Abstract
With the rapid development of China’s industrial sector, solid waste emissions have exploded along with the mismatch between treatment efficiency and economic development becoming increasingly prominent. The Yangtze River Economic Belt (YREB) is one of the most important core areas for the country to participate in economic globalization. Its expansive area and intensive industrial distribution mean more severe challenges for the treatment of industrial solid waste. Through the dynamic data envelopment analysis (DEA) method, this research selects 30 provinces to evaluate the efficiency of industrial solid waste treatment in YREB and non-Yangtze River Economic Belt (NYREB) and discusses the relationships among input, production, re-use, and disposal of industrial solid waste in the two regions. Findings show that the average efficiency of NYREB in recent years for many provinces shows a downward trend and an unstable solid waste treatment effect. Most average efficiency values of solid waste treatment in YREB provinces concentrate at a higher level, but the overall trend is positive. Finally, we note clear and polarizing differences between the two regions.
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Notes
Data source: Ministry of Ecology and Environment of the People's Republic of China.
Data source: https://cn.chinadaily.com.cn/.
Law of the People’s Republic of China on the prevention and control of environmental pollution by solid waste (revised on November 7, 2016).
Abbreviations
- YREB:
-
Yangtze River Economic Belt
- NYREB:
-
Non-Yangtze River Economic Belt
- MSW:
-
Municipal solid waste
- DMUs:
-
Decision-making units
- D-DEA:
-
Dynamic DEA
- SBMs:
-
Slack-based measures
- OE:
-
Overall efficiency
- TE:
-
Term efficiency
- DMU:
-
Decision-making unit
- DEA:
-
Data envelopment analysis
- CCRI:
-
Charnes and Cooper and Rhodes input orientation [1]
- CCRO:
-
Banker and Charnes and Cooper output orientation [2]
References
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
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Xi JP (2018) Restoring the ecological environment of the Yangtze River is an overwhelming priority. http://www.gov.cn/xinwen/2018-10/17/content_5331623.htm. Accessed 17 Oct
Mohee R, Mauthoor S, Bundhoo ZM et al (2015) Current status of solid waste management in small island developing states: a review. Waste Manag 43:539–549. https://doi.org/10.1016/j.wasman.2015.06.012
Guo W, Xi BD, Huang CH et al (2021) Solid waste management in China: policy and driving factors in 2004–2019. Resour Conserv Recycl 173:105727. https://doi.org/10.1016/j.resconrec.2021.105727
Vyas S, Prajapati P, Shah AV et al (2022) Municipal solid waste management: dynamics, risk assessment, ecological influence, advancements, constraints and perspectives. Sci Total Environ 814:152802. https://doi.org/10.1016/j.scitotenv.2021.152802
Komilis DP, Liogkas V (2014) Full cost accounting on existing and future municipal solid waste management facilities in Greece. Global NEST J 16(4):787–796
Aleluia J, Ferrão P (2017) Assessing the costs of municipal solid waste treatment technologies in developing Asian countries. Waste Manag 69:592–608. https://doi.org/10.1016/j.wasman.2017.08.047
Chifari R, Lo Piano S, Matsumoto S et al (2017) Does recyclable separation reduce the cost of municipal waste management in Japan? Waste Manag 60:32–41. https://doi.org/10.1016/j.wasman.2017.01.015
Foggia GD, Beccarello M (2020) Drivers of municipal solid waste management cost based on cost models inherent to sorted and unsorted waste. Waste Manag 114:202–214. https://doi.org/10.1016/j.wasman.2020.07.012
Tong X, Yu HF, Liu T (2021) Using weighted entropy to measure the recyclability of municipal solid waste in China: exploring the geographical disparity for circular economy. J Clean Prod 312:127719. https://doi.org/10.1016/j.jclepro.2021.127719
Klopp G (1985) The analysis of the efficiency of production system with multiple inputs and outputs. The University of Illinois at Chicago, Industrial and Systems Engineering College, Chicago, IL
Färe R, Grosskopf S (1994) Cost and revenue constrained production. Springer, New York. https://doi.org/10.1007/978-1-4612-2626-0
Färe R, Grosskopf S (1996) Productivity and intermediate products: a frontier approach. Econ Lett 50(1):65–70. https://doi.org/10.1016/0165-1765(95)00729-6
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
Mohamed SR, Ghazali NF, Mohd AH (2017) The input and output management of solid waste using Dea models: a case study at Jengka, Pahang. In: Proceedings of the 24th national symposium o mathematical sciences. AIP Conference Proceedings 1870(1):040057. https://doi.org/10.1063/1.4995889
Rogge N, Jaeger SD (2012) Evaluating the efficiency of municipalities in collecting and processing municipal solid waste: a shared input DEA-model. Waste Manag 32(10):1968–1978. https://doi.org/10.1016/j.wasman.2012.05.021
Rogge N, Jaeger SD (2013) Measuring and explaining the cost efficiency of municipal solid waste collection and processing services. Omega 41(4):653–664. https://doi.org/10.1016/j.omega.2012.09.006
Zhou J, Zhang R (2019) Efficiency evaluation of industrial solid waste recycling utilization based on improved DEA model. IOP Conf Ser Earth Environ Sci 295(3):032024. https://doi.org/10.1088/1755-1315/295/3/032024
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. https://doi.org/10.1016/j.enpol.2013.03.011
Iftikhar Y, He WJ, Wang ZH (2016) Energy and CO2 emissions efficiency of major economies: a non-parametric analysis. J Clean Prod 139:779–787. https://doi.org/10.1016/j.jclepro.2016.08.072
Hu JL, Wang SC (2006) Total-factor energy efficiency of regions in China. Energy Policy 34(17):3206–3217. https://doi.org/10.1016/j.enpol.2005.06.015
Tang JX, Wang QW, Choi GY (2020) Efficiency assessment of industrial solid waste generation and treatment processes with carry-over in China. Sci Total Environ 726:1–11. https://doi.org/10.1016/j.scitotenv.2020.138274
Li D, Wang MQ, Lee C (2020) The waste treatment and recycling efficiency of industrial waste processing based on two-stage data envelopment analysis with undesirable inputs. J Clean Prod 242:118279. https://doi.org/10.1016/j.jclepro.2019.118279
Funding
This study was supported by the Later Funded Projects of National Social Science Foundation (21FJYB047) and the Fundamental Research Funds for the Central Universities (B210207018).
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Conceptualization, F-RR; methodology, F-RR; software, ZT; validation K-JC; formal analysis, K-JC; investigation, F-RR and K-JC; resources, ZT; data curation, F-RR; writing—original draft preparation, YZ; writing—review and editing, K-JC and YZ; visualization, YZ; supervision, ZT; project administration, F-RR; funding acquisition, F-RR.
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Ren, Fr., Chen, Kj., Tian, Z. et al. The investment and treatment efficiencies of industrial solid waste in China’s Yangtze and non-Yangtze River Economic Belts. J Mater Cycles Waste Manag 24, 900–916 (2022). https://doi.org/10.1007/s10163-022-01364-2
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DOI: https://doi.org/10.1007/s10163-022-01364-2