Do ESG Frameworks Capture Corporate Health Impacts? An Analysis of the Food and Beverage Industry
Bibliographic record
Abstract
Investors use information about companies’ social and environmental performance to make investment decisions, a strategy known as Environmental, Social, and Governance (ESG) investment analysis. ESG screening may offer a mechanism to incentivize corporations to improve their health impact. However, there has been limited investigation of the extent to which ESG investment frameworks capture corporate health impacts in major industries. In this study, we sought to characterize the extent to which ESG frameworks address the health-impacting activities of the food and beverage (F&B) industry. To do this, we conducted a deductive framework analysis during the period of September 2023 to March 2024. Specifically, we identified gaps in existing ESG frameworks by comparing the content of five ESG reporting standards and rating systems to the HEALTH-CORP-FB typology, an evidence-based typology that describes the health-impacting activities of the F&B industry across seven domains (Governance Practices, Political Practices, Preference and Perception Shaping Practices, Economic Practices, Employment Practices, Products and Services, and Environmental Practices). To further assess how ESG frameworks account for the health-impacting activities of the F&B industry, we classified health-focused ESG fields in the packaged foods subindustry by two attributes: relevance to the assigned HEALTH-CORP-FB activity (low, medium, high) and type of business operations addressed (e.g., process, performance). Results indicate that, on average, the ESG fields (n = 1348) covered 39% of the 89 HEALTH-CORP-FB activities (range across frameworks: 27–48%). Higher proportions of activities in the Governance, Environmental, Employment, and Economic Practices domains (range across domains: 43–87%) were represented than activities in the Products and Services, Preference and Perception-Shaping Practices, and Political Practices domains (17–36%). Fields assigned to the latter domains were also less likely to be deemed highly relevant and to measure corporate performance. We conclude that the ESG frameworks included in this study capture some of the activities of the F&B industry that affect population health and health equity; however, critical gaps remain. We discuss how integrating key health-focused ESG indicators (e.g., revenue generation from ultra-processed foods) into existing frameworks could enable investors, public health organizations, civil society, and shareholder advocates to strengthen the accountability of the F&B sector with respect to health.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".