Li Dandan, Guan Xin, Tang Tingting, Zhao Luyang, Tong Wenrui, Wang Zeyu
Collaborative Innovation Center for Emissions Trading System Co-constructed By the Province and Ministry, Hubei University of Economics, Wuhan, 430072, China; School of Low Carbon Economics, Hubei University of Economics, Wuhan, 430072, China.
Guangzhou Xinhua University, Dongguan, 523133, China.
J Environ Manage. 2023 Dec 15;348:119426. doi: 10.1016/j.jenvman.2023.119426. Epub 2023 Oct 23.
Clean energy is urgently needed to realize mining projects' sustainable development (SD). This study aims to discuss the clean energy development path and the related issues of SD in the ecological environment driven by big data for mining projects. This study adopts a comprehensive research approach, including a literature review, case analysis, and model construction. Firstly, an in-depth literature review of the development status of clean energy is carried out, and the existing research results and technology applications are explored. Secondly, some typical mining projects are selected as cases to discuss the practice and effect of their clean energy application. Finally, the corresponding clean energy development path and the SD analysis model of the ecological environment are constructed based on big data technology to evaluate the feasibility and potential benefits of promoting and applying clean energy in mining projects. (1) It is observed that under different Gross Domestic Product (GDP) growth rates, the new and cumulative installed capacities of wind energy show an increasing trend. In 2022, under the low GDP growth rate, the cumulative installed capacity of global wind energy was 370.60 Gigawatt (GW), and the new installed capacity was 45 GW. With the high GDP growth rate, the cumulative and new installed capacities were 367.83 GW and 46 GW. As the economy grows, new wind energy capacity is expected to increase significantly by 2030. In 2046, 2047, and 2050, carbon dioxide (CO) emissions reductions are projected to be 8183.35, 8539.22, and 9842.73 Million tons (Mt) (low scenario), 8750.68, 9087.16, and 10,468.75 Mt (medium scenario), and 9083.03, 9458.86, and 10,879.58 Mt (high scenario). By 2060, it is expected that CO emissions reduction will continue to increase. (2) The proposed clean energy development path model has achieved a good effect. Through this study, it is hoped to provide empirical support and decision-making reference for the development of mining projects in clean energy, and promote the SD of the mining industry, thus achieving a win-win situation of economic and ecological benefits. This is of great significance for protecting the ecological environment and realizing the sustainable utilization of resources.
实现矿业项目的可持续发展迫切需要清洁能源。本研究旨在探讨大数据驱动下矿业项目在生态环境中的清洁能源发展路径及可持续发展相关问题。本研究采用综合研究方法，包括文献综述、案例分析和模型构建。首先，对清洁能源发展现状进行深入的文献综述，探索现有研究成果和技术应用。其次，选取一些典型矿业项目作为案例，探讨其清洁能源应用的实践与效果。最后，基于大数据技术构建相应的清洁能源发展路径及生态环境可持续发展分析模型，以评估在矿业项目中推广应用清洁能源的可行性和潜在效益。（1）观察发现，在不同的国内生产总值（GDP）增长率下，风能的新增装机容量和累计装机容量均呈上升趋势。2022年，在低GDP增长率下，全球风能累计装机容量为370.60吉瓦（GW），新增装机容量为45GW。在高GDP增长率下，累计装机容量和新增装机容量分别为367.83GW和46GW。随着经济增长，预计到2030年新风能装机容量将显著增加。在2046年、2047年和2050年，预计二氧化碳（CO）减排量分别为8183.35、8539.22和9842.73百万吨（Mt）（低情景）、8750.68、9087.16和10468.75Mt（中情景）以及9083.03、9458.86和10879.58Mt（高情景）。到2060年，预计CO减排量将继续增加。（2）所提出的清洁能源发展路径模型取得了良好效果。通过本研究，希望为清洁能源领域矿业项目的发展提供实证支持和决策参考，推动矿业行业的可持续发展，从而实现经济和生态效益的双赢。这对于保护生态环境和实现资源的可持续利用具有重要意义。
