This paper analyzes the 2018 Logistics Performance Index (LPI) from the World Bank to determine the spatial effects of countries’ logistics performance. Although the standardized ordinary least square (OLS) models show good results, the spatial lags and Moran’s I of LPI suggest the OLS assumptions are violated. Consequently, an improved geographically weighted regression (IGWR) model using multivariate kernel functions (MKF) is implemented. Through the analysis of the Moran scatter plot, the authors identified the countries that have different logistics performance development trends in the four quadrants representing the relationship between the spatial lags and the LPI. Using trade activity (i.e., import/export) in the MKF, the authors compared different MKF types and bandwidths to ensure the model’s predictability and accuracy and found that the adaptive Gaussian MKF is suitable. Finally, the IGWR model indicates both positive and negative influencing factors on LPI overall score. Specifically, the improvements of LPI are more associated to economic variables in mid- and low-income countries around the world, and are more related to import of construction equipment in the Middle East. Also, business environment is more important in Latin America and the Pacific. European countries are more sensitive to customs efficiency, whereas Pacific-Asian countries are more sensitive to quality of infrastructure and have higher coefficients than African and American countries. This spatial heterogeneity is related to the specific factors that promote the development of their logistics performance.

本文分析了世界银行 2018 年物流绩效指数 (LPI)，以确定各国物流绩效的空间效应。尽管标准化普通最小二乘 (OLS) 模型显示出良好的结果，但 LPI 的空间滞后和 Moran's I 表明违反了 OLS 假设。因此，实现了使用多元核函数 (MKF) 的改进的地理加权回归 (IGWR) 模型。通过对莫兰散点图的分析，作者在代表空间滞后与LPI关系的四个象限中确定了物流绩效发展趋势不同的国家。使用 MKF 中的贸易活动（即进口/出口），作者比较了不同的 MKF 类型和带宽，以确保模型的可预测性和准确性，发现自适应高斯 MKF 是合适的。最后，IGWR 模型显示了对 LPI 总分的正面和负面影响因素。具体而言，LPI的改善更多地与全球中低收入国家的经济变量相关，更多地与中东的建筑设备进口相关。此外，商业环境在拉丁美洲和太平洋地区更为重要。欧洲国家对海关效率更敏感，而亚太国家对基础设施质量更敏感，系数高于非洲和美国国家。