Skip to main content
SpringerLink
Log in

Analysis of resource allocation and environmental performance in China’s three major urban agglomerations

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

This study focuses on the analysis of optimal energy allocation and environmental performance of China’s three major urban agglomerations. Specifically, this paper first uses a fixed-input DEA model to obtain the optimal allocation of energy input. Then, an evaluation model based on the optimal allocation of energy input is proposed to evaluate environmental performance. Finally, these models are applied to the empirical analysis of the three major urban agglomerations in China. This article mainly draws the following conclusions. First, energy is wasted in most cities in the Beijing-Tianjin-Hebei region. Second, from 2012 to 2016, the environmental performance of each urban agglomeration showed a downward trend and then an upward trend. Third, the overall environmental performance of the three major urban agglomerations is not high. Some specific regions, such as Handan, Anyang, Xingtai, and Baoding, behave not well in protecting the environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Adler N, Volta N (2016) Accounting for externalities and disposability: a directional economic environmental distance function. Eur J Oper Res 250(1):314–327

    Google Scholar 

  • Al-Mansour F (2011) Energy efficiency trends and policy in Slovenia. Energy 36(4):1868–1877

    Google Scholar 

  • Bian Y, Yang F (2010) Resource and environment efficiency analysis of provinces in China: a DEA approach based on Shannon’s entropy. Energy Policy 38(4):1909–1917

    Google Scholar 

  • Chang YT, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58:277–283

    Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Google Scholar 

  • Chen N, Xu L, Chen Z (2017) Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models. Energy 134:659–671

    Google Scholar 

  • Chen L, Xu L, Yang Z (2019) Inequality of industrial carbon emissions of the urban agglomeration and its peripheral cities: a case in the Pearl River Delta, China. Renew Sust Energ Rev 109:438–447

    Google Scholar 

  • Choi Y, Zhang N, Zhou P (2012) Efficiency and abatement costs of energy-related CO2 emissions in China: a slacks-based efficiency measure. Appl Energy 98:198–208

    Google Scholar 

  • Deng W, Peng Z, Tang Y (2019) A quick assessment method to evaluate sustainability of urban built environment: case studies of four large-sized Chinese cities. Cities 89:57–69

    Google Scholar 

  • Feng C, Chu F, Ding J, Bi G, Liang L (2015) Carbon emissions abatement (CEA) allocation and compensation schemes based on DEA. Omega 53:78–89

    Google Scholar 

  • Geng Z, Dong J, Han Y, Zhu Q (2017) Energy and environment efficiency analysis based on an improved environment DEA cross-model: case study of complex chemical processes. Appl Energy 205:465–476

    CAS  Google Scholar 

  • Giannakitsidou O, Giannikos I, Chondrou A (2020) Ranking European countries on the basis of their environmental and circular economy performance: a DEA application in MSW. Waste Manag 109:181–191

    Google Scholar 

  • Hao Y, Deng Y, Lu Z, Chen H (2018) Is environmental regulation effective in China? Evidence from city-level panel data. J Clean Prod 188:966–976

    Google Scholar 

  • Hu JL, Lio MC, Yeh FY, Lin CH (2011) Environment-adjusted regional energy efficiency in Taiwan. Appl Energy 88(8):2893–2899

    Google Scholar 

  • Kuosmanen T (2005) Weak disposability in nonparametric production analysis with undesirable outputs. Am J Agric Econ 87(4):1077–1082

    Google Scholar 

  • Lee CY, Zhou P (2015) Directional shadow price estimation of CO2, SO2 and NOx in the United States coal power industry 1990–2010. Energy Econ 51:493–502

    Google Scholar 

  • Li Z, Ouyang X, Du K, Zhao Y (2017) Does government transparency contribute to improved eco-efficiency performance? An empirical study of 262 cities in China. Energy Policy 110:79–89

    Google Scholar 

  • Li Y, Wang L, Li F (2019a) A data-driven prediction approach for sports team performance and its application to National Basketball Association. Omega. https://doi.org/10.1016/j.omega.2019.102123

  • Li G, Sun J, Wang Z (2019b) Exploring the energy consumption rebound effect of industrial enterprises in the Beijing–Tianjin–Hebei region. Energy Efficiency 12(4):1007–1026

