (2017). Study on eco-efficiency of industrial parks in China based on data envelopment analysis. Journal of Environmental Management, 192, 107–115. https://doi.org/10.1016/j.jenvman.2017.01.048
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. https://doi.org/10.1016/s0038-0121(99)00012-9
Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90–98. https://doi.org/10.2307/1928055
Feng, N., Feng, H. H., Li, D. H., & Li, M. Q. (2020). Online media coverage, consumer engagement and movie sales: A PVAR approach. Decision Support Systems, 131, 113267. https://doi.org/10.1016/j.dss.2020.113267
Gharaei, A., Karimi, M., & Shekarabi, S. A. H. (2019). An integrated multi-product, multi buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm. Applied Mathematical Modelling, 69, 223–254. https://doi.org/10.1016/j.apm.2018.11.035
Global Carbon Project. (2021). Global Carbon Budget Report. https://www.globalcarbonproject.org/carbonbudget/index.htm
Golshani, H., Khoveyni, M., Valami, H. B., & Eslami, R. (2019). A slack-based super efficiency in a two-stage network structure with intermediate measures. Alexandria Engineering Journal, 58(1), 393–400. https://doi.org/10.1016/j.aej.2019.01.002
Hampf, B. (2014). Separating environmental efficiency into production and abatement efficiency: A nonparametric model with application to US power plants. Journal of Productivity Analysis, 41(3), 457–473. https://doi.org/10.1007/s11123-013-0357-8
Han, Y., Geng, Z., Zhu, Q., & Qu, Y. (2015). Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry. Energy, 83, 685–695. https://doi.org/10.1016/j.energy.2015.02.078
Hatami-Marbini, A., & Saati, S. (2020). Measuring performance with common weights: Network DEA. Neural Computing and Applications, 32(8), 3599–3617. https://doi.org/10.1007/s00521-019-04219-4
He, J. Q., Wang, S. J., Liu, Y. Y., Ma, H. T., & Liu, Q. Q. (2017). Examining the relationship between urbanization and the eco-environment using a coupling analysis: Case study of Shanghai, China. Ecological Indicators, 77, 185–193. https://doi.org/10.1016/j.ecolind.2017.01.017
He, W., Zhang, B., & Ding, T. (2020). Sources of provincial carbon intensity reduction potential in China: A non-parametric fractional programming approach. Science of the Total Environment, 730, 139037. https://doi.org/10.1016/j.scitotenv.2020.139037
Helmut, H. (2001). How to calculate and interpret ecological footprint for long periods of time: The case of Austria 1926–2995. Ecological Economics, 38(1), 25–45. https://doi.org/10.1016/S0921-8009(01)00152-5
Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395. https://doi.org/10.2307/1913103
Jawadi, F., Mallick, S. K., & Sousa, R. M. (2016). Fiscal and monetary policies in the BRICS: A panel VAR approach. Economic Modeling, 58, 535–542. https://doi.org/10.1016/j.econmod.2015.06.001
Jiang, Q., & Tan, Q. (2020). Can government environmental auditing improve static and dynamic ecological efficiency in China? Environmental Science and Pollution Research, 27, 21733–21746. https://doi.org/10.1007/s11356-020-08578-7
Jouida, S. (2018). Diversification, capital structure and profitability: A panel VAR approach. Research in International Business and Finance, 45, 243–256. https://doi.org/10.1016/j.ribaf.2017.07.155
Kourtzidis, S., Matousek, R., & Tzeremes, N. G. (2021). Modelling a multi-period production process: Evidence from the Japanese regional banks. European Journal of Operational Research, 294(1), 327–339. https://doi.org/10.1016/j.ejor.2021.01.036
Kuang, B., Lu, X. H., Han, J., Fan, X. Y., & Zuo, J. (2020). How urbanization influence urban land consumption intensity: Evidence from China. Habitat International, 100, 102103. https://doi.org/10.1016/j.habitatint.2019.102103
Li, Z., Crook, J., & Andreeva, G. (2017a). Dynamic prediction of financial distress using Malmquist DEA. Expert Systems with Applications, 80, 94–106. https://doi.org/10.1016/j.eswa.2017.03.017
Li, Z., Ouyang, X., Du, K., & Zhao, Y. (2017b). Does government transparency contribute to improved eco-efficiency performance? An empirical study of 262 cities in China. Energy Policy, 110, 79–89. https://doi.org/10.1016/j.enpol.2017.08.001
Liang, H. W., Dong, L., Luo, X., Ren, J. Z., Zhang, N., Gao, Z. Q., & Dou, Y. (2016). Balancing regional industrial development: Analysis on regional disparity of China’s industrial emissions and policy implications. Journal of Cleaner Production, 126, 223–235. https://doi.org/10.1016/j.jclepro.2016.02.145
Liao, K. C., Yue, M. Y., Sun, S. W., Xue, H. B., Liu, W., Tsai, S. B., & Wang, J. T. (2018). An evaluation of coupling coordination between tourism and finance. Sustainability, 10(7), 2320. https://doi.org/10.3390/su10072320
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. Q., & Wang, Y. (2019). Inconsistency of economic growth and electricity consumption in China: A panel VAR approach. Journal of Cleaner Production, 229, 144–156. https://doi.org/10.1016/j.jclepro.2019.04.396
Lin, B. Q., & Zhu, J. P. (2017). Energy and carbon intensity in China during the urbanization and industrialization process: A panel VAR approach. Journal of Cleaner Production, 168, 780–790. https://doi.org/10.1016/j.jclepro.2017.09.013
Lin B. Q., & Zhu, J. (2019). Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China. Applied Energy, 239, 12–22. https://doi.org/10.1016/j.apenergy.2019.01.166
Lin, B. Q., & Zhu, R. (2021). Energy efficiency of the mining sector in China, what are the main influence factors? Resources, Conservation and Recycling, 167, 105321. https://doi.org/10.1016/j.resconrec.2020.105321
