Sales in Commercial Alleys and Their Association with Air Pollution: Case Study in South Korea

Khadija Ashraf, Kangjae Lee, Geunhan Kim and Jeon-Young Kang ()
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Khadija Ashraf: Department of Convergence and Fusion System Engineering, Kyungpook National University, 2559 Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Republic of Korea
Kangjae Lee: Department of Convergence and Fusion System Engineering, Kyungpook National University, 2559 Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Republic of Korea
Geunhan Kim: Department of Environmental Planning, Korea Environment Institute, Sejong 30147, Chungcheongnam-do, Republic of Korea
Jeon-Young Kang: Department of Geography, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea

Sustainability, 2024, vol. 16, issue 2, 1-19

Abstract: We investigate the dynamic interplay between air pollution (PM 10 ) and income and their joint association with quarterly sales in commercial alleys, focusing on the pre-COVID-19 (2018–2019) and COVID-19 (2020–2021) periods in Seoul, South Korea. The objective of this study is to identify how air pollution and income collectively influence consumer spending patterns by looking at the increase and decrease in sales in commercial alleys, with a focus on contrasting these effects before and during the COVID-19 pandemic, utilizing advanced machine learning techniques for deeper insights. Using machine learning techniques, including random forest, extreme gradient boosting, catboost, and lightGBM, and employing explainable artificial intelligence (XAI), this study identifies shifts in the significance of predictor variables, particularly PM 10 , before and during the pandemic. The results show that before the pandemic, PM 10 played a notable role in shaping sales predictions, highlighting the sensitivity of sales to air quality. However, during the pandemic, the importance of PM 10 decreased significantly, highlighting the transformative indirect impact of external events on consumer behavior. This study also examines the joint association of PM 10 and income with sales, revealing distinctive patterns in consumer responses to air quality changes during the pandemic. These findings highlight the need for dynamic modeling to capture evolving consumer behavior and provide valuable insights for businesses and policymakers navigating changing economic and environmental conditions. While this study’s focus is on a specific region and time frame, the findings emphasize the importance of adaptability in predictive models and contribute to understanding the complex interplay between environmental and economic factors in shaping consumer spending behavior.

Keywords: air pollution; explainable artificial intelligence (XAI); joint association of sales and air pollution; machine learning; particulate matter (PM 10 ); quarterly sales (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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