English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Industrial co-agglomeration plays a significant role in the moving up of the manufacturing industry in the value chain and in transforming China from a manufacturing giant into a world manufacturing power. This study establishes a co-aggregation index to explore spatio-temporal changes of the co-agglomeration between manufacturing and producer services in 30 provinces of China from 2004 to 2019. Furthermore, we use spatial Durbin model to analyze the impact of industrial co-agglomeration on air pollution reduction. We find that (1) the co-agglomeration index varies remarkably at spatio-temporal scale; (2) high co-agglomeration index is mainly distributed in eastern and central China, while low co-agglomeration index is mainly located in the western region; (3) the co-agglomeration index presents a cluster pattern among provinces, with the cluster of high value in eastern China and the cluster of low value in western China; and (4) the co-agglomeration between manufacturing and producer services is proven effetely to reduce air pollution, which is accompanied with spatial spillover effect. We also provided policy implications in line with diverse industries, multi hierarchies, and different regions, promoting the coordination of manufacturing and producer services and improving air quality.

Industrial Co-Agglomeration and Air Pollution Reduction: An Empirical Evidence Based on Provincial Panel Data