Study on the Evaluation of the Development Efficiency of Smart Mine Construction and the Influencing Factors Based on the US-SBM Model
Abstract
:1. Introduction
2. Research Methodology and Data Sources
2.1. Research Methodology
2.1.1. Global Reference Method
2.1.2. US-SBM Model
2.1.3. KDE Model
2.1.4. Tobit Regression Model
2.2. Indicator System Construction and Data Sources
2.2.1. Construction of the Indicator System
- (1)
- The evaluation index system of smart mine construction development efficiency
- (2)
- Selection of impact factor indicators and model setting
2.2.2. Data Sources
3. Analysis of Results
3.1. Analysis of the Development Efficiency of Smart Mine Construction
3.2. Spatial Structure Evolution
- (1)
- In terms of peak, the first wave of the rise of smart mine construction development efficiency occured around 0.4, and the second wave occurred at around 1.1, indicating that the polarization phenomenon of smart mine construction development efficiency in each region is profound.
- (2)
- In kurtosis, the wave height shows an overall rising trend, and the kernel density curve for all years shows a trend of increasing peak height, decreasing width, and a broad ridge evolving into a sharp rise. The degree of difference in the development efficiency of smart mine construction in each region is becoming smaller.
- (3)
- In terms of location, the kernel density curve from 2011–2016 shows a left shift, and the turn from 2016–2020 offers a suitable dress, indicating that the level of efficiency of smart mine construction development shows a trend of change of first decreasing and then increasing.
3.3. Analysis of Influencing Factors
3.3.1. Internal Factors Analysis (Redundancy Analysis)
3.3.2. Analysis of External Factors
- (1)
- The level of economic development and technological innovation both have a positive promotion effect on the development efficiency of smart mine construction at a 10% significance level. Among these, the influence coefficient of the level of economic development reaches 0.384, showing that it is the main external factor enhancing the development efficiency of smart mine construction. This is because the higher economic level is conducive to the development and introduction of advanced mining technology and management models while enhancing the promotion of the transformation of human production and lifestyle. The concern of human beings for a green environment and health has made the changes in production methods in the mining industry inevitable. For example, people’s increased attention to the safe working environment and occupational health has forced mining enterprises to develop intelligent and unmanned work sites. The impact coefficient of technological innovation is only 0.000342. Even though scientific and technological innovation favorably support the transformation and development of the coal industry, the effect of technological innovation on enhancing the development efficiency of smart mine construction is weak due to the lack of key core technologies and weak innovation results in transformation ability in the domestic mine construction process.
- (2)
- Both the level of human development and environmental policy have a significant negative relationship on the development efficiency of smart mine construction at a 1% significance level, which is the main factor limiting the improvement in this area. Here, the regression coefficient of the human capital level reaches −0.346, which has the most significant impact on the development efficiency of smart mine construction, indicating a severe lack of talents related to smart mine construction. Skilled labor gradually moves to other fields due to the cognitive restrictions on the production environment and operational safety of traditional mining enterprises, as well as the pursuit of a high-quality lifestyle. With the development of the smart mine industry, the definition of mining enterprises by society will also change. This situation will be positively transformed by more relevant talents. The regression coefficient of the environmental policy is −0.156, which indicates that although extensive development in the mine construction process has been limited, it has not been eliminated. The government’s investment in legislation, supervision, technology, and finance has hindered the development of smart mine construction while reducing pollution emissions, indicating that the current level of smart mine construction is low and more investment in clean mine construction is needed.
- (3)
- Industrial structures and the degree of government intervention positively affect smart mine construction’s development efficiency at a 5% significance level, with influence coefficients of 0.0187 and 0.0204, respectively. When the ratio of primary business income and local fiscal expenditure of the coal industry increases by 1%, the development level of smart mine construction in the study area increases by 0.0187 and 0.0204, respectively, on average. Coal resource endowment, as a prerequisite for the development of smart mine construction, has an essential supporting role in the development of smart mine construction. The government has a substantial leading role at the energy development level. Government actions can directly guide the development direction of smart mine construction, promoting the development of smart mine construction.
