A Review on Consequential Life Cycle Assessment in the Power Sector
Bibliographic record
Abstract
The existing policy for greenhouse gas (GHG) abatement aims at decarbonisation of the power sector. The interrelations between the power sector and other economic sectors raise a question of whether the GHG emission reduction policy in the power sector is as effective as it is claimed. Consequential life cycle assessment (CLCA) has been developed to assess the environmental impacts of any industrial/productive sector in relation with changes in the policy and its indirect impacts on other economic sectors. This review is conducted on CLCA studies in the power sector in terms of system boundaries expansion and socio-economic interactions and the ability to quantify indirect environmental impacts. It is indicated that CLCA expanded the system boundaries by applying mutatis mutandis assumption to include several affected products with various scales of change. Economic modelling tools are frequently applied to make assumptions on the extent of change. The applications of these tools also help to identify the environmental profile of product systems and the socio-economic changes such as economic growth and consumer behaviours. Thanks for the expansion of system boundaries and inclusion of socio-economic interactions, the total environmental impacts of power sector are comprehensively quantified. The variations of the total environmental impacts, with different magnitude of change, were observed in several reviewed case studies. In term of GHG emissions, some products become cleaner, for example battery; however, in most of the cases, the power system in general becomes more polluted when indirect impacts on other economic sectors are included.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".