资源科学 ›› 2019, Vol. 41 ›› Issue (8): 1513-1525.doi: 10.18402/resci.2019.08.11

• 资源管理 • 上一篇    下一篇

湖北省绿色矿山建设影响因素及其效果分析

王永卿1,王来峰2,邓洪星3,董凯4   

  1. 1.中国地质大学(武汉)资源学院,武汉 430074
    2.中国地质大学(武汉)经济管理学院,武汉 430074
    3.广西壮族自治区地质环境监测总站,南宁 530029
    4.南宁师范大学自然资源与测绘学院,南宁 530001
  • 收稿日期:2019-01-30 修回日期:2019-07-12 出版日期:2019-08-28 发布日期:2019-08-21
  • 作者简介:王永卿,女,山东临沂人,博士生,主要研究领域为资源经济评价体系及应用。E-mail: 752784236@qq.com
  • 基金资助:
    国家社会科学基金项目(15CJY012);湖北省国土资源厅项目(2016296177);广西高校中青年教师基础能力提升项目(2019KY0401)

Influencing factors and performance evaluation of green mining in Hubei Province

Yongqing WANG1,Laifeng WANG2,Hongxing DENG3,Kai DONG4   

  1. 1.School of Resources, China University of Geosciences (Wuhan), Wuhan 430074, China
    2.School of Economics and Management, China University of Geosciences (Wuhan), Wuhan 430074, China
    3.Guangxi Zhuang Autonomous Region Geological Environment Monitoring Station, Nanning 530029, China
    4.School of Natural Resources and Surveying, Nanning Normal University, Nanning 530001, China
  • Received:2019-01-30 Revised:2019-07-12 Online:2019-08-28 Published:2019-08-21

摘要:

绿色矿山建设影响因素及建设效果分析对全面推动绿色矿山建设、形成绿色矿业格局具有重要意义。本文基于2011—2015年湖北省596家矿山企业规模、资源占用、生产效率等特征,结合矿山对地区经济贡献及与主体功能区匹配情况,运用面板Logit模型探究了湖北省绿色矿山建设的影响因素;运用倾向值匹配方法(PSM)分析了矿山规模、矿种、主体功能区等因素对绿色矿山建设效果的影响。结果表明:①绿色矿山建设的影响因素按照其影响大小依次为:矿山规模>矿山经济价值>“三率”调查矿种>主体功能区类别>城镇化水平>矿山经济类型>年投资比重。“三率”调查工作、城镇化进程对绿色矿山建设具有推动作用,大中型矿山、重点生态保护区内矿山、国有和集体企业对绿色矿山建设的落实更为积极,而企业投入不足是制约绿色矿山建设的因素之一。②绿色矿山建设对提高企业经济效益的效果:大型矿山>中型矿山>小型矿山,非金属矿山>金属矿山;对矿山环境治理投资改善效果:重点生态功能区>农产品主产区>重点开发区,非金属矿山>金属矿山;对废石和尾矿的治理效果短期内不显著。因此,矿山特征要素是影响绿色矿山建设的关键因素,尤其是矿山规模与占用资源储量;矿山经济效益受矿山规模和矿产资源类型的影响,矿山环境治理投资力度受主体功能区职能和矿产资源类型影响。建议在绿色矿山建设中应结合企业占用矿产资源储量,优化矿山规模结构,摸清矿产资源利用情况,重点改善矿山固体废弃物的综合利用,以实现绿色矿山全面建设、传统矿业转型升级。

关键词: 绿色矿山, 影响因素, 绩效评价, 面板Logit模型, 倾向值匹配模型, 湖北省

Abstract:

The analysis of influencing factors and performance of green mining is of great significance for promoting green mine development and forming green mining in an all-round way. Based on the data of 596 mining enterprises in Hubei Province from 2011 to 2015, this study used panel data Logit model to identify the influencing factors of green mine development and used propensity score matching (PSM) model to analyze the influence of some factors on the performance of green mining, including scale of the mines, mineral types, and main functional areas. The conclusions are as follows: (1) The importance of influencing factors is in the following order: mining scale, economic value of mines, “three-rate” survey of minerals, main functional area, urbanization level, economic type of mining, and annual investment. The “three-rate” survey and urbanization progress have promoted the green mine development. Large and medium-sized mines, mines in key ecological protection areas, state-owned and collective enterprises are more active in implementing the development of green mines. Insufficient investment of enterprises is a key factor restricting green mine development. (2) The effect of improving the economic efficiency of enterprises from green mine development is: large mines>medium-sized mines>small mines, non-metallic mines>metal mines; the improvement effect of mine environmental management investment is: key ecological functional areas>major agricultural products producing areas>key development areas, non-metallic mines>metal mines; the effects of tailings treatment and utilization from green mine development is: “three-rate” survey of minerals>non-“three-rate” survey of minerals. Our suggestions are as follows: The “three-rate” survey of mineral resources should be carried out in an all-round way to increase utilization rate of resource. To enhance the supervision and management of protection and restoration of mine eco-environment, mines are encouraged to increase investment in mine environmental management and the comprehensive utilization of tailings. Promote the development of green mines from isolated sites to areas, establish green demonstration area of mining, and promote the transformation and upgrading of traditional mining industry through demonstration and dissemination.

Key words: green mining, influencing factors, performance evaluation, panel data Logit model, propensity score matching, Hubei Province