Centralization or coordination? The role of green finance in industrial chain modernization at the scale of urban agglomeration networks
Yuan Tian, Yan Kuang, Yi RenPurpose
New quality productive forces (NPF) represent the latest manifestation of industrial chain modernization transformation, requiring industrial chains to develop towards innovation, synergy, and greening. Green finance (GF), through capital support and green technological innovation (GTI), has become a key force driving industrial chain modernization (ICM).
Design/methodology/approach
Based on panel data from 156 cities within China's top ten urban agglomerations (UAs) from 2010 to 2023, this paper scientifically measures the level of green finance and the modernization of UA industrial chains using the spatiotemporal range entropy weight method. It analyzes their spatio-temporal evolution characteristics. The practical effectiveness of green finance in empowering the modernization of industrial chains within UAs is empirically tested through the benchmark regression model, mediation effect model, and spatial econometric model.
Findings
The findings reveal: (1) GF guides financial resources towards green industries, directly driving ICM in UAs, and indirectly empowers ICM through GTI as an intermediary transmission mechanism. (2) The development of ICM in China's UAs exhibits a distinct east-to-west spatial gradient, with an overall trend toward synergistic development. (3) Under the empowerment of GF, the ICM of sub-UAs shows significant spatial heterogeneity and agglomeration characteristics. ICM in eastern UAs tends towards centralized development, with the Yangtze River Delta UA exhibiting a pronounced polarization effect. ICM in Chengdu-Chongqing and Middle Yangtze River UAs tends towards synergistic development.
Originality/value
First, in research perspective, we conduct the study from the UA perspective. Exploring the centralized, synergistic, and networked characteristics of industrial chains at the UA scale provides new ideas for constructing the ICM system. Second, in data processing, we integrate multi-source data. Combining micro-enterprise data with macro-economic data, we assign indicator values using information from both temporal and spatial dimensions. GF is divided into three sub-dimensions: “credits-securities-investments”. The ICM of UAs is divided into five sub-dimensions: “foundation-green-structure-collaboration and agglomeration-innovation”. This enriches the quantitative framework of the multi-dimensional system. Third, we incorporate spatial factors into the overall analytical framework.