Lin, F., Lin, S.-W., & Lu, W.-M. (2019). Dynamic eco-efficiency evaluation of the semiconductor industry: A sustainable development perspective. Environmental Monitoring and Assessment, 191(7), 435. https://doi.org/10.1007/s10661-019-7598-6
Liu, Q. Q., Wang, S. J., Li, B., & Zhang, W. Z. (2020). Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis. Journal of Environmental Management, 264, 110442. https://doi.org/10.1016/j.jenvman.2020.110442
Liu, Y. B., Yao, C. S., Wang, G. X., & Bao, S. M. (2011). An integrated sustainable development approach to modeling the eco-environmental effects from urbanization. Ecological Indicators, 11(6), 1599–1608. https://doi.org/10.1016/j.ecolind.2011.04.004
Ma, J., Qi, L., & Deng, L. (2018). Additive centralized and Stackelberg DEA models for two-stage system with shared resources. International Transactions in Operational Research, 27(4), 2211–2229. https://doi.org/10.1111/itor.12504
Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewanle and Sustainable Energy Reviews, 70, 1298–1322. https://doi.org/10.1016/j.rser.2016.12.030
Pastor, J. T., & Lovell, C. A. K. (2005). A global Malmquist productivity index. Economics Letters, 88(2), 266–271. https://doi.org/10.1016/j.econlet.2005.02.013
Pereira, M. A., Ferreira, D. C., Figueira, J. R., & Marques, R. C. (2021). Measuring the efficiency of the Portuguese public hospitals: A value modelled network data envelopment analysis with simulation. Expert Systems with Applications, 181, 115169. https://doi.org/10.1016/j.eswa.2021.115169
Piao, S. R., Li, J., & Ting, C. J. (2019) Assessing regional environmental efficiency in China with distinguishing weak and strong disposability of undesirable outputs. Journal of Cleaner Production, 227, 748–759. https://doi.org/10.1016/j.jclepro.2019.04.207
Picazo-Tadeo, A. J., Gómez-Limón, J. A., & Reig-Martínez, E. (2011). Assessing farming eco-efficiency: A Data Envelopment Analysis approach. Journal of Environmental Management, 92(4), 1154–1164. https://doi.org/10.1016/j.jenvman.2010.11.025
Qu, C., Shao, J., & Shi, Z. (2020). Does financial agglomeration promote the increase of energy efficiency in China? Energy Policy, 146, 111810. https://doi.org/10.1016/j.enpol.2020.111810
Rashidi, K., & Saen, R. F. (2015). Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energy Economics, 50, 18–26. https://doi.org/10.1016/j.eneco.2015.04.018
Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment. International Journal of Life Cycle Assessment, 13, 374–388. https://doi.org/10.1007/s11367-008-0009-9
Ren, S., Li, X., Yuan, B., Li, D., & Chen, X. (2018). The effects of three types of environmental regulation on eco-efficiency: A cross-region analysis in China. Journal of Cleaner Production, 173, 245–255. https://doi.org/10.1016/j.jclepro.2016.08.113
Roshdi, I., Hasannasab, M., Margaritis, D., & Rouse, P. (2018). Generalised weak disposability and efficiency measurement in environmental technologies. European Journal of Operational Research, 266(3), 1000–1012. https://doi.org/10.1016/j.ejor.2017.10.033
Ruggiero, J. (2005). Impact assessment of input omission on DEA. International Journal of Information Technology & Decision Making, 4(3), 359–368. https://doi.org/10.1142/s021962200500160x
Schaltegger, S., & Sturm, A. (1990). Ecological rationality: Approaches to design of ecology-oriented management instruments. Die Unternehmung, 4, 273–290.
Shen, D. N., & Li, Y. (2020). Panel vector autoregression model to study the dynamic relationship between meteorological S&T and the economic development of meteorologically sensitive industries in China. International Journal of Electrical Engineering Education. https://doi.org/10.1177/0020720920928459
Shen, Y., Yue, S., Pu, Z., & Guo, M. (2020). Sustainable total-factor ecology efficiency of regions in China. Science of The Total Environment, 741, 139241. https://doi.org/10.1016/j.scitotenv.2020.139241
Shen, Z. Y., Wu, H. T., Bai, K. X., & Hao, Y. (2022). Integrating economic, environmental and societal performance within the productivity measurement. Technological Forecasting & Social Change, 176, 121463. https://doi.org/10.1016/j.techfore.2021.121463
Shermeh, H. E., Najafi, S. E., & Alavidoost, M. H. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686–697. https://doi.org/10.1016/j.energy.2016.06.087
Song, M., Tan, K., Wang, J., & Shen, Z. (2022). Modeling and evaluating economic and ecological operation efficiency of smart city pilots. Cities, 124, 103575. https://doi.org/10.1016/l.cities.2022.103575
Statistical Communique on the National Economic and Social Development. (2021). Retrieved October 27, 2022, from http://www.gov.cn/shuju/2022-02/28/content_5676015.htm
Sueyoshi, T. (2000). Stochastic DEA for restructure strategy: An application to a Japanese petroleum company. Omega, 28(4), 385–398. https://doi.org/10.1016/s0305-0483(99)00069-9
Sueyoshi, T., & Yuan, Y. (2017). Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention. Energy Economics, 66, 154–166. https://doi.org/10.1016/j.eneco.2017.06.008
Sun, J., Li, G., & Wang, Z. (2019). Technology heterogeneity and efficiency of China’s circular economic systems: A game meta-frontier DEA approach. Resources, Conservation and Recycling, 146, 337–347. https://doi.org/10.1016/j.resconrec.2019.03.046
Sun, X. X., & Loh, L. (2019). Sustainability governance in China: An analysis of regional ecological efficiency. Sustainability, 11(7), 1958, 11071958. https://doi.org/10.3390/su11071958
