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Do Forests help environmental development of Cities in China?

Author

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  • Liang Chun Lu

    (Lunghwa University of Science and Technology)

  • Yung-ho Chiu

    (Soochow University)

  • Shih-Yung Chiu

    (Soochow University)

  • Tzu-Han Chang

    (Soochow University)

Abstract

Considering AQI and CO2 emission as an undesirable output in dynamic SBM DEA, we analyze the forest carbon sinks as an impact factor to explore the productivity efficiency of the 31 cities in China from 2013 to 2016. Our results found that the inclusion of forests and CO2 emission, some of the governance efficiency of urban indicators amended overestimated and underestimated efficiency score and ranking. According to our study, there are 6 cities with the best overall efficiency score of 1, including Beijing, Guangzhou, Nanjing, Nanning, Shanghai and Tianjin, and these cities all located along the coast. Overall average TFE scores of forests and fixed assets in the 31 cities are between 0.5453 and 0.5233. The scores are slowly improving year by year and still have considerable room for improvement. Most of urban’s forest governance efficiency stay low in long-term periods. The efficiency score of AQI is the worst in the urban governance. The severe air pollutant problem is not effectively improved for the long-term treatment.

Suggested Citation

  • Liang Chun Lu & Yung-ho Chiu & Shih-Yung Chiu & Tzu-Han Chang, 2022. "Do Forests help environmental development of Cities in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6602-6629, May.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:5:d:10.1007_s10668-021-01718-0
    DOI: 10.1007/s10668-021-01718-0
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    as
    1. Lansink, Alfons Oude & Reinhard, Stijn, 2004. "Investigating technical efficiency and potential technological change in Dutch pig farming," Agricultural Systems, Elsevier, vol. 79(3), pages 353-367, March.
    2. Li, Lan-Bing & Hu, Jin-Li, 2012. "Ecological total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 46(C), pages 216-224.
    3. R Ramanathan, 2005. "Estimating energy consumption of transport modes in India using DEA and application to energy and environmental policy," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 732-737, June.
    4. Fare, Rolf & Grosskopf, Shawna, 1996. "Productivity and intermediate products: A frontier approach," Economics Letters, Elsevier, vol. 50(1), pages 65-70, January.
    5. Wu, Jie & Lv, Lin & Sun, Jiasen & Ji, Xiang, 2015. "A comprehensive analysis of China's regional energy saving and emission reduction efficiency: From production and treatment perspectives," Energy Policy, Elsevier, vol. 84(C), pages 166-176.
    6. Park, Hojeong & Lim, Jaekyu, 2009. "Valuation of marginal CO2 abatement options for electric power plants in Korea," Energy Policy, Elsevier, vol. 37(5), pages 1834-1841, May.
    7. Grossman, G.M & Krueger, A.B., 1991. "Environmental Impacts of a North American Free Trade Agreement," Papers 158, Princeton, Woodrow Wilson School - Public and International Affairs.
    8. Zhang, Yingjie & Zhang, Tianzheng & Zeng, Yingxiang & Cheng, Baodong & Li, Hongxun, 2021. "Designating National Forest Cities in China: Does the policy improve the urban living environment?," Forest Policy and Economics, Elsevier, vol. 125(C).
    9. Teng, Xiangyu & Liu, Fan-peng & Chiu, Yung-ho, 2021. "The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model," Energy, Elsevier, vol. 216(C).
    10. Selden Thomas M. & Song Daqing, 1994. "Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?," Journal of Environmental Economics and Management, Elsevier, vol. 27(2), pages 147-162, September.
    11. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    12. Bi, Gong-Bing & Song, Wen & Zhou, P. & Liang, Liang, 2014. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model," Energy Policy, Elsevier, vol. 66(C), pages 537-546.
