Mapping biodiversity risk metrics in finance: A comparative analysis of MSCI and LSEG using the ENCORE framework
Bibliographic record
Abstract
Biodiversity loss, caused by factors such as habitat destruction, pollution, climate change, overexploitation, and invasive species, poses significant risks to ecosystems, businesses, and economies. This research paper examines the interaction between biodiversity risks and the integration of these risks into financial decision making through the availability of biodiversity metrics. The biodiversity metrics available through MSCI and LSEG were mapped against the ENCORE (Exploring Natural Capital Opportunities, Risks, and Exposure) impact factors to understand what risks and opportunities can be measured and used for decision-making. The findings reveal gaps in biodiversity coverage, particularly when looking at the environmental factors of non-GHG air pollutants, soil pollutants, and water pollutants. The analysis was done using the MSCI ACWI index (Morgan Stanley Capital International All Country World Index) dataset, comprising of more than 2800 international companies. The results showed that ENCORE environmental factors significant to industries had 35% coverage in MSCI, and 24% in LSEG. Furthermore, the variation in data coverage between the two sources identifies an opportunity to expand our understanding of environmental issues by using complementary data to analyze one factor such as freshwater ecosystem and GHG emissions. This research sheds light on the current gaps between the measurement of biodiversity issues and business reporting.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".