A solution to energy transition paradox: optimal subsidy policy for minimizing the carbon emissions from future hybrid electricity system with hydropower and variable renewables.
Bibliographic record
Abstract
The low-cost wind and solar energy may reduce the investments in hydropower and thus increase the share of fossil energy and finally increase the carbon emissions, leading to an "Energy Transition Paradox" [1]. To solve this problem, this study proposes to use subsidies to reconcile the conflicts involved in the capacity planning of hydropower and VRE, which has seldom been addressed in the shift to a low-carbon electricity system. The electricity system in Hubei Province, China is selected as a case study, where we examine the effects of different scenarios of fixed-subsidies, in addition to the market-clearing price, on renewable power generation. First, we estimate the long-term electricity prices based on the cost of marginal units. Next, we design several representative subsidy scenarios and determine the net present values and investments for increasing both hydropower and VRE capacity under these scenarios. Finally, the optimal or most effective subsidy scenario is identified by evaluating the carbon emissions and power generations. Results indicate that, 50% of the subsidy originally allocated to variable renewables should be re-allocated to hydropower to reduce the total carbon emissions. This means that a higher proportion of subsidies should be allocated to the hydropower rather than all subsidies are used to support the VRE alone. This study not only provides an effective economic policy to resolve the energy transition paradox but also shows the potential of enhancing the synergy between different renewable energies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".