    Google Scholar 

  • Li F, Yan Z, Zhu Q, Yin M, Kou G (2020) Allocating a fixed cost across decision making units with explicitly considering efficiency rankings. J Oper Res Soc. https://doi.org/10.1080/01605682.2020.1718561

  • Liu H, Zhang Y, Zhu Q, Chu J (2017) Environmental efficiency of land transportation in China: a parallel slack-based measure for regional and temporal analysis. J Clean Prod 142:867–876

    CAS  Google Scholar 

  • Miao Z, Geng Y, Sheng J (2016) Efficient allocation of CO2 emissions in China: a zero sum gains data envelopment model. J Clean Prod 112:4144–4150

    CAS  Google Scholar 

  • Qi T, Zhang X, Karplus VJ (2014) The energy and CO2 emissions impact of renewable energy development in China. Energy Policy 68:60–69

    Google Scholar 

  • Shi GM, Bi J, Wang JN (2010) Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy 38(10):6172–6179

    Google Scholar 

  • Simar L, Wilson PW (1999) Estimating and bootstrapping Malmquist indices. Eur J Oper Res 115(3):459–471

    Google Scholar 

  • Song M, Wang S (2017) Participation in global value chain and green technology progress: evidence from big data of Chinese enterprises. Environ Sci Pollut Res 24(2):1648–1661

    Google Scholar 

  • Song M, Chen Y, An Q (2018) Spatial econometric analysis of factors influencing regional energy efficiency in China. Environ Sci Pollut Res 25(14):13745–13759

    Google Scholar 

  • Song M, Peng J, Wang J, Zhao J (2018a) Environmental efficiency and economic growth of China: a ray slack-based model analysis. Eur J Oper Res 269(1):51–63

    Google Scholar 

  • Song M, Xie Q, Wang S, Zhou L (2020a) Intensity of environmental regulation and environmentally biased technology in the employment market. Omega:102201

  • Song M, Xie Q, Wang S, Zhang H (2020b) Could environmental regulation and R&D tax incentives affect green product innovation? J Clean Prod 120849

  • Sun J, Wang Z, Li G (2018b) Measuring emission-reduction and energy-conservation efficiency of Chinese cities considering management and technology heterogeneity. J Clean Prod 175:561–571

    Google Scholar 

  • Sun J, Li G, Wang Z (2018a) Optimizing China’s energy consumption structure under energy and carbon constraints. Struct Chang Econ Dyn 47:57–72

    Google Scholar 

  • Sun J, Li G, Wang Z (2019) Technology heterogeneity and efficiency of China’s circular economic systems: a game meta-frontier DEA approach. Resour Conserv Recycl 146:337–347

    Google Scholar 

  • Sun J, Xu S, Li G (2020) Analyzing sustainable power supply chain performance. J Enterp Inf Manag

  • Tan B, Yavuz Y, Otay EN, Çamlıbel E (2016) Optimal selection of energy efficiency measures for energy sustainability of existing buildings. Comput Oper Res 66:258–271

    Google Scholar 

  • Wang Y, Wang J (2019) Does industrial agglomeration facilitate environmental performance: new evidence from urban China? J Environ Manag 248:109244

    Google Scholar 

  • Wang Q, Zhou P, Zhou D (2012) Efficiency measurement with carbon dioxide emissions: the case of China. Appl Energy 90(1):161–166

    Google Scholar 

  • Wang K, Yu S, Zhang W (2013a) China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math Comput Model 58(5–6):1117–1127

    CAS  Google Scholar 

  • Wang K, Zhang X, Wei YM, Yu S (2013b) Regional allocation of CO2 emissions allowance over provinces in China by 2020. Energy Policy 54:214–229

    Google Scholar 

  • Wang Q, Chiu YH, Chiu CR (2017) Non-radial metafrontier approach to identify carbon emission performance and intensity. Renew Sust Energ Rev 69:664–672

    CAS  Google Scholar 

  • Wang Z, Wang X, Liang L (2019) Green economic efficiency in the Yangtze river delta: spatiotemporal evolution and influencing factors. Ecosyst Health Sustain 5:20–35. https://doi.org/10.1080/20964129.2018.1559000

    Article  Google Scholar 

  • Wang K, Mi Z, Wei YM (2019a) Will pollution taxes improve joint ecological and economic efficiency of thermal power industry in China?: a DEA-based materials balance approach. J Ind Ecol 23(2):389–401

