Pension Funds Carbon Footprint and Investment Trade-Offs

Boermans,; Galema, Rients

doi:10.2139/ssrn.2952338

Cited by 2 publications

(2 citation statements)

References 58 publications

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“…These changing perspectives on how to monitor, track, and analyze actions aiming to mitigate the effects of climate change are being encoded in various ways. For example, via the ESG scoring frameworks being developed in the financial investment space or via the development of the notion of carbon footprints of investment portfolios, see examples in Boermans & Galema (2019) and Boermans et al (2017) who studied carbon footprints of large pension funds. In the asset management space, there is an ongoing debate about how to best incentivize corporations in order to foster private sector initiatives that will actively create a paradigm shift in decision-making toward more sustainable and environmentally friendly corporate decisions, eventually leading to a reduction in environmental impact.…”

Section: Introduction To Divfolio Applicationmentioning

confidence: 99%

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Marupanthorn,

Peters,

Ofosu-Hene

et al. 2024

Ann. actuar. sci.

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This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

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Section: Introduction To Divfolio Applicationmentioning

confidence: 99%

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Marupanthorn,

Peters,

Ofosu-Hene

et al. 2024

Ann. actuar. sci.

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“…Further, Scope 3 represents the most significant emissions reduction opportunities going forward, and a full assessment of Scope 3 emissions is critical for understanding the end-to-end impacts of carbon taxes and climate policies on individual firms [10]. However, the analyses of firm-level emissions by external stakeholders are usually limited to Scope 1 and Scope 2 emissions [11][12][13]. This is due to the following three issues associated with Scope 3:…”

Section: Introductionmentioning

confidence: 99%

Scope 3 emissions: Data quality and machine learning prediction accuracy

Nguyen,

Diaz-Rainey,

Kitto

et al. 2023

PLOS Clim

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Investors’ sophistication on climate risk is increasing and as part of this they require high-quality and comprehensive Scope 3 emissions data. Accordingly, we investigate Scope 3 emissions data divergence (across different providers), composition (which Scope 3 categories are reported) and whether machine-learning models can be used to predict Scope 3 emissions for non-reporting firms. We find considerable divergence in the aggregated Scope 3 emissions values from three of the largest data providers (Bloomberg, Refinitiv Eikon, and ISS). The divergence is largest for ISS, as it replaces reported Scope 3 emissions with estimates from its economic input-output and life cycle assessment modelling. With respect to the composition of Scope 3 emissions, firms generally report incomplete composition, yet they are reporting more categories over time. There is a persistent contrast between relevance and completeness in the composition of Scope 3 emissions across sectors, with low materiality categories such as travel emissions being reported more frequently than typically high materiality ones, such as the use of products and processing of sold products. Finally, machine learning algorithms can improve the prediction accuracy of the aggregated Scope 3 emissions by up to 6% and up to 25% when each category is estimated individually and aggregated into total Scope 3 emissions. However, absolute prediction performance is low even with the best models, with the accuracy of estimates primarily limited by low observations in specific Scope 3 categories. We conclude that investors should be cognizant of Scope 3 emissions data divergence, incomplete reporting of Scope 3 categories, and that predictions for non-reporting firms have high absolute errors even when using machine learning models. For both reported and estimated data, caveat emptor applies.

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Pension Funds Carbon Footprint and Investment Trade-Offs

Cited by 2 publications

References 58 publications

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Scope 3 emissions: Data quality and machine learning prediction accuracy

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