Tang, Y. H., Yang, R., Chen, Y. W., & Miao, X. (2022). Assessment of China’s green governance performance based on integrative perspective of technology utilization and actor management. International Journal of Sustainable Development & World Ecology, 29(8), 827–839. https://doi.org/10.1080/13504509.2022.2107105
Teng, J. Y., & Wu, X. G. (2014). Eco-footprint-based life-cycle eco-efficiency assessment of building projects. Ecological Indicators, 39, 160–168. https://doi.org/10.1016/j.ecolind.2013.12.018
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
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/10.1016/j.ejor.2008.05.027
Vaezi, E., Najafi, S. E., Hajimolana, S. M., Lotfi, F. H., & Namin, M. A. (2021). Efficiency evaluation of a three-stage leader-follower model by the data envelopment analysis with double-frontier viewpoint. Scientia Iranica, 28(1), 492–515. https://doi.org/10.24200/sci.2019.51980.2459
Walsh, P. P., Murphy, E., & Horan, D. (2020). The role of science, technology and innovation in the UN 2030 agenda. Technological Forecasting and Social Change, 154, 119957. https://doi.org/10.1016/j.techfore.2020.119957
Wang, J., Wei, X., & Guo, Q. (2018b). A three-dimensional evaluation model for regional carrying capacity of ecological environment to social economic development: Model development and a case study in China. Ecological Indicators, 89, 348–355. https://doi.org/10.1016/j.ecolind.2018.02.005
Wang, K., Wei, Y. M., & Huang, Z. (2018a). Environmental efficiency and abatement efficiency measurements of China’s thermal power industry: A data envelopment analysis based materials balance approach. European Journal of Operational Research, 269(1), 35–50. https://doi.org/10.1016/j.ejor.2017.04.053
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., Tang, J., & Choi, G. (2021a). A two-stage eco-efficiency evaluation of China’s industrial sectors: A dynamic network data envelopment analysis (DNDEA) approach. Process Safety and Environmental Protection, 148, 879–892. https://doi.org/10.1016/j.psep.2021.02.005
Wang, S. J., Hua, G. H., & Yang, L. Z. (2020). Coordinated development of economic growth and ecological efficiency in Jiangsu, China. Environmental Science and Pollution Research, 27, 36664–36676. https://doi.org/10.1007/s11356-020-09297-9
Wang, S. H., Sun, X. L., & Song, M. L. (2021b). Environmental regulation, resource misallocation, and ecological efficiency. Emerging Markets Finance and Trade, 57(3), 410–429. https://doi.org/10.1080/1540496X.2018.1529560
Wang, Y., & Chen, X. Y. (2020). Natural resource endowment and ecological efficiency in China: Revisiting resource curse in the context of ecological efficiency. Resources Policy, 66, 101610. https://doi.org/10.1016/j.resourpol.2020.101610
Wang, Z., & He, W. (2017). CO2 emissions efficiency and marginal abatement costs of the regional transportation sectors in China. Transportation Research Part D: Transport and Environment, 50, 83–97. https://doi.org/10.1016/j.trd.2016.10.004
Wendling, Z. A., Emerson, J. W., Esty, D. C., Levy, M. A., de Sherbinin, A., et al. (2018). 2018 Environmental Performance Index. Yale Center for Environmental Law & Policy.
Wu, H. T., Li, Y. W., Hao, Y., Ren, S. Y., & Zhang, P. F. (2020). Environmental decentralization, local government competition, and regional green development: Evidence from China. Science of The Total Environment, 708, 135085. https://doi.org/10.1016/j.scitotenv.2019.135085
Wu, J., Wu, Z., & Hollaender, R. (2012). The application of Positive Matrix Factorization (PMF) to eco-efficiency analysis. Journal of Environmental Management, 98, 11–14. https://doi.org/10.1016/j.jenvman.2011.12.022
Xia, Y., Wang, X., Li, H., & Li, A. (2020). China’s provincial environmental efficiency evaluation and influencing factors of the mining industry considering technology heterogeneity. IEEE Acess, 8, 178924–178937. https://doi.org/10.1109/access.2020.3027698
Xing, Z. C., Wang, J. G., & Zhang, J. (2018). Expansion of environmental impact assessment for eco-efficiency evaluation of China’s economic sectors: An economic input-output based frontier approach. Science of the Total Environment, 635, 284–293. https://doi.org/10.1016/j.scitotenv.2018.04.076
Xu, M. X., & Hu, W. Q. (2020). A research on coordination between economy, society and environment in China: A case study of Jiangsu. Journal of Cleaner Production, 258, 120641. https://doi.org/10.1016/j.jclepro.2020.120641
Xu, Y., Zhang, H., Cheng, K., Zhang, Z., & Chen, Y. (2021). Efficiency measurement in multi-period network DEA model with feedback. Expert Systems with Applications, 175, 114815. https://doi.org/10.1016/j.eswa.2021.114815
Xue, Y., Tang, C., Wu, H. T., Liu, J., & Hao, Y. (2022). The emerging driving force of energy consumption in China: Does digital economy development matter? Energy Policy, 165, 112997. https://doi.org/10.1016/j.enpol.2022.112997
Yang, L., & Yang, Y. (2019). Evaluation of eco-efficiency in China from 1978 to 2016: Based on a modified ecological footprint model. Science of the Total Environment, 662, 581–590. https://doi.org/10.1016/j.scitotenv.2019.01.225
Yang, R., Wong, C. W. Y., & Miao, X. (2021a). Evaluation of the coordinated development of economic, urbanization and environmental systems: A case study of China. Clean Technologies and Environmental Policy, 23, 685–708. https://doi.org/10.1007/s10098-020-01999-5
