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Sustainable Cities and Society
Volume 79, 2022, 103693

A taxonomy of green governance: A qualitative and quantitative analysis towards sustainable development

Jahira Debbarma, Yongrok Choi

Program in Industrial Security Governance, Inha University; 100 Inha-ro, Michuhol-gu, Incheon 22212, South Korea.

Abstract

It is undeniable that our environment is constantly evolving and citizens are facing new issues and challenges related to the environment around the world. Green governance is essential to achieve the goals agreed upon by local and global governments. The concept of green governance makes it possible to understand the integration of the actors of each governance form during decision-making. In this article, we identify the research gap and propose a taxonomy of green governance for sustainable development. We used factor analysis to construct the taxonomy of green governance. We also proposed the critical influencing factors of green governance to build sustainable development. To evaluate the importance of green governance for reducing CO2 emissions and other energy-related consumption, this study conducted two case studies with empirical analysis on the OECD Indian dataset of green growth indicators. The Indian green growth indicators are predicted using a machine learning technique that employs linear digression, support vector machine (SVM), and Gaussian process. The analysis shows that the taxonomy of green governance—global governance, adaptive governance, climate governance, ecological governance, self-governance, energy governance, and information technology (IT) governance—are related to each other and can work on the same objective by pursuing different activities. In addition, the case study analysis shows that the SVM is the superior technique in terms of predicting the time series data in this study. Based on the analysis, this study suggest that green governance is vital for achieving global sustainable goals for future growth, and policy-makers should keep this in mind when making environmental policy decisions.

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