Sustainable Development in Engineering: A Review of Principles and Definition of a Conceptual Framework
Bibliographic record
Abstract
This article aims to provide engineers and the engineering community with a sustainability conceptual framework setting out the connections linking engineering projects to environmental and social systems. The main principles of sustainable development on the one hand and of sustainable engineering on the other hand are first reviewed, analyzed, and synthesized. Particular attention is paid to the principles put forward by international and national engineering organizations. Key issues emerging for sustainable development in general and sustainable engineering are identified through structured lists of principles. Second, concepts and models originating in natural and social sciences are outlined to shed more light on the ways the various aspects of sustainability are related. The conceptual framework we propose combines the reviewed concepts and models in a relevant manner for sustainable engineering. Engineering projects and physical or social systems prove to be related in manifold ways. Although the most common relations are exposed in the sustainability framework, others have to be further elaborated by eventual users to fully take into account the specificities of the various fields of engineering. Based in part on the framework, a novel systemic definition of sustainable development is also proposed. This definition brings forward a new perspective along which the relevance of the principles can be examined. Finally, applications of the sustainability framework in engineering practice and engineering education are discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".