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Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach

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  • Jie Wu
  • Qingyuan Zhu
  • Junfei Chu
  • Liang Liang

Abstract

The rapid development in economy of China has intensified the country’s many problems, such as environmental pollution and energy shortage. Thus, establishing a society with resource conservation and environmental harmony, typically reusing the pollution and waste from the industrial production, has attracted attention from both the government and the public in recent years. Data envelopment analysis (DEA) has been widely used in measuring two-stage network structures that constituted with homogenous decision making units. However, previous works failed to take the undesirable intermediate products into account in the two-stage network structures including production system and disposal system. In this study, we build an additive DEA approach to evaluate the efficiency of the proposed new two-stage network structures, and propose a better efficiency decomposition to the individual system. Finally, our approach is applied to analyze the industrial production in 30 provincial level regions in mainland China and some implications are given. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Jie Wu & Qingyuan Zhu & Junfei Chu & Liang Liang, 2015. "Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 455-477, October.
    • Handle: RePEc:kap:compec:v:46:y:2015:i:3:p:455-477
    DOI: 10.1007/s10614-015-9498-3
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    2. Xiaohong Zhuang & Zhuyuan Li & Run Zheng & Sanggyun Na & Yulin Zhou, 2021. "Research on the Efficiency and Improvement of Rural Development in China: Based on Two-Stage Network SBM Model," Sustainability, MDPI, vol. 13(5), pages 1-21, March.
    3. Liu, Hongwei & Wu, Jie & Chu, Junfei, 2019. "Environmental efficiency and technological progress of transportation industry-based on large scale data," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 475-482.
    4. Jie Wu & Panpan Xia & Qingyuan Zhu & Junfei Chu, 2019. "Measuring environmental efficiency of thermoelectric power plants: a common equilibrium efficient frontier DEA approach with fixed-sum undesirable output," Annals of Operations Research, Springer, vol. 275(2), pages 731-749, April.
    5. Hongwei Liu & Ronglu Yang & Zhixiang Zhou & Dacheng Huang, 2020. "Regional Green Eco-Efficiency in China: Considering Energy Saving, Pollution Treatment, and External Environmental Heterogeneity," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    6. Xiping Wang & Moyang Li, 2019. "The Spatial Spillover Effects of Environmental Regulation on China’s Industrial Green Growth Performance," Energies, MDPI, vol. 12(2), pages 1-13, January.
    7. Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    8. Jie Wu & Dacheng Huang & Zhixiang Zhou & Qingyuan Zhu, 2020. "The regional green growth and sustainable development of China in the presence of sustainable resources recovered from pollutions," Annals of Operations Research, Springer, vol. 290(1), pages 27-45, July.
    9. Jie Wu & Qingyuan Zhu & Pengzhen Yin & Malin Song, 2017. "Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices," Operational Research, Springer, vol. 17(3), pages 715-735, October.
    10. Xiaohong Liu & Qingyuan Zhu & Junfei Chu & Xiang Ji & Xingchen Li, 2019. "Environmental Performance and Benchmarking Information for Coal-Fired Power Plants in China: A DEA Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1287-1302, December.
    11. Qiong Xia & Min Li & Huaqing Wu & Zhenggang Lu, 2016. "Does the Central Government’s Environmental Policy Work? Evidence from the Provincial-Level Environment Efficiency in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    12. Junfei Chu & Jie Wu & Qingyuan Zhu & Qingxian An & Beibei Xiong, 2019. "Analysis of China’s Regional Eco-efficiency: A DEA Two-stage Network Approach with Equitable Efficiency Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1263-1285, December.
    13. Maryam Nematizadeh & Alireza Amirteimoori & Sohrab Kordrostami, 2019. "Performance analysis of two-stage network processes with feedback flows and undesirable factors," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(3), pages 51-66.
    14. Yanhong Tang & Yingwen Chen & Rui Yang & Xin Miao, 2020. "The Unified Efficiency Evaluation of China’s Industrial Waste Gas Considering Pollution Prevention and End-Of-Pipe Treatment," IJERPH, MDPI, vol. 17(16), pages 1-27, August.

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