Carbon reduction is an important aspect of achieving high-quality environmental development; environmental regulation and industrial agglomeration are important ways to affect carbon emissions. Therefore, studying the relationship between industrial agglomeration, environmental regulation, and carbon emissions has important theoretical and practical significance. Firstly, this article adopts the method of location entropy to measure the indicators of industrial agglomeration. Secondly, it proposes an environmental regulation indicator system based on the experience of previous scholars and measures the environmental regulation indicators using the entropy method. Next, eight types of energy consumption are used to measure carbon emissions based on the carbon emission coefficient method provided by the IPCC. Finally, based on the Moran index, the spatial correlation of carbon emission levels in various regions of the Yangtze River Economic Belt (YREB) is tested. A spatial econometric model was introduced to explore the relationship between industrial agglomeration, environmental regulation, and carbon emissions at a deeper level, and the following conclusions were drawn: (1) The regression coefficient of the spatial term of industrial agglomeration on carbon emissions is 0.848, which is significantly positive at the 10% level, indicating that under the influence of spatial effects, industrial agglomeration has a significant promoting effect on carbon emissions. (2) The regression coefficient of the spatial term of environmental regulation on carbon emissions is −0.011, which is significantly negative at the 10% level, indicating that environmental regulation has an inhibitory effect on carbon emissions under the influence of spatial effects. Based on the above conclusions, useful suggestions have been provided for optimizing industrial structure, improving environmental regulation levels, and alleviating carbon emission issues.

Trade has contributed to economic development and has brought countries or regions of the world closer together, but it has also had a significant impact on the global environment, particularly in terms of carbon dioxide emissions. Does an increase in a country’s trade necessarily contribute to an increase in its carbon emissions? This paper examines the impact of changes in the position of major countries in the world trade network on their carbon emission levels. In terms of research methodology, this paper achieves an innovation by adopting a complex network approach to analyze the structural characteristics of the trade relationship networks of major countries or regions in the world, providing a new perspective for the study of the relationship between trade development and carbon emissions. The results of the study show that: trade relations among countries are relatively stable from 2000 to 2020, trade ties among members of regional integration organizations are increasing, the top ten countries in terms of importance are mainly developed countries, and China has very close trade relations with most countries. Based on the analysis of structural characteristics, the impact of changes in the network status of each country on its ocarbon emissions is analyzed, using indicators such as the degree centrality of each node as the independent variable and its domestic carbon emission level as the dependent variable. It is found that developed countries have a significant positive impact on in-going degree centrality, and insignificant impact on out-going degree centrality and betweenness centrality. In contrast, developing countries have a significant positive impact on out-going degree centrality and a negative impact on carbon emissions by betweenness centrality, and this conclusion tells us that not all export growth will contribute to higher levels of carbon emissions in the country. Eigenvector centrality has a negative effect on carbon emissions for both developing and developed countries, and closeness to centrality has no effect on carbon emissions while closeness centrality has no effect on carbon emissions. The results of this study again show that the influence of developed countries is greater than that of developing countries on carbon emissions; therefore, the role of developed countries can be taken into account in subsequent studies on carbon emission reduction.

China’s economic development has made world-renowned achievements which have subsequently brought serious environmental pollution problems. As a large manufacturing country, the industrial value added in China accounts for nearly 30% of the global share. However, the manufacturing industry lacks competence. Therefore, considering environmental protection issues while promoting manufacturing industry upgrading (MIU) is a critical goal for China at present. By applying panel data from China’s Yangtze River Economic Belt (YREB) from 2010 to 2019, this paper uses a spatial econometric model to study the effect of green finance (GF) on upgrading (hereafter, upgrading refers to the improvement of the manufacturing structure and the transition toward green manufacturing) the manufacturing industry. This study found that the impact of GF on MIU in the YREB is significantly positive. GF has a good positive spatial spillover effect. Moreover, the high level of urbanization and high freight volume have positive impacts, the impacts of foreign investment and government fiscal expenditure are significantly negative, and the effect of the level of human capital on upgrading is not obvious. Therefore, vigorously developing green finance, optimizing the environment for manufacturing upgrades, and cultivating green and high-quality talents should be the future direction of government policy formulation.

Consumption plays an important role in economic growth and sustainable economic development. The Chinese government emphasizes the theme of promoting high-quality development. This aim has led to the implementation of strategies to expand domestic demand through a deep structural reform in the supply chain, enhance the endogenous power and reliability of the large domestic cycle, improve the quality and level of the international cycle, and accelerate the construction of a modern economic system. Based on the clarification of the consumption effect of housing support expenditure, this paper uses a combination of theoretical and empirical analysis to clarify the relationship between housing support expenditure and urban residents’ consumption. This is carried out to seek a breakthrough point for raising the total level of urban residents’ consumption expenditure in China from the level of housing support. This study revealed that government housing support has a certain degree of influence on the total level of urban residents’ consumption expenditure and that the government’s support of housing support is conducive to improving the total level of urban residents’ consumption expenditure. Also, if the housing price is not controlled and adjusted, the effect of government housing support on urban residents’ consumption expenditure will be greatly reduced, which will affect the lives of urban residents in the long run. Finally, suggestions and countermeasures for improving the overall level of urban residents’ consumption and the sustainable development of the social economy in China are provided.

Based on the outward direct investment data of each manufacturing industry segment in Jiangsu Province from 2000 to 2020, this paper establishes a correlation network by constructing a grey incidence model with the average value of absolute grey incidence as the threshold. We further analyze the relationship between each manufacturing industry segment in Jiangsu Province in the process of outward direct investment from two perspectives, namely, point and surface. The study shows that from the perspective of each node, the correlation coefficient between equipment manufacturing and other industries is significantly higher, i.e., the influence of equipment manufacturing on other industries is significantly greater. Chemical raw materials and chemical products manufacturing, general equipment manufacturing, special equipment manufacturing, and transportation equipment manufacturing are the important nodes in the network. From the perspective of the network as a whole, the Jiangsu manufacturing OFDI affiliation network is not concentrated. Still, it has small-world characteristics, which are conducive to disseminating information. In contrast, the close nature of the industry has more commonalities, leading to it being more easily divided into the same module in the network block model analysis.