Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO₂ emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO₂ reduction. This study calculated CO₂ emissions from 2005–2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO₂ across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO₂ emission in the area is a complex network pattern, and in the sample period, the CO₂ emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan—the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO₂ is greatest; and (4) the higher density of the global network of CO₂ emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

A crucial measure to accelerate the low-carbon transformation of enterprises in the industrial sector involves stringent control over carbon emissions attributed to logistics and transportation activities. In this study, a multi-objective workshop layout optimization model is developed, aiming to minimize logistics cost per unit area and carbon emissions, and maximize the non-logistics relationship. The objective is to mitigate avoidable transportation-related carbon emissions during enterprise operations, while facilitating the co-development of the enterprise’s economy and environment. The model is solved utilizing an enhanced NSGA-II algorithm, with the initial solution set optimized through a combination of system layout design method, dynamic adaptive crossover, and variation strategies. Additionally, the distribution function is introduced to enhance the elite retention strategy and boost the algorithm’s search rate. By using an actual case study, the usefulness of the enhanced algorithm is demonstrated, and the plant’s initial low-carbon layout is realized in order to advance the enterprises’ sustainable growth.

In light of the recent worldwide scientific and technological revolution, it is imperative that urban infrastructure undergo a digital transformation in order to lower carbon emissions and support sustainable urban growth. However, to date, there is a lack of empirical research on carbon emissions based on the digital transformation of urban infrastructure. This paper uses data from 178 prefecture-level cities in China from 2005 to 2020 to study the impact of digital transformation of urban infrastructure on carbon emissions based on the "local-neighbourhood" perspective using a spatial difference-in-differences model. The results show that the digital transformation of urban infrastructure reduces the intensity of local carbon emissions while also reducing the carbon emissions of neighbouring cities, with a spatial spillover effect, and the boundary of this spatial spillover is 600 km. Mechanistic analyses suggest that digital transformation of urban infrastructure can reduce carbon emissions locally as well as in nearby areas by promoting green technological innovations. In light of this, this study has important policy implications for maximising the contribution of digital transformation of infrastructure to reducing carbon emissions.