JOURNAL ARTICLE

Research on Ecological Restoration Strategies for Abandoned Mines Based on Ecology-Oriented Development.

  • Published In: Decision Analysis (INFORMS), 2025, v. 22, n. 1. P. 44 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Wang, Ning; Tan, Deqing 3 of 3

Abstract

This article focuses on the ecological restoration of abandoned mines in China within the framework of ecology-oriented development (EOD), employing a differential game model involving government and enterprise players. It analyzes four modes—cooperative, noncooperative without policy, noncooperative with ecological restoration compensation, and noncooperative with industrial support—examining investment levels, restoration quality, industry development, pricing strategies, and profit dynamics. Key findings indicate that extending the government’s concession period incentivizes greater enterprise investment in ecological restoration and environmentally sensitive industries; the cooperative mode yields the highest overall benefits; and differentiated government policies (ecological restoration compensation or industrial support) effectively promote enterprise inputs depending on industry characteristics. The study offers policy recommendations emphasizing concession period extension, preference for cooperative modes to attract social capital, and tailored subsidy policies to enhance restoration and industrial development efforts.

Additional Information

  • Source:Decision Analysis (INFORMS). 2025/03, Vol. 22, Issue 1, p44
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2025
  • ISSN:1545-8490
  • DOI:10.1287/deca.2023.0132
  • Accession Number:183294303
  • Copyright Statement:Copyright of Decision Analysis (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.