Abstract

Climate change represents one of the most critical challenges facing the global economy today. While environmental impacts receive considerable attention, the economic implications are equally significant and require rigorous quantitative analysis. This thesis investigates the relationship between climate change and major economic indicators including GDP growth, inflation, trade balances, and employment across 50 countries from 1990 to 2023. The research employs a comprehensive multi-methodological approach combining panel data econometrics, time-series analysis, and machine learning techniques. Fixed-effects and random-effects panel regression models reveal statistically significant relationships between climate-related disaster frequency and economic performance. Specifically, disaster count demonstrates a positive coefficient of 0.0769 (p < 0.001), indicating that while disasters may initially appear to boost economic activity through reconstruction spending, the underlying economic damage is substantial. Time-series analysis using ARIMA and Vector Autoregression (VAR) models captures dynamic responses of economic indicators to climate shocks. Machine learning approaches employing XGBoost and Random Forest regressors identify non-linear relationships and feature importance, with XGBoost achieving a test R² of 0.384, outperforming traditional linear models in capturing complex climate-economic interactions. Scenario projections under three warming scenarios (+1.5°C, +2.0°C, and +3.0°C) reveal that 17 to 19 countries experience negative GDP impacts, with mean negative impacts ranging from -0.105 to -0.225 percentage points. The worst-case impacts reach -0.625 percentage points, demonstrating the significant economic risks of unmitigated climate change. Comparative analysis identifies substantial geographic disparities in climate vulnerability. Developing countries exhibit mean vulnerability indices of 0.468 compared to 0.272 for developed countries, highlighting the disproportionate burden on lower-income nations. Regional analysis reveals South Asia and Africa as the most vulnerable regions, with mean vulnerability indices of 0.518 and 0.450 respectively. The research addresses critical gaps in existing literature by examining simultaneous interactions across multiple economic indicators, employing non-linear modeling techniques, and providing country-specific vulnerability assessments. The findings contribute to evidence-based policy formulation, identifying 15 high-priority countries requiring immediate intervention and developing comprehensive adaptation strategies with benefit-cost ratios of 2–5:1. This thesis provides policymakers, economists, and environmental planners with quantitative evidence and actionable recommendations for developing effective climate adaptation and mitigation strategies. The results quantify economic risks while investigating solutions supporting the construction of climate-resilient economic systems.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ioannis Karamitsos

Campus

RIT Dubai

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