Globalization has spurred rapid development and cooperation between China and "Belt and Road" initiative countries, significantly increasing their trade volume. By 2022, this trade accounted for 34.7% of China's total foreign trade, primarily concentrated in Southeast Asia, with ASEAN alone representing 50.3% of the total. However, a trade imbalance persists. The World Bank's Logistics Performance Index (LPI), introduced in 2007, measures logistics performance globally. A robust logistics sector is crucial for economic growth, facilitating industrial development, financial operations, and complete supply chains. The LPI of "Belt and Road" countries directly influences China's trade with them. This paper analyzes the relationship between international logistics performance and import/export trade, using data from 2011-2022 (excluding the 2008 financial crisis's impact) across 61 "Belt and Road" countries. The study's innovation lies in analyzing the interaction mechanism between logistics performance and trade, employing a fixed-effects model to assess the impact of LPI on countries of different sizes, and implementing robustness checks (outlier removal, sample interval reduction, tail processing, and lagged explained variables). The contributions are: enriching research on logistics performance and trade (considering both imports and exports); exploring the differential impact of logistics performance on various country sizes; and providing specific suggestions for improving logistics performance and trade volume, informing the "Belt and Road" initiative and national logistics/trade strategies.

Existing literature highlights the crucial role of logistics performance in supporting trade growth and overall economic development. Studies by Bhukiya and Patel (2023), Huong et al. (2024), and Barakat et al. (2023) demonstrate the positive relationship between logistics performance and international trade, with improvements in LPI contributing to increased trade openness and reduced costs. Jayathilaka et al. (2022) confirmed the positive role of LPI in promoting international trade, particularly in Asia, Europe, and Oceania. Conversely, the literature also shows that import and export trade influences the logistics industry's development. Guo (2018) found exports have a more significant impact than imports. Zhan et al. (2019) and Wang and Wang (2021) highlighted the impact of trade scale, efficiency, and structure on the logistics industry in the "Belt and Road" region. Wang (2015) and Yang et al. (2019) identified co-integration relationships between logistics development and trade. While the impact of international logistics performance varies with income levels, trade facilitation levels, and population sizes (Fan and Yu 2015, Çelebi 2019, Kumari and Bharti 2021), research on the differentiated impact across varying country sizes remains limited. This paper addresses this gap by analyzing the impact on countries categorized by population size.

This study employs an extended gravity model, incorporating the LPI and control variables, to analyze the impact of international logistics performance of "Belt and Road" countries on China's import and export trade. The sample includes 61 countries from 2011 to 2022. Data from the World Bank's WDI database were used, with missing LPI data linearly predicted using Stata15 software. Variables were standardized to mitigate unit differences. The explained variable is China's trade volume with "Belt and Road" countries (in billions of USD). Explanatory variables include the LPI and its sub-indicators (cargo tracking, logistics serviceability, international freight price competitiveness, customs clearance efficiency, delivery timeliness, and transportation infrastructure quality). Control variables encompass: distance (adjusted for Brent crude oil price), GDP of "Belt and Road" countries, China's GDP, trade openness, adjacency to China, and WTO membership. Countries were categorized into large, medium, and small based on average population (2011-2022). A basic gravity model (TRADE_{ij} = X_iX_j/DIS_{ij}) was transformed into logarithmic form and expanded to include the LPI and control variables (Equation 3). Similar models (Equations 4-9) were employed with LPI sub-indicators. Fixed-effects regression analysis was used via Stata15.0, following a Hausman test to confirm model suitability. Robustness checks involved: outlier removal (Bhutan, North Macedonia, Bosnia and Herzegovina, and Moldova), reduced sample interval (2011-2020), tail reduction processing (5-95%), and lagging the explained variable (TRADE) by one period to address potential two-way causality. Table 1 details variable definitions and data sources.

Descriptive statistics revealed significant disparities in trade volume, population, and LPI scores across "Belt and Road" countries. Regression analysis using the fixed-effects model (Table 5) showed a significantly positive impact of LPI on China's trade with these countries at the 10% significance level (Equation 10). Distance had a significantly negative impact, while the GDP of both "Belt and Road" countries and China, and trade openness, positively influenced trade volume. Border adjacency showed a negative correlation, while WTO membership was insignificant. The impact of LPI was most significant for large-scale countries (1% level), while it was negative and insignificant for small and medium-sized countries respectively. Analyzing LPI sub-indicators (Table 6), logistics service capacity and infrastructure quality showed significant positive impacts at the 10% and 1% significance levels, respectively; however, other sub-indicators were insignificant. For large-scale countries (Table 7), international freight price competitiveness, logistics service capacity, customs clearance efficiency, and infrastructure quality significantly positively influenced trade (1% significance level), while cargo tracking and delivery timeliness showed no significant impact. Robustness tests (Tables 8-11) consistently supported the main finding of a positive LPI-trade relationship, even after addressing potential outliers, shorter time frames, data tail adjustments, and time lags.

The findings confirm the importance of international logistics performance in promoting trade between China and "Belt and Road" countries. The differential impact across country sizes highlights the need for tailored strategies. The significant positive effect of logistics service capacity and infrastructure underscores the need for investment in these areas. While specific sub-indicators' impacts varied, overall improved logistics infrastructure and efficiency enhance trade. The negative impact of distance reiterates the importance of reducing trade barriers and optimizing transportation networks. The results align with existing literature demonstrating the positive correlation between logistics performance and trade but provide more nuanced insights by analyzing the impact on countries of varying scales. The study’s focus on both imports and exports broadens the understanding of the complex interplay between logistics and bilateral trade.

This study confirms the significant positive relationship between improved international logistics performance and increased trade volume between China and "Belt and Road" countries, particularly for larger economies. Logistics service capacity and infrastructure quality are key drivers. Future research should expand the sample to include a broader range of countries, consider additional factors like income levels and geographic location for country categorization, and utilize more advanced econometric techniques. Despite its limitations, the study contributes valuable insights to the literature on international logistics and trade.

This study's limitations include its focus on "Belt and Road" countries, limiting the generalizability of the findings. The reliance on World Bank data might introduce some limitations in data availability and consistency. The population-based categorization of country sizes could be complemented by incorporating other factors like income level or geographic location. The use of the fixed-effects model, while suitable for the data, might not capture all the nuances of the complex relationship being investigated. Finally, further exploration of potential endogeneity between variables is recommended.