Abstract
The ecological environment and economic development are double-edged swords. Nevertheless, we can still achieve green and coordinated development through environmental regulations and industrial agglomeration. Based on the panel data from 269 cities in China from 2008 to 2017, using the SBM-DEA model, the Malmquist-Luenberger (ML) index, and the spatial Durbin model (SDM) under different weight matrices, this paper explored the spatial pattern of ecological efficiency, the internal evolution mechanism, and the spillover effects of industrial agglomeration and environmental regulation on ecological efficiency. The results demonstrated that China’s urban ecological efficiency had an obvious spatial pattern of “high in the east and low in the west.” Due to the different life cycles of cities, the internal evolution mechanism of urban ecological efficiency had significant differences. Pure technological efficiency (PEFFCH), technological progress (TECH), and scale efficiency (SECH) have contributed the most to the ecological efficiency of the eastern, central, and western regions, respectively. Furthermore, a significant U-shaped relationship existed between industrial agglomeration and ecological efficiency. In particular, urban ecological efficiency will be improved when the industrial agglomeration level exceeds a certain scale. However, the spillover effects of industrial agglomeration were more sensitive to distance factors, leading to failure of the significance test under the economic distance and asymmetric economic distance matrix. The “innovation compensation effect” of environmental regulation was greater than the “compliance cost,” which verified the applicability of the “Porter Hypothesis” in urban ecological efficiency to a certain extent. Finally, the geographical detector showed that each variable had a certain impact on the urban ecological efficiency, and the impact of the interaction term was greater than that of a single variable.
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References
Ambec S, Cohen MA, Elgie S, Lanoie P (2013) The porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness? Rev Environ Econ. Policy 1:2–22. https://doi.org/10.1093/reep/res016
Anselin L (1988) Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7799-1.
Anselin L, Bera AK, Florax R, Yoon MJ (1996) Simple diagnostic tests for spatial dependence. Reg Sci Urban Econ 26(1):77–104. https://doi.org/10.1016/0166-0462(95)02111-6
Arabi B, Munisamy S, Emrouznejad A, Shadman F (2014) Power industry restructuring and eco-efficiency changes: a new slacks-based model in Malmquist–Luenberger index measurement. Energ Policy 68:132–145. https://doi.org/10.1016/j.enpol.2014.01.016
Bai J, Nie L (2018) Energy efficiency, environmental pollution and the transformation of China’s economic development mode. J Financ Res 10:1–18 http://www.jryj.org.cn/CN/.
Becker RA (2011) Local environmental regulation and plant-level productivity. Ecol Econ 70(12):2516–2522. https://doi.org/10.1016/j.ecolecon.2011.08.019
Bye B, Fæhn T, Rosnes O (2018) Residential energy efficiency policies: costs, emissions and rebound effects. Energy 143:191–201. https://doi.org/10.1016/j.energy.2017.10.103
Caves DW, Christensen LR, Diewert WE (1982) Multilateral comparisons of output, input, and productivity using superlative index numbers. Econ J 92(365):73–86. https://doi.org/10.2307/2232257
Chakraborty P, Chatterjee C (2017) Does environmental regulation indirectly induce upstream innovation? New evidence from India. Res Policy 46(5):939–955. https://doi.org/10.1016/j.respol.2017.03.004
Chang Y, Shi L, Wang Y (2016) The eco-efficiency of pulp and paper industry in China: an assessment based on slacks-based measure and Malmquist–Luenberger index. J Clean Prod 127:511–521. https://doi.org/10.1016/j.jclepro.2016.03.153
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen S, Wu D (2018) Adapting ecological risk valuation for natural resource damage assessment in water pollution. Environ Res 164:85–92. https://doi.org/10.1016/j.envres.2018.01.005
Chen X, Ye J (2019) When the wind blows: spatial spillover effects of urban air pollution. J Environ Plan Manag 62(8):1359–1376. https://doi.org/10.1080/09640568.2018.1496071
Chen S, Wei Z, Li J (2010) Comprehensive evaluation for construction performance in concurrent engineering environment. Int J Proj Manag 28:708–718. https://doi.org/10.1016/j.ijproman.2009.11.004
Chen C, Sun Y, Lan Q, Jiang F (2020) Impacts of industrial agglomeration on pollution and ecological efficiency-a spatial econometric analysis based on a big panel dataset of China’s 259 cities. J Clean Prod 258:120721. https://doi.org/10.1016/j.jclepro.2020.120721
Choi Y, Oh DH, Zhang N (2015) Environmentally sensitive productivity growth and its decompositions in China: a metafrontier Malmquist–Luenberger productivity index approach. Empir Econ 49(3):1017–1043. https://doi.org/10.1007/s00181-014-0896-5
Chung YHH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. Microeconomics 51(3):229–240. https://doi.org/10.1006/jema.1997.0146
Elhorst JP (2012) Dynamic spatial panels: models, methods, and inferences. J Geogr Syst 14(1):5–28. https://doi.org/10.1007/s10109-011-0158-4
Emrouznejad A, Yang GL (2016) A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries. Energy 115:840–856. https://doi.org/10.1016/j.energy.2016.09.032
ESRI (2016) How Hot Spot Analysis (Getis-Ord Gi*) Works. http://desktop.arcgis.com/en/arcmap/10.4/tools/ spatial-statistics-toolbox/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm.
