PREDICTING INTERCONNECTED CLIMATE IMPACTS USING THE COX PROPORTIONAL HAZARDS MODEL AND XAI

Climate change has systemic and cumulative effects on air quality, water availability, food production, and waste management, yet most current predictive studies examine each area separately and provide little guidance on how to interpret them to inform policy decisions. This paper interprets climate change as a systemic risk over time and proposes a feature that integrates the Cox proportional hazards model with explainable artificial intelligence (XAI) to measure and explain cascading climate effects. NASA, NOAA, and environmental monitoring agencies, as well as peer-reviewed studies, were combined to create multisource longitudinal datasets covering the 1980-2024 timeframe and including atmospheric, hydrological, agricultural, and waste-related variables. The Cox model can be used to estimate risks in time-dependent settings where all sectors are mutually dependent, whereas Shapley Additive Explanations (SHAP) can provide clear explanations of the effect of each variable. The experimental evidence shows that the suggested framework attains a concordance index of more than 0.82, which is better than traditional regression-based models for predicting changes in climate risk. Temperate variations and CO2 release become the predominant causes of agricultural and water-induced risks, with PM 2.5 concentration exerting the greatest influence on the escalation of health risk. In contrast to earlier models based on the concepts of staticity and black-boxing, the proposed method provides interpretable, testable, and policy-relevant insights into the dynamics of climate risks. The findings underscore the evidence-based approaches to climate adaptation and provide a scalable, explainable basis for integrated climate resilience planning, especially in susceptible and rapidly urbanizing areas.