External-Cost Estimation of Electricity Generation in G20 Countries: Case Study Using a Global Life-Cycle Impact-Assessment Method
Bibliographic record
Abstract
The external costs derived from the environmental impacts of electricity generation can be significant and should not be underrated, as their consideration can be useful to establish a ranking between different electricity generation sources to inform decision-makers. The aim of this research is to transparently evaluate the recent external cost of electricity generation in G20 countries using a global life-cycle impact-assessment (LCIA) method: life cycle impact assessment method based on endpoint modeling (LIME3). The weighting factors developed in the LIME3 method for each G20 country enable one to convert the different environmental impacts (not only climate change and air pollution) resulting from the emissions and resources consumption during the full lifecycle of electricity generation—from resource extraction to electricity generation—into a monetary value. Moreover, in LIME3, not only the weighting factors are developed for each G20 country but also all the impact categories. Using this method, it was possible to determine accurately which resources or emission had an environmental impact in each country. This study shows that the countries relying heavily on coal, such as India (0.172 $/kWh) or Indonesia (0.135 $/kWh) have the highest external costs inside the G20, with air pollution and climate accounting together for more than 80% of the costs. In these two countries, the ratio of the external cost/market price was the highest in the G20, at 2.3 and 1.7, respectively. On the other hand, countries with a higher reliance on renewable energies, such as Canada (0.008 $/kWh) or Brazil (0.012 $/kWh) have lower induced costs. When comparing with the market price, it has to be noted also that for instance Canada is able to generate cheap electricity with a low-external cost. For most of the other G20 countries, this cost was estimated at between about 0.020$ and 0.040 $/kWh. By estimating the external cost of each electricity generation technology available in each G20 country, this study also highlighted that sometimes the external cost of the electricity generated from one specific technology can be significant even when using renewables due to resource scarcity—for example, the 0.068 $/kWh of electricity generated from hydropower in India. This information, missing from most previous studies, should not be omitted by decision makers when considering which type of electricity generation source to prioritize.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".