Academic Journal of Science and Technology ISSN: 2771-3032 | Vol. 14, No. 2, 2025 129 Research on the Impact Factors of Logistics Industry Agglomeration on Logistics Industry Carbon Emissions in the Yangtze River Economic Belt Yue Li1, a 1School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China a237421306@qq.com Abstract: During the period of 2010-2021, an expert group consisting of 11 provinces and the Yang Economic Center Province conducted basic calculations on the carbon emissions and agglomeration of the logistics industry in the Yang Economic Center. A modern spatial Dubin a Γ© t Γ© tabli on the foundation of spatial economy, aimed at clarifying the impact of logistics industry alliances on the carbon mission of logistics industry. The main conclusions are as follows: (1) The logistics industry carbon emission representative offices in Shanghai, Jiangsu, Zhejiang, Hubei and other provinces pointed out that the carbon emissions of the logistics industry in these provinces mainly come from provinces within the Yangtze River Economic Circle, while the logistics industry carbon dioxide emission representative offices in Western regions continue to maintain stability . (2) The logistics industry agglomeration centers in Shanghai, Jiangsu, and Anhui have the highest status in the Yangtze River Economic Circle, which is an indicator of the level of industrial agglomeration. The localization coefficient of the logistics industry in the central region is fluctuating, showing a trend of "foreign trade". (3) In the Durbin model, the impact of industrial agglomeration on logistics carbon emissions tasks is positive, as well as on spatial visual restructuring. Firstly, the impact of energy integration, population and network capacity, as well as the influence of government intervention and network infrastructure construction. Keywords: Yangtze River Economic Belt, logistics industry carbon emissions, logistics industry agglomeration level, spatial Durbin model, location entropy. 1. Introduction As the strongest comprehensive strength and strategic support region in China, the modern logistics industry is an important guarantee for the rapid development of the Yangtze River Economic Belt and a pillar industry for the region's economic development. The agglomeration of logistics industry can improve regional innovation level, and the improvement of regional innovation level can promote regional economic growth. To a certain extent, industrial agglomeration has a positive promoting effect on regional economic growth. However, with the continuous expansion of production scale, excessive agglomeration will form between industries, and the crowding effect caused by excessive agglomeration will affect the generation of carbon emissions. In order to address the issue of carbon emissions, governments at all levels have proposed a series of sustainable development strategies, such as accelerating the construction of ecological civilization in the Yangtze River Economic Belt and promoting the green development of the logistics industry. Therefore, in the context of the "joint protection" of the Yangtze River Economic Belt, studying the impact of logistics industry agglomeration level on logistics industry carbon emissions is not only a response to the implementation of energy-saving and emission reduction policies in the Yangtze River Economic Belt, but also plays an important role in achieving regional sustainable development. 2. Overview of Relevant Theories In the study of industrial agglomeration and carbon emissions, some scholars have analyzed the linear relationship between the level of industrial agglomeration and carbon emissions. The research results of Tian Yun et al. show that both industrial agglomeration and agricultural net carbon effect have spatial autocorrelation, and have a "positive N" - shaped correlation [1]; The research results of Zhu Dongbo et al. show that the relationship between industrial agglomeration and environmental pollution follows an inverted U-shaped curve, and industrial agglomeration is beneficial for reducing pollutant emissions [2]; The research by Cheng Jiesheng et al. shows that there is a non-linear U- shaped relationship between the carbon emission efficiency of ecotourism and environmental regulation [3]; Research by Miao Jianjun et al. found that increasing the level of manufacturing and productive service industry agglomeration would exacerbate environmental