Goldsmith RW (1951) A perpetual inventory of national wealth. In Studies in Income and Wealth 14:5-73.
Han J (2020) Can urban sprawl be the cause of environmental deterioration? Based on the provincial panel data in China. Environ Res 189:109954. https://doi.org/10.1016/j.envres.2020.109954
Hawkins T, Hendrickson C, Higgins C, Matthews HS, Suh S (2007) A mixed-unit input-output model for environmental life-cycle assessment and material flow analysis. Environ Sci Technol 41(3):1024–1031. https://doi.org/10.1021/es060871u
Krugman P (1990) "Increasing Returns and Economic Geography," NBER Working Papers 3275. National Bureau of Economic Research, Inc 99:483–499. https://doi.org/10.1086/261763
Lanoie P, Laurent LJ, Johnstone N, Ambec S (2011) Environmental policy, innovation and performance: new insights on the Porter hypothesis. J Econ Manag Strateg 20(3):803–842. https://doi.org/10.1111/j.1530-9134.2011.00301.x.
LeSage JP, Pace RK (2009) Introduction to spatial econometrics. Boca Raton: Taylor and Francis. https://doi.org/10.1201/9781420064254
Li K, Lin B (2017) Impact of energy conservation policies on the green productivity in China’s manufacturing sector: evidence from a three-stage DEA model. Appl Energy 168:351–363. https://doi.org/10.1016/j.apenergy.2016.01.104
Li J, Liu Z (2019) Spatial differences and influential factors of GTFP in Chinese three major urban agglomeration. Soft Science 33(02) 61-64+80. https://doi.org/10.13956/j.ss.1001-8409.2019.02.13
Li B, Wu S (2017) Effects of local and civil environmental regulation on green total factor productivity in China: a spatial Durbin econometric analysis. J Clean Prod 153:342–353. https://doi.org/10.1016/j.jclepro.2016.10.042
Li J, Han X, Jin M, Zhang X, Wang S (2019) Globally analysing spatiotemporal trends of anthropogenic PM2.5 concentration and population’s PM2.5 exposure from 1998 to 2016. Environ Int 128:46–62. https://doi.org/10.1016/j.envint.2019.04.026
Liu J, Cheng Z, Li L (2016) Industrial agglomeration and environmental pollution. Sci Res Manag 6:134–140. https://doi.org/10.19571/j.cnki.1000-2995.2016.06.016
Liu R, Ma Z, Liu Y (2020) Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: a machine learning approach. Environ Int 142:105823. https://doi.org/10.1016/j.envint.2020.105823
Marshall A (2009) Principles of economics: unabridged eighth edition. Cosimo, Inc.
Morrissey K (2016) A location quotient approach to producing regional production multipliers for the Irish economy. Pap Reg Sci 95(3):491–506. https://doi.org/10.1111/pirs.12143
Ord J, K, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27(4):286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x.
Pace RK, LeSage JP (2009) A sampling approach to estimate the log determinant used in spatial likelihood problems. J Geogr Syst 11(3):209–225. https://doi.org/10.1007/s10109-009-0087-7
Pei Y, Zhu Y, Liu S, Xie M (2021) Industrial agglomeration and environmental pollution: based on the specialized and diversified agglomeration in the Yangtze River Delta. Environ Dev Sustain 23:4061–4085. https://doi.org/10.1007/s10668-020-00756-4
Polykretis C, Alexakis DD (2021) Spatial stratified heterogeneity of fertility and its association with socio-economic determinants using Geographical Detector: the case study of Crete Island, Greece. Appl Geogr 127:102384. https://doi.org/10.1016/j.apgeog.2020.102384
Rubashkina Y, Galeotti M, Verdolini E (2015) Environmental regulation and competitiveness: empirical evidence on the Porter Hypothesis from European manufacturing sectors. Energy Policy 83:288–300. https://doi.org/10.1016/j.enpol.2015.02.014
Taylor WA (2000) Change-point analysis: a powerful new tool for detecting changes. 01-01. https://variation.com/.
