LAUSR

Abstract 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.