Application of resource allocation models to the problems of regional energy policy in large developing coutries
Bibliographic record
Abstract
The interregional allocation of, and competition for, natural resources for energy development is assuming increasing importance in many large developing countries, particularly in countries such as India that are in the more advanced stages of industrialization, and where state governments and regional agencies have significant planning powers. This paper explores the applicability of optimal resource allocation models designed primarily for already-developed economies, such as that of the U.S., Canada, and Western Europe, to the emerging problems of such developing countries. In particular, the potential application of electric-utility-siting models is discussed in the context of electric grid expansion in the State of Andhra Pradesh in Southern India. Specific topics addressed include the degree to which models can be effectively used in the rather different conditions and priorities of industrializing countries; the requirements for basic-model reformulations to account for unique factors of geography (climate, hyydrology, the extant conditions of the infrastructure within which any development must occur); and the ability to include and quantify environmental impacts of energy deveelopment as well as policy considerations relevant in the context of developing countries. The paper identifies desirable research directions, indicates data needs, and assesses those aspects of subnational energy-planning problems that should receive better emphasis in national and international energy-plannng efforts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".