- (4)
- The regression coefficient of the degree of opening up to the outside world is 0.0617, which positively promotes the development of smart mine construction, and the influence coefficient of the degree of coal resource advantage is −0.0916, which has a negative influence on the development of smart mine construction, but none of them are significant. For example, the “One Belt, One Road” strategy has helped China’s coal industry to relieve excess capacity, enrich supply and demand channels, and improve the competitiveness of the international market. However, because most of China’s coal resource cities are located in relatively backward economic regions lacking foreign investment attraction and a low degree of openness to the outside world, improvement in the development efficiency of smart mine construction is not apparent. Although coal resources are the basis for the development of smart mine construction, the severe environmental pollution and excessive waste of resources generated by the long-term rough development mode seriously limit the development of smart mine construction, especially the construction of clean mines.
4. Discussion
- The evaluation of the development efficiency of smart mine construction shows that the lack of crucial technologies mainly limits the current low efficiency of smart mine construction. Because of the complex and diverse coal seam conditions, the degree of difficulty, technical paths, and effects of smart mine construction vary depending on the coal seam conditions. The lack of “transparent geology” detection technology does not provide a reasonable basis for achieving fully smart mining. At the same time, intelligent mining technology cannot flexibly adapt to complex working face environments, and the self-adaptability and reliability of smart mining equipment are poor [46]. Therefore, we should strengthen the research and development of “transparent geology” technology, realize the “transparency” and “visualization” of geological mine information, increase the study of smart equipment and self-adaptive control technology in combination with 5G technology, and improve the quality of mining equipment. At the same time, we should speed up the integration of 5G technology as the core technology ecology to facilitate the development of smart mining technology and promote technological changes in mine construction [2].
- In order to enhance the regional targeting of mining development policies, it is imperative for regions with varying levels of development efficiencies to formulate appropriate strategies for the development of smart mine construction based on their unique circumstances. Regions such as Guangxi and Shandong, where the total development efficiency is above 0.85, should focus on technological innovation in smart mining and strive to break through technical development bottlenecks. Areas such as Fujian and Jilin, where the total development efficiency is 0.6~0.85, should focus on expanding the application of advanced mining equipment and technology, introducing advanced mine management models at home and abroad and accelerating the comprehensive benefits of their mine construction. Hebei, Henan, and other regions with a total development efficiency below 0.6 are seriously lagging in the development of smart mine construction. They should accelerate the merger and reorganization of coal enterprises, eliminate backward production capacity, and improve coal mines’ modernization, intensification, and scale. At the same time, they should also increase investment in infrastructure and technology and improve enterprises’ scientific and technological capabilities.
- The analysis of the factors influencing the development efficiency of smart mine construction shows that internal factors, accounting for 46% of the regions, have a severe redundancy phenomenon, indicating that the mining enterprises in the study area have a poor ability to optimize resource allocation. Mining enterprises should increase the importance of the rational allocation of resources and strengthen communication and cooperation with management consulting companies [47]. They should also combine their objective conditions and develop an enterprise that meets their development strategy. Regarding external environmental factors, economic development has the most significant positive impact on the development efficiency of smart mine construction. Therefore, the government and mining enterprises should strive to promote technological innovation and improve incentive mechanisms for independent creation and technological innovation, as well as form a talent introduction system. This would accelerate industrial transformation and strengthen the opening-up to the outside world to improve their economic competitiveness and the level of openness of the economy. Environmental policies and human capital levels have inhibiting effects on the development of smart mine construction. Meanwhile, the government should improve the corresponding reward and punishment system to increase the investment in earning rewards and avoiding punishment. Smart mine construction is seriously unattractive to current talents and lacks a sound smart talent training system. We should strengthen the training of talents for smart mine construction through school–government cooperation and school–enterprise cooperation, while at the same time increasing the skills training for practitioners to improve the talent training system [47].
