Support for the Task Force on Climate-Related Financial Disclosures (TCFD) and Impact on Non-Renewable Energy Sector Investments in United States Public Pension Funds
Bibliographic record
Abstract
The recommendations of the Task Force on Climate-Related Financial Disclosures (TCFD) are \nexpected to play an important role in advancing the transition towards a climate target-aligned \neconomy. However, the impact of the TCFD framework on investment decisions in sectors \nvulnerable to climate-related risks, such as the non-renewable energy sector, has yet to be \nstudied. Applying the lens of institutional theory, this study investigates whether normative \npressures stemming from voluntary support for the TCFD influence non-renewable energy sector \ninvestment decisions by public pension funds in the United States. This study employs a \nquantitative approach to pursue three interconnected objectives. First, identify the public pension \nfunds in the United States that support the TCFD, and among those, identify their stage of \nimplementation of the TCFD’s recommendations. Second, assess whether fund size and location \nare influential in determining TCFD support or implementation stage. Third, examine whether \nthe exposure to non-renewable energy sector investments before and after the release of the \nTCFD recommendations in 2017 significantly differs depending on whether a fund supports the \nTCFD or not. The study’s findings reveal that 8 of 191 sampled public pension funds in the \nUnited States support the TCFD and are at various stages of implementation of the \nrecommendations. Fund size was identified as a significant predictor of both TCFD support and \nstage of implementation, with larger funds more likely to be supporters and more advanced in \nimplementation. California and New York were the only states with public pension funds that \nsupport the TCFD. Location, specifically whether a fund is in California or New York, emerged \nas a significant predictor of TCFD implementation stage, with funds in these two states being \nmore advanced in implementation. Lastly, no significant differences in exposure to non-renewable \nenergy sector investments before and after the TCFD recommendations were released \nbetween TCFD supporters and non-supporters were found. These findings contribute to the \nliterature on the implementation of the TCFD framework and its impacts on investment decision-making. \nThey also apply institutional theory in a new context and demonstrate that normative \npressures resulting from voluntary TCFD support have not redirected institutional investments \naway from the non-renewable energy sector, despite its significant climate-related risks. These \nfindings may be of interest to policymakers working towards a climate target-aligned economy \nand considering regulatory measures to influence institutional investment decisions. They also may be of interest to public and private pension funds seeking to understand market engagement \nwith the TCFD and its impact on investment decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".