Understanding Nature Related Challenges and Opportunities in O&G Company
Bibliographic record
Abstract
Abstract It is increasingly recognized that companies need to accelerate actions to halt and reverse the loss of nature and contribute to nature positive. In 2022, the "Kunming-Montreal Global Biodiversity Framework (GBF)" was adopted during the fifteenth meeting of the Conference of the Parties (COP15), and nature-related initiatives such as the TNFD Framework have been published. INPEX recognizes the importance of addressing the increasingly diverse global environmental issues connected to our business activities, including climate change, biodiversity, water, and waste management. In INPEX, six of the seven core sustainability themes in ISO 26000 were identified as areas of high importance for INPEX and our stakeholders, and these form our Material Issues. One of the Material Issues is biodiversity conservation and water management. Recognizing biodiversity conservation as a significant global environmental issue, the "Policy and Commitments on Biodiversity Conservation" was established and published in December 2022, following a resolution by the Board of Directors. The type and degree of impacts that our operations have on biodiversity differ depending on the scale, activities, and location of each project. Accordingly, the biodiversity conservation efforts required for each project also differ. Therefore, we assess the importance of biodiversity in the areas in which we operate, and the risks and impacts on biodiversity that each project brings. We then apply a mitigation hierarchy to plan strategies for avoiding, mitigating, and compensating for those risks and impacts, and to conserve biodiversity, in environmentally sensitive areas that are particularly important. This paper presents the challenges and opportunities that INPEX has identified through the internal nature readiness assessment based on WBCSD's assessment tool (2021), and the internal assessment trial using the TNFD Framework "LEAP Approach". The internal self-assessment using WBCSD's nature readiness assessment tool (2021) revealed several company-wide challenges and opportunities in INPEX's nature readiness. Some progressing areas were the establishment and publication of nature related policies and commitments, and the implementation of actions based on mitigation hierarchy in operations. On the contrary, setting quantitative targets and KPIs on material topics and evaluating dependencies have been identified as challenges. An internal assessment trial of TNFD Framework "LEAP Approach" was implemented for INPEX's operational sites in Japan. Through the assessment, the practicality and challenges of applying the LEAP Approach were identified. The LEAP Approach provides the concepts and guidance to collect data and assess location-specific impacts, dependencies, risks, and opportunities. Some key challenges identified were the scoping of the assessment, prioritization of material locations, availability and quality of location-specific data, and selection of appropriate metrics and indicators.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".