    13. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    14. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    17. Chi Chen & Taejin Park & Xuhui Wang & Shilong Piao & Baodong Xu & Rajiv K. Chaturvedi & Richard Fuchs & Victor Brovkin & Philippe Ciais & Rasmus Fensholt & Hans Tømmervik & Govindasamy Bala & Zaichun , 2019. "China and India lead in greening of the world through land-use management," Nature Sustainability, Nature, vol. 2(2), pages 122-129, February.
    18. Bhatti, M. Ishaq & Nguyen, Cuong C., 2012. "Diversification evidence from international equity markets using extreme values and stochastic copulas," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 622-646.
    19. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    20. Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
    21. Zhou, Peng & Poh, Kim Leng & Ang, Beng Wah, 2007. "A non-radial DEA approach to measuring environmental performance," European Journal of Operational Research, Elsevier, vol. 178(1), pages 1-9, April.
    22. Emrouznejad, Ali & Yang, Guo-liang, 2016. "CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index," Energy Policy, Elsevier, vol. 96(C), pages 397-410.
    23. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    24. Dyckhoff, H. & Allen, K., 2001. "Measuring ecological efficiency with data envelopment analysis (DEA)," European Journal of Operational Research, Elsevier, vol. 132(2), pages 312-325, July.
    25. Li, Ying & Chiu, Yung-ho & Lu, Liang Chun, 2018. "Energy and AQI performance of 31 cities in China," Energy Policy, Elsevier, vol. 122(C), pages 194-202.
    26. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    27. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    28. Al Rahahleh, Naseem & Bhatti, M. Ishaq, 2017. "Co-movement measure of information transmission on international equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 119-131.
    29. Guo, Xiaoying & Lu, Ching-Cheng & Lee, Jen-Hui & Chiu, Yung-Ho, 2017. "Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China," Energy, Elsevier, vol. 134(C), pages 392-399.
    30. Zhang, Yufei & Chen, Jiayin & Han, Yi & Qian, Mengxi & Guo, Xiaona & Chen, Ruishan & Xu, Di & Chen, Yi, 2021. "The contribution of Fintech to sustainable development in the digital age: Ant forest and land restoration in China," Land Use Policy, Elsevier, vol. 103(C).
    31. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    32. Li, Ke & Lin, Boqiang, 2015. "Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China," Energy Economics, Elsevier, vol. 48(C), pages 230-241.
    33. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2012. "A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs?," Energy Policy, Elsevier, vol. 46(C), pages 574-584.
    34. Chen, Chien-Ming, 2009. "A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks," European Journal of Operational Research, Elsevier, vol. 194(3), pages 687-699, May.
    35. Li, Yi & Sun, Linyan & Feng, Taiwen & Zhu, Chunyan, 2013. "How to reduce energy intensity in China: A regional comparison perspective," Energy Policy, Elsevier, vol. 61(C), pages 513-522.
    36. Parker, Steven & Bhatti, M. Ishaq, 2020. "Dynamics and drivers of per capita CO2 emissions in Asia," Energy Economics, Elsevier, vol. 89(C).
    37. Shafik, Nemat & Bandyopadhyay, Sushenjit, 1992. "Economic growth and environmental quality : time series and cross-country evidence," Policy Research Working Paper Series 904, The World Bank.
    38. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    39. Guo, Xiao-Dan & Zhu, Lei & Fan, Ying & Xie, Bai-Chen, 2011. "Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA," Energy Policy, Elsevier, vol. 39(5), pages 2352-2360, May.
    40. Satoshi Honma & Jin-Li Hu, 2018. "A meta-stochastic frontier analysis for energy efficiency of regions in Japan," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-16, December.
    41. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    42. Fan, Meiting & Shao, Shuai & Yang, Lili, 2015. "Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)," Energy Policy, Elsevier, vol. 79(C), pages 189-201.
    43. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
    44. Park, K. Sam & Park, Kwangtae, 2009. "Measurement of multiperiod aggregative efficiency," European Journal of Operational Research, Elsevier, vol. 193(2), pages 567-580, March.
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