Yang R., Wong, C. W. Y., Wang, T., Du, M. J., & Miao, X. (2021b). Assessment on the interaction between technology innovation and eco-environmental systems in China. Environmental Science and Pollution Research, 28(44), 63127–63149. https://doi.org/10.1007/s11356-021-15149-x
Yao, J. D., Xu, P. P., & Huang, Z. J. (2021). Impact of urbanization on ecological efficiency in China: An empirical analysis based on provincial panel data. Ecological Indicators, 129, 107827. https://doi.org/10.1016/j.ecolind.2021.107827
Yu, S., Liu, J., & Li, L. (2019a). 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
Yu, Y., Chong, P., & Li, Y. (2019b). Do neighboring prefectures matter in promoting eco-efficiency? Empirical evidence from China. Technological Forecasting and Social Change, 144, 456–465. https://doi.org/10.1016/j.techfore.2018.03.021
Yue, H., Lin, L., & Yantuan, Y. (2018a). Do urban agglomerations outperform non-agglomerations? A new perspective on exploring the eco-efficiency of Yangtze River Economic Belt in China. Journal of Cleaner Production, 202, 1056–1067. https://doi.org/10.1016/j.jclepro.2018.08.202
Yue, H., Lin, L., & Yu, Y. (2018b). Does urban cluster promote the increase of urban eco-efficiency? Evidence from Chinese cities. Journal of Cleaner Production, 197, 957–971. https://doi.org/10.1016/j.jclepro.2018.06.251
Zameer, H., Yasmeen, H., Wang, R., Tao, J., & Malik, M. N. (2020). An empirical investigation of the coordinated development of natural resources, financial development and ecological efficiency in China. Resources Policy, 65, 101580. https://doi.org/10.1016/j.resourpol.2020.101580
Zhan, C., & De Jong, M. (2018). Financing eco-cities and low carbon cities: The case of Shenzhen International Low Carbon City. Journal of Cleaner Production, 180, 116–125. https://doi.org/10.1016/j.jclepro.2018.01.097
Zhang, X., Wang, G. S., & Wang, Y. W. (2014). Spatial-temporal differences of provincial eco-efficiency in China based on matrix-type network DEA. Economic Geography, 12, 153–160. https://doi.org/10.15957/j.cnki.jjdl.2014.12.023
Zhang, Y.-J., Liu, J.-Y., & Su, B. (2020). 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
Zhong, R., & Zeng, J. (2022). The impact of digital economy on household consumption – Empirical analysis based on the Spatial Durbin Model. Inquiry into Economic Issues, 3, 31–43 (in Chinese).
Zhou, C., Shi, C., Wang, S., & Zhang, G. (2018). Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models. Ecological Indicators, 86, 67–80. https://doi.org/10.1016/j.ecolind.2017.12.011
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
Acheampong, A. O. (2018). Economic growth, CO2 emissions and energy consumption: What causes what and where? Energy Economics, 74, 677–692. https://doi.org/10.1016/j.eneco.2018.07.022
Adler, N., & Volta, N. (2016). Accounting for externalities and disposability: A directional economic environmental distance function. European Journal of Operational Research, 250(1), 314–327. https://doi.org/10.1016/j.ejor.2015.10.064
Alsaedi, Y. H., & Tularam, G. A. (2020). The relationship between electricity consumption, peak load and GDP in Saudi Arabia: A VAR analysis. Mathematics and Computers in Simulation, 175, 164–178. https://doi.org/10.1016/j.matcom.2019.06.012
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264. https://doi.org/10.1287/mnsc.39.10.1261
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Beltrán-Esteve, M., Reig-Martínez, E., & Estruch-Guitart, V. (2017) Assessing eco-efficiency: A metafrontier directional distance function approach using life cycle analysis. Environmental Impact Assessment Review, 63, 116–127. https://doi.org/110.1016/j.eiar.2017.01.001
Berdiev, A. N., & Saunoris, J. W. (2016). Financial development and the shadow economy: A panel VAR analysis. Economic Modelling, 57, 197–207. https://doi.org/10.1016/j.econmod.2016.03.028
Bing, Z., Bi, J., Fan, Z., Yuan, Z., & Ge, J. (2008). Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological Economics, 68(1–2), 306–316. https://doi.org/10.1016/j.ecolecon.2008.03.009
Bostian, M., Färe, R., Grosskopf, S., Lundgren, T., & Weber, W. L. (2018). Time substitution for environmental performance: The case of Swedish manufacturing. Empirical Economics, 54(1), 129–152. https://doi.org/10.1007/s00181-016-1180-7
Boussemart, J.-P., Leleu, H., Shen, Z., & Valdmanis, V. (2020). Performance analysis for three pillars of sustainability. Journal of Productivity Analysis, 53, 305–320. https://doi.org/10.1007/s11123-020-00575-9
BP. (2021). Statistical review of world energy. http://www.bo.com/statisticalreview
Carrasco-Gutierrez, C. E., Souza, R. C., & Guillén, O. (2009). Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features. Brazilian Review of Econometrics, 29(1), 1–14. https://doi.org/10.12660/bre.v29n12009.2696
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen, C.-M., & Delmas, M. A. (2012). Measuring eco-inefficiency: A new frontier approach. Operations Research, 60(5), 1064–1079. https://doi.org/10.1287/opre.1120.1094
Chen, H. B., Dong, K., Wang, F. F., & Emmanuel, C. A. (2020). The spatial effect of tourism economic development on regional ecological efficiency. Environmental Science and Pollution Research, 27, 38241–38258. https://doi.org/10.1007/s11356-020-09004-8
Chen, X., Liu, X., Gong, Z., & Xie, J. (2021a). Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156, 107234. https://doi.org/10.1016/j.cie.2021.107234
Chen, Y. W., Wong, C. W. Y., Yang, R., & Miao, X. (2021b). Optimal structure adjustment strategy, emission reduction potential and utilization efficiency of fossil energies in China. Energy, 237, 121623. https://doi.org/10.1016/j.energy.2021.121623
