Evaluating Sustainability on Projects Using Indicators
Bibliographic record
Abstract
The concept of balancing people, planet, and profit to maximize the absolute value of an enterprise is known as sustainability. It is concerned with the economic, social, and environmental effects of an enterprise in the long term. However, in practice, this definition does not provide companies with a meaningful framework to integrate sustainability into their projects, which by definition are one-off endeavors. Given this divide between the long-term nature of sustainability and the temporary nature of projects, companies have found it difficult to incorporate relevant sustainability indicators into project baselines. In this chapter, the authors examine a methodology for integrating sustainability into project baselines for consultants in the industrial and resource extraction fields. The methodology is comprised of an indicator set and a procedure for using the indicator set. This chapter’s goal is to help standardize the sustainability process, making it easier to implement and more mainstream. The objectives of this chapter are: (1) identify different sustainability indicator sets and their strengths and weaknesses; (2) explain what a multi-level analytical hierarchy project is and why it is important to integrating sustainability into such projects; and (3) state the steps in a procedure to integrate sustainability into project baselines.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".