The balance between carbon stocks (CS) and carbon emissions (CE) is crucial for optimizing regional ecosystem management and enhancing ecosystem carbon sink capacity. Taking the main urban area of Chongqing (MUAC) as the study area, this study integrated the InVEST model, carbon emission coefficient method, CS-CE ratio (CSER), hotspot analysis, and spatial flow field strength model to investigate the spatial mismatch characteristics of CS and CE and their flows patterns from 2000 to 2020. The results showed the following: (1) The average CS declined steadily from 328.63 t to 313.76 t, with high-CS areas coinciding with high-altitude regions. The CE exhibited an inverted U-shaped trend over time, with an overall upward tendency. And high-CE areas were mainly concentrated in the midwestern and northern parts of MUAC. (2) The CSER exhibited a decreasing trend, with the deficit area expanding by 8.3
Mangrove forests are among the most carbon-dense coastal ecosystems, yet estimates of mangrove biomass and carbon stocks derived from remote sensing remain sensitive to methodological choices. In particular, how canopy structure is represented and aggregated from LiDAR data can systematically influence biomass estimates, with direct implications for carbon accounting and climate mitigation assessments. Few studies, however, have directly contrasted individual-crown and area-based biomass pathways within a single LiDAR dataset, leaving it unclear how much of the reported variability in mangrove carbon stocks reflects structural assumptions rather than genuine ecological difference. This study examines the structural sensitivity of LiDAR-derived mangrove biomass estimates by comparing two pathways within a consistent spatial framework: (i) individual-crown above-ground biomass (AGB) estimated from diameter at breast height (DBH) proxies derived from LiDAR-based canopy diameter, and (ii) stand-level AGB estimated from area-based-weighted Lorey’s mean canopy height. High-resolution airborne LiDAR data were acquired over a fringing mangrove system in eastern Java, Indonesia, and used to derive digital surface models, digital terrain models, and canopy height models. The individual-crown-based AGB estimates aggregated to 10 m × 10 m grid cells yielded lower and more constrained biomass values (mean = 153.8 ± 74.7 Mg ha− 1; median = 162.73 Mg ha− 1), reflecting a tree-level structural perspective. In contrast, the area-based-weighted Lorey’s pathways produced higher biomass estimates at the same spatial scale, with mean AGB values of 167.3 ± 121.7 Mg ha− 1 for the canopy-cover-weighted equation and 183.6 ± 132.5 Mg ha− 1 for the height- weighted equation, representing a mean difference of approximately 10–20
To address high emissions and low efficiency in signal control for urban arterial traffic under mixed traffic conditions, this study proposes a deep reinforcement learning signal control method oriented toward carbon emission reduction. First, a discretized state encoding matrix integrating vehicle position, speed, and acceleration is constructed to quantify the microscopic state of arterial traffic flow. Second, a multi-objective reward function that considers both fuel-vehicle carbon emissions and waiting time is designed, with dynamic weights adapted to the coupling intensity of three traffic flow states, thereby transforming the coupling mechanism into a quantifiable optimization objective. Finally, an improved deep Q-network with a dueling architecture and noisy networks is employed to optimize signal timing, smoothing traffic fluctuations and regulating the coupling relationship. Simulation results show that under free flow, stable flow, and unstable flow, average waiting times decrease to 18.3 s, 42.1 s, and 68.5 s, respectively, with a maximum reduction of 18.08
As carbon pricing mechanisms gain global prominence, understanding their distributional impacts, particularly in developing economies, becomes crucial for the design of equitable climate policies. We explore the effect of carbon emission trading (CET) policy on income inequality in China through integrated theoretical and empirical analysis. We find that the CET policy significantly reduces income inequality, and this conclusion passed the robustness tests. Theoretical analysis shows that if carbon emission allowances are treated as an input factor of production, when carbon prices are relatively low, firms tend to substitute labor for carbon emission allowances, thereby increasing the labor income share (LIS). Empirical testing also confirms that the mitigation of income inequality through CET is attributable to the increase in the LIS. In addition, the equalizing effect is more pronounced in cities with higher initial income inequality and greater marketization. Government environmental expenditure and smooth labor market mobility also have positive synergistic effects. Our research provides policy insights on balancing emissions reduction with social equity.