Chen, Y. W., Yang, R., Wong, C. W., Ji, J., & Miao, X. (2022). Efficiency and productivity of air pollution control in Chinese cities. Sustainable Cities and Society, 76, 103423. https://doi.org/10.1016/j.scs.2021.103423
Cheng, X., Long, R., Chen, H., & Li Q. (2019). Coupling coordination degree and spatial dynamic evolution of a regional green competitiveness system: A case study from China. Ecological Indicators, 104, 489–500. https://doi.org/10.1016/j.ecolind.2019.04.003
China Civil Affairs’ Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2017110010
China Energy Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022060061
China Industry Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022010304
China Statistical Yearbook on Science and Technology. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022010277
China Statistical Yearbook. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2021110004
China Statistics Yearbook on Environment. (2021). China Statistics Press. https://data.cnki.net/yearbook/Single/N2022030234
Choi, Y., Zhang, N., & Zhou, P. (2012). Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure. Applied Energy, 98, 198–208. https://doi.org/10.1016/j.apenergy.2012.03.024
Cui, Q., & Li, Y. (2018). Airline dynamic efficiency measures with a Dynamic RAM with unified natural & managerial disposability. Energy Economics, 75, 534–546. https://doi.org/10.1016/j.eneco.2018.09.016
De Simone, L., & Popoff, F. with the WBCSD. (1997). Eco-efficiency: The business link to sustainable development. The MIT Press.
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
Duan, X., Dai, S., Yang, R., Duan, Z., & Tang, Y. (2020). Environmental collaborative governance degree of government, corporation and public. Sustainability, 12(3), 1138. https://doi.org/10.3390/su12031138
Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132(2), 312–325. https://doi.org/10.1016/s0377-2217(00)00154-5
Fan, Y., Bai, B., Qiao, Q., Kang, P., Zhang, Y., & Guo, J. (2017). Study on eco-efficiency of industrial parks in China based on data envelopment analysis. Journal of Environmental Management, 192, 107–115. https://doi.org/10.1016/j.jenvman.2017.01.048
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. https://doi.org/10.1016/s0038-0121(99)00012-9
Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90–98. https://doi.org/10.2307/1928055
Feng, N., Feng, H. H., Li, D. H., & Li, M. Q. (2020). Online media coverage, consumer engagement and movie sales: A PVAR approach. Decision Support Systems, 131, 113267. https://doi.org/10.1016/j.dss.2020.113267
Gharaei, A., Karimi, M., & Shekarabi, S. A. H. (2019). An integrated multi-product, multi buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm. Applied Mathematical Modelling, 69, 223–254. https://doi.org/10.1016/j.apm.2018.11.035
Global Carbon Project. (2021). Global Carbon Budget Report. https://www.globalcarbonproject.org/carbonbudget/index.htm
Golshani, H., Khoveyni, M., Valami, H. B., & Eslami, R. (2019). A slack-based super efficiency in a two-stage network structure with intermediate measures. Alexandria Engineering Journal, 58(1), 393–400. https://doi.org/10.1016/j.aej.2019.01.002
Hampf, B. (2014). Separating environmental efficiency into production and abatement efficiency: A nonparametric model with application to US power plants. Journal of Productivity Analysis, 41(3), 457–473. https://doi.org/10.1007/s11123-013-0357-8
Han, Y., Geng, Z., Zhu, Q., & Qu, Y. (2015). Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry. Energy, 83, 685–695. https://doi.org/10.1016/j.energy.2015.02.078
Hatami-Marbini, A., & Saati, S. (2020). Measuring performance with common weights: Network DEA. Neural Computing and Applications, 32(8), 3599–3617. https://doi.org/10.1007/s00521-019-04219-4
He, J. Q., Wang, S. J., Liu, Y. Y., Ma, H. T., & Liu, Q. Q. (2017). Examining the relationship between urbanization and the eco-environment using a coupling analysis: Case study of Shanghai, China. Ecological Indicators, 77, 185–193. https://doi.org/10.1016/j.ecolind.2017.01.017
He, W., Zhang, B., & Ding, T. (2020). Sources of provincial carbon intensity reduction potential in China: A non-parametric fractional programming approach. Science of the Total Environment, 730, 139037. https://doi.org/10.1016/j.scitotenv.2020.139037
Helmut, H. (2001). How to calculate and interpret ecological footprint for long periods of time: The case of Austria 1926–2995. Ecological Economics, 38(1), 25–45. https://doi.org/10.1016/S0921-8009(01)00152-5
Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395. https://doi.org/10.2307/1913103
Jawadi, F., Mallick, S. K., & Sousa, R. M. (2016). Fiscal and monetary policies in the BRICS: A panel VAR approach. Economic Modeling, 58, 535–542. https://doi.org/10.1016/j.econmod.2015.06.001
Jiang, Q., & Tan, Q. (2020). Can government environmental auditing improve static and dynamic ecological efficiency in China? Environmental Science and Pollution Research, 27, 21733–21746. https://doi.org/10.1007/s11356-020-08578-7
Jouida, S. (2018). Diversification, capital structure and profitability: A panel VAR approach. Research in International Business and Finance, 45, 243–256. https://doi.org/10.1016/j.ribaf.2017.07.155
Kourtzidis, S., Matousek, R., & Tzeremes, N. G. (2021). Modelling a multi-period production process: Evidence from the Japanese regional banks. European Journal of Operational Research, 294(1), 327–339. https://doi.org/10.1016/j.ejor.2021.01.036
