IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12076-d923911.html
   My bibliography  Save this article

Assessment of Urban Green Development Efficiency Based on Three-Stage DEA: A Case Study from China’s Yangtze River Delta

Author

Listed:
  • Qi Yang

    (School of Public Administration, Hohai University, Nanjing 211100, China)

  • Zhonggen Sun

    (School of Public Administration, Hohai University, Nanjing 211100, China)

  • Hubiao Zhang

    (School of Public Administration, Hohai University, Nanjing 211100, China)

Abstract

With the march of global urbanization, there are looming problems including environmental degradation and remediation all over the world. In this case, urban green development is the key to overcoming climate crisis, biodiversity loss and pollution. In this paper, a three-stage DEA model was employed to study the urban green development efficiency (GDE), with cities in the Yangtze River Delta (YRD) as the object. In the study, the regional economic foundation, urbanization level, industrial structure and government planning were used as external environmental variables, and the impact of objective external environmental factors was tested empirically, thereby eliminating the adverse environmental impact and statistical noise to obtain more truthful GDE. According to the results, first, the influence of external environmental factors and stochastic disturbance on GDE was effectively removed by virtue of the three-stage DEA model, and the GDE of the YRD was measured in a true and objective manner. The GDE of the YRD in Stage III was notably higher than that in Stage I since the GDE in Stage I was underestimated under the influence of objective environmental variables. Second, the GDE level showed heterogeneity in different cities, which behaved better in coastal and southeastern regions than in central, western and northern regions. Third, regarding the impact of external environmental variables, the GDE was enhanced by increasing the proportion of the tertiary industry and the green area of built districts but weakened when the area of built districts (ABD) reflecting urban construction was expanded. The index gross regional product (GRP) reflects local economic development level, the impact of which on GDE was not determined in this paper. As a consequence, in the process of urban development, it is suggested to focus on the innovation and application of green technology, upgrade the industrial structure, cultivate green talents, and formulate reasonable green transformation policies.