pollution, while industrial synergy agglomeration would reduce pollution levels [4]; Guo Anning et al. showed that the coordination between carbon emission efficiency and industrial structure in the Yellow River Basin showed a trend of first decreasing and then increasing [5]. Domestic and foreign scholars have also conducted research and analysis on the mediating role and spatial spillover effects of industrial agglomeration level and carbon emissions, exploring the impact of industrial agglomeration level on carbon emissions. Research by Yang Chuan et al. shows that the energy consumption level of logistics enterprises is one of the mechanisms by which logistics industry agglomeration affects the carbon emissions of logistics enterprises [6]; Li Xiaofan et al. used the level of urbanization as a threshold variable to verify the threshold effects of different industrial agglomerations of productive services, manufacturing, and their synergistic industries on carbon emissions [7]; Zhao Fan et al. found through 130 establishing a GMM model that when the industrial scale is large, the carbon emissions level of the Yangtze River Economic Belt is also high, but then there is a downward trend [8]; Xu Yingzhi et al. studied the impact mechanism of industrial agglomeration on haze pollution [9]; Yan Caozheng and others explored the impact mechanism of logistics industry agglomeration on China's green total factor productivity [10]; Wang Jian et al. used mediation effects, dynamic threshold effects, and asymmetry to study the impact of China's logistics industry agglomeration on cross regional carbon emissions transfer. The study found that carbon transfer under logistics agglomeration has an inverted U- shaped characteristic [11]; Pang Jiangang et al. analyzed the spatiotemporal characteristics and causes of the coupling and synergistic effect of industrial structure upgrading and carbon emission efficiency, revealing the coupling mechanism of the two [12]; Wei et al. explored the response mechanism and spatial effects under the logical framework of "regional development agricultural industry upgrading carbon emissions" [13]; Song et al. used the Kaya LMDI model to analyze the factors affecting carbon emissions and explored the relationship between industrial structure and carbon emissions through coefficient of variation (CV) [14]; Li H et al. used dynamic panel models and mediation effects models to verify the impact of industrial agglomeration on carbon emissions, and found that there is an inverted U-shaped relationship between industrial agglomeration and carbon emissions [15]. From the above literature, it can be seen that most of the relevant literature at home and abroad focuses on manufacturing, agriculture, and tourism industries, with relatively little research on logistics industry clustering and carbon emissions. The logistics industry accounts for about 9% of carbon emissions, making it the third largest source of carbon emissions in China after industry and construction. China is also vigorously developing the Yangtze River Economic Belt to drive the economic development of the western region, which cannot be achieved without the support of the logistics industry. Therefore, based on panel data from 11 provinces and cities in the Yangtze River Economic Belt from 2010 to 2021, this study used fossil fuel calculation method, location entropy method, and spatial Durbin model to analyze the influencing factors of logistics industry agglomeration level on logistics industry carbon emissions in the Yangtze River Economic Belt. The aim is to study the relationship between logistics industry agglomeration and logistics industry carbon emissions while the Yangtze River Economic Belt is developing rapidly under China's dual carbon goals, and promote the sustainable development of the logistics industry economy in the Yangtze River Economic Belt. 3. Research Methods and Data Sources (1) Decomposition and Calculation of Carbon Emissions Fossil fuel combustion is the main source of carbon dioxide emissions, and existing research typically estimates carbon emissions using fossil fuel consumption [16]. This study takes the Yangtze River Economic Belt as the research object, and selects eight energy sources including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, and natural gas as the research objects according to the methods in the 2006 IPCC National Greenhouse Gas Inventory Guidelines. The