Enhancing climate action evaluation using artificial neural networks: An analysis of SDG 13
Bibliographic record
Abstract
This study aims to enhance the evaluation of climate-related Sustainable Development Goals (SDGs), with a focus on SDG 13 ("Climate Action"), using Artificial Neural Networks (ANNs) methods. It examines seven critical 2023 SDG Global Index indexes to model and predict environmental performance. The innovative use of ANNs allows for capturing complex and non-linear interactions among sustainability indicators, surpassing traditional linear models. A key component of the research is the application of Garson's algorithm, which identifies the relative importance of each of the seven indexes in influencing climate outcomes. The study optimizes the ANN's parameters through a grid search, ensuring robust and precise predictions. This research offers valuable insights for policymakers and researchers aiming to improve climate action strategies by providing a more nuanced understanding of the factors driving environmental performance. The findings demonstrate the potential of advanced AI techniques in refining sustainability assessments and guiding more effective environmental policies. Key policy insights drawn from the study include expanding interventions aimed at promoting more sustainable consumption and production policies, given the significant contribution of SDG 12 in driving climate goals; reviewing the methods for measuring economic growth to account for the planetary crises; and increasing the use of AI tools to guide policymaking.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".