ORIGINAL RESEARCH
Spatial Dynamics Change of Mining Eco-Efficiency in Chinese Provinces: A Novel Assessment Framework Integrating SBM-DEA and Malmquist-Luenberger Models
,
 
 
 
 
More details
Hide details
1
Chengdu Gas Group Corporation, Chengdu, 610041, China
 
2
College of Preschool and Primary Education, China West Normal University, Nanchong, 637000, Sichuan, China
 
 
Submission date: 2024-01-23
 
 
Final revision date: 2024-02-28
 
 
Acceptance date: 2024-04-14
 
 
Online publication date: 2024-09-02
 
 
Publication date: 2025-01-09
 
 
Corresponding author
Haolang Yang   

Chengdu Gas Group Corporation, Chengdu, 610041, China;, No.19 Shaoling Road, Wuhou District, 610041, Chengdu City, China
 
 
Pol. J. Environ. Stud. 2025;34(2):1823-1839
 
KEYWORDS
TOPICS
ABSTRACT
In the context of global sustainable development goals and heightened environmental awareness, the imperative to evaluate the eco-efficiency of mining becomes paramount. However, the escalated mining activities result in the generation of wastewater, exhaust gas, and solid waste, posing a threat to the environment. This study endeavors to assess the mining eco-efficiency across Chinese provinces by introducing a pioneering assessment index system and framework. The framework incorporates the non-desired output SBM-DEA model and the Malmquist-Luenberger total factor productivity index model. To validate the reliability of the model, it is applied to 27 provinces in China. The findings unveil significant insights: (1) The mining eco-efficiency of Chinese provinces exhibits an overall positive trend but displays notable spatial variations; (2) East China demonstrates superior technical progress and overall technical efficiency, while North, Northeast, and Northwest China lag in technical progress but excel in overall technical efficiency. Notably, non-desired outputs exert an influence on China’s level of green mining development, particularly in Northeast and Central China. The study recommends that enterprises shoulder the responsibility of environmental management and mine restoration, enhance their capacity for innovation in green technology, and expedite the construction of green mines to augment mining eco-efficiency. These results furnish valuable perspectives and pertinent information for decision-making related to green mining development, energy structure transformation, and the implementation of large-scale mining projects.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (47)
1.
FLEXER V., BASPINEIRO C.F., GALLI C.I. Lithium recovery from brines: A vital raw material for green energies with a potential environmental impact in its mining and processing. *Science of the Total Environment*, 639, 1188, 2018. <https://doi.org/10.1016/j.scit...> PMid:29929287.
 
2.
DE-CARVALHO J.M.F., FONTES W\.C., DE-AZEVEDO C.F. Enhancing the eco-efficiency of concrete using engineered recycled mineral admixtures and recycled aggregates. *Journal of Cleaner Production*, 257, 2020. <https://doi.org/10.1016/j.jcle...>.
 
3.
LIU X., GUO P., GUO S. Assessing the eco-efficiency of a circular economy system in China's coal mining areas: Emergy and data envelopment analysis. *Journal of Cleaner Production*, 206, 1101, 2019. <https://doi.org/10.1016/j.jcle...>.
 
4.
YIN Q., WANG Y., XU Z., WAN K., WANG D. Factors influencing green transformation efficiency in China's mineral resource-based cities. *Journal of Cleaner Production*, 330, 2022. <https://doi.org/10.1016/j.jcle...>.
 
5.
OLIVEIRA R., CAMANHO A.S., ZANELLA A. Expanded eco-efficiency assessment of large mining firms. *Journal of Cleaner Production*, 142, 2364, 2017. <https://doi.org/10.1016/j.jcle...>.
 
6.
WANG D., WAN K., YANG J. Ecological efficiency of coal cities in China: evaluation and influence factors. *Natural Hazards*, 95 (1), 363, 2019. <https://doi.org/10.1007/s11069...>.
 
7.
CHIU Y.H., HUANG K.Y., CHANG Z.H. Efficiency assessment of coal mine use and land restoration: Considering climate change and income differences. *Resources Policy*, 73, 2021. <https://doi.org/10.1016/j.reso...>.
 
8.
KITHEKA B.M., BALDWIN E.D., POWELL R.B. Grey to green: tracing the path to environmental transformation and regeneration of a major industrial city. *Cities*, 108, 2021. <https://doi.org/10.1016/j.citi...>.
 
9.
LI J.X., ZHANG H.Q., XU E.Q. Quantifying production-living-ecology functions with spatial detail using big data fusion and mining approaches. *Ecological Indicators*, 142, 2022. <https://doi.org/10.1016/j.ecol...>.
 
10.
JISKANI I.M., CAI Q., ZHOU W., LU X., SHAH S.A. An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining. *Expert Systems with Applications*, 188, 2022. <https://doi.org/10.1016/j.eswa...>.
 
