Advancing Sustainable Development: Emerging Factors and Futures for the Engineering Field
Bibliographic record
Abstract
This study set out to identify emerging trends in advancing engineering for sustainable development, supporting the engineering workforce to address wicked problems, and strengthening pathways between engineering education, industry, and policy. The following question guided this work: What are the emerging factors impacting the future of global sustainability efforts within engineering, and how can these be amplified to increase the impact of engineering for sustainable development? Using an adapted Delphi method with surveys, focus groups, and member-checking interviews, we hosted the American Society of Mechanical Engineers (ASME) 2022 Engineering Global Development (EGD) Stakeholder Summit. The summit convened industry leaders, innovators, and academics to explore emerging factors impacting the future of global sustainability efforts in engineering. This manuscript synthesizes emerging trends and proposes recommendations for engineering, particularly in the specific focus area of engineering for sustainable development (e.g., ‘humanitarian engineering’, ‘global engineering’). Critical recommendations include the adoption of emerging cultural mindsets, which include: (1) take an interdisciplinary and multi-stakeholder approach, (2) consider dynamic and interconnected systems, (3) increase humility and intercultural competence, (4) prioritize diversity and inclusion, (5) increase localization and center community perspectives, (6) challenge the perception that engineering is neutral, and (7) broaden the goals of engineering. Ultimately, this study highlights pathways forward for the broader engineering community to more effectively contribute to advancing the United Nations Sustainable Development Goals.
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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 | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.022 | 0.035 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".