Financial Modelling Strategies for Social Life Cycle Assessment: A Project Appraisal of Biodiesel Production and Sustainability in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Social Life Cycle Assessment (S-LCA) is a rapidly evolving social impact assessment tool that allows users to identify the social impacts of products along with their life cycles. In recent years, S-LCA methodologies have been increasingly applied to energy systems and resources with notable success yet with limited reliability and even less flexibility or geographic specificity. In response, this study develops a novel assessment tool, named the GreenZee model, to reflect the social impacts of products and their sustainability using local currency units. The model is developed through evaluating both qualitative and quantitative inputs that capture the perceived monetary value of social impacts. To demonstrate the operationalization of the model, we explore a hypothetical case study of the biodiesel industry in Newfoundland and Labrador (NL), Canada. Results indicate that biodiesel production in NL would have positive socio-cultural impacts, high cultural values, and would create employment opportunities for locals. Overall, the GreenZee model provides users with a relatively simple approach to translate a variety of qualitative and quantitative social impact inputs (as importance levels) into meaningful and understandable financial outputs (as strength levels). We argue that building and testing models such as the GreenZee are crucial to supporting more flexible approaches to life cycle assessments that need to address increasingly complex social categories, cultural values, and geographic specificity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".