Study maps nonlinear links between industry clustering and welfare in China

A study of 19 urban agglomerations in China finds that the relationship between emerging-industry clustering and welfare performance during the renewable-energy transition is nonlinear, with diminishing rather than reversing returns. The analysis also identifies spatial spillovers and substantial variation across stages of development, indicating that the benefits associated with industrial concentration cannot be described by a single uniform effect.

The researchers based their analysis on point-of-interest data collected from 2014 through 2023 across eight emerging sectors. They used kernel density estimation to characterize the spatial patterns of industry clustering and assessed welfare performance under renewable-energy transition, abbreviated WPRE, with an improved enhanced group-frontier dynamic data envelopment analysis model incorporating super-efficiency. This model was combined with an integrated XGBoost-SHAP-GEO framework to examine nonlinear associations between industrial agglomeration and WPRE, including spatial spillover effects and interactions among geospatial characteristics.

The spatial analysis shows that emerging-industry clusters in the 19 urban agglomerations followed a polycentric evolution rather than developing around a single dominant center. At the same time, the core-periphery structure became stronger. The study describes the broader trajectory of welfare performance under renewable-energy transition as “plateaued fluctuation and volatile growth,” suggesting that progress was neither steady nor evenly distributed across the urban system. These patterns are based on modeled WPRE estimates and observed spatial clustering rather than direct measurements of household welfare outcomes.

Geospatial characteristics and their interaction effects emerged as the principal predictors of WPRE in the integrated model. However, their associations varied markedly by stage, reinforcing the conclusion that emerging-industry agglomeration does not have one constant effect. The relationship with welfare performance was non-monotonic and consistent with diminishing returns: additional clustering was not associated with endlessly larger gains. The findings therefore point to a more complex picture than a simple trade-off in which either concentrated or dispersed industry is uniformly preferable.

Spatial spillovers add another layer to the results. Industrial clusters can be influenced by conditions beyond their immediate boundaries, so the welfare association in one agglomeration cannot be evaluated in isolation from the wider urban system. The strengthening core-periphery architecture and the stage-dependent predictor relationships both indicate that the consequences of emerging-industry clustering depend on where clusters form, how their broader geography evolves and which stage of development is considered. The authors frame these spatial disparities as part of a “new resource curse,” linking the energy transition to uneven welfare conversion between production and consumption zones.

The study offers a quantitative description rather than evidence that a particular industrial policy will improve welfare. Its accepted article presents the findings as a scientific basis for understanding how industrial spatial allocation interacts with renewable-energy transition, while identifying further possible revisions before the final version of record. By combining spatial density analysis, dynamic efficiency assessment and machine-learning-based interpretation, the work highlights why aggregate measures may conceal important regional differences. For China’s 19 urban agglomerations, the central result is that emerging-industry clustering has nonlinear, geographically dependent associations with welfare performance, with diminishing returns and meaningful spatial spillovers shaping the pattern.

Subject of Research: Nonlinear associations between emerging-industry clustering and welfare performance in Chinese urban agglomerations

Article Title: Nonlinear effects and spatial spillovers of emerging industry clustering on welfare performance under renewable energy transition in China''s urban agglomerations

Article References: Tang, T., Mo, J., & Wei, X. (2026). Nonlinear effects and spatial spillovers of emerging industry clustering on welfare performance under renewable energy transition in China''s urban agglomerations. Scientific Reports. https://doi.org/10.1038/s41598-026-75886-2

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75886-2

Keywords: renewable energy transition, industrial clustering, urban agglomerations, welfare performance, spatial spillovers, kernel density estimation, data envelopment analysis, XGBoost, SHAP analysis, China, Nonlinear, effects