Accurate assessment of expressway roadside vegetation carbon storage is essential for achieving carbon balance and ecological sustainability in the transportation sector. However, this is challenging due to the extensive spatial coverage, marked climatic variation, and pronounced spatial dependence of these vegetation corridors. Focusing on the expressway network of Shanxi Province, this study integrates UAV sample data with Sentinel-2 imagery. A graph convolutional network (GCN) is used to extract road network spatial topology, and estimation models for aboveground vegetation carbon storage are constructed for Shanxi's northern, central, and southern climatic zones. The performance of XGBoost, Random Forest, and Support Vector Machine models is then compared across these climatic zones, emphasizing the importance of model adaptability and spatial feature integration. The results showed that the incorporation of GCN-derived spatial features significantly enhanced the model's ability to characterize spatial autocorrelation along linear corridors. Among all models, the XGBoost-GCN model performed best in southern Shanxi, with a validation R² of 0.723. Compared with the overall model, the climate-zoned models improved estimation accuracy by an average of 18.3%, indicating that zoned modeling is more suitable for estimating carbon storage of roadside vegetation under strong climatic gradients. The carbon storage of expressway roadside vegetation in Shanxi Province exhibited an overall spatial pattern of decreasing from south to north. Hydrothermal conditions were identified as the main factors driving its spatial differentiation, while finely managed areas such as interchanges showed relatively high carbon sink potential. These findings indicate that the multi-scale remote sensing approach integrating GCN-derived spatial features with climate-zoned modeling can improve both the accuracy and regional adaptability of carbon storage estimation for roadside vegetation along long-distance expressways, and can provide methodological support for carbon monitoring, carbon sink identification, and differentiated ecological management of transportation infrastructure ecosystems.

Carbon emissions trading systems have become increasingly prevalent amid rising global climate concerns and serve as key market-based tools for sustainable transformation. Agriculture is central to advancing China's "dual carbon" strategy, requiring both emission control and reduction, while rapid digital agricultural development enables more precise carbon monitoring and management. This study examines whether China's pilot carbon emissions trading pilot policy improves the eco-efficiency of digital agriculture. Using the dynamic data envelopment analysis (DEA)-Malmquist index method, we construct an evaluative framework to measure digital agricultural eco-efficiency, and based on panel data from 30 Chinese provinces over the period 2011-2022, we employ a difference-in-differences (DID) model to identify the policy effects. The empirical findings demonstrate that the ecological efficiency of China's digital agriculture has successfully increased because of the implementation of the pilot policy for carbon emissions trading, and this conclusion passes several robustness tests. Heterogeneity analysis indicates that the effects of the policy vary across regions and different levels of development of digital agricultural eco-efficiency. According to the results of the mediating effect analysis, the pilot carbon emissions trading pilot policy increases forest coverage, which in turn increases digital agricultural eco-efficiency. The results of this research offer guidance for promoting environmentally sustainable agricultural development in China and for supporting international initiatives aimed at lowering agricultural greenhouse gas emissions.

Understanding carbon emissions of the railway transportation industry in China is critical for effective climate action. This study applied machine learning models to analyze railway operational conditions and socio-economic driving factors of carbon emissions across four railway operational regions with different terrain, climate, and economic development characteristics in China from 2009 to 2021. This study indicates that People, Electricity Carbon Emission Factor, Tertiary Industry Gross Domestic, Railway Passenger Volume, Railway freight Volume, Research and Development Investment and Railway Infrastructure Level have a significant impact on carbon emissions. The Lasso_LR model shows strong fitting performance, as the mean absolute error is 4.98% of the average carbon emissions. By 2021, the Qinghai-Tibet Plateau, Yunnan Province, the Guangxi Zhuang Autonomous Region, and the Yangtze River Delta Region showed a decline in carbon emissions, with emission reductions of 41.1%, 36.6%, 21.4%, and 21.5% compared to their levels in 2009. Model predictions indicate that carbon emissions from railways in the Qinghai-Tibet Plateau, Yunnan Province, the Guangxi Zhuang Autonomous Region, and the Yangtze River Delta Region are projected to decline by 15.8%, 12.23%, 34.15%, and 0.03% respectively by 2030, relative to 2021 levels in the regional emission-reduction scenario. This study offers insights into the socio-economic and internal mechanisms of emissions, guiding tailored reduction targets for different railway operational regions to aid China in achieving '3060' target.

