A non-convex metafrontier DEA model with natural and managerial disposability for pollutant tax levels and environmental efficiencies analysis

Zhongbao Zhou, Wenting Sun, Helu Xiao, Qianying Jin and Wenbin Liu

Journal of the Operational Research Society, 2022, vol. 73, issue 10, 2294-2308

Abstract: Efficiency analysis is an important step in the environmental performance evaluation research, especially in the context of the implementation of the Environmental Protection Tax Law in China. In this paper, we first consider regional heterogeneity and divide regions into high, medium, and low-cost groups based on pollution tax levels. Then, we construct a non-convex metafrontier Data Envelopment Analysis (DEA) model that can solve the heterogeneity problem well and measure the environmental efficiency of each region. In addition to considering natural disposability, we further discuss managerial disposability here. The empirical results show the difference between the efficiency and the benchmark test based on the convex or non-convex metafrontier assumption. Besides, middle and low-cost groups are encouraged to increasing environmental protection expenditure under managerial disposability. Finally, we summarize the improvement methods of provincial administrative regions under different tax rates: high-cost groups develop a green environmental economy and control pollution emissions; middle-level groups optimize industrial structure and increase investment in technological innovation; the low-tax groups increase the tax on pollutants and strengthen government control.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2021.1979903 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:73:y:2022:i:10:p:2294-2308

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2021.1979903

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().