Suggested Citation

  • Qi Yang & Zhonggen Sun & Hubiao Zhang, 2022. "Assessment of Urban Green Development Efficiency Based on Three-Stage DEA: A Case Study from China’s Yangtze River Delta," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12076-:d:923911
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12076/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12076/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhu, Bangzhu & Zhang, Mengfan & Zhou, Yanhua & Wang, Ping & Sheng, Jichuan & He, Kaijian & Wei, Yi-Ming & Xie, Rui, 2019. "Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach," Energy Policy, Elsevier, vol. 134(C).
    2. Zhou, Xiaoyang & Xu, Zhongwen & Chai, Jian & Yao, Liming & Wang, Shouyang & Lev, Benjamin, 2019. "Efficiency evaluation for banking systems under uncertainty: A multi-period three-stage DEA model," Omega, Elsevier, vol. 85(C), pages 68-82.
    3. Xin Yang & Guangyin Shang, 2020. "Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model," IJERPH, MDPI, vol. 17(20), pages 1-12, October.
    4. Zhang, Chunhong & Liu, Haiying & Bressers, Hans Th.A. & Buchanan, Karen S., 2011. "Productivity growth and environmental regulations - accounting for undesirable outputs: Analysis of China's thirty provincial regions using the Malmquist–Luenberger index," Ecological Economics, Elsevier, vol. 70(12), pages 2369-2379.
    5. Xiang Liu & Jia Liu, 2016. "Measurement of Low Carbon Economy Efficiency with a Three-Stage Data Envelopment Analysis: A Comparison of the Largest Twenty CO 2 Emitting Countries," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
    6. 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.
    7. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    8. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    9. Yang, Yuying & Guo, Haixiang & Chen, Linfei & Liu, Xiao & Gu, Mingyun & Ke, Xiaoling, 2019. "Regional analysis of the green development level differences in Chinese mineral resource-based cities," Resources Policy, Elsevier, vol. 61(C), pages 261-272.
    10. Dian Li & Ziheng Shangguan & Malan Huang & Xinyue Zhang & Lu Tang, 2022. "Impacts of Urban Development on Regional Green Development Efficiency—A Case of the Yangtze River Delta in China," Energies, MDPI, vol. 15(13), pages 1-18, June.
    11. Susmita Dasgupta & Benoit Laplante & Hua Wang & David Wheeler, 2002. "Confronting the Environmental Kuznets Curve," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 147-168, Winter.
    12. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    13. 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.
    14. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    15. Yikun Su & Hong Xue & Huakang Liang, 2019. "An Evaluation Model for Urban Comprehensive Carrying Capacity: An Empirical Case from Harbin City," IJERPH, MDPI, vol. 16(3), pages 1-25, January.
    16. Walter Musakwa & Adriaan Niekerk, 2015. "Monitoring sustainable urban development using built-up area indicators: a case study of Stellenbosch, South Africa," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 17(3), pages 547-566, June.
    17. 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.
    18. Cook, Wade D. & Zhu, Joe, 2007. "Classifying inputs and outputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 180(2), pages 692-699, July.
    19. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
    20. R G Dyson & E A Shale, 2010. "Data envelopment analysis, operational research and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 25-34, January.
    21. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weixin Yang & Yue Hu & Qinyi Ding & Hao Gao & Lingguang Li, 2023. "Comprehensive Evaluation and Comparative Analysis of the Green Development Level of Provinces in Eastern and Western China," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    2. MATEI Gheorghe, 2023. "Spatial Efficiency Of Romaniaʼs Development Regions From The Perspective Of Sustainable Development," Management of Sustainable Development, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 15(2), pages 19-27, December.
    3. Ming Chen & Lina Song & Xiaobo Zhu & Yanshuo Zhu & Chuanhao Liu, 2023. "Does Green Finance Promote the Green Transformation of China’s Manufacturing Industry?," Sustainability, MDPI, vol. 15(8), pages 1-22, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    2. Qingxian An & Xiangyang Tao & Bo Dai & Jinlin Li, 2020. "Modified Distance Friction Minimization Model with Undesirable Output: An Application to the Environmental Efficiency of China’s Regional Industry," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1047-1071, April.
    3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    4. Ying Li & Yung-Ho Chiu & Tai-Yu Lin & Tzu-Han Chang, 2020. "Pre-Evaluating the Technical Efficiency Gains from Potential Mergers and Acquisitions in the IC Design Industry," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 525-559, April.
    5. Trinks, Arjan & Mulder, Machiel & Scholtens, Bert, 2020. "An Efficiency Perspective on Carbon Emissions and Financial Performance," Ecological Economics, Elsevier, vol. 175(C).
    6. Andreas Eder & Bernhard Mahlberg, 2018. "Size, Subsidies and Technical Efficiency in Renewable Energy Production: The Case of Austrian Biogas Plants," The Energy Journal, , vol. 39(1), pages 185-210, January.
    7. Andreas Eder & Bernhard Mahlberg & Bernhard Stürmer, 2021. "Measuring and explaining productivity growth of renewable energy producers: An empirical study of Austrian biogas plants," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(1), pages 37-63, February.
    8. Ruiz, José L. & Segura, José V. & Sirvent, Inmaculada, 2015. "Benchmarking and target setting with expert preferences: An application to the evaluation of educational performance of Spanish universities," European Journal of Operational Research, Elsevier, vol. 242(2), pages 594-605.
    9. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    10. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    11. Victoria Wojcik & Harald Dyckhoff & Marcel Clermont, 2019. "Is data envelopment analysis a suitable tool for performance measurement and benchmarking in non-production contexts?," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 559-595, December.
    12. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    13. Gouveia, M.C. & Dias, L.C. & Antunes, C.H. & Boucinha, J. & Inácio, C.F., 2015. "Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method," Omega, Elsevier, vol. 53(C), pages 104-114.
    14. Si-Si Dong & Liang-Qun Qi & Jia-Quan Li, 2022. "Evaluation of the Implementation Effect of China’s Industrial Sector Supply-Side Reform: From the Perspective of Energy and Environmental Efficiency," Energies, MDPI, vol. 15(9), pages 1-17, April.
    15. Wen-Chih Chen, 2021. "On performance evaluation with a dual-role factor," Annals of Operations Research, Springer, vol. 304(1), pages 63-84, September.
    16. Silvia Saravia-Matus & T. S. Amjath-Babu & Sreejith Aravindakshan & Stefan Sieber & Jimmy A. Saravia & Sergio Gomez y Paloma, 2021. "Can Enhancing Efficiency Promote the Economic Viability of Smallholder Farmers? A Case of Sierra Leone," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    17. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    18. Amar Oukil & Slim Zekri, 2021. "Investigating farming efficiency through a two stage analytical approach: Application to the agricultural sector in Northern Oman," Papers 2104.10943, arXiv.org.
    19. Tarnaud, Albane Christine & Leleu, Hervé, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Omega, Elsevier, vol. 75(C), pages 57-76.
    20. Angela Stefania Bergantino & Enrico Musso, 2011. "A Multi-step Approach to Model the Relative Efficiency of European Ports: The Role of Regulation and Other Non-discretionary Factors," Chapters, in: Kevin Cullinane (ed.), International Handbook of Maritime Economics, chapter 18, Edward Elgar Publishing.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12076-:d:923911. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.