Kuang, B., Lu, X. H., Han, J., Fan, X. Y., & Zuo, J. (2020). How urbanization influence urban land consumption intensity: Evidence from China. Habitat International, 100, 102103. https://doi.org/10.1016/j.habitatint.2019.102103
Li, Z., Crook, J., & Andreeva, G. (2017a). Dynamic prediction of financial distress using Malmquist DEA. Expert Systems with Applications, 80, 94–106. https://doi.org/10.1016/j.eswa.2017.03.017
Li, Z., Ouyang, X., Du, K., & Zhao, Y. (2017b). Does government transparency contribute to improved eco-efficiency performance? An empirical study of 262 cities in China. Energy Policy, 110, 79–89. https://doi.org/10.1016/j.enpol.2017.08.001
Liang, H. W., Dong, L., Luo, X., Ren, J. Z., Zhang, N., Gao, Z. Q., & Dou, Y. (2016). Balancing regional industrial development: Analysis on regional disparity of China’s industrial emissions and policy implications. Journal of Cleaner Production, 126, 223–235. https://doi.org/10.1016/j.jclepro.2016.02.145
Liao, K. C., Yue, M. Y., Sun, S. W., Xue, H. B., Liu, W., Tsai, S. B., & Wang, J. T. (2018). An evaluation of coupling coordination between tourism and finance. Sustainability, 10(7), 2320. https://doi.org/10.3390/su10072320
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. Q., & Wang, Y. (2019). Inconsistency of economic growth and electricity consumption in China: A panel VAR approach. Journal of Cleaner Production, 229, 144–156. https://doi.org/10.1016/j.jclepro.2019.04.396
Lin, B. Q., & Zhu, J. P. (2017). Energy and carbon intensity in China during the urbanization and industrialization process: A panel VAR approach. Journal of Cleaner Production, 168, 780–790. https://doi.org/10.1016/j.jclepro.2017.09.013
Lin B. Q., & Zhu, J. (2019). Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China. Applied Energy, 239, 12–22. https://doi.org/10.1016/j.apenergy.2019.01.166
Lin, B. Q., & Zhu, R. (2021). Energy efficiency of the mining sector in China, what are the main influence factors? Resources, Conservation and Recycling, 167, 105321. https://doi.org/10.1016/j.resconrec.2020.105321
Lin, F., Lin, S.-W., & Lu, W.-M. (2019). Dynamic eco-efficiency evaluation of the semiconductor industry: A sustainable development perspective. Environmental Monitoring and Assessment, 191(7), 435. https://doi.org/10.1007/s10661-019-7598-6
Liu, Q. Q., Wang, S. J., Li, B., & Zhang, W. Z. (2020). Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis. Journal of Environmental Management, 264, 110442. https://doi.org/10.1016/j.jenvman.2020.110442
Liu, Y. B., Yao, C. S., Wang, G. X., & Bao, S. M. (2011). An integrated sustainable development approach to modeling the eco-environmental effects from urbanization. Ecological Indicators, 11(6), 1599–1608. https://doi.org/10.1016/j.ecolind.2011.04.004
Ma, J., Qi, L., & Deng, L. (2018). Additive centralized and Stackelberg DEA models for two-stage system with shared resources. International Transactions in Operational Research, 27(4), 2211–2229. https://doi.org/10.1111/itor.12504
Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewanle and Sustainable Energy Reviews, 70, 1298–1322. https://doi.org/10.1016/j.rser.2016.12.030
Pastor, J. T., & Lovell, C. A. K. (2005). A global Malmquist productivity index. Economics Letters, 88(2), 266–271. https://doi.org/10.1016/j.econlet.2005.02.013
Pereira, M. A., Ferreira, D. C., Figueira, J. R., & Marques, R. C. (2021). Measuring the efficiency of the Portuguese public hospitals: A value modelled network data envelopment analysis with simulation. Expert Systems with Applications, 181, 115169. https://doi.org/10.1016/j.eswa.2021.115169
Piao, S. R., Li, J., & Ting, C. J. (2019) Assessing regional environmental efficiency in China with distinguishing weak and strong disposability of undesirable outputs. Journal of Cleaner Production, 227, 748–759. https://doi.org/10.1016/j.jclepro.2019.04.207
Picazo-Tadeo, A. J., Gómez-Limón, J. A., & Reig-Martínez, E. (2011). Assessing farming eco-efficiency: A Data Envelopment Analysis approach. Journal of Environmental Management, 92(4), 1154–1164. https://doi.org/10.1016/j.jenvman.2010.11.025
Qu, C., Shao, J., & Shi, Z. (2020). Does financial agglomeration promote the increase of energy efficiency in China? Energy Policy, 146, 111810. https://doi.org/10.1016/j.enpol.2020.111810
Rashidi, K., & Saen, R. F. (2015). Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energy Economics, 50, 18–26. https://doi.org/10.1016/j.eneco.2015.04.018
Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment. International Journal of Life Cycle Assessment, 13, 374–388. https://doi.org/10.1007/s11367-008-0009-9
Ren, S., Li, X., Yuan, B., Li, D., & Chen, X. (2018). The effects of three types of environmental regulation on eco-efficiency: A cross-region analysis in China. Journal of Cleaner Production, 173, 245–255. https://doi.org/10.1016/j.jclepro.2016.08.113
Roshdi, I., Hasannasab, M., Margaritis, D., & Rouse, P. (2018). Generalised weak disposability and efficiency measurement in environmental technologies. European Journal of Operational Research, 266(3), 1000–1012. https://doi.org/10.1016/j.ejor.2017.10.033
Ruggiero, J. (2005). Impact assessment of input omission on DEA. International Journal of Information Technology & Decision Making, 4(3), 359–368. https://doi.org/10.1142/s021962200500160x
Schaltegger, S., & Sturm, A. (1990). Ecological rationality: Approaches to design of ecology-oriented management instruments. Die Unternehmung, 4, 273–290.