- There are still some shortcomings in this study. Firstly, due to the unique nature of mine construction development, the lack of comprehensive resource utilization data in each region restricts the selection of input and output indicators in the evaluation index system. Secondly, the lack of valuable statistical data in each area limits the choice of decision-making units, resulting in the inability to evaluate the development efficiency of smart mine construction in all resource-based cities in China. Finally, future studies will require an in-depth analysis of the efficiency differences between the weighted SBM model and the current research to further optimize the evaluation index system for the development efficiency of smart mine construction.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Wang, G.F.; Ren, S.H.; Pang, Y.H.; Qu, S.J.; Zheng, D.Z. Development achievements of China’s coal industry during the 13th Five-Year Plan period and implementation path of “dual carbon” target. Coal Sci. Technol. 2021, 49, 1–8. [Google Scholar] [CrossRef]
- Fan, J.D.; Li, C.; Yan, Z.G. Overall architecture and core scenario of a smart coal mine in-corporating 5G technology ecology. J. China Coal Soc. 2020, 45, 1949–1958. [Google Scholar] [CrossRef]
- Qiu, S.H.; Tan, Z.L. Study on index system of intelligent mine construction degree in coal enterprises. Coal Sci. Technol. 2019, 47, 259–266. [Google Scholar] [CrossRef]
- He, Y.Y. Discussion on evaluation index system and architecture of smart mine. Ind. Mine Autom. 2017, 43, 16–20. [Google Scholar] [CrossRef]
- Hosseinzadeh, A.; Smyth, S.; Valadkhani, A. Analyzing the efficiency performance of major Australian mining companies using bootstrap data envelopment analysis. Econ. Model. 2016, 57, 26–35. [Google Scholar] [CrossRef]
- Tsolas, I.E. Assessing performance in Greek bauxite mining by means of frontier estimation methodologies. IMA J. Manag. Math. 2009, 21, 253–265. [Google Scholar] [CrossRef]
- Wang, S.M. Evaluation of Safety Input-Output Efficiency of Coal Mine Based on DEA Model. Procedia Eng. 2011, 26, 2270–2277. [Google Scholar] [CrossRef] [Green Version]
- Cooper, W.W.; Seiford, L.M.; Tone, K. Introduction to Data Envelopment Analysis and Its Uses; Springer Books: Berlin, Germany, 2006. [Google Scholar]
- Kasap, Y. The effect of work accidents on the efficiency of production in the coal sector. S. Afr. J. Sci. 2011, 107, 77–85. [Google Scholar] [CrossRef]
- Tsolas, I.E. Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA. Resour. Policy 2011, 36, 159–167. [Google Scholar] [CrossRef]
- Fang, H.; Wu, J.; Zeng, C. Comparative study on efficiency performance of listed coal mining companies in China and the US. Energy Policy 2009, 37, 5140–5148. [Google Scholar] [CrossRef]
- Kulshreshtha, M.; Parikh, J.K. Study of efficiency and productivity growth in opencast and underground coal mining in India: A DEA analysis. Energy Econ. 2002, 24, 439–453. [Google Scholar] [CrossRef]
- Li, S.X.; Wang, J.C.; Cheng, J.H. Efficiency Levels and Regulation Effects of Coal Industry in China. China J. Popul. Resour. Environ. 2015, 25, 169–176. [Google Scholar]
- Xu, J.F.; Tian, S.Y.; Zhan, Q.Y. Ecological efficiency evaluation of coal resource-based cities in China. Urban Probl. 2016, 95, 85–93. [Google Scholar] [CrossRef]
- Che, L.L.; Han, X.; Zhao, L.S.; Wu, C.Y. Coal Use Efficiency Evaluation and Decoupling Analysis between Coal Use Efficiency and Economic Growth in China. China J. Popul. Resour. Environ. 2015, 25, 104–110. [Google Scholar] [CrossRef]
- Yang, S.W.; Zhang, K.G.; Niu, M.M. Research on Evaluation of ecological and economic efficiency of China’s mining industry. Macroeconomics 2011, 6, 66–71, 78. [Google Scholar] [CrossRef]
- Geissler, B.; Mew, M.C.; Weber, O. Efficiency performance of the world’s leading corporations in phosphate rock mining. Resour. Conserv. Recycl. 2015, 105 (Pt B), 246–258. [Google Scholar] [CrossRef]
- Zhang, L.H.; Luo, Z.R.; Zhang, X.L. Research on Financial Support Efficiency of Scientific and Technological Innovation in Coal Enterprises under the Background of Supply-side Reform. East China Econ. Manag. 2019, 33, 149–157. [Google Scholar] [CrossRef]