    Google Scholar 

  • Wang K, Wu M, Sun Y, Shi X, Sun A, Zhang P (2019b) Resource abundance, industrial structure, and regional carbon emissions efficiency in China. Res Policy 60:203–214

    Google Scholar 

  • Wang S, Xing L, Chen H (2020) Impact of marine industrial structure on environmental efficiency. Manag Environ Qual 31(1):111–129

    Google Scholar 

  • Wu J, Lv L, Sun J, Ji X (2015) A comprehensive analysis of China’s regional energy saving and emission reduction efficiency: from production and treatment perspectives. Energy Policy 84:166–176

    Google Scholar 

  • Wu J, Zhang W, Zhou Z (2019) Construction resource allocation for industrial solid waste treatment centers in cities of Anhui Province, China. Manag Environ Qual 30(5):1190–1202

    Google Scholar 

  • Yin JY, Cao YF, Tang BJ (2019) Fairness of China’s provincial energy environment efficiency evaluation: empirical analysis using a three-stage data envelopment analysis model. Nat Hazards 95(1–2):343–362

    Google Scholar 

  • Yu A, You J, Rudkin S, Zhang H (2019) Industrial carbon abatement allocations and regional collaboration: re-evaluating China through a modified data envelopment analysis. Appl Energy 233:232–243

    Google Scholar 

  • Zha Y, Zhao L, Bian Y (2016) Measuring regional efficiency of energy and carbon dioxide emissions in China: a chance constrained DEA approach. Comput Oper Res 66:351–361

    Google Scholar 

  • Zhang N, Choi Y (2013) Total-factor carbon emission performance of fossil fuel power plants in China: a metafrontier non-radial Malmquist index analysis. Energy Econ 40:549–559

    Google Scholar 

  • Zhang B, Lu D, He Y, Chiu YH (2018) The efficiencies of resource-saving and environment: a case study based on Chinese cities. Energy 150:493–507

    Google Scholar 

  • Zhao H, Guo S, Zhao H (2019) Provincial energy efficiency of China quantified by three-stage data envelopment analysis. Energy 166:96–107

    Google Scholar 

  • Zhou PABW, Ang BW, Poh KL (2006) Slacks-based efficiency measures for modeling environmental performance. Ecol Econ 60(1):111–118

    Google Scholar 

  • Zhou P, Ang BW, Han JY (2010) Total factor carbon emission performance: a Malmquist index analysis. Energy Econ 32(1):194–201

    Google Scholar 

  • Zhou Z, Liu C, Zeng X, Jiang Y, Liu W (2018) Carbon emission performance evaluation and allocation in Chinese cities. J Clean Prod 172:1254–1272

    Google Scholar 

  • Zhu Q, Li X, Li F, Zhou D (2020a) The potential for energy saving and carbon emission reduction in China’s regional industrial sectors. Sci Total Environ 716:135009

    CAS  Google Scholar 

  • Zhu Q, Song M, Wu J (2020b) Extended secondary goal approach for common equilibrium efficient frontier selection in DEA with fixed-sum outputs. Comput Ind Eng 144:106483

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the editor and anonymous reviewers for their insightful comments and suggestions. This research was financially supported by the National Natural Science Foundationof China, China (Grants 71871153, 71904084, 71501139), the Natural Science Foundation for Jiangsu Province (Grant BK20190427), the Social Science Foundation of Jiangsu Province (Grant 19GLC017), and theFundamental Research Funds for the Central Universities (Grant NR2019003), and the Innovation and Entrepreneurship Foundation for Doctor of Jiangsu Province.

Author information

Authors and Affiliations

  1. Research Center for Smarter Supply Chain, School of Business & Dongwu Think Tank, Soochow University, Suzhou, China

    Jiasen Sun & Zheng Wang

  2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing, 211106, People’s Republic of China

    Qingyuan Zhu

  3. Research Center for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing, 211106, China

    Qingyuan Zhu

Authors
  1. Jiasen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingyuan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingyuan Zhu.

Additional information

Responsible editor: Nicholas Apergis

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, J., Wang, Z. & Zhu, Q. Analysis of resource allocation and environmental performance in China’s three major urban agglomerations. Environ Sci Pollut Res 27, 34289–34299 (2020). https://doi.org/10.1007/s11356-020-09665-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-020-09665-5

Keywords

Search

Navigation