From “Policy-Driven” to “Park Clustering”: Evolution and Attribution of Location Selection for Pollution-Intensive Industries in the Beijing–Tianjin–Hebei Urban Agglomeration

Pollution-intensive industries (PIIs) generate substantial economic benefits while posing serious environmental challenges, making the optimization of their spatial distribution a critical issue for sustainable development. Understanding the spatiotemporal dynamics behind PII location patterns is essential for effective land-use planning and industrial policy. This study investigates the location patterns of newly established PIIs in the Beijing–Tianjin–Hebei urban agglomeration of China between 2007 and 2019. By integrating principal component analysis with a geographically and temporally weighted regression model, the research explores how key drivers influence PII distribution across both spatial and temporal dimensions. The results indicate that government intervention has historically been the most significant factor shaping PII distribution, although its influence has gradually declined due to increasing marketization and technological progress. PIIs are more likely to cluster in areas with moderate levels of economic development, as both very high and very low development levels tend to discourage agglomeration. Over time, improvements in infrastructure, transportation and market conditions have enabled PIIs to overcome geographical constraints. Moreover, industrial parks have emerged as a critical factor by offering cost-efficiency and resource optimization, thereby attracting new PII investment. These findings underscore the importance of accounting for spatiotemporal heterogeneity when analyzing industrial distribution. The study provides policy-relevant insights into industrial land-use planning, highlighting the need for differentiated land supply strategies and the strategic development of industrial parks. It also offers useful references for other developing countries facing similar challenges amid the ongoing restructuring of global manufacturing.