11.
YUAN S., STAINSBY W., LI M., XU K., WAITE M., ZIMMERLE D., FEIOCK R. Future energy scenarios with distributed technology options for residential city blocks in three climate regions of the United States. *Applied Energy*, 237, 60, 2019. <https://doi.org/10.1016/j.apen...>.
 
12.
LIU Y., ZHU J., LI E.Y., MENG Z., SONG Y. Environmental regulation, green technological innovation, and eco-efficiency: the case of Yangtze River economic belt in China. *Technological Forecasting & Social Change*, 155, 60, 2020. <https://doi.org/10.1016/j.tech...>.
 
13.
SHAO L., YU X., FENG C. Evaluating the eco-efficiency of China's industrial sectors: A two-stage network data envelopment analysis. *Journal of Environmental Management*, 247, 551, 2019. <https://doi.org/10.1016/j.jenv...> PMid:31260921.
 
14.
YANG Y., GUO H., CHEN L., LIU X., GU M., KE X. Regional analysis of the green development level differences in Chinese mineral resource-based cities. *Resources Policy*, 61, 261, 2019. <https://doi.org/10.1016/j.reso...>.
 
15.
YAN D., KONG Y., REN X., SHI Y., CHIANG S. The determinants of urban sustainability in Chinese resource-based cities: a panel quantile regression approach. *Science of the Total Environment*, 686, 1210, 2019. <https://doi.org/10.1016/j.scit...> PMid:31412517.
 
16.
WANG Y., WU X.L., HE S.Y. Eco-environmental assessment model of the mining area in Gongyi, China. *Scientific Reports*, 11 (1), 17549, 2021. <https://doi.org/10.1038/s41598...> PMid:34475428 PMCid:PMC8413286.
 
17.
CHEN W., SI W., CHEN Z.M. How technological innovations affect urban eco-efficiency in China: a prefecture-level panel data analysis. *Journal of Cleaner Production*, 270, 2020. <https://doi.org/10.1016/j.jcle...>.
 
18.
JIA H., LI T., WANG A. Decoupling analysis of economic growth and mineral resources consumption in China from 1992 to 2017: A comparison between tonnage and exergy perspective. *Resources Policy*, 74, 2021. <https://doi.org/10.1016/j.reso...>.
 
19.
ALI S.H., PERRONS R.K., TOLEDANO P. A model for "smart" mineral enterprise development for spurring investment in climate change mitigation technology. *Energy Research & Social Science*, 58, 2019. <https://doi.org/10.1016/j.erss...>.
 
20.
GUO Y., TONG L., MEI L. The effect of industrial agglomeration on green development efficiency in Northeast China since the revitalization. *Journal of Cleaner Production*, 258, 2020. <https://doi.org/10.1016/j.jcle...>.
 
21.
SUN J.F., YUAN X.Z., LIU H. Emergy evaluation of a swamp dike-pond complex: A new ecological restoration mode of coal-mining subsidence areas in China. *Ecological Indicators*, 107, 2019. <https://doi.org/10.1016/j.ecol...>.
 
22.
LIU Q., WANG S., LI B., ZHANG W. Dynamics, differences, influencing factors of eco-efficiency in China: a spatiotemporal perspective analysis. *Journal of Environmental Management*, 264, 2020. <https://doi.org/10.1016/j.jenv...> PMid:32250887.
 
23.
JISKANI I.M., CAI Q., ZHOU W. Green and climate-smart mining: A framework to analyze open-pit mines for cleaner mineral production. *Resources Policy*, 71, 2021. <https://doi.org/10.1016/j.reso...>.
 
24.
CUI C.Q., WANG B., ZHAO Y.X. China's regional sustainability assessment on mineral resources: Results from an improved analytic hierarchy process-based normal cloud model. *Journal of Cleaner Production*, 210, 105, 2019. <https://doi.org/10.1016/j.jcle...>.
 
25.
DELLA BOSCA H., GILLESPIE J. The coal story: Generational coal mining communities and strategies of energy transition in Australia. *Energy Policy*, 120, 734, 2018. <https://doi.org/10.1016/j.enpo...>.
 
26.
CHEN H., ZHANG X., WU R., CAI T. Revisiting the environmental Kuznets curve for city-level CO₂ emissions: based on corrected NPP-VIIRS nighttime light data in China. *Journal of Cleaner Production*, 268, 2020. <https://doi.org/10.1016/j.jcle...>.
 
27.
ZHANG Y.J., SONG Y. Unified efficiency of coal mining enterprises in China: An analysis based on meta-frontier non-radial directional distance functions. *Resources Policy*, 65, 2020. <https://doi.org/10.1016/j.reso...>.
 
28.
CHEN Y., LIU L. Improving eco-efficiency in coal mining area for sustainability development: An emergy and super-efficiency SBM-DEA with undesirable output. *Journal of Cleaner Production*, 339, 2022. <https://doi.org/10.1016/j.jcle...>.
 
29.
LUO D., HUANG J., WU H. Measuring green development index and coupling coordination of mining industry: An empirical analysis based on panel data in China. *Journal of Cleaner Production*, 401, 2023. <https://doi.org/10.1016/j.jcle...>.
 
