From investment to net benefits: A review of guidelines and methodologies for cost–benefit analysis in the electricity sector
Bibliographic record
Abstract
The electricity sector is transforming to integrate renewable energy sources while ensuring grid quality, efficiency, and reliability. Such a transformation demands major investments from both private and public stakeholders. Economic appraisal tools such as cost–benefit analysis (CBA) have become increasingly relevant in identifying investments that optimize financial and social net benefits. Despite this, many CBA applications in the electricity sector, such as those for transmission and distribution infrastructure, tend to prioritize financial metrics and single-criterion evaluations, often neglecting broader social and environmental considerations. This highlights the need for a more inclusive approach to addressing these limitations. To this end, this paper provides a comprehensive review of the literature on CBA as applied to electricity infrastructure appraisals. First, the review examines various facets of CBA methodology, including its key steps, scope, standing, metrics, models, and approaches for addressing uncertainty. Second, this study analyzes relevant CBA guidelines employed to assess electricity projects’ social costs and benefits across the entire value chain, encompassing power generation, transmission, distribution, and end-use. Third, the paper highlights challenges and barriers within CBA guidelines, noting significant variations in their development and applicability across electricity domains and regions. The review categorizes these barriers into CBA into methodological, regulatory, and domain-specific barriers. Advancing CBA requires standardizing scope, unveiling cost and benefit causal chains, enhancing uncertainty handling, and leveraging synergies across regions to bridge gaps between theory and practice. • Review of cost-benefit analysis (CBA) for electricity infrastructure projects. • CBA guidelines vary in modeling tools, sophistication, and uncertainty handling. • Cross-domain insights enhance CBA theory and practice. • CBA faces methodological, regulatory, and domain-specific barriers. • Solutions: systemic view, dynamic modeling, transparency, and probabilistic methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".