To better understand the impacts of infrastructure investment on economic growth and, we used Cobb-Douglas production function model to develop the stock of public infrastructure capital into the economic growth model. It applies spatial panel data model effect analysis to statistical data of the Yangtze River Economic Zone with 131 cities from 2003 to 2016 and investigates the relationship between different types of public infrastructure capital stock and regional economic growth in different periods. The empirical results show that (1) the economic growth of the cities in the Yangtze River Economic Zone has characteristics of significant spatial dependence, the degree and significance of spatial dependence are gradually declining, and the spatial agglomeration of the economic growth in the cities in the region is relatively stable. (2) Different types of public infrastructure capital stock have distinct spatial effects on regional economic growth. The capital stock of energy infrastructure significantly promotes global economic growth. The capital stock of transportation infrastructure significantly stimulates the local economic growth and inhibits the economic growth of the adjacent areas. The capital stock of water-related infrastructure restrains local economic growth and promotes economic growth in adjacent areas. These findings indicate that increasing investment in public infrastructure development in the Yangtze River Economic Zone remains an effective measure to promote regional economic growth. Under the premise of limited resources, taking full account of the effects of various types of investment can promote the mutual benefit of the economies in the region and effectively achieve the strategic objectives for the Yangtze River Economic Zone.

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Social stability risk assessment (SSRA) has become the mainstream policy instrument for assessing potential risks of large-scale development projects across all sectors in China. In this paper, fuzzy qualitative comparative analysis (fsQCA) is used to quantify the impact of SSRA policy on economic competitiveness across China’s 31 provinces using a SSRA policy dataset (2003–2020) and a provincial economic competitiveness dataset (2019–2020). QCA combines Boolean algebra and set theory to identify configurations of conditions that are necessary or sufficient for a given outcome. Rather than following the mainstream statistical method of developing a single causal model that best fits the data, QCA explores multiple concurrent causality. A typology of SSRA policies was developed to guide our analysis. The research concluded that to support high economic competitiveness within provinces, SSRA policies must be structured around solving social stability problems and addressing a specific industry issue (e.g., pollution) in a particular industry (e.g., resources). Policies that only include one of these factors or that focus on the performance of government officials were found to contribute to low economic competitiveness. Reorienting the focus of SSRA policies could support more rigorous risk assessments and enhance economic competitiveness, particularly in provinces that host large-scale development projects. These findings have implications for China’s policymakers given their dual objectives of driving economic reform while maintaining a harmonious society.