- Cheng, G. Data Envelopment Analysis and MaxDEA Software; Intellectual Property Publishing House: Beijing, China, 2014; Volume 27. [Google Scholar]
- Pastor, J.T.; Lovell, C. A global Malmquist productivity index. Econ. Lett. 2005, 88, 266–271. [Google Scholar] [CrossRef]
- Kaoru, T. A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res. 2002, 143, 32–41. [Google Scholar] [CrossRef] [Green Version]
- Huang, J.; Yang, X.; Cheng, G.; Wang, S. A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China. J. Clean. Prod. 2014, 67, 228–238. [Google Scholar] [CrossRef]
- Charnes, A.W.; Cooper, W.W.; Rhodes, E.L. Measuring The Efficiency of Decision Making Units. Eur. J. Oper. Res. 1979, 2, 429–444. [Google Scholar] [CrossRef]
- Huppes, G.; Ishikawa, M. A Framework for Quantified Eco-efficiency Analysis. J. Ind. Ecol. 2005, 9, 25–41. [Google Scholar] [CrossRef]
- Banker, R.D.; Thrall, R.M. Estimation of returns to scale using data envelopment analysis. Eur. J. Oper. Res. 1992, 62, 74–84. [Google Scholar] [CrossRef]
- Tone, K. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 2001, 130, 498–509. [Google Scholar] [CrossRef] [Green Version]
- Andersen, P.; Petersen, N.C. A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Manag. Sci. 1993, 39, 1261–1264. [Google Scholar] [CrossRef]
- Ye, A.Z. Nonparametric Econometrics; Nankai University Press: Tianjin, China, 2003; pp. 24–25. [Google Scholar]
- Fan, Z.G.; Deng, Z.K.; Zhang, P.W.; Wu, Y.F. Green development efficiency measurement and its impact mechanism in ecological civilization pilot area. Stat. Decis. 2022, 38, 75–79. [Google Scholar] [CrossRef]
- Zhang, P.; Yu, W. The Spatial Interaction Spillover Effects of Financial Agglomeration and Urban Development Efficiency—An Empirical Study on the Spatial Simultaneous Equations in and among Cities above the Prefecture Level. J. Shanxi Univ. Financ. Econ. 2019, 41, 1–16. [Google Scholar] [CrossRef]
- Yang, L.; Chen, J.J.; Shi, P.F.; Huang, G.Q. The evaluation of red tourism development efficiency and its influencing factors: Acase study of the red tourism region in Northern and Western Guizhou. J. Nat. Resour. 2021, 36, 2763–2777. [Google Scholar] [CrossRef]
- Hou, D.; Liang, Z.X. Research on Green Mining Efficiency of Coal Enterprises Based on Two-Stage DEA Model. Saf. Coal Mines 2020, 51, 255–259. [Google Scholar] [CrossRef]
- Ma, X.J.; Li, Y.D.; Wang, C.X.; Yang, Q. Ecological efficiency in the development of circular economy of China under hard constraints based on an optimal super efficiency SBM-Malmquist-Tobit model. China Environ. Sci. 2018, 38, 3584–3593. [Google Scholar] [CrossRef]
- Tong, J.P.; Ma, J.F.; Wang, S.; Qin, T.; Wang, Q. Research agricultural water use efficiency in Yangtze River Basin based on super-efficiency DEA Tobit model. Resour. Environ. Yangtze Basin 2015, 24, 603–608. [Google Scholar] [CrossRef]
- Gu, S.Z.; Wu, Y.B.; Liu, W. Reflection on the transformation and upgrading of the coal industry during the “13th Five-Year” Plan period. Study Explor. 2015, 37, 78–82. [Google Scholar]
- Wang, J.N.; Xu, Z.C.; Hu, X.B.; Peng, X.C. Analysis of environmental efficiencies and their changes in China based on DEA theory. China Environ. Sci. 2010, 30, 565–570. [Google Scholar]
- Qin, B.T.; Yu, R.Y.; Ge, Y.M. The impact of environmental regulation on the industrial structure transformation of resource-based cities. China Environ. Sci. 2021, 41, 3427–3440. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhang, H.Y.; Ge, L.M. Study on Green Development Efficiency Measurement and Synergistic Improvement Mechanism: A Case Study of Central Plains City Group. Ecol. Econ. 2022, 38, 92–99. [Google Scholar]
- Wang, Z.F.; Zhao, S.S. Temporal and Spatial Dynamic Evolution and Influencing Factors of Tourism Efficiency in Hunan Province Based on DEA-Malmquist Model. Resour. Environ. Yangtze Basin 2019, 28, 1886–1897. [Google Scholar] [CrossRef]
- Liu, D.D.; Zhaosong, Y.Y.; Guo, Y. Energy efficiency and its determinants of western China: Total factor perspective. China Environ. Sci. 2015, 35, 1911–1920. [Google Scholar]
- National Bureau of Statistics of China (NBS). Available online: http://www.stats.gov.cn/tjsj/ndsj/ (accessed on 12 March 2022).
