Author(s)

Li Chen, RuoXi Zhang^*

Affiliation(s)

School of Management, Ningbo University, Ningbo 215000, Zhejiang, China.

Corresponding Author

RuoXi Zhang

ABSTRACT

Climate change poses one of the most significant challenges to humanity, with profound implications for ecosystems, societies, and economies. This paper explores the integration of deep learning techniques into climate-economic modeling, aiming to enhance predictive accuracy and inform policy decisions in the face of escalating climate-related risks. Traditional climate-economic models, such as Integrated Assessment Models (IAMs), have been foundational in understanding the interplay between climate change and economic systems. However, they often rely on linear assumptions and simplified relationships that fail to capture the complex, non-linear dynamics of climate-economics interactions. This paper underscores the urgent need for continued research into the integration of deep learning techniques in climate-economic modeling. Policymakers are encouraged to invest in data infrastructure, foster interdisciplinary collaborations, and prioritize the ethical use of deep learning tools in decision-making processes. By harnessing the power of deep learning, we can enhance our understanding of climate change impacts and develop more effective strategies for resilience and adaptation, ultimately paving the way for a more sustainable future.

KEYWORDS

Deep Learning; Climate-Economic modeling; Policy decisions

CITE THIS PAPER

Li Chen, RuoXi Zhang. Deep learning for climate-economic modeling. Social Science and Management. 2024, 1(2): 36-43. DOI: https://doi.org/10.61784/ssm3019.

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