Abstract
Traditional theories and methods for comprehensive environmental performance evaluation are challenged by the appearance of big data because of its large quantity, high velocity, and high diversity, even though big data is defective in accuracy and stability. In this paper, we first review the literature on environmental performance evaluation, including evaluation theories, the methods of data envelopment analysis, and the technologies and applications of life cycle assessment and the ecological footprint. Then, we present the theories and technologies regarding big data and the opportunities and applications for these in related areas, followed by a discussion on problems and challenges. The latest advances in environmental management based on big data technologies are summarized. Finally, conclusions are put forward that the feasibility, reliability, and stability of existing theories and methodologies should be thoroughly validated before they can be successfully applied to evaluate environmental performance in practice and provide scientific basis and guidance to formulate environmental protection policies.
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Notes
The UN Climate Change Conference was held on October 23, 2013 in Warsaw, the capital of Poland. Agreements on some important subjects were reached.
NSF has released the plan “Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA)”.
These data include emission loads and emission ratios of oxynitride, sulfur dioxide, carbon dioxide, methane, nitrous oxide, net-generated energy, etc.
International Data Corporation (IDC) presented relevant research in their report “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East”.
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We appreciate the support of the Program for the Major Projects in Philosophy and Social Science Research of the Ministry of Education of China (No. 14JZD031), National Natural Science Foundation of China (Nos. 71471001, 71171001 and 71503001), and New Century Excellent Talents in University (No. NCET-12-0595).
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Song, ML., Fisher, R., Wang, JL. et al. Environmental performance evaluation with big data: theories and methods. Ann Oper Res 270, 459–472 (2018). https://doi.org/10.1007/s10479-016-2158-8
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DOI: https://doi.org/10.1007/s10479-016-2158-8