The industrial sector is the primary source of anthropogenic carbon emissions. Research on industrial carbon peaking and its drivers play a fundamental role in formulating emission reduction measures. The provincial industrial CO2 emissions were calculated from 2000 to 2021 in China. The Logarithmic Mean Divisia Index (LMDI) method was employed to quantitatively measure the contributions of industrial output, industrial structure, energy intensity, and energy structure to the changes in emissions. The results show that: (1) During the research period, the industrial CO2 emissions of various provinces experienced differentiated growth processes. Among those 11 provinces have already achieved industrial CO2 emission peak. (2) Industrial output value and energy intensity are the essential driving forces affecting industrial CO2 emissions, while the contributions of industrial structure and energy structure to CO2 emissions are relatively low. Spatio-Temporal differences exist in the contributions of various influencing factors. (3) 9 provinces have proactively peaked their emissions by improving energy efficiency, optimizing structure in energy use and industrial structure. While 2 passively emission declined provinces reduce emissions through a decline in industrial output value. (4) The provinces that have not yet reached their peak emissions are mainly industrial provinces, energy based provinces and developing central-western provinces. Their industrial output value has been steadily increasing, and the inhibitory effect of energy intensity and energy structure on CO2 is relatively low. Only under the low-carbon scenario can they almost achieve carbon peaking by 2030. The paper discusses industrial emission reduction strategies by controlling "super emitters" provinces, formulating differentiated emission reduction measures based on influencing factors, and practicing a low-carbon development path.

Since the Industrial Revolution, the increasing emissions of greenhouse gases have posed unprecedented challenges to sustainable human development. As one of the most vital terrestrial ecosystems, farmland ecosystems play an irreplaceable role in balancing carbon emissions and absorption, attracting growing scholarly attention. Taking Jiangsu Province, one of China's major grain-producing regions, as the study area, this research integrates the Slacks-Based Measure (SBM) model, the entropy-weighted method, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to analyze the spatiotemporal evolution of farmland carbon effects-including carbon emissions, carbon absorption, and net carbon sequestration-during 2011-2021. Furthermore, a Grey Prediction Model was employed to forecast the carbon effects of 13 cities over the next 12 years. The results show that Jiangsu's farmland carbon emission efficiency exhibited an overall upward trend with fluctuations, with an average value of 0.76. The multi-year mean fitting degrees of resource input and agricultural output were relatively low, at 0.426 and 0.358, respectively, with substantial intercity differences. The average coupling coordination degree between resource input and agricultural output was 0.66, indicating a primary coordination state. The constructed GM (1,1) model achieved a qualification rate exceeding 73.80%, demonstrating its reliability for predicting farmland carbon effects. Forecasts suggest a potential weakening of the province's agricultural carbon sink effect, with the net carbon sequestration in 2033 expected to decline by 15.55% compared with the maximum value during the observation period. This study reveals the spatiotemporal characteristics and potential evolution patterns of farmland carbon effects, providing theoretical support for region-specific agricultural emission reduction policies and promoting the sustainable development of efficient, low-carbon agriculture.

