Committee on Climate Change

AI advancements can advance environmental inequities and health harms

In recent years, artificial intelligence (AI) is progressively being utilized in many industries. In order to meet the increasingly computational AI demands, more data centers are being developed. Unfortunately, health concerns related to climate change, energy consumption, air p...

Transforming Agricultural Water Management for Healthy People and Planet: Thinking Beyond Water

ABSTRACT This paper develops the concept of Thinking Beyond Water , a transdisciplinary paradigm that reframes agricultural water management (AWM) as a systems catalyst for planetary health, equity, justice and resilience. Moving beyond conventional irrigation‐centric models, we...

Extreme sea level inundation maps for Vanuatu

Abstract Extreme sea level (ESL) inundation poses a growing risk for Pacific Island Countries (PICs), yet few nationwide inundation maps exist to inform national-scale flood risk management. This study presents a new dataset of ESL inundation maps for Vanuatu. ESL inundation was...

Fifteen years of regional climate downscaling in CORDEX-Australasia

Regional climate modelling plays a central role in understanding and projecting climate change across Australasia, a region characterised by complex topography, diverse climate regimes, and high exposure to climate extremes. Over the past fifteen years, CORDEX-Australasia has coo...

Prioritizing key performance indicators for maintenance management in public hospitals: a hybrid SEM–machine learning approach

Purpose Public hospital maintenance operates under growing service pressure, yet existing frameworks often rely on data-intensive systems or broader facility outcomes, offering limited guidance on which maintenance key performance indicators (KPIs) should be prioritized under res...

The Role of Energy Market Uncertainties in Forecasting US State-Level Stock Market Volatility: A GARCH-MIDAS Approach

Abstract In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States based on monthly metrics of oil price uncertainty (OPU) a...