As the world's largest carbon dioxide (CO₂) emitter and a major trading country, both anthropogenic and natural factors play an important role in China's carbon budget. However, previous studies mostly focused on evaluating anthropogenic emissions or the natural carbon cycle separately, and few included trade-related (import and export) CO₂ emissions and its contribution to global warming. Using the CarbonTracker CT2019 assimilation dataset and China trade emissions from the Global Carbon Project, we found that the change trend of global CO₂ flux had obvious spatial heterogeneity, which was mainly affected by anthropogenic CO₂ flux. From 2000 to 2018, carbon emissions from fossil fuels in the world and in China showed an obvious increasing trend, but the magnitude of the increase tended to slow down. In 2018, the radiative forcing (RF) caused by China's import and export trade was 0.0038 W m^-2, and the RF caused by natural carbon budget was -0.0027 W m^-2, offsetting 1.54% and 1.13% of the RF caused by fossil fuels that year, respectively. From 2000 to 2018, the contribution of China's carbon emission from fossil fuels to global RF was 11.32%. Considering China's import and export trade, the contribution of anthropogenic CO₂ emission to global RF decreased to 9.50%. Furthermore, taking into account the offset of carbon sink from China's terrestrial ecosystems, the net contribution of China to global RF decreased to 7.63%. This study demonstrates that China's terrestrial ecosystem and import and export trade are all mitigating China's impact on global anthropogenic warming, and also confirms that during the research process on climate change, comprehensively considering the carbon budget from anthropogenic and natural carbon budgets is necessary to a systematic understanding of the impacts of regional or national carbon budgets on global warming.

Urban agglomerations serve as crucial platforms for constructing substantial domestic circulation and fostering harmonious regional development in China. Given the evolution of the integrated development of urban agglomerations, the characteristics of their internal spatial networks inevitably lead to the carbon peak paths of individual cities being influenced by their proximate counterparts. Consequently, this study focused on the Yangtze River Delta urban agglomeration, which boasts a high degree of integration within China, constructed a spatial weight matrix based on composite geographic and economic dimensions, applied a spatial econometric model to analyze the spatial correlation of carbon emissions in this urban agglomeration, and further applied the spatially embedded Genetic Algorithm-Long Short-Term Memory (GA-LSTM) model to simulate dynamically the peak paths of carbon emissions in this urban agglomeration. The empirical results revealed several important findings: (1) Considering the spatial correlation effects of the urban agglomeration, the carbon peaks of several cities occur sooner than expected, and most cities experience a reduction in their peak level, indicating that the spatial correlation effect can effectively optimize the spatial pattern of carbon emissions. However, the post-peak emission dynamics of these cities are not significantly affected. (2) In the baseline scenarios, with the exception of Suzhou (Jiangsu) and Bozhou, all cities attain their carbon peak by 2030, with most cities in Anhui province maintaining a steady decrease in carbon emissions after 2019, some cities in Jiangsu and Zhejiang provinces experiencing a relatively slow decrease in carbon emissions after reaching the peak, and Shanghai and Nantong showing a rebound trend of slow increase in carbon emissions after reaching their peak at an early stage. (3) Under the green scenarios, the total carbon emissions from the Yangtze River Delta urban agglomeration follow a steady downward trend since 2019, effectively reversing the inertial growth under the baseline scenarios, and the cities within the urban agglomeration show significant improvement in the time to peak, peak level, and post-peak situation, which contributes to a synergistic emission reduction pattern.

The digital economy is a pivotal driver for fostering high-quality economic development in China, which is crucial in attaining carbon peak and neutrality objectives. Given its significance as a core vehicle for national urbanization initiatives, elucidating the influence and spatial implications of the digital economy on carbon emission efficiency at the county and district levels is imperative for China's new urbanization strategy, bolstering ecological civilization and advancing green transformation and development in the contemporary era. Utilizing the entropy weight method and the SBM-DEA model, this study assessed the digital economy's development level and carbon emission efficiency in 19 counties within the Changsha-Zhuzhou-Xiangtan metropolitan area spanning 2011 to 2021. Spatial analysis techniques were employed to delineate the spatiotemporal patterns and clustering characteristics of the digital economy and carbon emission efficiency at the county scale within this metropolitan region. Furthermore, we leveraged the OLS model and the spatial Durbin model to empirically scrutinize the impact and spatial effect of the digital economy on carbon emission efficiency in the study area. The key findings are as follows: (1) There exists a significant disparity in the development of the digital economy and carbon emission efficiency across the Changsha-Zhuzhou-Xiangtan metropolitan area, exhibiting a spatial distribution pattern characterized by "high in the north and low in the south". (2) The digital economy in this metropolitan area consistently exerts a noteworthy stimulatory effect on carbon emission efficiency, and this influence is markedly strengthened upon the inclusion of various control variables. (2) The digital economy in the Changsha-Zhuzhou-Xiangtan metropolitan area exhibits a substantial positive spatial effect on carbon emission efficiency, with a more significant impact on local carbon emission efficiency compared with neighboring counties.

In the context of rapid urbanization, promoting low-carbon school trips is a crucial strategy for fostering sustainable urban development. However, the factors influencing carbon emissions from school trips during compulsory education remain unclear. This study focuses on Kaifeng, a city experiencing rapid spatial expansion and reconstruction. Utilizing multi-source spatiotemporal big data, large-scale micro-surveys, and the SHAP model (an interpretable machine learning method), we explore the nonlinear mechanisms and threshold effects of low-carbon school trips. The results reveal the following: (1) Carbon emissions from school trips show significant spatial disparities, making rapid expansion of outer areas critical for emission reduction efforts. (2) Temporal and spatial accessibility are the most critical factors impacting carbon emissions. Secondary schools should be planned within a 15-minute walking circle, while primary schools need optimization to be within a 13-minute one. (3) Effective management of schools surrounding the built environment can support low-carbon school trips. Optimal conditions for low-carbon school trips include a road network density of 10-14 km/km², a land use mix of 2.4-2.7, and primary schools accommodating up to 1000 people. (4) The lack of students' ability to travel independently also restricts low-carbon school trips. Developing safe routes and promoting walking school buses are effective strategies for creating child-friendly cities. The research results provide valuable insights for the refined planning and governance of urban education resource distribution and low-carbon development.