Enhancing Environmental Policy Decisions in Korea and Japan Through AI-Driven Air Pollution Forecast

Yushin Kim, Jungin Kim, Sunghyun Cho, Hyein Sim and Ji-Young Kim ()
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Yushin Kim: Major in Bio-Artificial Intelligence, Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Republic of Korea
Jungin Kim: Major in Bio-Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
Sunghyun Cho: Department of Computer Science and Engineering, Hanyang University ERICA, Ansan 15588, Republic of Korea
Hyein Sim: Major in Japanese Politics & Diplomacy, Department of Japanese Language and Culture, Hanyang University, Ansan 15588, Republic of Korea
Ji-Young Kim: Major in Japanese Politics & Diplomacy, Department of Japanese Language and Culture, Hanyang University ERICA, Ansan 15588, Republic of Korea

Sustainability, 2024, vol. 16, issue 23, 1-20

Abstract: (1) Background: Although numerous artificial intelligence (AI)-based air pollution prediction models have been proposed, research that links key pollution drivers, such as regional industrial facilities, to actionable policy recommendations is required. (2) Methods: This study employs the radial basis function (RBF) and spatial lag features to capture spatial interactions among regions, utilizing a transformer model for analysis. The model was trained on air quality and industrial data from South Korea (2010–2022) and Japan (2017–2020). (3) Results: The transformer model achieved a mean squared error of 0.045 for the Korean dataset and 0.166 for the Japanese dataset, outperforming benchmark models, including Support Vector Regression, neural networks, and the AutoRegressive Integrated Moving Average model. (4) Conclusions: By capturing complex spatial dynamics, the proposed model provides valuable insights that can assist policymakers in developing effective, data-driven strategies for air pollution reduction at the national and regional levels, thereby supporting the broader goals of sustainability through informed, equitable environmental interventions.

Keywords: artificial intelligence; air pollution; sustainable environmental policy; transformer; radial basis function; spatial Durbin model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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