Resource Accounting and Waste Impact: Limitations of Exergy as a Sustainability Tool
Bibliographic record
Abstract
Exergy is a thermodynamic concept that has been widely promoted for assessing and improving sustainability, notably in the characterization of resources and wastes. Despite having notable benefits, exergy is often misused by authors who tend to apply it as an intrinsic characteristic of an object (i.e., as a static thermodynamic property). Using both theoretical and empirical evidence the authors introduce the challenges involved with applying exergy as an intrinsic characteristic matter with particular focus on resource value and waste impact. These challenges lead to an in-depth discussion of current major reference environment formulations and reveals that the properties of exergy reference environments are not reconcilable with the properties of the natural environment. The authors conclude by arguing that exergy practitioners should abandon attempts to formulate standard comprehensive reference environments and return to process dependent reference environments that exergy was originally based upon. In this regard, the authors are proposing that exergy be seen as a context- or environment-dependent decision-making tool and not as an intrinsic characteristic of matter.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".