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Record W3159615113 · doi:10.3390/en14092673

The Economic Performance of Hydropower Dams Supported by the World Bank Group, 1975–2015

2021· article· en· W3159615113 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHydropower, Displacement, Environmental Impact
Canadian institutionsQueen's University
Fundersnot available
KeywordsHydropowerPortfolioRenewable energyCost overrunBusinessElectricity generationSmall hydroNatural resource economicsEnvironmental economicsEnvironmental resource managementEngineeringFinanceEconomicsPower (physics)

Abstract

fetched live from OpenAlex

This paper assesses the economic benefits of 57 World Bank Group-sponsored hydropower dam plant investments. Hydropower dams are among the main sources for producing electricity and the largest renewable source for power generation throughout the world. Hydropower dams are often a lower-cost option for power generation in Clean Energy Transition for addressing global climate change. Despite its conspicuous aspects, constructing hydropower dams has been controversial. Considering the World Bank’s long history as the largest hydropower development financier, this study investigates its performance in supporting hydropower dams. The outcomes of this study apply to the wider hydropower development community. Of the projects in this study, 70% experienced a cost overrun, and more than 80% of projects experienced time overruns, incurring potential additional costs as a result. Despite the high cost and time overruns, this hydropower portfolio of dams produced a present value of net economic benefits by 2016 of over half a trillion USD. Based on our findings, the evaluated hydropower portfolio helped avoid over a billion tonnes of CO2 for an estimated global environmental benefit valued at nearly USD 350 billion. The projects’ additional environmental benefits raise the real rate of return from 15.4% to 17.3%. The implication for hydropower developers is that the projects’ assessment should consider cost and time overrun and factor them into the project-planning contingency scenarios. There is a considerable benefit for developing countries to exploit their hydropower resources if they can be developed according to industry practices and international standards. The case for developing hydropower may be stronger when considering its climate benefits. The net economic benefits of hydropower can be even higher if there is a greater effort to manage cost and time overruns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.011
GPT teacher head0.342
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it