Artificial intelligence and ESG in resources-intensive industries: Reviewing the use of AI in fisheries, mining, plastics, and forestry
Bibliographic record
Abstract
• The implementation of artificial intelligence systems to ensure supply chain sustainability is increasing in numerous industries. • AI is mobilized to address specific sustainability issues in the fisheries, mining, plastics, and forestry industries. • AI has the potential to address identified issues but generally risks increasing consumption patterns of these industries, thus leading to unsustainable growth. • On one hand, AI has the ability to sift through extensive datasets and offer pragmatic insights to navigate the complex landscape of ESG. On the other, its mobilization can exacerbate inequalities and give rise to novel risks. • Finally, we also recognize AI’s environmental impacts with high energy and water consumption. An important application of artificial intelligence (AI) is to facilitate the implementation of Environmental, Social, and Governance (ESG) across complex and multi-tiered value chains. Solving the challenges faced by (un)sustainable practices in global industries has become a priority for corporate actors, states, and civil society, legitimized by both ethical and legal arguments. In fisheries, mining, plastics, and forestry, despite its potential to improve ESG practices, AI technologies also risk furthering consumption practices, thus negatively affecting socio-economic and environmental sustainability. We highlight the potential for direct positive impacts of AI on each of the environmental, social, and governance mechanisms throughout the four industries. However, we also point to the risks that indirect impacts of AI-powered tools entail on ESG practices and the potential for industries to disconnect themselves from these indirect impacts. Finally, we suggest that the widespread use of AI to optimize consumption processes, without questioning overconsumption patterns, risks creating unsustainable practices at the global level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".