specific calculation formula is as follows: Carbon emissions=βˆ†π‘ͺ＝ βˆ‘ π‘ͺπ’Š βˆ‘ πœΆπ’Š πœ·π’Š π‘΄π’Šπ’Šπ’Š (1) In formula (1), is the carbon emissions generated by the consumption of the i-th type of energy in the logistics industry, Ξ± i is the conversion coefficient of the i-th type of energy to standard coal benchmark, Ξ² i is the carbon emission coefficient of the i-th type of energy, and Mi is the physical consumption of the i-th type of energy. (2) Measurement of Industrial Agglomeration Level Industrial agglomeration refers to the high concentration of the same industrial production factors in a certain geographical area under certain location conditions, and the sustained agglomeration of the same industrial production factors in space. The existing research mainly uses measurement methods such as location entropy, spatial Gini coefficient, and Hirschman Herfindahl index. The latter two methods are more suitable for measuring the overall clustering degree and absolute concentration degree, but lack consideration for the relative clustering degree of data. The location entropy method can reduce the differential impact between regions. Based on the analysis of the characteristics and limitations of logistics industry agglomeration in the Yangtze River Economic Belt, a comprehensive evaluation of logistics industry agglomeration in various provinces and cities within the Yangtze River Economic Region was conducted using the location entropy method: 𝐿𝑄 / / (2) In equation (2), represents the location entropy of the logistics industry in the Yangtze River Economic Belt region in region Z, represents the added value of the logistics industry in the Yangtze River Economic Belt region in region Z, represents the added value of the domestic logistics industry, represents the gross domestic product in region Z, and represents the gross domestic product. If the ratio is greater than 1, it indicates that there is a high degree of agglomeration in the logistics industry in the Yangtze River Economic Belt region. (3) Spatial Durbin model The Spatial Durbin Model (SDM) is a combined extended model of SLM and SEM, which can add corresponding constraint conditions to SLM and SEM. It predicts the impact of one variable on another variable by considering the interdependence between geographic spatial units. The regression model is presented in the following form: πΆπ‘Žπ‘Ÿ πœŒπ‘ŠπΆπ‘Žπ‘Ÿ π‘Ž πΆπ‘Žπ‘Ÿ π‘Ž 𝑍 π‘Ž π‘ŠπΏπ‘Žπ‘” πœ€ (3) Among them, i and t respectively represent the province, city, and year, represent the carbon emissions of the logistics industry, represent the level of logistics industry agglomeration, represent the control variable, represent the random error term, and a represents the comprehensive impact of logistics industry agglomeration on logistics carbon emissions. If it is positive, it indicates that logistics agglomeration will increase logistics industry carbon emissions, and if it is negative, it indicates that logistics agglomeration will reduce logistics industry carbon dioxide emissions. By deforming the three factor spatial correlation regression coefficients, we can obtain: 131 𝑙𝑛Yit 𝛽 𝜌 βˆ‘ π‘Šπ‘™π‘›Yit 𝛽 𝑙𝑛𝑅𝑑𝑖 𝛽 βˆ‘ 𝑙𝑛𝑅𝑑𝑖 πœ† βˆ‘ 𝑙𝑛𝑋 πœƒπ‘Šπ‘™π‘›π‘‹ πœ€ (4) In the formula, Yit is the dependent variable, representing the carbon emissions of the logistics industry in region i during period t; Rdi is the explanatory variable of logistics industry agglomeration level; Among them, W represents the spatial weighting matrix, represents the spatial autocorrelation regression coefficient, Ξ² represents the coefficient of the corresponding variable, represents the random error, X represents the control variable. At 𝜌 =0， Ξ²=0οΌŒπœ† =0, it is a spatial autoregressive mode; At 𝜌 =0，β=0， πœ†β‰ 0, it is a spatial error model; When πœŒβ‰ 0οΌŒΞ²β‰ 0οΌŒπœ† =0, we call it the spatial Durbin model. If the significance test is passed, the spatial econometric model can be used. The relevant indicators are listed in Table 1. Table 1. Index System of Factors Influencing Logistics Carbon Emissions Caused by Logistics Industry Agglomeration variable index Indicator Description Explained Variable carbon footprint Calculated out explanatory variable industrial agglomeration Calculated out control variable energy consumption Calculated out government intervention The ratio of fiscal expenditure to fiscal revenue Number of industrial population Employees in the transportation, postal, and warehousing