Under the "Dual Carbon" goals (carbon peaking and carbon neutrality), China's Carbon Emissions Trading System (CETS) represents a key policy tool in addressing climate change, significantly contributing to carbon reduction and the enhancement of Energy Eco-Efficiency (EEE). As a comprehensive measure of coordination within the "energy-economy-environment" system, EEE effectively captures a country's or region's ability to harmonize energy consumption, green and sustainable development, and environmental protection. This study computes the EEE index using the SBM model and examines how a carbon emission trading pilot policy (CETPP) affects EEE in China and its spatial spillover by employing a difference-in-differences (DID) model and spatial econometric model. The results indicate that CETPP implementation significantly enhances regional EEE and advances China's green and low-carbon transition process by improving the balance between economic and environmental goals, increasing employment, diversifying the energy supply, and strengthening economic resilience. Mechanistic research reveals that the CETPP promotes the improvement of EEE by reducing pollution emission intensity and that regional innovation ability can enhance the positive impact of the CETPP on regional EEE. Further analysis revealed significant endogenous spatial interactions in EEE across Chinese regions. However, EEE in the eastern and western regions can create a "siphon" effect on production factors that hinders development in neighbouring areas. Implementing a CETPP in a region not only advances local EEE but also stimulates EEE improvements in adjacent areas, with the strongest spillover effect observed in eastern China. To this end, it is essential to enhance the management of carbon dioxide emissions, actively advance the establishment of carbon trading markets. Moreover, region-specific measures should be implemented to promote the coordinated improvement of regional EEE.

Changes in climate are altering plant growth patterns and associated phenological events like the Start of Season (SOS), End of Season (EOS), and Length of the Growing Season (LGS). However, there is limited research quantifying the impact of these changes on key vegetation-atmospheric interaction processes such as the carbon and water cycles. This study uses 914 site years of data across 132 flux tower sites in the FLUXNET2015 dataset to explore the relationships between carbon sequestration, expressed by Gross Primary Productivity (GPP), and multiple phenological variables, including LGS, changes in SOS (ΔSOS), and changes in EOS (ΔEOS). LGS explains 23% of the variability in GPP across all sites. Significant correlations were found in deciduous broadleaf forests (R² = 0.5) and evergreen needleleaf forests (R² = 0.44), while ecosystems such as shrublands, savannas, and wetlands displayed weaker connections. Changes in the SOS also affected GPP, with an earlier SOS increasing the total annual GPP. Deciduous Broadleaf Forests (R² = 0.54), Evergreen Needleleaf Forests (R² = 0.5), Grasslands (R² = 0.47) showed a significant negative association between ΔSOS and ΔGPP, whereas Croplands showed weaker correlations. Conversely, EOS variations had little impact on GPP. Upscaled to global vegetated land area these relationships suggest that each additional day in the growing season could increase carbon uptake by 1.035 Gt C yr- 1, while an earlier SOS by 0.93 Gt C yr- 1 and a one-day delay in EOS by approximately 0.65 Gt C yr- 1. These findings underscore the need to account for seasonal shifts and phenological changes in global carbon models.

Digitalization and decarbonization are reshaping urban production, yet the relationship between factor-level digitalization and cities' carbon total factor productivity (CTFP) remains underexplored, particularly concerning its mechanisms, boundary conditions, and spatial reach. This study develops a Digitalization Index of Urban Elements (DIUE), encompassing labor, capital, and energy, to examine its association with CTFP in 213 Chinese cities from 2011 to 2023. CTFP is measured using an undesirable-output slacks-based measure, and the empirical analysis employs a two-step system GMM with Windmeijer correction, mediation analysis, spatial-lag models, and dynamic panel threshold tests. Robustness checks utilize an alternative productivity index and a difference-in-differences design based on early smart-city pilots. Three key findings emerge. First, factor-level digitalization is positively associated with urban CTFP; a one-standard-deviation increase in DIUE corresponds to a 1.5-1.7% increase in CTFP, which accumulates to approximately 4% in the long run. Second, this relationship is primarily mediated by green innovation, accounting for approximately 47% of the mediated effect, while industrial upgrading and agglomeration provide additional support, accounting for about 24% and 6%, respectively. Third, the benefits of digitalization exhibit both spatial spillovers and conditional effects: the indirect spillover effect constitutes roughly one-third of the direct effect, and positive gains are more pronounced in contexts with lower labor and capital misallocation and stronger local low-carbon commitment. By integrating a transparent and replicable measure of factor digitalization with evidence on its underlying mechanisms, spatial spillovers, and activation thresholds, this study clarifies the conditions under which digitalization can be effectively translated into enhanced carbon productivity. The policy implication is that cities should complement digital investment with measures to reduce factor misallocation, strengthen low-carbon commitment, and foster innovation.