Sustainability Assessment of Electricity Generation Development under the Implementation of Support Policies with Endogenous Financial Resources Using a Hybrid Decision Support Model
Bibliographic record
Abstract
Sustainable electricity development is one of the requirements for achieving sustainable development in global communities. However, due to barriers, especially in less developed countries, there is little incentive to invest in the development of sustainable electricity technologies. Therefore, there should be a change in market mechanisms, and broad support policies have to be implemented for the sustainable development of electricity. In the long run, these policies must lead to the sustainable development of energy systems. To evaluate the efficiency and effects of the proposed support policies on the sustainability of electricity generation development, this study intends to analyze the multiple and complex dimensions of the problem using a hybrid decision support model. Moreover, by defining an indicator to assess the electricity generation expansion sustainability, this study assists policymakers in making logical decisions about sustainable support programs for the electricity development based on the characteristics of the electricity market of each country. Despite uncertainties in the electricity market, simulations show that the results of this hybrid model have approximately 88% conformance with historical data. Consequently, the model can evaluate the sustainability of the system under the implementation of the proposed support programs and compare them to select the most effective one. The results show that by assuming a competitive market and rational behavior and implementing support programs with endogenous financial resources, the installed renewable capacity can be improved by up to 70.4% compared with the direct subsidy policies. Regardless of the financial burden of policies (e.g., direct subsidies) and the possibility of facing a budget deficit, these programs can be up to 79.2% more effective in the sustainability of the energy system compared with the direct subsidy policy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".