industries infrastructure construction Regional highway mileage the volume of freight transport Total freight volume of highways, railways, and waterways (4) Data source The research subjects are Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan provinces in the Yangtze River Economic Belt. Panel data on energy consumption, transportation, postal, and warehousing industries in the logistics industry from 2010 to 2021 were selected from 11 provinces and cities in the Yangtze River Economic Belt. The data came from the China Statistical Yearbook and national data of the National Bureau of Statistics from 2010 to 2021, as well as the statistical yearbooks and bulletins of 11 provinces and cities in the Yangtze River Economic Belt from 2010 to 2021. According to geographical location, Shanghai, Jiangsu, and Zhejiang are defined as the eastern region of the Yangtze River Economic Belt; Anhui, Jiangxi, Hubei, and Hunan are the central regions; Chongqing, Sichuan, Guizhou, and Yunnan are the western regions. The observation period of the study is from 2010 to 2021. 4. Empirical Results and Analysis (1) Calculation results of carbon emissions in the logistics industry Calculate the carbon emissions of the logistics industry in the Yangtze River Economic Belt according to equation (1), and use Arcmap 10.7 software to conduct spatial visualization analysis of the carbon emissions of the logistics industry in four time periods of 2010, 2014, 2018, and 2021 in various regions of the Yangtze River Economic Belt, as shown in Figure 1. Year Shanghai Jiangsu Zhejiang Anhui Jiangxi Hubei Hunan Chongqing Sichuan Yunnan Guizhou 2010 5469.836 4246.365 3328.836 1537.453 1382.462 3577.826 2563.352 1589.264 2865.835 1506.254 2493.632 2011 5687.831 4300.139 3581.773 1622.76 1399.487 3686.713 2676.44 1749.135 2519.11 1545.651 2680.585 2012 5790.524 4652.812 3758.146 2355.447 1458.908 3708.19 2404.128 2051.985 2707.715 1849.645 2868.469 2013 5790.67 5001.967 3921.831 2592.673 1810.693 3733.706 2986.394 2268.708 1904.084 1730.511 2718.643 2014 5776.233 5475.105 3993.251 2871.722 1852.851 4045.335 3264.955 2157.900 2819.968 1856.652 3075.984 2015 6061.846 5696.434 4236.578 2887.157 2019.087 4117.823 3701.786 2573.619 2747.905 2116.005 2976.711 2016 6748.001 5856.783 4243.384 2919.209 2045.492 5079.985 3847.019 2745.529 3971.708 2334.836 3115.895 2017 7384.861 6123.549 4410.57 3097.723 2118.099 5152.173 3901.762 2887.364 4158.544 2052.315 3185.194 2018 7227.292 6488.622 4302.24 3232.698 2443.427 5273.352 4213.967 2586.715 4174.513 2201.104 3581.684 2019 7510.278 6873.724 4009.272 3119.959 2645.154 5855.251 4365.663 2670.493 4371.078 2353.092 3901.407 2020 6233.596 6935.005 4180.278 3042.595 2619.383 4957.042 4271.2 2482.986 4176.293 2392.482 3752.68 2021 6541.341 6660.567 4342.581 2975.559 2635.962 5802.778 4753.949 2482.644 4334.195 2677.92 3856.296 2022 5387.82 6338.807 4198.295 2740.627 2577.904 5029.147 4715.92 2050.322 4324.754 2673.95 3592.224 Figure 1. Spatial distribution of carbon emissions from logistics industry in various provinces and cities along the Yangtze River Economic Belt From Figure 1, it can be seen that from 2010 to 2021, the overall carbon emissions level of the logistics industry in provinces and cities along the Yangtze River Economic Belt was relatively stable, and the carbon emission intensity of the logistics industry in the western region continued to steadily improve during the observation period. Shanghai, Jiangsu, Zhejiang, and Hubei provinces are the main sources of carbon emissions from the logistics industry in the provinces and cities along the Yangtze River Economic Belt. The total annual carbon emissions from the logistics industry account for over 67.54% of the total regional carbon emissions for that year. The logistics industry in Shanghai has the highest carbon emissions, accounting for 23.33% of the regional logistics industry's carbon emissions, while the logistics industry in Yunnan Province has the lowest carbon emissions. The overall control of carbon emissions in the logistics industry in Chongqing is relatively stable. (2) Analysis of the Agglomeration Level of Logistics Industry in the Yangtze River Economic Belt By collecting relevant statistical data from various regions of the Yangtze River Economic Belt, the logistics industry location entropy coefficients of each province and city in the Yangtze River Economic Belt from 2010 to 2021, as well as their development and