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

Abstract

We investigate the dynamic interplay between air pollution (PM10) 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 PM10, before and during the pandemic. The results show that before the pandemic, PM10 played a notable role in shaping sales predictions, highlighting the sensitivity of sales to air quality. However, during the pandemic, the importance of PM10 decreased significantly, highlighting the transformative indirect impact of external events on consumer behavior. This study also examines the joint association of PM10 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.