Empowering the Next Generation with Sustainability: Integrating Life Cycle Assessment into Undergraduate Science and Engineering Education
Bibliographic record
Abstract
In an era marked by climate change, resource depletion, and environmental degradation, there is a growing imperative to integrate sustainability into science and engineering education. To effectively address the present global challenges, tertiary-level students pursuing science and engineering, must not only develop expertise in their respective technological fields but also acquire competencies to analyze the sustainability aspects of their work. Tertiary education, therefore, entails an effective integration of sustainable competencies into science and engineering curricula. One essential competency in this aspect is the ability to perform Life Cycle Assessment (LCA)─a useful tool that helps to evaluate the environmental impact of materials and products across their entire lifespan. While the incorporation of quantitative LCA is common for postgraduate programs, its integration at the undergraduate level remains limited. This article describes an instructional approach for introducing LCA to undergraduates as part of a science-based sustainability module. This pedagogical model aims to foster critical thinking and environmental awareness by encouraging students to assess the environmental impact of a product holistically throughout its life cycle (i.e., cradle-to-grave) from raw material acquisition to production, use, and end-of-life disposal. Designed for first-year undergraduate students at Singapore University of Technology and Design (SUTD) with limited prior exposure to sustainability science, this methodology implements an innovative dual-tool pedagogical strategy─use of both Excel-based spreadsheets and commercial LCA software (Ansys Granta EduPack). Furthermore, this pedagogical approach is complemented by guided worksheets as well as real-world case studies providing students with structured support and practical context for applying Life Cycle Assessment. Subsequently, students apply these insights in their design projects. Given the size of the first-year enrollment at SUTD (approximately 500 students), this instructional model also addresses logistical challenges in delivering individualized learning experiences. Nonetheless, this paper offers a practical guide for educators in sustainability, science, and engineering to incorporate LCA into their teaching, thereby empowering the next generation with tools to tackle global sustainability challenges.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".