- National Bureau of Statistics of China (NBS). Available online: http://www.stats.gov.cn/tjgz/wzlj/dftjwz/ (accessed on 22 March 2022).
- Ministry of Land and Resources (MLMR). Available online: https://www.gov.cn/fuwu/bumendifangdating/bumendating/guotuziyuanbu/ (accessed on 28 March 2022).
- China National Coal Association (CNCA). Available online: http://www.coalchina.org.cn/ (accessed on 29 March 2022).
- China Emission Accounts and Datasets (CEADs). Available online: https://www.ceads.net.cn/data/ (accessed on 10 April 2022).
- Yuan, L.; Zhang, P.S. Key technology and path thinking of dynamic reconstruction of mine transparent geological model. J. China Coal Soc. 2023, 48, 1–14. [Google Scholar] [CrossRef]
- Price Waterhouse Coopers (PWC). Mining for Efficiency. Price Waterhouse Coopers, Sydney. 2014. Available online: https://www.pwc.com.au/industry/energy-utilities-mining/assets/mining-efficiency-aug14.pdf/ (accessed on 15 December 2022).
Model Name | Advantages |
---|---|
Traditional DEA | It can evaluate the effectiveness of the same type of DMUs with multiple inputs and outputs. |
SBM | It can effectively avoid the bias caused by the radial and angular metrics of traditional DEA models. |
Undesirable SBM | It is more reflective of the nature of efficiency evaluation by considering non-desired output factors. |
Super-efficiency SBM | It can further compare and distinguish the effective DMUs in the frontier. |
US-SBM | It combines the advantages of non-desired and super-efficient SBM models. |
Model Name | Advantages |
---|---|
KDE | Good smoothness |
Good statistical characteristics | |
High accuracy | |
Weak model dependency | |
Good visualization |
Indicator Type | Indicator Meaning | Indicators | Explanation of Indicators | Unit |
---|---|---|---|---|
Input elements | Capital Investment | Total fixed asset investment in the coal industry | Measuring the level of investment in infrastructure development | RMB 100 million |
Labor input | The annual average number of employees in the coal industry | Measuring the degree of realization of unmanned work surfaces | 10,000 people | |
Resource input | Total electricity consumption in the coal industry | Measures the degree of input of production resources in the industry | 100 million kWh | |
Technical input | Internal expenditure of R&D funds in the coal industry | Measures the degree of industrial development of science and technology funding investment | RMB 10,000 | |
R&D personnel equivalent (full-time equivalent) | Measures the degree of industry development science and technology in terms of human input | Person-year | ||
Output elements | Expected output | Revenue from the main business of the coal industry | Measuring the degree of comprehensive revenue-based construction in the industry | RMB 100 million |
Coal production | Measuring the degree of construction of efficient mines in the industry | 10 kilo-tons | ||
Number of smart, unmanned work surfaces | Measuring the degree of smart mine construction in the industry | Each | ||
Total current assets of the coal industry | Measuring the construction degree of industry management information technology | RMB 100 million | ||
The proportion of green mines in enterprises above the scale | Measuring the extent to which the industry has achieved green mining | % | ||
