Improving Organizational Life Cycle Assessment (O-LCA) through a Hospital Case Study
Bibliographic record
Abstract
In this thesis, I advance methods and data for organizational life cycle assessment (O-LCA) through a novel application that supports sustainability management in healthcare. In essence, the term sustainability management broadly encompasses decision-making for sustainability – considering environmental, social, and economic aspects – at all levels of coupled human and natural systems (e.g., at the level of individuals, organizations, municipalities, provinces, countries, and international bodies). In Chapter 1, I establish the conceptual foundations for the work presented in Chapters 2-4. In Chapter 2 (co-authored with Jair Santillán-Saldivar, Cassandra Thiel, Guido Sonnemann, and Steven B. Young), I conduct a comprehensive scoping review of the literature on “healthcare sustainability” – based on a representative sample from over 1,700 articles published between 1987 and 2017 – that highlights largely untapped opportunities to use industrial ecology approaches (such as LCA) to build an evidence base for this burgeoning domain of sustainability management. In Chapter 3 (co-authored with Steven B. Young), I constructively critique the existing methodology for O-LCA, with concrete proposals – particularly the use of basic statistical sampling and inference techniques – to strike a better balance of scientific rigour and practical feasibility in O-LCA. In Chapter 4 (co-authored with Steven B. Young), I test and demonstrate these proposals through an O-LCA of a Canadian hospital, in which I compiled new LCA data for approximately 200 goods and services used in healthcare. Finally, in Chapter 5, I reflect upon my contributions in this thesis, and on opportunities for future work.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".