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 J, Li X, Christakos G, Liao Y, Zhang T, Gu X, Zheng X (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Nt J Geogr Inf Sci 24(1):107–127. https://doi.org/10.1080/13658810802443457
Wang J, Zhang T, Fu B (2016) A measure of spatial stratified heterogeneity. Ecol Indic 67:250–256
Wang Z, Liang L, Sun Z, Wang X (2019) Spatiotemporal differentiation and the factors influencing urbanization and ecological environment synergistic effects within the Beijing-Tianjin-Hebei urban agglomeration. J Environ Manag 243(1):227–239. https://doi.org/10.1016/j.jenvman.2019.04.088
Wen L, Sharp B, Sbai E (2020) Spatial effects of wind penetration and its implication for wind farm investment decisions in New Zealand. Energy J 41(2):47–72. https://doi.org/10.5547/01956574.41.2.lwen
Wen L, Sheng M, Sharp B (2021) The impact of COVID-19 on changes in community mobility and variation in transport modes. N Z Econ Pap:1–8. https://doi.org/10.1080/00779954.2020.1870536
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 Econ 78:468–480. https://doi.org/10.1016/j.eneco.2018.11.036
Wu J, Xu H, Tang K (2021) Industrial agglomeration, CO2 emissions and regional development programs: a decomposition analysis based on 286 Chinese cities. Energy 225:120239. https://doi.org/10.1016/j.energy.2021.120239
Xu H, Demetriades A, Reimann C, Jiménez JJ, Filser J, Zhang C (2019) Identification of the co-existence of low total organic carbon contents and low pH values in agricultural soil in north-central Europe using hot spot analysis based on GEMAS project data. Sci Total Environ 678(15):94–104.https://doi.org/10.1016/j.scitotenv.2019.04.382
Xu H, Croot P, Zhang C (2021) Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis. Environ Int 151:106456. https://doi.org/10.1016/j.envint.2021.106456
Yang L, Yang Y (2019) Evaluation of eco-efficiency in China from 1978 to 2016: based on a modified ecological footprint model. Sci Total Environ 662:581–590. https://doi.org/10.1016/j.scitotenv.2019.01.225
Yang L, Ouyang H, Fang K, Ye L, Zhang J (2015) Evaluation of regional environmental efficiencies in China based on super-efficiency-dea. Ecol Indic 51:13–19. https://doi.org/10.1016/j.ecolind.2014.08.040
Yang F, Choi Y, Lee H (2021) Life-cycle data envelopment analysis to measure efficiency and cost-effectiveness of environmental regulation in China’s transport sector. Ecol Indic 126:107717. https://doi.org/10.1016/j.ecolind.2021.107717
Yasmeen H, Tan Q, Zameer H, Tan J, Nawaz K (2020) Exploring the impact of technological innovation, environmental regulations and urbanization on ecological efficiency of China in the context of COP21. J Environ Manag 274:111210. https://doi.org/10.1016/j.jenvman.2020.111210
Zhang K, Dou J (2016) Does industrial agglomeration reduce emissions. J Huazhong Univ Sci Technol 30(4):99–109. https://doi.org/10.19648/j.cnki.jhustss1980.2016.04.018
Zhang J, Wu G, Zhang J (2004) The Estimation of China’s provincial capital stock:1952—2000. Econ Res J 10:35–44
Zhang C, Liu H, Bressers HTA, Buchanan KS (2011) Productivity growth and environmental regulations- accounting for undesirable outputs: analysis of China’s thirty provincial regions using the Malmquiste- Luenberger index. Ecol Econ 70(12):2369–2379. https://doi.org/10.1016/j.ecolecon.2011.07.019
Zhang J, Liu Y, Chang Y, Zhang L (2017a) Industrial eco-efficiency in China: a provincial quantification using three-stage data envelopment analysis. J Clean Prod 143:238–249. https://doi.org/10.1016/j.jclepro.2016.12.123
Zhang B, Cao C, Hughes RM, Davis WS (2017b) China’s new environmental protection regulatory regime: Effects and gaps. J Environ Manag 187:464–469. https://doi.org/10.1016/j.jenvman.2016.11.009
Zhang K, Shao S, Fan S (2020) Market integration and environmental quality: evidence from the Yangtze River Delta region of China. J Environ Manag 261:110208. https://doi.org/10.1016/j.jenvman.2020.110208
Zhang X, Lin M, Wang Z, Jin F (2021) The impact of energy-intensive industries on air quality in China’s industrial agglomerations. J Geogr Sci 31(04):584–602. https://doi.org/10.1007/s11442-021-1860-x
Zheng X, Yu Y, Wang J, Deng H (2014) Identifying the determinants and spatial nexus of provincial carbon intensity in China: a dynamic spatial panel approach. Reg Environ Chang 14(4):1651–1661. https://doi.org/10.1007/s10113-014-0611-2
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. Ecol Indic 86:67–80. https://doi.org/10.1016/j.ecolind.2017.12.011
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The authors are grateful to the Editor and two anonymous referees for helpful comments and suggestions.
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This work was supported by the National Natural Science Foundation of China (Grant/Award Number: 41471103) and the Jiangsu Provincial Humanities and Social Sciences Major Fund (Grant/Award Number: 2020SJZDA135).
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Four authors provided critical feedback and helped shape the research, analysis, and manuscript. Yizhen Zhang—conceptualization, investigation, and writing of the original draft and analysis. Han Zhang—software and data curation; and corresponding author Tao Wang—visualization, reviewing, and editing. Liuwei Wang and Yu Fu—data curation and writing.
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Zhang, Y., Zhang, H., Fu, Y. et al. Effects of industrial agglomeration and environmental regulation on urban ecological efficiency: evidence from 269 cities in China. Environ Sci Pollut Res 28, 66389–66408 (2021). https://doi.org/10.1007/s11356-021-15467-0
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DOI: https://doi.org/10.1007/s11356-021-15467-0