The Environmental Impacts of AI and Digital Technologies
Bibliographic record
Abstract
This study aims to investigate the environmental impacts of AI and digital technologies, identify potential mitigation strategies, and assess the role of policy, regulation, and public awareness in fostering sustainable practices within this domain. Employing a qualitative research methodology, this study collected data through semi-structured interviews with 27 professionals across the technology sector, environmental research, policy-making, and academia. Thematic analysis was used to analyze the interview transcripts, allowing for the identification of main themes and categories related to the environmental impacts of AI and digital technologies and the exploration of potential mitigation strategies. Five main themes emerged from the analysis: Direct Environmental Impact, Mitigation Strategies, Technological Innovations, Policy and Regulation, and Public Awareness and Engagement. Each theme encompasses various categories and concepts, such as Energy Consumption, E-Waste, Renewable Energy Adoption, Sustainable Design, Energy-Efficient Hardware, Legislation and Standards, Educational Campaigns, and Digital Literacy. The findings highlight the complex and multifaceted nature of AI and digital technologies' environmental impacts, along with the crucial role of innovative mitigation strategies and comprehensive policy frameworks in addressing these challenges. The study concludes that while AI and digital technologies offer tremendous potential for advancing sustainable development, their deployment must be carefully managed to minimize negative environmental impacts. It underscores the importance of integrating sustainability considerations into the development and deployment of these technologies, alongside fostering robust policy and regulatory frameworks and enhancing public awareness and engagement to achieve a sustainable digital future.
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 | 0.000 |
| 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 | 0.000 |
| 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".