Land use optimization represents an important pathway toward regional low-carbon transition and sustainable development. As a territorial spatial governance instrument centered on the coordinated optimization of production, living, and ecological spaces, Whole-Region Comprehensive Land Consolidation (WRCLC) still lacks systematic empirical evidence regarding its effects on regional carbon balance. Based on panel data from 9,162 townships in China during the period 2013–2022, this study combines the staggered difference-in-differences model with fuzzy-set qualitative comparative analysis to systematically examine the impact, mechanisms, and pathways of WRCLC on carbon balance.. The results show that: (1) WRCLC exerts a positive effect on carbon balance and generates positive adjacent indirect effects; (2) production space optimization, living space optimization, and ecological space optimization constitute the key mechanisms through which WRCLC promotes carbon balance; (3) market-oriented and diversified collaborative configurations are important pathways for achieving high-level carbon balance. Therefore, this study provides new empirical evidence and policy implications for how global land governance can synergistically promote spatial optimization, ecological restoration, and low-carbon development.
This study examines how artificial intelligence (AI), renewable energy technology innovation (RETI), and regulatory quality (RQ) shape the low-carbon energy transition across countries at different income levels. Using panel data from 50 high-, middle-, and low-income countries over the period 2002–2019, the low-carbon energy transition is measured by the share of renewable energy in total final energy consumption, which represents a key pathway for reducing fossil-fuel dependence and supporting carbon mitigation. The empirical analysis applies two-way fixed-effects and instrumental-variable (2SLS) models, complemented by several panel econometric and robustness tests. The findings show that AI significantly promotes the energy transition across all income groups by improving energy efficiency, system optimization, and the integration of renewable resources. Regulatory quality constrains the transition in high-income countries but stimulates it in middle- and low-income countries, indicating that the effectiveness of carbon-related regulatory frameworks varies according to countries’ institutional and developmental conditions. RETI consistently accelerates the transition in high-income countries, whereas its effects are weak or inconsistent in middle- and low-income countries, reflecting persistent disparities in technological capacity, financing, and innovation infrastructure. Overall, the results demonstrate that AI, technological innovation, and regulatory institutions jointly shape countries’ capacity to shift toward low-carbon energy systems. Although the study does not directly estimate carbon emissions, it identifies the institutional and technological mechanisms through which renewable energy expansion can contribute to decarbonization and carbon management objectives. The findings support differentiated policies for strengthening AI capabilities, facilitating clean-technology transfer, and aligning regulatory frameworks with national carbon-mitigation strategies.
Iran is currently grappling with critical environmental and economic challenges, including rising CO2 emissions, frequent blackouts, and ongoing economic stagnation. Despite a growing body of literature on the energy-emissions nexus, much of it has overlooked the integration of macroeconomic drivers with underlying structural constraints in the Iranian context. This study aims to examine the determinants of CO2 emissions in Iran over the period 1990–2022 by combining econometric and system-based approaches. The study employs an Autoregressive Distributed Lag (ARDL) model to estimate short- and long-run relationships, while Principal Component Analysis (PCA) is used to construct a composite energy structure index from oil, natural gas, electricity, and coal consumption. A Causal Loop Diagram (CLD) is further applied to support the interpretation of structural interactions within Iran’s energy system. The empirical results indicate that the PCA-derived energy-structure index is positively associated with CO2 emissions in the long run, reflecting the importance of fossil-fuel-related energy structure. Economic growth is also associated with increased emissions, reflecting the carbon-intensive nature of Iran’s development path. In contrast, renewable energy consumption is negatively associated with emissions in the long run, although it is associated with a temporary increase in emissions in the short run. The agricultural sector shows a negative short-run association, while its long-run impact remains statistically insignificant. This study contributes to the Iran-focused literature by addressing multicollinearity among major energy variables using a PCA-derived composite index, offering an alternative representation of fossil fuel dependence compared with approaches based on separate fuel variables or aggregate energy consumption. The findings offer insights into the interaction between energy structure, economic activity, and environmental outcomes, with implications that may be relevant for other energy-subsidized or structurally constrained economies.
