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Integrated modelling of the impacts of hydropower projects on the water-food-energy nexus in a transboundary Himalayan river basin

2019· article· en· W2911985491 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

VenueApplied Energy · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Waterloo
FundersJohn D. and Catherine T. MacArthur Foundation
KeywordsHydropowerEnvironmental scienceFlood mythWater resource managementClimate changeWater resourcesNatural resource economicsEnvironmental resource managementGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

The sustainable development goals (SDGs) and the Paris agreement target a global cleaner energy transition with wider adaptation, poverty reduction and climate resilience benefits. Hydropower development in the transboundary Koshi river basin in the Himalayan region presents an intervention that can support the SDGs whilst meeting the regional commitments to the Paris agreement. This study aims to quantify the benefits of proposed water resource development projects in the transboundary basin (4 storage and 7 run-of-the-river hydropower dams) in terms of hydroelectric power generation, crop production and flood damage reduction. A hydro-economic model is constructed by soft coupling hydrological and crop growth simulation models to an economic optimization model. The model assesses the potential of the interventions to break the vicious cycle of poverty and water, food, and energy insecurity. Unlike previous studies, the model (a) incorporates the possibility of using hydropower to pump groundwater for irrigation as well as flood regulation and (b) quantifies the resilience of the estimated benefits under future climate scenarios from downscaled general circulation models affecting both river flows and crop growth. The results show significant potential economic benefits generated from electricity production, increased agricultural production, and flood damage control at the transboundary basin scale. The estimated annual benefits are around USD 2.3 billion under the baseline scenario and USD 2.4 billion under a future (RCP 4.5) climate scenario, compared to an estimated annual investment cost of USD 0.7 billion. The robustness of the estimated benefits illustrates the climate resilience of the water resource development projects. Contrary to the commonly held view that the benefits of these proposed projects are limited to hydropower, the irrigation and flood regulation benefits account for 40 percent of the total benefits. The simulated scenarios also show substantial irrigation gains from the construction of the ROR schemes, provided the generated power is also used for groundwater irrigation. The integrated modelling framework and results provide useful policy insights for evidence-based decision-making in transboundary river basins around the globe facing the challenges posed by the water-food-energy nexus.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.179
Teacher spread0.165 · 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