evolution in the eastern, central, and 132 western regions, were calculated using formula (2), as shown in Figures 2 and 3. -1 0 1 2 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 0 2 0 2 1 Shanghai Jiangsu Zhejiang Anhui Jiangxi Hubei Hunan Chongqing Sichuan Yunnan Guizhou Figure 2. Trend of Entropy Coefficient Changes in Logistics Industry Location in Various Provinces of the Yangtze River Economic Belt from 2010 to 2021 0 0.2 0.4 0.6 0.8 1 1.2 1.4 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 0 2 0 2 1 eastern region central region western region Figure 3. Trend of Entropy Coefficient Changes in Logistics Industry Location in the Three Major Regions of the Yangtze River Economic Belt from 2010 to 2021 From Figures 2 and 3, it can be seen that from 2010 to 2021, there were three provinces and cities in the Yangtze River Economic Belt region with a location entropy of logistics industry clustering level greater than 1, indicating a high degree of industry clustering, namely Shanghai, Jiangsu Province, and Anhui Province. The location entropy of the logistics industry agglomeration level in the Yangtze River Economic Belt is highest in Shanghai, with an average value of 1.4416, followed by Anhui Province, with an average value of 1.2427. Among the 11 provinces and cities, Sichuan Province has the lowest location entropy value for logistics industry clustering, which is 0.7272. Hubei Province had a negative location entropy (-0.6351) in 2020, which may be due to the regression of GDP level that year. The entropy coefficient of logistics industry location in the eastern region of the Yangtze River Economic Belt has shown a slow upward trend in all years except for 2020 when it fell to a low point due to the impact of the epidemic. The entropy coefficient of logistics industry location in some regions shows a fluctuating trend, presenting a "midstream subsidence" trend of logistics industry agglomeration development. The location entropy coefficient of the logistics industry in its western region shows a double "U" - shaped development characteristic of first decreasing and then increasing. The location entropy coefficient reached its minimum value in 2015, then increased year by year, and reached its peak in 2018, gradually narrowing the gap with the central region. (3) Analysis of the influencing factors of industrial agglomeration on carbon emissions in the logistics industry 1. Selection and verification of spatial econometric models Currently, based on the different types of spatial interaction effects, there are three commonly used spatial econometric models: Spatial Lag Model (SLM), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). On this basis, we use spatial econometric models to test this spatial correlation. Using formulas (3) and (4), based on the dependent variable, explanatory variable, control variable, and spatial econometric model described in Table 1, LM test was performed using Stata15.1 to identify the required spatial econometric model. The results are listed in Table 2. 133 Table 2. LM Inspection Test Statistic df p-value Spatial error: Moran's I 1.394 1 0.000*** Lagrange multiplier 0.915 1 0.000*** Robust Lagrange multiplier 3.469 1 0.000*** Spatial lag: Lagrange multiplier 8.903 1 0.000*** Robust Lagrange multiplier 11.456 1 0.034** Note: *, * *, * * * respectively indicate passing the significance tests of 10%, 5%, and 1%. The results of the LM test in Table 2 show that both the LM test and Robust LM test of SEM are significant at the 1% level, The LM test of SLM reached a significance level of 1%, and the Robust LM test reached a significance level of 5%. The results showed that both SEM and SLM tests passed the significance level test, indicating that spatial econometric models can be used for research and SDM models should be selected. To verify the accuracy of the results, perform LR and Wald tests again to determine whether to choose the SDM model. The results of LR test and Wald test are shown in Table 3. Table 3. Spatial econometric model verification Testing methods Spatial Error Model (SEM) Spatial Lag Model (SLM) Statistical P Statistical P LM-test 0.915*** 0.008 8.903*** 0.006 Robust LM-test 3.469 0.122 11.456* 0.082 LR-test 22.42*** 0.0042 19.94** 0.0106 Wald-test 20.31*** 0.0024 21.05*** 0.0037 Hausman 25.56*** 0.0006 Note: *, * *, * * * respectively indicate passing the significance tests of 10%, 5%, and 1%. From Table 3-LR and Wald test