Navigating the selection of renewable energy trading partners: A multi-objective optimization approach
Bibliographic record
Abstract
Decarbonizing economies and energy systems is urgently needed in order to meet current climate change mitigation targets. However, many countries, particularly in Europe, will not be able to meet their rapidly growing demand for renewable energy by expanding domestic production alone in the near future. Consequently, these countries are planning to import renewable energy using chemical carriers such as hydrogen and metals. This raises the question of which countries to partner with for renewable energy trade. Selecting the appropriate trading partners is a complex task that requires balancing several potentially conflicting objectives, including cost-efficiency, sustainability, governance, and security of supply. In this article, we present a novel approach to selecting partner countries in the presence of such trade-offs. Our approach uses empirical indicators, abstract selection rules, and the epsilon constraint method to combine these objectives into a single objective optimization problem with additional constraints. We demonstrate our approach by examining the case of Germany as an importer of renewable energy using iron as an energy carrier. Our approach identifies the optimal set of potential trading partners and their respective shares of supplied renewable energy by minimizing costs while meeting the added constraints. For instance, under the most stringent sustainability and security constraints, the model identifies Australia, the United States, Brazil, Spain, Canada, and Chile as potential trading partners for Germany. Relaxing these constraints adds more countries such as Morocco and Oman. Our approach is the first to identify trade networks, i.e., concrete sets of partner countries, that can bridge gaps in renewable energy supply, offering valuable guidance for developing trading partnerships. • We present a novel optimization approach to select renewable energy trading partners. • The approach considers economic, sustainability, governance, and security objectives • It identifies partner countries, their supply shares, and trade-offs between objectives. • We illustrate the approach for Germany using iron as a renewable energy carrier. • Optimization can aid with informed decision-making in renewable energy trade.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".