Shen, D. N., & Li, Y. (2020). Panel vector autoregression model to study the dynamic relationship between meteorological S&T and the economic development of meteorologically sensitive industries in China. International Journal of Electrical Engineering Education. https://doi.org/10.1177/0020720920928459
Shen, Y., Yue, S., Pu, Z., & Guo, M. (2020). Sustainable total-factor ecology efficiency of regions in China. Science of The Total Environment, 741, 139241. https://doi.org/10.1016/j.scitotenv.2020.139241
Shen, Z. Y., Wu, H. T., Bai, K. X., & Hao, Y. (2022). Integrating economic, environmental and societal performance within the productivity measurement. Technological Forecasting & Social Change, 176, 121463. https://doi.org/10.1016/j.techfore.2021.121463
Shermeh, H. E., Najafi, S. E., & Alavidoost, M. H. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686–697. https://doi.org/10.1016/j.energy.2016.06.087
Song, M., Tan, K., Wang, J., & Shen, Z. (2022). Modeling and evaluating economic and ecological operation efficiency of smart city pilots. Cities, 124, 103575. https://doi.org/10.1016/l.cities.2022.103575
Statistical Communique on the National Economic and Social Development. (2021). Retrieved October 27, 2022, from http://www.gov.cn/shuju/2022-02/28/content_5676015.htm
Sueyoshi, T. (2000). Stochastic DEA for restructure strategy: An application to a Japanese petroleum company. Omega, 28(4), 385–398. https://doi.org/10.1016/s0305-0483(99)00069-9
Sueyoshi, T., & Yuan, Y. (2017). Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention. Energy Economics, 66, 154–166. https://doi.org/10.1016/j.eneco.2017.06.008
Sun, J., Li, G., & Wang, Z. (2019). Technology heterogeneity and efficiency of China’s circular economic systems: A game meta-frontier DEA approach. Resources, Conservation and Recycling, 146, 337–347. https://doi.org/10.1016/j.resconrec.2019.03.046
Sun, X. X., & Loh, L. (2019). Sustainability governance in China: An analysis of regional ecological efficiency. Sustainability, 11(7), 1958, 11071958. https://doi.org/10.3390/su11071958
Tang, Y. H., Yang, R., Chen, Y. W., & Miao, X. (2022). Assessment of China’s green governance performance based on integrative perspective of technology utilization and actor management. International Journal of Sustainable Development & World Ecology, 29(8), 827–839. https://doi.org/10.1080/13504509.2022.2107105
Teng, J. Y., & Wu, X. G. (2014). Eco-footprint-based life-cycle eco-efficiency assessment of building projects. Ecological Indicators, 39, 160–168. https://doi.org/10.1016/j.ecolind.2013.12.018
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
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/10.1016/j.ejor.2008.05.027
Vaezi, E., Najafi, S. E., Hajimolana, S. M., Lotfi, F. H., & Namin, M. A. (2021). Efficiency evaluation of a three-stage leader-follower model by the data envelopment analysis with double-frontier viewpoint. Scientia Iranica, 28(1), 492–515. https://doi.org/10.24200/sci.2019.51980.2459
Walsh, P. P., Murphy, E., & Horan, D. (2020). The role of science, technology and innovation in the UN 2030 agenda. Technological Forecasting and Social Change, 154, 119957. https://doi.org/10.1016/j.techfore.2020.119957
Wang, J., Wei, X., & Guo, Q. (2018b). A three-dimensional evaluation model for regional carrying capacity of ecological environment to social economic development: Model development and a case study in China. Ecological Indicators, 89, 348–355. https://doi.org/10.1016/j.ecolind.2018.02.005
Wang, K., Wei, Y. M., & Huang, Z. (2018a). Environmental efficiency and abatement efficiency measurements of China’s thermal power industry: A data envelopment analysis based materials balance approach. European Journal of Operational Research, 269(1), 35–50. https://doi.org/10.1016/j.ejor.2017.04.053
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., Tang, J., & Choi, G. (2021a). A two-stage eco-efficiency evaluation of China’s industrial sectors: A dynamic network data envelopment analysis (DNDEA) approach. Process Safety and Environmental Protection, 148, 879–892. https://doi.org/10.1016/j.psep.2021.02.005
Wang, S. J., Hua, G. H., & Yang, L. Z. (2020). Coordinated development of economic growth and ecological efficiency in Jiangsu, China. Environmental Science and Pollution Research, 27, 36664–36676. https://doi.org/10.1007/s11356-020-09297-9
Wang, S. H., Sun, X. L., & Song, M. L. (2021b). Environmental regulation, resource misallocation, and ecological efficiency. Emerging Markets Finance and Trade, 57(3), 410–429. https://doi.org/10.1080/1540496X.2018.1529560
Wang, Y., & Chen, X. Y. (2020). Natural resource endowment and ecological efficiency in China: Revisiting resource curse in the context of ecological efficiency. Resources Policy, 66, 101610. https://doi.org/10.1016/j.resourpol.2020.101610
Wang, Z., & He, W. (2017). CO2 emissions efficiency and marginal abatement costs of the regional transportation sectors in China. Transportation Research Part D: Transport and Environment, 50, 83–97. https://doi.org/10.1016/j.trd.2016.10.004
Wendling, Z. A., Emerson, J. W., Esty, D. C., Levy, M. A., de Sherbinin, A., et al. (2018). 2018 Environmental Performance Index. Yale Center for Environmental Law & Policy.
