Broadening GHG accounting with LCA: application to a waste management business unit
Bibliographic record
Abstract
In an effort to obtain the most accurate climate change impact assessment, greenhouse gas (GHG) accounting is evolving to include life-cycle thinking. This study (1) identifies similarities and key differences between GHG accounting and life-cycle assessment (LCA), (2) compares them on a consistent basis through a case study on a waste management business unit. First, GHG accounting is performed. According to the GHG Protocol, annual emissions are categorized into three scopes: direct GHG emissions (scope 1), indirect emissions related to electricity, heat and steam production (scope 2) and other indirect emissions (scope 3). The LCA is then structured into a comparable framework: each LCA process is disaggregated into these three scopes, the annual operating activities are assessed, and the environmental impacts are determined using the IMPACT2002+ method. By comparing these two approaches it is concluded that both LCA and GHG accounting provide similar climate change impact results as the same major GHG contributors are determined for scope 1 emissions. The emissions from scope 2 appear negligible whereas emissions from scope 3 cannot be neglected since they contribute to around 10% of the climate change impact of the waste management business unit. This statement is strengthened by the fact that scope 3 generates 75% of the resource use damage and 30% of the ecosystem quality damage categories. The study also shows that LCA can help in setting up the framework for a annual GHG accounting by determining the major climate change contributors.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".