Carbon capture, utilization, and storage (CCUS) technology is considered an effective way to reduce greenhouse gases, such as carbon dioxide (CO2), which is significant for achieving carbon neutrality. Based on Derwent patent data, this paper explored the technology topics in CCUS patents by using the latent Dirichlet allocation (LDA) topic model to analyze technology’s hot topics and content evolution. Furthermore, the logistic model was used to fit the patent volume of the key CCUS technologies and predict the maturity and development trends of the key CCUS technologies to provide a reference for the future development of CCUS technology. We found that CCUS technology patents are gradually transforming to the application level, with increases in emerging fields, such as computer science. The main R&D institutes in the United States, Europe, Japan, Korea, and other countries are enterprises, while in China they are universities and research institutes. Hydride production, biological carbon sequestration, dynamic monitoring, geological utilization, geological storage, and CO2 mineralization are the six key technologies of CCUS. In addition, technologies such as hydride production, biological carbon sequestration, and dynamic monitoring have good development prospects, such as CCUS being coupled with hydrogen production to regenerate synthetic methane and CCUS being coupled with biomass to build a dynamic monitoring and safety system.

Taking 57 prefecture-level cities in the Yellow River basin as a research area, this study evaluates the coupling coordination level of the water–energy–carbon (WEC) system in the Yellow River basin from 2012 to 2021 and explores the driving factors of coupling coordinated development. The study revealed that: (1) the development level of the three subsystems all showed an upward trend. The development level of the carbon system exhibited the highest level. The development index of the carbon and energy systems rose steadily, whereas the development index of the water system fluctuated considerably during the research period, although the magnitude of the fluctuation gradually slowed down. (2) The coupling coordination degree displayed a distribution characteristic of “high in the east and low in the west, high in the south and low in the north”. While the coupling coordination degree improved year by year, the spatial heterogeneity gradually increased. (3) The coupling coordination degree presented a positive correlation, and the agglomeration level was dominated by “high-high” and “low-low” agglomeration types. The “high-high” agglomeration area had a certain degree of spatial mobility, while the “low-low” agglomeration areas showed a tendency for spreading towards the middle reaches of the Yellow River basin. (4) Technological innovation, and the economic basis, had a significant positive impact on the coupling coordinated development, while the industrial structure bias showed a clear inhibitory effect. The positive role of opening up is not yet significant. Meanwhile, the indirect effect of each driving factor was greater than the direct effect.

Based on a detailed analysis of the impact mechanism of industrial restructuring on carbon dioxide emissions in the Yellow River Basin, this paper first calculated the carbon dioxide emission data of 57 prefecture-level cities in the Yellow River Basin from 2009 to 2019 and constructed indicators from two dimensions: the advancement and the rationalization of the industrial structure. Then, the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was used to empirically analyze the influencing factors of industrial structure adjustments on carbon dioxide emissions in the Yellow River Basin. Consequently, changing carbon dioxide emission trends in the Yellow River Basin under various scenarios were predicted. The research observed the following: (1) the eastern part of the Shandong Peninsula Urban Agglomeration and the Energy Golden Triangle have higher carbon dioxide emissions; (2) the advancement of industrial structures in the Yellow River Basin has a better emission reduction effect than the rationalization of industrial structures; (3) increased foreign investment will lead to an increase in carbon dioxide emissions in the Yellow River Basin, and a “Pollution Refuge Effect” will emerge; (4) accelerated industrial transformations and upgrades, high-quality economic development, and a moderate population growth rate are consistent with future development trends.

By analyzing the mechanism by which industrial structure adjustment influences the reduction in pollution and carbon emissions (RPCE) in the Yellow River Basin, in this study, we calculated data for the RPCE in 57 prefecture-level cities from 2011 to 2020. Based on the Regression on Population, Affluence, and Technology (STIRPAT) model, we empirically examined factors affecting the RPCE in the Yellow River Basin. Additionally, different scenarios were established in order to simulate and predict the future trend of the RPCE in the Yellow River Basin. In the study, we found the following: (1) The RPCE in the Yellow River Basin shows a positive trend, with lower levels in upstream Gansu and Ningxia and particularly severe conditions in Zhongwei, Shizuishan, and Wuhai, making these key areas for RPCE. (2) Moreover, the RPCE effect of the advanced industrial structure in the Yellow River Basin is superior to that of the rationalized industrial structure, economic growth and population increases are conducive to RPCE, foreign investors in the Yellow River Basin tend to invest more in high-energy-consuming industries, and there is a “pollution haven” effect. (3) In terms of regional heterogeneity, the impact of industrial structure adjustment on the RPCE in the lower and middle reaches is greater than that in the upstream regions. (4) The acceleration of the transformation and upgrading of industry, stabilization of the population growth rate, and promotion of high-quality economic development are the optimal development paths for RPCE in the Yellow River Basin.

This study employs DMSP-OLS and NPP-VIIS nighttime light remote sensing data to develop a carbon emission regression model based on energy consumption, analyzing the spatiotemporal evolution of carbon emissions in 57 cities within the Yellow River Basin from 2012 to 2021. The analysis uses a quantile regression model to identify factors affecting carbon emissions, aiming to enhance the basin’s emission mechanism and foster low-carbon development. Key findings include: 1) Carbon emissions from energy consumption increased in this period, with a decreasing growth rate. 2) Emissions were concentrated along the Yellow River and its tributaries, forming high-density carbon emission centers. 3) The Yellow River Basin has mainly formed a “high-high” agglomeration area centered on resource-based cities such as Shanxi and Inner Mongolia’s coal, and a “low-low” agglomeration area centered on Gansu and Ningxia. The standard deviation ellipse of carbon emissions in the Yellow River Basin generally extends from east to west, and its center of gravity tends to move northward during the study period. 4) Technological innovation, economic development, and population agglomeration suppressed emissions, with digital economy and foreign investment increasing them in certain cities. Urbanization correlated positively with emissions, but adjusting a single industrial structure showed insignificant impact.