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.

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.

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.

The rapid expansion of the electronics market has led to a sharp increase in waste electrical and electronic equipment (WEEE), intensifying environmental pressure and carbon emissions associated with inefficient recycling systems. Traditional WEEE reverse logistics are characterized by high energy consumption and low resource recovery efficiency, limiting their contribution to carbon reduction goals. To address this issue, this study develops a tripartite evolutionary game model involving the government, consumers, and recycling platforms to analyze strategic interactions and system dynamics in low-carbon WEEE recycling. Numerical simulations are conducted to examine the effects of key parameters on equilibrium stability and convergence behavior. The results indicate that appropriately calibrated government subsidies, consumer participation incentives, and platform-level cooperative benefits accelerate system convergence toward a stable low-carbon equilibrium. However, excessive subsidies generate diminishing marginal effects and potential fiscal inefficiencies. Enhancing long-term cooperative benefits and technological efficiency is shown to improve equilibrium stability and carbon performance. This study contributes to the literature by integrating carbon emission considerations into evolutionary game modeling of WEEE reverse logistics and provides managerial insights into mechanism design for sustainable supply chain coordination.

The transportation sector, as a significant contributor to global CO2 emissions, demands urgent attention to align its decarbonization with national carbon neutrality agendas. Existing research disproportionately focuses on quantifying TCE (transportation CO2 emissions) and mapping their spatio-temporal distributions. However, the evolutionary trajectories and sequential peaking dynamics of TCE across different national contexts remain unclear. To address this question, this study was conducted. A global comparative analysis of 115 countries was conducted, establishing a four-stage TCE development typology through three metrics: TCE intensity (A), per capita TCE (B), and total TCE (C). The analysis revealed a universal A → B → C peaking sequence, with Stage II (A to B transition) exhibiting a significantly prolonged duration (mean = 8.37years) compared to Stage III (B to C transition; mean = 2.12years). Developed economies predominantly occupy Stage IV, while developing countries cluster in Stage II and Stage III. Regionally, North American countries demonstrated extended durations in both stages, exceeding global averages. Regression analysis indicated that socioeconomic indicators have limited explanatory power in predicting stage durations, underscoring the individualized nature of TCE progression across nations. This study contributes by revealing the unified and diverse peak patterns of three core TCE indicators at national levels, while addressing a critical gap in global emission reduction strategies through cross-economy analysis. The findings confirm a predictable evolution in TCE across most nations but highlight significant variations between developed and developing economies. The prolonged duration of Stage II compared to Stage III suggests a more challenging transition phase for many countries. Moreover, the limited influence of standard socioeconomic metrics on stage durations emphasizes the need for nuanced, country-specific approaches to emissions transitions. The study proposes targeted TCE reduction measures differentiated by development stage and transportation sub-sector, providing scientific guidance for policy formulations.

Emergence of blockchain technology has disrupted a number of economic sectors, particularly financial institutions, with significant effects on their operations. This paper investigates the impact of asset tokenization on the issuance and trading process of financial assets, specifically bonds. It examines the effect of tokenizing the High Yield Bond on the Ethereum blockchain across two key dimensions: On costs, a comparative cost-benefit analysis is conducted before and after tokenization, and on green sustainability, through a comparative analysis on the carbon footprint of the bond before and after Ethereum's merge to proof of stake. The results show that Tokenization improves cost-savings, and it promotes a greener, more sustainable approach when using the Ethereum blockchain post-transition to proof of stake.

Artificial intelligence (AI) has rapidly expanded across multiple industries and technologies, driving economic growth and offering innovative solutions to structural challenges. However, its environmental impact remains contested. While firms investing in AI aim to lower its carbon footprint, its widespread use continues to generate significant emissions. This study examines the environmental effects of AI investments, particularly on carbon emissions, while also accounting for human and economic development indicators. The analysis employs the Panel ARDL-PMG approach using data from 2012–2023 for nine technologically advanced economies characterized by extensive use of robotics (South Korea, Japan, Germany, the United States, China, Singapore, Sweden, Italy, and France). The findings reveal the existence of a stable long-run equilibrium among the variables. The negative and significant ECT indicates that about 32% of short-term imbalances are corrected each year, suggesting that the system steadily moves toward its long-run equilibrium. In the long run, per capita GDP and renewable energy consumption reduce carbon emissions, whereas AI investments (AIINV), Foreign Direct Investment (FDI), and the Human Development Index (HDI) increase them. The results show that AIINV and FDI do not contribute to reducing carbon emissions. In this context, the findings suggest that investments in the energy sector are not directed toward encouraging the transformation of energy sources. These results highlight the environmental risks posed by the growing prevalence of AI. However, AIINV and FDI have the potential to help reduce carbon emissions if they are aligned with the transformation of energy sources. Thus, aligning AI with green innovation and sustainable environmental policies is essential. This study emphasizes the importance of enabling the energy transition to reduce carbon emissions arising from AIINV and FDI in the sector. Promoting eco-efficient technologies and sustainable innovation processes can help mitigate the carbon-intensive effects of digital transformation.

