Water Management and Corporate Social Performance in the Food and Beverage Industry
Bibliographic record
Abstract
The food and beverage industry is one of the most water intensive industries. Therefore, an effective and efficient water management, based on eco-system related indicators, is crucial. This study analyzes the connection between indicators that address sustainable water management as a subgroup of ecosystem management and the general corporate social performance of firms. The study explores which water eco-system indicators are used in the food and beverage industry to assess corporate water risk management. Secondly, we analyzed the relationship between corporate water risk management and overall corporate social performance. Based on an analysis of 61 firms in the food and beverage sector, our results suggest that the most used indicators were Operations’ Dependency on Freshwater, Change in Water Supply, Use of Water in the Facilities, Collaboration with Communities, and Water Risks for Agricultural Inputs. Indicators addressing an inside-out perspective, such as Impacts on Communities were less often used. Furthermore, we found that the firms’ general corporate social performance, measured by MSCI KLD-ESG indicators, is a good predictor for their use of water indicators. We conclude that the firms in the sample follow an outside-in approach for their water management activities and that water management is a significant part of corporate social responsibility activities in the sector because the business performance of food and beverage firms is interwoven with their water management activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".