Undesired outputs | Mortality rate per million tons | Measuring the degree of construction of safe mines in the industry | People | |
CO2 emissions from the coal industry | Measuring the degree of construction of clean mines in the industry | Million tons |
Region | TE | PTE | SE |
---|---|---|---|
Fujian | 0.6520 | 0.6770 | 0.9546 |
Guangxi | 1.1126 | 1.2104 | 0.9239 |
Hebei | 0.4494 | 0.4917 | 0.9157 |
Henan | 0.1551 | 0.1760 | 0.7475 |
Hunan | 0.3374 | 0.5439 | 0.6562 |
Jilin | 0.7935 | 0.8865 | 0.8795 |
Jiangsu | 0.7587 | 0.8358 | 0.8539 |
Shandong | 0.9112 | 0.9729 | 0.8936 |
Shanxi | 0.8638 | 1.0084 | 0.8223 |
Shaanxi | 0.7890 | 0.9324 | 0.8520 |
Xinjiang | 0.7235 | 0.8343 | 0.8567 |
Yunnan | 0.3599 | 0.3922 | 0.9278 |
Chongqing | 1.1199 | 1.2966 | 0.9005 |
Average value | 0.6943 | 0.7891 | 0.8603 |
Region | Fixed Asset Investment Redundancy Rate | Number of Practitioners’ Redundancy Rate | Total Electricity Consumption Redundancy Rate | Redundancy Rate of Internal Expenditure of R&D Funds | R&D Staff Redundancy Rate |
---|---|---|---|---|---|
Fujian | 0.4105 | 0.2314 | 0.0163 | 0.2146 | 0.5393 |
Guangxi | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Hebei | 0.0000 | 0.0397 | 0.0887 | 0.1971 | 0.1693 |
Henan | 0.0000 | 0.4459 | 0.0000 | 0.2210 | 0.5116 |
Hunan | 0.1396 | 0.4493 | 0.0532 | 0.1325 | 0.6394 |
Jilin | 0.0825 | 0.1331 | 0.0770 | 0.2300 | 0.1954 |
Jiangsu | 0.0679 | 0.3170 | 0.2200 | 0.2702 | 0.3152 |
Shandong | 0.0000 | 0.0014 | 0.0000 | 0.0401 | 0.0413 |
Shanxi | 0.0571 | 0.0249 | 0.0000 | 0.0332 | 0.0395 |
Shaanxi | 0.1641 | 0.0473 | 0.0036 | 0.0892 | 0.1588 |
Xinjiang | 0.3948 | 0.3281 | 0.2600 | 0.1868 | 0.4146 |
Yunnan | 0.4284 | 0.3942 | 0.0207 | 0.0285 | 0.5777 |
Chongqing | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Region | Regression Coefficient | Standard Error | Z-Value | p-Value |
---|---|---|---|---|
Economic Development Level | 0.384 * | 0.1990 | 1.93 | 0.053 |
Industry Structure | 0.0187 ** | 0.0075 | 2.51 | 0.012 |
Environmental Policy | −0.156 *** | 0.0586 | −2.67 | 0.008 |
Level of government intervention | 0.0204 ** | 0.0101 | 2.02 | 0.044 |
Human capital level | −0.346 *** | 0.1230 | −2.80 | 0.005 |
Degree of openness to the outside world | 0.0617 | 0.0649 | 0.95 | 0.342 |
Technology Innovation | 0.000342 * | 0.0002 | 1.76 | 0.079 |
Coal Resource Advantage Degree | −0.0916 | 0.0699 | −1.31 | 0.190 |
_cons | 1.296 | 2.4900 | 0.52 | 0.603 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tao, M.; Lv, S.; Feng, S. Study on the Evaluation of the Development Efficiency of Smart Mine Construction and the Influencing Factors Based on the US-SBM Model. Sustainability 2023, 15, 5183. https://doi.org/10.3390/su15065183
Tao M, Lv S, Feng S. Study on the Evaluation of the Development Efficiency of Smart Mine Construction and the Influencing Factors Based on the US-SBM Model. Sustainability. 2023; 15(6):5183. https://doi.org/10.3390/su15065183
Chicago/Turabian StyleTao, Mei, Shanshan Lv, and Shiqian Feng. 2023. "Study on the Evaluation of the Development Efficiency of Smart Mine Construction and the Influencing Factors Based on the US-SBM Model" Sustainability 15, no. 6: 5183. https://doi.org/10.3390/su15065183