Technological challenges of green innovation and sustainable resource management with large scale data

Malin Song, Ron Fisher and Yusen Kwoh

Technological Forecasting and Social Change, 2019, vol. 144, issue C, 361-368

Abstract: This Special Volume (SV) focuses on finding effective ways to achieve sustainable development. This introductory article contains an overview of the articles, divided into four topics: (a) sustainable natural resource management based on large scale data (b) environmental pollution and prevention through large scale data, (c) technological challenges involving green innovation, and (d) big data modeling and mining involving green growth. Taking into account the seriousness of environmental pollution, sustainable development papers based on big data are clearly the most important contributions of this SV. Some authors reported on the relationship between environmental pollution and influencing factors, such as shelling, traffic density, e-commerce environmental regulation, technological innovation and green supply chain behavior. Some authors investigated sustainable natural resource management based on large scale data, including energy, generation and agricultural land. Some authors studied green innovation from various perspectives, and built different models to analyze green growth problems. The findings and recommendations are positive and encouraging. However, it is clear that many additional challenges must be solved in order to establish and support systems which will guide and monitor transformations into sustainable, livable, and low pollution. Much work remains, but the wisdom in the articles in this SV will help societies transition.

Keywords: Green innovation; Sustainable natural resource management; Big data application; Environmental pollution; Pollution prevention (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (78)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:144:y:2019:i:c:p:361-368

DOI: 10.1016/j.techfore.2018.07.055

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