River basin cities, while crucial for economic activity, often face ecological challenges. China's pursuit of high-quality development (HQD), encompassing economic, social, ecological, and cultural dimensions, necessitates understanding the factors influencing this multifaceted goal, particularly in environmentally sensitive areas like the Yellow River Basin (YRB). The YRB, a significant focus of China's ecological protection and HQD plan, has seen substantial financial investment since 2021. However, the efficacy and spatial distribution of this investment remain empirically unclear. This study addresses this gap by examining the spatial effects of financial growth on HQD within the YRB. The research is timely and crucial because it explores the impact of financial growth on a comprehensive measure of HQD, considering not just economic progress but also social well-being, environmental sustainability, and cultural enrichment. Existing literature, while highlighting the importance of financial growth in development, presents conflicting views on its environmental impacts, lacking systematic empirical evidence. This research fills this gap by offering an empirical investigation.

The literature review examines three key areas: defining HQD, evaluating HQD, and the impact of financial growth on HQD, particularly in the YRB. Existing definitions of HQD vary, often emphasizing a balance between economic, social, and ecological aspects. Evaluation methods frequently involve multi-indicator approaches, employing techniques like the SBM model, entropy methods, and principal component analysis. Studies on the relationship between financial growth and HQD within the YRB context are limited, with theoretical discussions predominating, and these often present contradictory perspectives. While some posit a positive correlation, others caution against potential negative environmental effects from increased credit and consumption of energy-intensive goods.

This study employs a four-step methodology. First, it constructs a HQD evaluation system for the YRB using twelve indicators across four dimensions: economy, society, ecology, and culture. The entropy-weighted TOPSIS method is used to calculate a composite HQD index. Second, it tests for spatial autocorrelation in HQD using Moran's I. Third, it uses spatial econometric models, specifically the Spatial Dubin Model (SDM), to analyze the spatial effects of financial growth on HQD. The SDM incorporates spatial lag and spatial error terms to account for spatial dependence. Financial growth is modeled as both a linear and non-linear (quadratic and cubic) term to capture potential non-linear relationships. Control variables include human capital, foreign direct investment, infrastructure level, and government control. Finally, heterogeneity analysis is performed by dividing the sample into high- and low-economy regions based on per capita GDP. The study uses panel data from 99 cities in the YRB from 2006 to 2019, obtained from China Statistical Yearbooks. Robustness checks are conducted using an instrumental variable approach (GMM), OLS model substitution, and sample cropping to address potential endogeneity issues and spatial discontinuity.

The study finds strong positive spatial autocorrelation in HQD within the YRB. Spatial visual analysis reveals clusters of high and low HQD values, supporting this finding. The empirical regression results using the SDM reveal a significant N-shaped relationship between financial growth and local HQD. The inverted U-shaped relationship is evident initially as financial development promotes investment and growth but then becomes negative due to environmental concerns and structural unemployment at higher levels of financial growth. Decomposition of the SDM results shows that financial growth exhibits an inverted U-shaped spatial spillover effect on surrounding regions' HQD. Human capital and foreign direct investment positively influence local HQD but don't show significant spatial spillover effects. Government control negatively impacts local HQD with no significant spillover effects. Infrastructure has a negative impact both locally and in surrounding regions. Robustness checks using instrumental variables confirm the N-shaped relationship between financial growth and HQD. Further, Green innovation acts as a mediating factor in the relationship between financial growth and HQD, exhibiting an inverted U-shaped relationship with financial growth. Heterogeneity analysis indicates that the N-shaped relationship between financial growth and local HQD holds for both high- and low-economy regions, but the spatial spillover effects are significantly stronger in high-economy regions. This reflects that the low economic development environment inhibits the spatial spillover effect of financial growth on HQD in surrounding areas.

The findings suggest a complex and nuanced relationship between financial growth and HQD in the YRB. The N-shaped curve highlights the importance of balanced and efficient financial development. Initially, financial growth stimulates economic expansion and improves well-being, but excessive or unbalanced financial growth can lead to negative consequences for the environment and employment. The spatial spillover effects further underscore the interconnectedness of regional development. Policies aimed at promoting HQD should consider not only local economic growth but also the wider regional context. The mediating role of green innovation suggests that promoting environmentally friendly technologies can enhance the positive impacts of financial growth while mitigating potential negative effects. The stronger impacts of financial growth in higher-economy regions suggest that creating a favorable economic environment is crucial for realizing the benefits of financial development.

This study offers valuable insights into the spatial dynamics of financial growth's impact on HQD in the YRB. The N-shaped relationship and the varying spatial spillover effects highlight the need for context-specific policies. Future research could explore the precise mechanisms through which green innovation mediates the relationship, delve deeper into the interplay between different dimensions of HQD, and analyze the role of green finance and digital finance in this context. Investigating policy implications at different levels of economic development would also add value.

The study is limited to the YRB in China, and the findings may not be generalizable to other contexts. The use of a composite HQD index involves inherent challenges in weighting different indicators. The study focuses on financial growth as a single indicator of financial development, which might not fully capture the complexities of the financial system. Additionally, despite efforts to address endogeneity, the possibility of omitted variables affecting the results remains.