4.7 Article

A critical review of green growth indicators in G7 economies from 1990 to 2019

Journal

SUSTAINABILITY SCIENCE
Volume -, Issue -, Pages -

Publisher

SPRINGER JAPAN KK
DOI: 10.1007/s11625-023-01397

Keywords

Green growth; Economic growth; Environmental taxes; Environmental win-wins; Build back better; Machine learning

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Green growth policies aim to address both climate change and economic growth, but quantitative and cross-country analyses are still limited. This study develops a classification framework to quantitatively analyze green growth and identifies key policy inputs and environmental/economic outputs. The findings suggest caution in drawing generalized conclusions about the effectiveness of green growth policies, even among highly developed economies.
Green growth policies aim to address both climate change and economic growth and are now prevalent throughout many economies. While green growth is sufficiently assessed in qualitative, case-study-based literature, quantitative and cross-country analyses are still limited. In response to this research deficit, our aim is twofold: (1) to develop a classification framework to quantitatively analyse green growth and (2) to identify key policy inputs and techno-economic or environmental outputs for green growth through a novel taxonomy. We focus on the G7 countries, since they have, historically, tended to align their economic policies. We employ a machine-automated K-means clustering algorithm, as well as correlation analyses, to assess where green growth win-wins, or co-benefits to the economy and environment, might exist. Our findings suggest that enthusiasm should be tempered for public policy commitments for green growth; despite unified green growth policy in G7 countries-significant differences are observed for both policy inputs and environmental/economic outputs. As a result, we caution policymakers and researchers against drawing generalised conclusions about the effectiveness of green growth policies, even among highly developed economies. Finally, our research draws attention to data deficiencies which, evidently, reduce more robust assessment across countries and over time.

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