A spatial econometric investigation into road traffic accessibility and economic growth: insights from the Chengdu-Chongqing twin-city economic circle

The impact of transport infrastructure investment on regional and local economic growth is a significant research area. This study focuses on the Chengdu-Chongqing twin-city economic circle, a rapidly developing region in China. The research explores the complex interplay of economic, investment, and political-institutional factors in generating economic benefits from transport infrastructure. The central research question is how road traffic accessibility influences economic growth within this specific geographic context, considering the spatial distribution of economic activity and the potential for spillover effects. Understanding this relationship is crucial for effective policymaking and regional planning. The study's importance stems from its potential to inform future infrastructure investments and strategies aimed at maximizing economic growth within the Chengdu-Chongqing region, and potentially, other similar urban economic circles globally. The study uses 2019 data to avoid the confounding effects of the COVID-19 pandemic.

Existing literature extensively explores the link between transportation infrastructure and economic growth, with studies examining the impact of various transport modes and infrastructure investments on regional development. Several researchers, including Aschauer (1989), Hong et al. (2011), and Banister and Berechman (2003), have demonstrated a positive correlation between transport investment and economic growth. However, the literature also points towards the potential for bidirectional causality – economic growth can stimulate infrastructure investment, and improved infrastructure can, in turn, accelerate economic growth. Acheampong et al. (2022), Hua et al. (2022), and Sun et al. (2022) further emphasize the interplay of transport infrastructure, technological innovation, energy consumption, and environmental impacts. Studies by Arbués et al. (2015) and Konno et al. (2021) specifically address the spatial productivity of transport infrastructure and the incorporation of spatial spillover effects in their analysis. The literature review also includes studies on accessibility metrics and applications (Jie et al., 2007; Morris et al., 1978; Hansen, 1959), regional economic resilience (Chacon-Hurtado et al., 2020), and the role of transportation in urban development (Knowles et al., 2020; Yu et al., 2022). The specific focus on the spatial econometric approach, incorporating spatial autocorrelation and spillover effects, distinguishes this study from previous research. Previous studies like Bo (2019) and Chen et al. (2020) have touched upon the spatial aspects but this study provides a more in-depth analysis.

The study employs a spatial econometric approach to analyze the relationship between road traffic accessibility and economic growth in the Chengdu-Chongqing twin-city economic circle. The researchers utilize robust indicators of road traffic accessibility, transportation investments, and economic outputs. The data for 2019 is analyzed to avoid the confounding factors introduced by the COVID-19 pandemic. A spatial weight matrix, based on the Queen neighborhood, was created, recognizing the influence of road development on neighboring areas. The Moran's I index was used to test for spatial autocorrelation. Two spatial econometric models were considered: the spatial lag model (which considers spatial spillover effects in the dependent variable – GDP) and the spatial error model (which accounts for spatial autocorrelation in the error term). The spatial lag model is formulated as: GDPpi = ρWGDPpi + β0 + β1Acc i + β2Urbi + β3Indi + β4L abi + β5Edui + β6Trai + εi. The spatial error model is formulated as: GDPpi = β0 + β1Acc i + β2Urbi + β3Indi + β4L abi + β5Edui + β6Trai + εi, where ε = λWe + v. The models are estimated using maximum likelihood estimation to account for spatial correlation. The choice between the spatial lag and spatial error models was based on Lagrange Multiplier tests and the log-likelihood function, Akaike Information Criterion (AIC), and Schwarz Criterion (SC). The ordinary least squares (OLS) model is also estimated as a benchmark. The study acknowledges the potential endogeneity problem, where economic growth and transport infrastructure may have a bidirectional relationship. While not directly addressed in this model, the authors suggest future research could explore instrumental variable techniques to address this limitation. Variables included are GDP per capita (dependent), road traffic accessibility (Acc), urbanization level (Urb), industrial structure (Ind), labor force (Lab), education investment (Edu), and trade openness (Tra).

The accessibility analysis reveals a 'core-periphery' pattern, with higher accessibility in central regions (around Chengdu and Chongqing) compared to peripheral areas. Suining has the lowest accessibility, while Wanzhou and Kaizhou have the highest. Chongqing's overall accessibility is lower than Sichuan's, likely due to geographical factors and urbanization patterns. Moran's I index (0.210) shows significant spatial correlation in per capita GDP. The spatial error model is chosen as the best-fitting model based on LogL, AIC, and SC values. The spatial autocorrelation coefficient (λ) for the error term is 0.535, significant at the 1% level, strongly indicating spatial dependence. Key findings from the spatial error model are: * Urbanization (Urb) has a positive and significant relationship with GDP per capita (a 1% increase in urbanization leads to a ¥315.642 increase in per capita GDP). * Industrial structure (Ind) shows a significant positive impact on GDP (a 1% improvement leads to a ¥2,029.470 increase). * Education investment (Edu) has a significant positive relationship with GDP (a ¥10,000 increase in per capita educational expenditure leads to a ¥108,469.000 increase in per capita GDP). * Labor force (Lab) demonstrates a positive and significant association with GDP (a 1% increase in the employed population leads to a ¥322,575.700 increase in per capita GDP). * Accessibility (Acc) exhibits a negative and significant relationship with GDP (a 1-hour decrease in accessibility leads to a ¥5,006.560 increase in per capita GDP). * Trade openness (Tra) did not show a significant relationship with GDP.

The findings confirm the importance of spatial econometric modeling in analyzing the relationship between road traffic accessibility and economic growth. The significant spatial error effect highlights the need to consider the spatial dependence of economic variables and the influence of neighboring regions. The negative relationship between accessibility and GDP, seemingly counterintuitive, may reflect the fact that the areas with the highest accessibility are already economically developed, implying that further improvement in accessibility might not lead to proportionate economic growth. The significant positive relationships found for urbanization, industrial structure, education investment, and labor force align with existing economic theories. The lack of a significant relationship between trade openness and GDP may warrant further investigation. The 'core-periphery' pattern in accessibility and its association with the spatial distribution of economic activity underscore the importance of targeted infrastructure development strategies. The study’s results highlight the need for policy interventions that focus on improving the supporting economic environment in conjunction with infrastructure improvements.

This study demonstrates the significant impact of road traffic accessibility on economic growth within the Chengdu-Chongqing twin-city economic circle, although the relationship is more nuanced than initially hypothesized. Spatial econometric modeling proves crucial for accurately capturing the spatial dependencies in the data. The study emphasizes the importance of a holistic approach to regional development, combining infrastructure investment with supportive economic policies focused on factors like urbanization, industrial structure, education, and labor force. Future research could address the potential endogeneity bias, expand the scope to include other transportation modes, and investigate the long-term effects of infrastructure investments on economic growth.

The study's analysis is limited to 2019 data to avoid the confounding effects of the COVID-19 pandemic. Future research could incorporate more recent data to assess the impact of post-pandemic recovery on the relationship between accessibility and economic growth. The potential endogeneity issue, although acknowledged, wasn't directly addressed in this study, limiting the strength of the causal inferences. The choice of the spatial weight matrix also influences the results, and alternative specifications should be considered in future work. Further, the study focuses solely on road traffic accessibility; incorporating other modes of transportation would offer a more comprehensive understanding of the overall transport infrastructure impact. Finally, while the study presents compelling insights into the Chengdu-Chongqing region, the generalizability of these findings to other regions requires further investigation.