results, it can be seen that the LR test of SLM is significant at the 5% level, and the Wald test is significant at the 1% level. The SEM statistics all pass the significance test at the 1% level, indicating that SDM cannot degrade into SEM or SLM. In this case, SDM, i.e. the spatial Durbin model, is chosen. The Hausman results in the table passed the 1% significance test, indicating that fixed effects are superior to random effects models. Therefore, the fixed effects spatial Durbin model is used to study the spatial spillover effect of logistics industry agglomeration on logistics industry carbon emissions in the Yangtze River Economic Belt. 2. Regression analysis of logistics carbon emissions and industrial agglomeration results For the spatial Durbin model, due to the lack of consideration of the spatial lag term of the independent variable on the mutual influence between adjacent regions, there is a certain degree of bias. However, the effect decomposition of the spatial Durbin model can reflect the direct and indirect effects of logistics carbon emissions in a region and its surrounding areas. The decomposition results of relevant factors are shown in Table 4, and the specific analysis is as follows: Table 4. Decomposition Results of Durbin Model Effects variable direct effect indirect effect Total effect Lnx1 0.870***(15.860) 0.431**(2.100) 1.300***(5.580) Lna1 0.676*(1.780) 5.223***(3.280) 5.899***(3.200) Lna2 - 0.232*(-1.760) 0.701 (1.750) - 0.470(0.960) Lna3 - 0.444***(6.650) 1.379***(-3.740) 0.935**(-2.250) Lna4 -0.110(-1.510) -0.940***(-4.010) -1.049***(-3.830) Lna5 0.349***(7.050) 0.478***(2.770) 0.827***(4.180) Note: * * *, * *, * represent significant levels of 1%, 5%, and 10%, respectively, with Z values in parentheses. From the decomposition of effects in Table 4, it can be seen that: (1) The direct and indirect effects of industrial agglomeration (lnx1) are significantly positive, indicating that industrial agglomeration not only increases the logistics carbon emissions of the local area, but also increases the logistics carbon emissions of its adjacent areas. Specifically, the direct effect coefficient is 0.870, significant at the 1% significance level, and the spillover effect coefficient is 0.431, significant at the 5% significance level, with direct effects being greater than indirect effects. For every 1% increase in the overall industrial agglomeration level of the Yangtze River Economic Belt, logistics carbon emissions will increase by 1.300%, indicating that industrial specialization agglomeration can promote the increase of logistics carbon emissions. (2) The direct and indirect effects of energy consumption (lna1) are both positive. For every 1% increase, logistics carbon emissions will increase by 5.8990%, indicating that energy consumption can promote the increase of logistics carbon emissions. Among them, the direct effect coefficient is 0.676, and the significance level is not high. The spillover effect coefficient is 2.223, which is significant at the 1% significance level. Promoting energy consumption by logistics enterprises will lead to an increase in logistics carbon emissions in neighboring areas. (3) The direct effect of government intervention (lna2) is 134 positive, and the indirect effect is negative. For every 1% increase, logistics carbon emissions will decrease by 0.470%, indicating that government intervention can suppress the increase of logistics carbon emissions, but the significance level is not high. The direct effect coefficient is -0.232, significant at the 10% significance level, while the spillover effect coefficient is 0.701, not significant. Government intervention has a positive spillover effect, and it can cause regional industrial constraints. (4) The direct effect of the industrial population size (lna3) is negative, and the indirect effect is positive, both of which have passed the significance test. Among them, the direct effect coefficient is 0.444, significant at the 1% significance level, and the spillover effect coefficient is -1.612, significant at the 1% significance level. The direct effect of industrial population size is greater than the indirect effect. For every 1% increase in the overall logistics industry population in the Yangtze River Economic Belt, logistics carbon emissions will increase by 0.935%, indicating that the number of industrial population can promote the increase of logistics carbon emissions. (5) The direct