30.
LIU H.C., YOU J.X., YOU X.Y., SHAN M.M. A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method. *Applied Soft Computing*, 28, 579, 2015. <https://doi.org/10.1016/j.asoc...>.
 
31.
SHI Y., SHI S., WANG H. Reconsideration of the methodology for estimation of land population carrying capacity in Shanghai metropolis. *Science of the Total Environment*, 652, 367–381, 2019. <https://doi.org/10.1016/j.scit...> PMid:30366337.
 
32.
ZHANG Y., FU Z.H., XIE Y.L. Dynamic metabolism network simulation for energy-water nexus analysis: A case study of Liaoning Province, China. *Science of the Total Environment*, 779, 2021. <https://doi.org/10.1016/j.scit...> PMid:33752008.
 
33.
SALMI O. Eco-efficiency and industrial symbiosis – a counterfactual analysis of a mining community. *Journal of Cleaner Production*, 15, 1696, 2007. <https://doi.org/10.1016/j.jcle...>.
 
34.
WANG D., SHI Y., WAN K. Integrated evaluation of the carrying capacities of mineral resource-based cities considering synergy between subsystems. *Ecological Indicators*, 108, 2020a. <https://doi.org/10.1016/j.ecol...>.
 
35.
YUAN Q.S., WANG P.F., WANG X. Abundant microbial communities act as more sensitive bio-indicators for ecological evaluation of copper mine contamination than rare taxa in river sediments. *Environmental Pollution*, 305, 2022. <https://doi.org/10.1016/j.envp...> PMid:35430312.
 
36.
WANG D., WANG Y., HUANG Z., CUI R. Understanding the resilience of coal industry ecosystem to economic shocks. *Resources Policy*, 67, 2020. <https://doi.org/10.1016/j.reso...>.
 
37.
CHENG Y.Y., ZHOU K.F., WANG J.L. Regional metal pollution risk assessment based on a long short-term memory model: A case study of the South Altai Mountain mining area, China. *Journal of Cleaner Production*, 379, 2022. <https://doi.org/10.1016/j.jcle...>.
 
38.
WANG Y., ZHAO H., LI L., LIU Z., LIANG S. Carbon dioxide emission drivers for a typical metropolis using input–output structural decomposition analysis. *Energy Policy*, 58, 312, 2013. <https://doi.org/10.1016/j.enpo...>.
 
39.
ZHONG Z., PENG B., XU L., ANDREWS A., ELAHI E. Analysis of regional energy economic efficiency and its influencing factors: A case study of Yangtze River urban agglomeration. *Sustainable Energy Technologies and Assessments*, 41, 2020. <https://doi.org/10.1016/j.seta...>.
 
40.
YANG Z., SONG J., CHENG D., XIA J., LI Q. Comprehensive evaluation and scenario simulation for the water resources carrying capacity in Xi’an city, China. *Journal of Environmental Management*, 230, 221, 2019. <https://doi.org/10.1016/j.jenv...> PMid:30290309.
 
41.
ZHU Y., XU D., ALI S.H., CHENG J. A hybrid assessment model for mineral resource availability potentials. *Resources Policy*, 74, 102283, 2021. <https://doi.org/10.1016/j.reso...>.
 
42.
WANG D., WAN K., YANG J. Measurement and evolution of eco-efficiency of coal industry ecosystem in China. *Journal of Cleaner Production*, 209, 803, 2019. <https://doi.org/10.1016/j.jcle...>.
 
43.
QIU S., WANG Z., LIU S. The policy outcomes of low-carbon city construction on urban green development: Evidence from a quasi-natural experiment in China. *Sustainable Cities and Society*, 66, 2021. <https://doi.org/10.1016/j.scs....>.
 
44.
LIU Q., WU S., LEI Y., LI S., LI L. Exploring spatial characteristics of city-level CO₂ emissions in China. *Science of the Total Environment*, 754, 2021. <https://doi.org/10.1016/j.scit...> PMid:32920414 PMCid:PMC12212555.
 
45.
YAN J., SHENG Y., YANG M., YUAN Q., GU X. Local government competition, new energy industry agglomeration and urban ecological total factor energy efficiency. *Journal of Cleaner Production*, 429, 139511, 2023. <https://doi.org/10.1016/j.jcle...>.
 
46.
XIA L., WAN L., WANG W., LUO J., YAN J. Energy accessibility via natural resources: Do natural resources ensure energy accessibility in low income countries? *Resources Policy*, 86, 104145, 2023. <https://doi.org/10.1016/j.reso...>.
 
47.
CUI X., WANG W., ISIK C., UDDIN I., YAN J., GU X., AHMAD M. Do geopolitical risk and economic policy uncertainty cause CO₂ emissions in BRICS? *Stochastic Environmental Research and Risk Assessment*, 1–15, 2024.
 
eISSN:2083-5906
ISSN:1230-1485
Journals System - logo
Scroll to top