Board 26: Reducing Environmental Impact in Higher Education: Curriculum Design for the Sustainable-Unit Operations Laboratory
Bibliographic record
Abstract
As outlined in the Paris Agreement, the global commitment to achieving net-zero emissions by 2050 necessitates a multifaceted approach encompassing clean energy initiatives and carbon taxation.Higher education institutions, recognizing their role as key contributors to sustainability, are increasingly focusing on reducing their carbon footprint.The teaching laboratories, essential for various disciplines, contribute significantly to the university's carbon footprint.In this study, we applied the common practices of Life Cycle Analysis (LCA) in the industry to the Unit Operations Laboratory, which resembles the industrial settings yet focuses on teaching and learning that may not have set production scopes nor define operation conditions and processes (i.e. for learning purposes and study impact of various factors on common chemical processes).As learning is the main objective in undergraduate laboratories, LCA methodologies related to laboratory equipment and incorporating technical information on global climate initiatives, clean energy, and the Paris Agreement need to be followed, but some modifications to make such calculations possible.To illustrate the feasibility of this approach, a case study on bioethanol production through yeast fermentation and subsequent distillation processes is employed as a proof-ofconcept.This case study serves as a platform for estimating LCA and redesigning experiments with the aim of reducing the carbon footprint.Since not all chemical process units are designed the same (i.e.sizes, power/production capacity), this project is a collaborative effort internationally amongst universities with similar equipment but different sizes.The carbon footprint approaches, and the preliminary data collected can enable fine-tuning and test the robustness of the approaches and models.The Unit Operations Laboratory emerges as a valuable platform for students to assess their carbon footprint and actively engage in practical LCA applications.This research contributes to the broader goal of embedding sustainability principles within the educational framework, fostering a generation of professionals equipped with the knowledge and skills necessary to address environmental 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".