Environmental performance evaluation with big data: theories and methods

Ma-Lin Song (), Ron Fisher, Jian-Lin Wang and Lian-Biao Cui
Additional contact information
Ma-Lin Song: Anhui University of Finance and Economics
Ron Fisher: Griffith University
Jian-Lin Wang: Dongbei University of Finance and Economics
Lian-Biao Cui: Anhui University of Finance and Economics

Annals of Operations Research, 2018, vol. 270, issue 1, No 23, 459-472

Abstract: 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.

Keywords: Big data; Environmental management; Environmental performance; Data envelopment analysis; Life cycle assessment (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (42)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-016-2158-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2158-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-016-2158-8

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().