The transportation sector plays a pivotal role in China’s efforts to achieve CO₂ reduction targets. As the capital of China, Beijing has the responsibility to lead the era’s demand for low-carbon development and provide replicable and scalable low-carbon transportation development experience and knowledge for other cities in China. This study calculates the CO₂ emissions of the transportation sector in Beijing from 1999 to 2019, constructs an extended STIRPAT model (population, affluence, technology, and efficiency), employs ridge regression to mitigate the effects of multicollinearity among the eight indicators, reveals the extent and direction of influence exerted by different indicators on CO₂ emissions, and predicts the development trends, peak times, and quantities of transportation CO₂ emissions in nine scenarios for Beijing from 2021 to 2035. Finally, adaptive low-carbon planning strategies are proposed for Beijing pertaining to population size and structure, industrial layout optimization, urban functional reorganization and adjustment, transportation infrastructure allocation, technological research and promotion, energy transition planning, and regional collaborative development. The results are as follows: (1) The total amount of CO₂ emissions from Beijing’s transportation sector exhibits a trend of gradually stabilizing in terms of growth, with a corresponding gradual deceleration in the rate of increase. Kerosene, gasoline, and diesel are the main sources of transportation CO₂ emissions in Beijing, with an annual average proportion of 95.78%. (2) The degree of influence of the indicators on transportation CO₂ emissions, in descending order, is energy intensity, per capita GDP, population size, GDP by transportation sector, total transportation turnover, public transportation efficiency, possession of private vehicles, and clean energy structure. Among them, the proportion of clean energy structure and public transportation efficiency are negatively correlated with transportation CO₂ emissions, while the remaining indicators are positively correlated. (3) In the nine predicted scenarios, all scenarios, except scenario 2 and scenario 4, can achieve CO₂ emission peaks by 2030, while scenarios 7 and 9 can reach the peak as early as 2025. (4) The significant advancement and application of green carbon reduction technologies have profound implications, as they can effectively offset the impacts of population, economy, and efficiency indicators under extensive development. Effective population control, sustainable economic development, and transportation efficiency improvement are viable means to help achieve carbon peaking and peak value in the transportation sector.

The low-carbon development of the transportation sector is crucial for China to achieve its national goals of carbon peaking and carbon neutrality. Since China is a vast country with unbalanced regional development, there are considerable differences in the levels of carbon dioxide emissions from the transportation sector across regions. Therefore, revealing the influencing factors that shape the characteristics of transportation carbon dioxide emissions (TCO₂) can inform tailored sub-national carbon reduction strategies based on local conditions, which is an important technical approach for achieving national goals. Based on an extended Kaya identity, we derived indicators of the impacts on provincial TCO₂ from factors such as economic development, population density, energy structure, transportation efficiency, technology research and development (R&D), infrastructure construction, transportation operation conditions, and residents’ transportation behavior. Using a multi-indicator joint characterization method, we explored the characteristics of provincial TCO₂ in China in 2019. By applying Ward’s method to hierarchical clustering, the thirty provinces of China were classified into six characteristic types (Types I to VI). Based on the total TCO₂ (TC), the intensity of TCO₂ (TI), and the per capita TCO₂ (TP) calculated for each province in 2019, the priority control directions and indicators for carbon reduction in each type were obtained through relative relationships with provincial averages and correlation analysis with the indicators. Specifically, Type I and Type IV can be categorized as TP-controlled, Type II and Type III as TC-controlled, and Type V and Type VI as TI-controlled. Finally, we provided typological strategies and key performance indicators (KPIs) relevant to local governments to better achieve carbon reduction goals in each provincial type. It can promote cooperative development and collaborative governance in carbon reduction across regions and the unified implementation of China’s dual-carbon goals.

Against the backdrop of global economic digital transformation and the rapid flow of creative factors, innovation spaces, as the key carriers of inventive activities, drive high-quality development in urban agglomerations. This study develops a three-dimensional framework of “Spatial Structure–Factor Synergy–Institutional Drivers” to uncover the evolution of innovation spaces and industrial shifts in the Beijing–Tianjin–Hebei urban agglomeration, China. Methodologically, spatial econometric techniques were applied to capture both the overall concentration and spatial disparities of innovation. Spatial Gini and variation coefficients measured innovation clustering, while standard deviation ellipses and location entropy identified spatial linkages among high-tech innovation clusters. Geographically weighted regression models explored spatial heterogeneity in influencing factors, and a policy intensity index was constructed to assess the effectiveness of differentiated policy interventions in optimizing innovation resources. Key findings include the following: (1) Innovation spaces are spatially polarized in a “core–periphery” pattern, yet require cross-regional collaboration. Concurrently, high-tech industries demonstrate a gradient structure: central cities leading in R&D, sub-central cities driving industrial applications, and node cities achieving specialized development through industrial transfer. (2) The driving mechanisms exhibit significant spatial heterogeneity: economic density shows diminishing returns in core areas, whereas R&D investment and ecological quality demonstrate increasingly positive effects, with foreign investment’s role evolving positively post-institutional reforms. (3) Regional innovation synergy has formed a preliminary framework, but strengthening sustainable policy mechanisms remains pivotal to advancing market-driven coordination and dismantling administrative barriers. These findings underscore the importance of integrated policy reforms for achieving balanced and high-quality innovation development in administratively coordinated urban agglomerations like BTH.

The Yellow River Basin (YRB) stands as one of China’s most significant river basins, and the maintenance of its ecological functionality is of paramount importance for national well-being. The Guanzhong Plain Urban Agglomeration (GPUA), situated in the middle reaches of the YRB, represents the central hub of human activities. The rapid expansion of cities within this region poses formidable challenges to the ecological security framework of the highly sensitive middle reaches of the YRB. In this study, the dynamic equivalent coefficient method was employed to evaluate the changes in Ecological Service Values (ESVs) within the GPUA from 1990 to 2020, as well as the ecological costs incurred due to urban expansion. The results indicate the following: (1) Over the past three decades, the land-use pattern within the GPUA has undergone significant transformations. The area designated for urban development has expanded by a factor of 1.16 compared to its original extent, while the areas encompassing forests, shrubs, agricultural land, grassland, wetland, and bare land have experienced continuous reductions. (2) The ESV of the study area displays a declining trend initially, followed by a subsequent increase over the 30-year period. Forests play a predominant role in contributing to the ESV of the GPUA, with regulating services and supporting services standing out as the primary ecosystem functions. (3) The expansion of the GPUA between 1990 and 2020 has resulted in a net loss of 3772.10 km² of ecological land. The ecological costs associated with urban expansion soar to an astonishing CNY 2.54 billion, with the highest costs attributed to the loss of hydrological regulation and soil conservation services; this issue demands significant attention. The outcomes of this research contribute to a better comprehension of the ecological costs and benefits that accompany the development of urban agglomerations in the middle reaches of the YRB. Furthermore, they provide invaluable insights for decision makers seeking to implement more effective strategies for sustainable land-use management.