Wu, H. T., Li, Y. W., Hao, Y., Ren, S. Y., & Zhang, P. F. (2020). Environmental decentralization, local government competition, and regional green development: Evidence from China. Science of The Total Environment, 708, 135085. https://doi.org/10.1016/j.scitotenv.2019.135085
Wu, J., Wu, Z., & Hollaender, R. (2012). The application of Positive Matrix Factorization (PMF) to eco-efficiency analysis. Journal of Environmental Management, 98, 11–14. https://doi.org/10.1016/j.jenvman.2011.12.022
Xia, Y., Wang, X., Li, H., & Li, A. (2020). China’s provincial environmental efficiency evaluation and influencing factors of the mining industry considering technology heterogeneity. IEEE Acess, 8, 178924–178937. https://doi.org/10.1109/access.2020.3027698
Xing, Z. C., Wang, J. G., & Zhang, J. (2018). Expansion of environmental impact assessment for eco-efficiency evaluation of China’s economic sectors: An economic input-output based frontier approach. Science of the Total Environment, 635, 284–293. https://doi.org/10.1016/j.scitotenv.2018.04.076
Xu, M. X., & Hu, W. Q. (2020). A research on coordination between economy, society and environment in China: A case study of Jiangsu. Journal of Cleaner Production, 258, 120641. https://doi.org/10.1016/j.jclepro.2020.120641
Xu, Y., Zhang, H., Cheng, K., Zhang, Z., & Chen, Y. (2021). Efficiency measurement in multi-period network DEA model with feedback. Expert Systems with Applications, 175, 114815. https://doi.org/10.1016/j.eswa.2021.114815
Xue, Y., Tang, C., Wu, H. T., Liu, J., & Hao, Y. (2022). The emerging driving force of energy consumption in China: Does digital economy development matter? Energy Policy, 165, 112997. https://doi.org/10.1016/j.enpol.2022.112997
Yang, L., & Yang, Y. (2019). Evaluation of eco-efficiency in China from 1978 to 2016: Based on a modified ecological footprint model. Science of the Total Environment, 662, 581–590. https://doi.org/10.1016/j.scitotenv.2019.01.225
Yang, R., Wong, C. W. Y., & Miao, X. (2021a). Evaluation of the coordinated development of economic, urbanization and environmental systems: A case study of China. Clean Technologies and Environmental Policy, 23, 685–708. https://doi.org/10.1007/s10098-020-01999-5
Yang R., Wong, C. W. Y., Wang, T., Du, M. J., & Miao, X. (2021b). Assessment on the interaction between technology innovation and eco-environmental systems in China. Environmental Science and Pollution Research, 28(44), 63127–63149. https://doi.org/10.1007/s11356-021-15149-x
Yao, J. D., Xu, P. P., & Huang, Z. J. (2021). Impact of urbanization on ecological efficiency in China: An empirical analysis based on provincial panel data. Ecological Indicators, 129, 107827. https://doi.org/10.1016/j.ecolind.2021.107827
Yu, S., Liu, J., & Li, L. (2019a). 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
Yu, Y., Chong, P., & Li, Y. (2019b). Do neighboring prefectures matter in promoting eco-efficiency? Empirical evidence from China. Technological Forecasting and Social Change, 144, 456–465. https://doi.org/10.1016/j.techfore.2018.03.021
Yue, H., Lin, L., & Yantuan, Y. (2018a). Do urban agglomerations outperform non-agglomerations? A new perspective on exploring the eco-efficiency of Yangtze River Economic Belt in China. Journal of Cleaner Production, 202, 1056–1067. https://doi.org/10.1016/j.jclepro.2018.08.202
Yue, H., Lin, L., & Yu, Y. (2018b). Does urban cluster promote the increase of urban eco-efficiency? Evidence from Chinese cities. Journal of Cleaner Production, 197, 957–971. https://doi.org/10.1016/j.jclepro.2018.06.251
Zameer, H., Yasmeen, H., Wang, R., Tao, J., & Malik, M. N. (2020). An empirical investigation of the coordinated development of natural resources, financial development and ecological efficiency in China. Resources Policy, 65, 101580. https://doi.org/10.1016/j.resourpol.2020.101580
Zhan, C., & De Jong, M. (2018). Financing eco-cities and low carbon cities: The case of Shenzhen International Low Carbon City. Journal of Cleaner Production, 180, 116–125. https://doi.org/10.1016/j.jclepro.2018.01.097
Zhang, X., Wang, G. S., & Wang, Y. W. (2014). Spatial-temporal differences of provincial eco-efficiency in China based on matrix-type network DEA. Economic Geography, 12, 153–160. https://doi.org/10.15957/j.cnki.jjdl.2014.12.023
Zhang, Y.-J., Liu, J.-Y., & Su, B. (2020). 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
Zhong, R., & Zeng, J. (2022). The impact of digital economy on household consumption – Empirical analysis based on the Spatial Durbin Model. Inquiry into Economic Issues, 3, 31–43 (in Chinese).
Zhou, C., Shi, C., Wang, S., & Zhang, G. (2018). Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models. Ecological Indicators, 86, 67–80. https://doi.org/10.1016/j.ecolind.2017.12.011
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