Title

National eco-innovation analysis with big data: A common-weights model for dynamic DEA

Document Type

Journal Article

Publication Title

Technological Forecasting and Social Change

Volume

162

Publisher

Elsevier

School

School of Business and Law

RAS ID

32491

Comments

Kiani Mavi, R., & Kiani Mavi, N. (2021). National eco-innovation analysis with big data: A common-weights model for dynamic DEA. Technological Forecasting and Social Change, 162, article 120369. https://doi.org/10.1016/j.techfore.2020.120369

Abstract

© 2020 Eco-innovations (EI) are activities that are strongly focused on innovation in products, processes, and organizational philosophies to improve environmental performance. Because eco-innovation is a multi-faceted concept comprising of inputs, outputs, operations, the efficiency of resources, and socioeconomic outcomes, big data analytics helps to better understand its dynamics. In this paper, dynamic data envelopment analysis (Dynamic DEA) is employed to analyze the eco-innovation efficiency over time. This paper proposes a novel technique based on goal programming to find a common set of weights (CSW) in relational dynamic DEA. To validate the applicability of the proposed method, eco-innovation of 27 members of the European Union (EU-27) is evaluated during the period 2011–2013 at the national level. Findings show that the discrimination power of the proposed method is higher than relational dynamic DEA and this approach can provide a full ranking of decision-making units (DMUs). Findings further highlight that Germany and Estonia are the highest and the lowest-ranked countries in terms of eco-innovation, respectively.

DOI

10.1016/j.techfore.2020.120369

Research Themes

Natural and Built Environments

Priority Areas

Environmental management, governance and policy

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