Measurement and influence factors of ecological efficiency of the Yangtze River economic belt under high quality development conditions
【Abstract】The four-stage analysis framework of NSUSBM-SFA-NSUSBM-Tobit was constructed in this paper. Based on the panel data of 107 cities in the Yangtze River economic belt from 2007 to 2016, the global DEA technology was used to calculate the eco-efficiency of cities under the inseparable assumption. Then the three-stage DEA model was used to introduce natural factors. And combined with the new development concept, The Tobit model was used to analyze the influencing factors of urban eco-efficiency. Regardless of the environmental differences in the cities, the average comprehensive ecological efficiency in the Yangtze River economic zone increased from 0.288 to 0.617 in 2007–2016, with an increase by 114.24%. It has crossed the low-quality development stage of 0.4, but was still lower than the high-quality development standard of 0.8 in 2016. The ecological efficiency of Hunan Province grew fastest, with an increase by 213.85%. Considering the environmental differences, the average comprehensive ecological efficiency of the Yangtze River economic zone increased from 0.150 to 0.395 in 2007–2016, with an increase by 163.33%. Shanghai grew fastest, with an increase by 311.52%. Shanghai, Jiangsu and Zhejiang ranked the top three in most years, while Jiangxi, Guizhou and Yunnan ranked the bottom three. Innovation, urban-rural coordination, government environmental investment, foreign investment and urbanization all contributed to eco-efficiency.
【Keywords】 Yangtze River economic belt; ecological efficiency; high quality development; SBM model; three-stage DEA; Tobit regression;
 Tone K, Tsutsui M. Applying an efficiency measure of desirable and undesirable outputs in dea to U.S. electric utilities [J]. Social Science Electronic Publishing, 2011, 4(2): 236–249.
 Wang B, Wu Y R, Yan P F. Environmental efficiency and environmental total factor productivity growth in China’s regional economies [J]. Economic Research Journal, 2010, 5(45): 95–109.
 Luo B N, Bu Y. Study on the performance evaluation and influencing factors of urban ecological welfare in the Yangtze River economic belt: a case study of 110 cities in the Yangtze River economic belt [J]. Enterprise Economy, 2018, 8(37): 30–37.
 Liu Y Y, Tuo J. Study on ecological efficiency of the Yangtze River economic belt based on super efficiency DEA model [J]. Special Zone Economy, 2019, 2: 112–115.
 Luo Y. DEA-based research on indicator selection and environmental performance measurement [D]. Hefei: University of Science and Technology of China, 2012.
 O Donnell C J, Rao D S P, Battese G E. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios [J]. Empirical Economics, 2008, 2(34): 231–255.
 Qian L, Xiao R Q, Chen Z W. Research on green technological innovation efficiency and regional differences of industrial enterprises in China: based on common frontier theory and DEA model [J]. Economic Theory and Business Management, 2015, 1: 26–43.
 Fried H O, Lovell C A K, Schmidt S S, et al. Accounting for environmental effects and statistical noise in data envelopment analysis [J]. Journal of Productivity Analysis, 2002, 1/2(17): 157–174.
 Wu L, Xiong Y. Measurement and construction of improvement model of ecological effciency in Yangtze River economic belt [J]. Ecological Economy, 2018, 12(34): 166–172+177.
 Zhou W Q. China’s industrial productivity growth and its influencing factors under the constraint of carbon emissions [D]. Wuhan: Huazhong University of Science and Technology, 2013.
 Lu L W, Song D Y, Huang C. Measurement of green total factor productivity of cities in the Yangtze River economic belt: a case study of 108cities in the Yangtze River economic belt [J]. Urban Problems, 2017, 1: 61–67.
 Tone K. A slacks-based measure of efficiency in data envelopment analysis [J]. European Journal of Operational Research, 2001, 3 (130): 498–509.
 Yang H L, Shi D, Xiao J. Influence of environmental factors on energy eiffcieney-analysis on regional theoretic energy saving potential and real energy saving potential [J]. China Industrial Economics, 2009, 4: 73–84.
 Oh D H. A global Malmquist-Luenberger productivity index [J]. Journal of Productivity Analysis, 2010, 3(34): 183–197.
 Tu Z G. The coordination of industrial growth with environment and resource [J]. Economic Research Journal, 2008, 2: 93–105.
 Zeng X G. Environmental efficiency and its determinants across Chinese regions [J]. Economic Theory and Business Management, 2011, 10: 103–110.
 Cheng D R, Li J. Eco-efficiency differences across provinces in China in the presence of environmental constraints: 1990–2006 [J]. Finance and Trade Research, 2009, 1(20): 13–17.
 Zhang J, Wu G Y, Zhang J P. The estimation of China’s provincial capital stock: 1952–2000 [J]. Economic Research Journal, 2004,10: 35–44.
 Yang J F, Gong L T, Zhang Q H. Human capital formation land its effects on economic growth [J]. Management World, 2006, 5: 10–18.
 Wang W. Aging population, reform of endowment insurance system and China's economic growth: theoretical analysis and numerical simulation [J]. Journal of Financial Research, 2012, 10: 29–45.
 Geng Z X, Sun Q X, Zheng W. Population aging, asset price and capital accumulation [J]. Economic Research Journal, 2016, 9(51): 29–43.
 Zhang R, Li G, Li A L, et al. A statistical investigation of China's manufacturing industry growth model under the background of supply-side reform [J]. Statistics & Decision, 2018, 17(34): 141–145.
 National Bureau of Statistics. The gross domestic product of China 1952–1995 [M]. Dalian: Northeastern University Press, 1997.
 National Bureau of Statistics. China statistical yearbook [M]. Beijing: China Statistics Press, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
 National Bureau of Statistics. China city statistical yearbook [M]. Beijing: China Statistics Press, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
 Guo W. Research on China’s regional environmental efficiency based on perspectives of spatial economics and environmental regulation [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2016.
 Luo D Y. A note on estimating managerial inefficiency of three-stage DEA model [J]. Statistical Research, 2012, 4(29): 104–107.
 Chen W W, Zhang L, Ma T H, et al. Research on three-stage DEA model [J]. Systems Engineering, 2014, 9(32): 144–149.
 Jin B. Study on the “high-quality development” economics [J]. China Industrial Economics, 2018, 4: 5–18.
 Li J C, Shi D M, Ge A T. Probe into the assessment indicator system on high-quality development [J]. Statistical Research, 2019, 1(36): 4–14.
 Lv W. Exploring the evaluation index system embodying high quality development [J]. The people’s congress of China, 2018, 11: 23–24.
 Mlachila M, Tapsoba R, Tapsoba S J A. A quality of growth index for developing countries: a proposal [J]. Social Indicators Research, 2017, 2(134): 675–710.
 Xu R H. Constructing quality of growth indices for China and determinants analysis [J]. Journal of Financial Development Research, 2018, 10: 36–45.
 National Bureau of Statistics. China Statistical Yearbook on Environment [M]. Beijing: China Statistics Press, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
 Zeng X G. Study on the ecological economic system of socialism with Chinese characteristics [M]. Beijing: China Environmental Science Press, 2019: 6.
 Zeng X G, Niu M C. Evaluation of urban environmental efficiency in China under high quality development conditions [J]. China Environmental Science, 2019, 39(6): 2667–2677.