Modeling Climate Impacts on Agricultural Output in South Korea: Evidence from Artificial Neural Networks

DOI

10.2991/978-94-6239-680-7_10How to use a DOI?

Keywords

Climate change; South Korea; Machine learning; Agricultural GDP; Artificial Neural Networks; Adaptation strategies

Abstract

Agriculture in South Korea, even with its limited share in the national economy, remains critical for food security, rural livelihoods and regional resilience. This study investigates the impact of climate change on agricultural gross domestic product in South Korea using advanced machine learning techniques. The model chosen in this research was trained using historical climate and economic data from 1990 to 2020 and validated using cross regional application to 26 Asian countries. Results indicate that precipitation is the most influential climatic variable across the four variables chosen, especially in water-intensive, rice producing provinces. While temperature plays a big role in colder, high-altitude regions. Wind speed on the other hand, exhibits minimal influence except in specific microclimates. Also, in this study, forecasted agricultural GDP projections for the year of 2030 were generated, underlining regional disparities in future climate vulnerability. Furthermore, this study proposes targeted and tailored climate-smart policy recommendations on a provincial level. Overall, this research demonstrates the value of machine learning in climate agriculture modeling and offers a scalable framework to inform adaptive policy recommendation in South Korea´s evolving agriculture and climate landscape.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Raouh ElMehdi
AU  - Cherkaoui Mounia
AU  - En-Nia Samir
AU  - Liouaeddine Mariem
AU  - Mansouri Zakaria
PY  - 2026
DA  - 2026/06/20
TI  - Modeling Climate Impacts on Agricultural Output in South Korea: Evidence from Artificial Neural Networks
BT  - Proceedings of the Conference Morocco-Korea Cooperation: A Lever for Afro-Asian Development (MKC 2025)
PB  - Atlantis Press
SP  - 125
EP  - 156
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-680-7_10
DO  - 10.2991/978-94-6239-680-7_10
ID  - ElMehdi2026
ER  -