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

机译:大数据的国家生态创新分析:一种动态DEA的共同权重模型

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摘要

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.
机译:生态创新(EI)是强烈专注于产品,流程和组织哲学的创新,以改善环境绩效的活动。由于生态创新是一种多方面的概念,包括输入,产出,操作,资源效率和社会经济结果,大数据分析有助于更好地了解其动态。本文采用动态数据包络分析(动态DEA)随着时间的推移分析了生态创新效率。本文提出了一种基于目标规划的新技术,以在关系动态DEA中找到一组常见的重量(CSW)。为验证拟议方法的适用性,在2011 - 2013年期间在国家一级的情况下评估了27名欧盟成员(EU-27)的生态创新。调查结果表明,所提出的方法的辨别力高于关系动态DEA,这种方法可以提供决策单元(DMUS)的全部排名。结果进一步强调德国和爱沙尼亚分别在生态创新方面是最高和最低的国家。

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