and indirect effects of infrastructure construction (lna4) are both negative, with a direct effect coefficient of -0.110, which is not significant, and a spillover effect coefficient of -0.940, which is significant at the 1% significance level, indicating that infrastructure construction will lead to a 0.940% reduction in logistics carbon emissions in neighboring areas. For every 1% increase, the overall logistics carbon emissions in the Yangtze River Economic Belt will decrease by 1.049%, indicating that infrastructure construction can curb the increase in logistics carbon emissions. (6) The direct and indirect effects of freight volume (lna5) are both significantly positive, with a direct effect coefficient of 0.349, significant at the 1% significance level, and a spillover effect coefficient of 0.478, significant at the 1% significance level. From the perspective of freight volume, the larger the freight volume, the greater the logistics carbon emissions. For every 1% increase in freight volume, it will lead to a 0.349% increase in logistics carbon emissions in the local area, while promoting a 0.478% increase in carbon emissions in neighboring areas. The indirect effect on neighboring areas is greater than the direct effect. On an overall level, for every 1% increase in freight volume, logistics carbon emissions will also increase by 0.827%, indicating that the transportation of goods will cause an increase in carbon emissions in the logistics industry. 5. Conclusion and Implications This article is based on panel data from 11 provinces and cities in the Yangtze River Economic Belt from 2010 to 2021. Firstly, the location entropy method and carbon emission measurement method are used to measure and analyze the industrial agglomeration level and carbon emission level of the logistics industry in the Yangtze River Economic Belt. Then, a spatial Durbin model is constructed to explore the direct and indirect effects of logistics industry agglomeration on logistics carbon emissions in the Yangtze River Economic Belt. The conclusions are as follows: (1) From the perspective of carbon emissions in the logistics industry, the overall level of carbon emissions in the logistics industry of provinces and cities along the Yangtze River Economic Belt is relatively stable. Shanghai, Jiangsu, Zhejiang, and Hubei provinces are the main sources of carbon emissions in the logistics industry of provinces and cities along the Yangtze River Economic Belt. The carbon emission intensity of the logistics industry in the western region has continued to steadily improve during the observation period. (2) From the perspective of logistics industry agglomeration level, from 2010 to 2021, there were three provinces and cities with logistics industry agglomeration level location entropy greater than 1, indicating a high degree of industry agglomeration, namely Shanghai, Jiangsu Province, and Anhui Province. The entropy coefficient of logistics industry location in the central region shows a fluctuating trend, indicating a "midstream subsidence" trend. (3) From the Durbin model, industrial agglomeration has a significant positive impact on the total carbon emissions of China's logistics industry, and has a significant spatial spillover effect. In addition, the total effect of energy consumption, population size, and freight volume is significantly positive, while the total effect of government intervention and infrastructure construction is significantly negative. Based on the above research conclusions, this article draws the following insights: (1) Improve the configuration mechanism of logistics and environment in the development process, accelerate the construction of logistics infrastructure in various provinces and cities of the Yangtze River Economic Belt, maximize its role in promoting regional economic efficiency, actively enhance industrial diversification and agglomeration, promote industry specialization and scale production, enhance the green emission reduction technology innovation efficiency of logistics enterprises, and provide strong support for the healthy development of logistics in the Yangtze River Economic Belt. (2) The spillover effect of logistics industry agglomeration on carbon emission intensity has significant spatial heterogeneity, and different regions should formulate differentiated strategies according to their own development situation. 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