Harnessing the Power of AI for Climate Change Impact Assessment
Notice bibliographique
Résumé
Climate change impact assessment is critical for creating adequate climate change mitigation and adaptation policies and plans. Critical infrastructure systems (e.g., water and energy systems) are particularly at risk for climate change impacts. In fact, a better understanding of climate change impacts on the water and energy systems would facilitate the fulfillment of SDG2 (end hunger), SDG 6 (clean water and sanitation), SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), and SDG 13 (climate action), with many indirect benefits across many other areas. Nonetheless, conducting climate change impact assessment, particularly at the community-level, is not an easy task. Often, the impact assessment models require access to substantial computational resources to run the complex models, as well as the expertise to work with those models and interpret their results, which may not be possible for all communities. As such, there is a need to expand climate change impact assessment to include more accessible models that can handle high-resolution, local data that is of interest to communities. Here, we highlight how climate impact assessment studies can benefit from the power of artificial intelligence (AI). The report details the use of AI model to conduct a computationally efficient climate change impact assessment. This model is applied to a case study across the United States of America (U.S.) as an example to showcase the insights it generates in real-world applications. To demonstrate this process, the study will focus on the impacts on coupled water and electricity demand (e.g., the water-electricity demand nexus). To conduct the impact assessment, the report demonstrates two different means of collecting future climate data—Coupled Model Intercomparison Project 5 (CMIP5) Earth System Models (ESMs) and contemporary climate analogs. Our results show significant increases across the Midwestern U.S. when using ESM-derived data. Similar results were found through the climate analog-derived data, suggesting that the analogs can be used successfully as proxies for traditional ESM data in communities that might not have access to the larger CMIP suite of models. Understanding the impacts of climate change on critical infrastructure is important for building sustainable and equitable policies for climate change mitigation and adaptation. These infrastructure systems are often interconnected (e.g., the water-energy nexus) and managed by local entities. Thus, while climate change is a global problem requiring cooperation across countries and sectors, many solutions require local action. In this sense, the results presented here can be used to deepen our scientific understanding of climate change impacts on the water-energy nexus, as well as develop novel methodologies that integrate AI with traditional climate change impact assessment to better prepare local communities for the future.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».