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We investigate how conventional asset managers account for environmental, social, and governance (ESG) factors in their investment process. We do so on the basis of an international survey among fund managers. We find that many conventional managers integrate responsible investing in their investment process. Furthermore, we find that ESG information in particular is being used for red flagging and to manage risk. We find that many conventional fund managers have already adopted features of responsible investing in the investment process. Furthermore, we argue and show that ESG investing is highly similar to fundamental investing. We also reveal that there is a substantial difference in the ways in which U.S. and European asset managers view ESG.

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The fossil fuel divestment movement tries to increase awareness about the need for climate action and heralds divestment from fossil fuel producers as a means to combat climate change. Financial investors are increasingly showing interest in the nonfinancial impact of companies they invest in, i.e. responsible investing. However, they also want to be assured of sufficient returns and limited risks to support the living costs of their ultimate beneficiaries. In this context, we investigate the impact of divestment and the transition of the energy system on investment performance. We rely on an international sample of almost seven thousand companies and study a period of forty years. Further, we investigate scenarios with very different pathways to the transition of the energy system. We find that the investment performance of portfolios that exclude fossil fuel production companies does not significantly differ in terms of risk and return from unrestricted portfolios. This finding holds even under market conditions that would benefit the fossil fuel industry. We conclude that divesting from fossil fuel production does not result in financial harm to investors, even when fossil fuels continue to play a dominant role in the energy mix for some time. Key policy insights. Financing the exploration and exploitation of fossil fuel resources is increasingly being regarded as controversial, leading to divestment from this industry.. Fossil fuel divestment does not seem to significantly harm financial investors and is not at odds with the fiduciary duty of institutional investors. This paves the way for more extensive initiatives to promote fossil fuel divestment.. A smooth energy transition will most likely erode the profitability of fossil fuel firms and their ability to invest. Therefore, governments cannot rely on the fossil fuel industry to finance the energy transition.

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Socially responsible investing and management style of mutual funds in the euronext stock markets. s.n.

The environmental Kuznets curve (EKC) explains the dynamics associated with income and environmental quality. This study utilizes bibliometric analysis and data visualization techniques and empirically determines the tendencies and patterns in the EKC literature. Furthermore, the study explains intellectual structure, construct development, evolution, collaborations, and research clusters within the EKC research domain during the last two decades, from 2000 to 2020. The study empirically analyzes 2,218 articles and 55,051 references from 328 journals, 4,146 authors, 99 countries, and 50 subject categories. The study used co-citation analysis to examined the noticeable research articles, journals and authors' contribution. Moreover, the co-occurrence analysis examined the prominent countries, institutions and keywords in the concerned literature. Most studies in EKC domain focus on developing regions facing the dual challenge of growth and environmental sustainability. The current initiative categorizes the EKC knowledge domain into major research areas with the help of different clusters namely causality analysis, non-renewable energy, energy consumption, the EKC, and industrial pollution. The study further discusses emerging trends that provide future research fronts and intellectual development within the EKC framework.

Background: Factor analysis (FA) has been widely applied in microarray studies as a data-reduction-tool without any a-priori assumption regarding associations between observed data and latent structure (Exploratory Factor Analysis).