Analysing the development of the climate, land, energy, and water systems (CLEWs) modelling framework: a state-of-the-art review
Bibliographic record
Abstract
Abstract This comprehensive state-of-the-art literature review explores recent scientific developments in climate, land, energy, and water systems (CLEWs) modelling by systematically analysing 41 peer-reviewed studies published between 2020 and 2024. This research uncovered insights into the evolving interdisciplinary landscape, revealing various trends, such as approximately 74% of studies publishing their data as open-access and 50% employing an open-source analytical tool, or tools, in combination with open-access data. This study identified four areas of significance: (1) the connections between CLEWs and the sustainable development goals, (2) how the CLEWs framework is linked to capacity development, (3) the critical interplay between energy and water systems, and (4) the transformative potential for comprehensive system integration using the CLEWs modelling framework. By pinpointing promising research directions such as soft-linking CLEWs models with geographic information systems, applying robust decision making methodologies, adapting the CLEWs framework to the city level, and highlighting the need to assess real world impact of CLEWs research, the review provides a strategic roadmap for future interdisciplinary research. Notably, the analysis emphasised the urgent need for enhanced institutional coordination and collaborative communities of practice, particularly for open-source modelling tools like the open-source energy modelling system, to further accelerate knowledge dissemination and foster innovative, integrated approaches to complex systemic challenges.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| 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".