###### Abstract  
This paper investigates the relationship between port productivity and carbon dioxide (CO\({}_{2}\)) emissions in port cities. The study initially employs the global Malmquist productivity index (MPI) to measure productivity growth in 16 major inland ports along the Yangtze River, obtaining data on the ports' total factor productivity (TFP). Through an analysis using the panel data model with two-way fixed effects, we find a positive correlation between the improvement of port TFP and the increase in CO\({}_{2}\) emissions in port cities. Further panel quantile regression analysis reveals the heterogeneity of this impact, especially in cities with medium and higher CO\({}_{2}\) emissions, where the positive effects of TFP on carbon emissions are particularly significant. The study also indicates a threshold effect of port size in the relationship between TFP  
also become major sources of environmental pollution [7; 8]. Particularly, the excessive consumption of fossil fuels has led to an overabundance of CO\({}_{2}\) emissions, placing immense pressure on the Yangtze River Economic Belt to achieve CO\({}_{2}\) reductions and affecting the realization of sustainable development [9; 10]. In 2019, the CO\({}_{2}\) emissions from the Yangtze River Economic Belt accounted for more than 50% of the country's total, posing a significant challenge to China's \"dual carbon\" goals of reaching a carbon peak by 2030 and achieving carbon neutrality by 2060 [11; 12]. Therefore, considering both the reduction in CO\({}_{2}\) emissions and the enhancement of port productivity has become a dual objective for policymakers. Understanding the interaction between the CO\({}_{2}\) emissions and port productivity of Yangtze River inland ports and their cities is of great importance for guiding policy formulation. The current research on carbon emissions in cities along the Yangtze River focuses on the extensive range of the Yangtze River Economic Belt, covering over 108 cities [13]. These studies explore the impact of factors such as construction land [12; 14], industrialization [15], urbanization [16], foreign direct investment [16], economic growth [17], green innovation [13], industrial agglomeration [18; 19], digital economy [20], and regional integration [21] on CO\({}_{2}\) emissions. However, due to the broad scope of the Yangtze River Economic Belt, most of the cities within it are not port cities, which leads to a general oversight in existing research on the potential impact of port activities on urban CO\({}_{2}\) emissions. In contrast, our study focuses on cities along the Yangtze River, thereby considering the ports' role in filling the research gap in this field. Meanwhile, the existing literature on the impact of transportation infrastructure and its operational efficiency on urban CO\({}_{2}\) emissions, while considering the effects of roads, railways [22; 23; 24; 25; 26; 27], and new logistics policies [28; 29], often overlooks the crucial area of ports. Specifically, the total factor productivity (TPF) of ports, as a core indicator for measuring the production and service capacity of ports [3; 30], is vital for ports and has become the focus of many studies [3]. In summary, we chose the TFP of ports along the Yangtze River to explore how it affects the CO\({}_{2}\) emissions of port cities for the following reasons: 1. Yangtze River ports, as economic and social linchpins, are crucial for the Yangtze River Basin, and their TFP is directly related to port operational efficiency, which could impact the CO\({}_{2}\) emissions levels of port cities. 2. The current literature on the impact of transportation infrastructure on urban CO\({}_{2}\) emissions lacks consideration of port impacts. 3. Reducing CO\({}_{2}\) emissions and enhancing port productivity are goals for policymakers, and our research aims to provide a basis for policymakers to formulate policies that can reduce CO\({}_{2}\) emissions and enhance port productivity simultaneously, supporting the coordinated development of these two goals.