High-integrity carbon offset systems require scientifically robust and spatially explicit frameworks to quantify carbon pools and fluxes across ecosystems. We present an integrative methodology that combines eddy covariance measurements, airborne and satellite remote sensing, and modeling to extrapolate near real-time carbon flux monitoring to larger areas, using the old-growth temperate forests of Chilean Patagonia as a case study. Our approach delivers high-resolution aboveground biomass carbon density (30 m) and net ecosystem exchange (NEE, 30 m—30 min) estimates using flux tower data. By integrating ground-based flux measurements with high-resolution remote sensing, the proposed methodology constrains model parameters and spatial extrapolation, thereby reducing uncertainty relative to conventional inventory-based approaches. Our approach offers a replicable framework for informing climate policy, conservation planning, and emerging nature-based finance instruments while meeting operational needs in terms of scalability, technological integration, reproducibility, and traceability.

Understanding the spatial effects of the synergistic development between agricultural carbon sequestration and emission reduction (ACSER) and food security (FS) is essential for promoting sustainable and high-quality agrarian development. Based on a constructed indicator system for ACSER and FS, this study measures the Coupling Coordination Degree (CCD) between the two in different grain functional areas of China from 2000 to 2023 using a modified coupling synergy model. Furthermore, a Spatial Durbin Model (SDM) is employed to explore the underlying driving mechanisms and spatial spillover effects of their synergistic relationship. (1) From 2000 to 2023, China's CCD between ACSER and FS exhibited a clear spatial gradient, higher in the north and lower in the south. Specifically, spatial clustering intensified in the main production and production-marketing areas but did not reach statistical significance, while the main marketing regions consistently exhibited a trend of spatial dispersion; (2) The spatial Durbin model analysis reveals that the CCD of ACSER and FS in China exhibits significant spatial spillover effects. Among the influencing factors, CO₂ uptake by major crops emerges as the primary driver of their synergistic development, while a higher proportion of cultivated land and increased pesticide and fertilizer use exert negative effects on both local and neighboring regions. (3)The study of regional heterogeneity shows that the CO₂ absorption of agricultural crops in the main production area promotes synergistic development; the synergistic mechanism of ACSER and FS in the production and marketing areas is more complicated, and resource mismatch is the main factor affecting the coupling and synergism of the two; The total power of agricultural machinery in the main marketing areas has a catalytic effect on the local synergy between ACSER and FS, while transregional transmission exhibits negative spillovers, highlighting resource allocation imbalances. Therefore, to promote the synergistic development of ACSER and FS, it is necessary to adopt region-specific measures based on spatial differences and to strengthen the synergistic effects among key factors within the agricultural system, to enhance resource allocation efficiency and system resilience.

Escalating environmental challenges necessitate accelerated green technology innovation and diffusion. This paper introduces a novel theoretical framework synthesizing differential game theory, endogenous growth with directed technical change, and network analysis to investigate the interplay between strategic national R&D investments, the endogenous direction of innovation, and structured international knowledge spillovers. The model contrasts non-cooperative and cooperative equilibria, revealing that non-cooperation yields suboptimal global outcomes: underinvestment in green R&D, delayed transitions, and a failure to curb long-term pollution, driven by free-riding on environmental benefits and knowledge spillovers. The spillover network's architecture critically mediates these dynamics. Conversely, cooperative solutions markedly improve environmental and technological trajectories. Numerical simulations confirm these findings and demonstrate that globally coordinated policy mixes, specifically carbon pricing combined with green R&D subsidies, can effectively approach cooperative outcomes. The analysis underscores the critical roles of relative green R&D productivity and spillover intensity in determining the pace and success of the global green transition. This research provides a comprehensive lens for understanding and shaping the global green technology landscape and formulating effective international environmental and technology policies.