The Role of Artificial Intelligence in Reducing Environmental Impacts in The Oil and Gas Industry from A Legal Perspective: A Comparative and Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite playing a significant role in energy supply and advancing the global economy, the oil and gas industry is recognized as one of the main sources of environmental pollution. This pollution includes the destruction of natural habitats, contamination of water and soil resources, and the widespread emission of greenhouse gases, which necessitates effective intervention by legal regulations and new technologies. In this context, artificial intelligence, as a transformative technology, has unique capabilities in identifying and mitigating these environmental impacts. This research takes a legal-analytical approach to examine the role of artificial intelligence in managing the environmental challenges of the oil and gas industry, exploring the legal and executive dimensions related to the application of this technology. In this regard, topics such as identifying and preventing oil spills, optimizing energy consumption, and continuously monitoring the quality of biological resources are discussed from a legal perspective. Additionally, challenges such as legal responsibility in the errors of artificial intelligence systems, data ownership, compliance with international environmental standards, and legal dimensions of cybersecurity are investigated and analyzed. The results of the research indicate that despite the high potential of artificial intelligence in reducing harmful environmental impacts and enhancing operational efficiency, the lack of clear legal frameworks and existing regulatory gaps pose serious obstacles to the optimal utilization of this technology. Comparative studies conducted with the legal systems of advanced countries such as Norway, Canada, and the Netherlands illustrate that success in integrating artificial intelligence with environmental requirements necessitates the formulation of precise regulations and advanced technological infrastructures. Ultimately, this research emphasizes the necessity of synergy between technology and legal regulations by providing practical suggestions such as formulating special legal standards, strengthening administrative oversight, and designing transparent mechanisms regarding data ownership and responsibility. The main objective of this research is to create a foundation for the responsible use of artificial intelligence in the oil and gas industry and to move towards sustainable development and environmental protection
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.
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 | 0.001 |
| 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 it