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Energy Environment and Economy

Volume 3 Number 1 2025

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 3 (1)

Exploring Climate Change Prediction and Mitigation Strategies with Large Language Models (LLM)

Md Saiful Islam1, Li Xiangdong1, Md Mahmudul Hasan3,Jubayer Ahmed1, Md Ziaul Haque Shipon1, Soyed Md Mahid Hasan1, Shaharia Ahmed2*

+ Author Affiliations

Energy Environment and Economy 3 (1) 1-11 https://doi.org/10.25163/energy.3110401

Submitted: 27 September 2025 Revised: 26 October 2025  Published: 26 November 2025 

Abstract

Concerning climate change, there is a growing demand for accessible tools that can provide reliable future climate information to support planning, finance, and other decision-making processes. Large language models (LLMs), such as GPT-4 and BERT, present a promising approach to bridging the gap between complex climate data, with the potential to revolutionize data analysis and decision-making across various sectors. However, a significant challenge remains in assessing LLMs' accuracy and reliability in predicting future climate trends. In this study, we introduce a hybrid LLM framework that combines Large Language Models (LLMs) with General Circulation Models (GCMs) to improve climate modeling by increasing prediction accuracy, uncovering hidden patterns in historical data, simulating policy outcomes, and encouraging public engagement. A series of experiments was designed to evaluate the performance of GPT-4 as a hybrid LLM and BERT as a traditional LLM under different conditions. Our results indicate that GPT-4, as a hybrid LLM, reduces prediction errors by up to 19% and produces policy analyses aligned with expert assessments. Although challenges remain in energy efficiency and data biases, our findings demonstrate the transformative potential of LLMs in integrating qualitative human insights with quantitative climate science, emphasizing their crucial role in advancing global climate goals.

Keywords: Climate prediction, Large Language Models, Multimodal Data Fusion, AI-Driven Climate Policy Analysis, Hybrid and Traditional LLM

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