Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research

Abstract

Sustainable finance is a rich field of research. Yet, existing reviews remain limited due to the piecemeal insights offered through a sub-set rather than the entire corpus of sustainable finance. To address this gap, this study aims to conduct a large-scale review that would provide a state-of-the-art overview of the performance and intellectual structure of sustainable finance. To do so, this study engages in a review of sustainable finance research using big data analytics through machine learning of scholarly research. In doing so, this study unpacks the most influential articles and top contributing journals, authors, institutions, and countries, as well as the methodological choices and research contexts for sustainable finance research. In addition, this study reveals insights into seven major themes of sustainable finance research, namely socially responsible investing, climate financing, green financing, impact investing, carbon financing, energy financing, and governance of sustainable financing and investing. To drive the field forward, this study proposes several suggestions for future sustainable finance research, which include developing and diffusing innovative sustainable financing instruments, magnifying and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, devising and unifying policies and frameworks for sustainable finance, tackling greenwashing of corporate sustainability reporting in sustainable finance, shining behavioral finance on sustainable finance, and leveraging the power of new-age technologies such as artificial intelligence, blockchain, internet of things, and machine learning for sustainable finance.