Using sustainable development indicators (SDIs) for sustainability assessment of surface mining operations in the oil sands projects: applicability, usefulness and cost
Bibliographic record
Abstract
Sustainable development indicators (SDIs) can be found within currentlyexisting approaches, strategies, models, appraisals, and methodologies for environmental and sustainability assessment. Conceptually, the design and implementation of SDIs brings together different stakeholders towards finding the balance among economic, social, and environmental development; however, questions surround SDIs for the assessment of sustainability of projects (e.g. surface mining operations) or industries (e.g. oil and gas) for which the development of SDIs still is in its infancy: (1) Do the SDIs properly align theory with practice?; (2) Do the SDIs meet their intent?; and (3) Can the stakeholders and project proponents afford the implementation of SDIs? Individual efforts have been made to establish a set of SDIs by companies developing projects; and regulatory systems (in some way predecessors of SDIs) require certain levels of investment to meet a minimum level of performance, particularly on environmental grounds. But large industrial projects (such as oil sands projects, which include surface mining operations) do not have a comprehensive set of SDIs to benchmark sustainable performance and/or measure sustainable development. Questions remain regarding the rate at which extractive industry companies align with more sustainable practices, whether it is the applicability of SDIs, their degree of usefulness, or the costs of development and implementation of SDIs, or other factors. The present work analyzes the development and implementation of SDIs in surface mining operations for oil sands projects, highlights the benefits of using SDIs, proposes an alternative framework for SDI in the Canadian oil sands industry, and offers Energy and Sustainability IV 55 www.witpress.com,
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 | 0.001 | 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 | 0.000 |
| 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".