Against the backdrop of intensifying global climate change and increasingly prominent resource constraints, achieving synergies between resilience enhancement and low-carbon urban development has become a critical issue for advancing sustainable urban development. To address this challenge, this study constructs an evaluation framework for urban resilience (UR) and low-carbon development (LCD). It quantifies the resilience and low-carbon development levels of 60 cities in the Yellow River Basin (YRB) from 2006 to 2021. Using a coupling coordination degree model, the research reveals the interactive mechanisms between UR and LCD, further analyzing how their synergistic evolution promotes regional sustainable development. Results indicate that from 2006 to 2021, the UR in the Yellow River Basin rose from 0.13 to 0.24, with an average annual growth rate of 5.64
The daily Normalized Difference Vegetation Index (NDVI) is a critical indicator of terrestrial carbon sequestration capacity, essential for accurately quantifying vegetation carbon sink functions and their dynamic responses to climate change. We previously developed a long-term daily NDVI dataset across China, but the over smoothed polynomial fitting method constrained reconstruction accuracy in complex scenarios, hindering precise fine-scale carbon sink monitoring. To address this limitation, we improved the reconstruction framework by integrating meteorological legacy effects and the machine learning algorithm, with a focus on validating its application value for net primary productivity (NPP) estimation. The reconstructed daily gap-free NDVI (1982–2023) shows strong consistency with original valid NDVI, achieving a national average R2 of 0.9, percentage bias (PB) of − 0.09
Agricultural production is crucial to food security and the realization of carbon reduction targets. Whether green and low-carbon development of agriculture can be achieved on the basis of ensuring food security is a common challenge faced by all economies. This study takes China’s 2004 designation of 13 major grain-producing areas as a quasi-natural experiment. Using provincial panel data from 2000 to 2022, a difference-in-differences model is constructed to explore if the policy can boost green transformation while ensuring yield growth. The findings reveal that the policy significantly reduces carbon emissions and agricultural non-point source pollution, with robustness confirmed by placebo tests, PSM-DID, and double machine learning. Crop structure adjustment and green technological progress have been identified as two key channels for promoting carbon reduction effects, among which labor force transfer plays a non-linear moderating role. Heterogeneity analysis indicates that excessive concentration of grain production is detrimental to the reduction and control of agricultural pollution, while the pollution reduction effect of the policy is more pronounced in regions with moderate to high levels of labor aging. It is recommended to further encourage appropriately scaled operations, promote the application and innovation of green agricultural technologies, and tailor strategies to local conditions to achieve agricultural green transformation, while continuing to uphold the major grain-producing areas policy.
Understanding how regionally determined carbon-pricing signals are transmitted into globally traded clean energy equities is increasingly important as climate policy and financial markets become more integrated. Yet, evidence remains limited on whether these linkages are nonlinear, state-dependent, and heterogeneous across clean energy technologies. This study examines how movements in European Union Emissions Trading System (EU-ETS) futures, a regional carbon market and policy-linked price signal, are reflected in the returns of ten globally listed clean energy subsectors using daily data from 19 October 2010 to 6 July 2024. Using disaggregated NASDAQ OMX Clean Energy subsector indices, the analysis captures how carbon-pricing information originating in the EU is incorporated into globally integrated equity markets. The study combines the Cross-Quantilogram (CQ) and Multivariate Time-Varying Quantile Regression (MTVQR) frameworks to examine directional predictability and conditional impacts, while controlling for financial uncertainty (VIX) and oil-market conditions (Brent crude oil prices). The CQ results show that EU-ETS futures contain significant predictive information for clean energy returns, with nonlinear, asymmetric dependence concentrated on the tails of the distribution. The MTVQR results further show that the conditional impact of carbon-pricing signals varies across market states and technologies. More mature subsectors, such as solar, wind, and smart grid, respond more strongly in weak and normal markets, while capital-intensive and enabling technologies, including biofuels, geothermal, storage, and fuel cells, exhibit stronger upper-tail or nonlinear responses. The findings highlight EU carbon pricing as a forward-looking transition-risk signal with implications for climate policy, portfolio allocation, and transition-risk management.
The Hulun Lake Basin in northern China harbors extensive temperate grasslands, whose carbon uptake and ecosystem functioning are effectively represented by gross primary productivity (GPP). However, the long-term response of grassland GPP to regional climate change remains insufficiently understood, due to the limited ability of global models to capture local ecosystem variability. This study improved GPP estimation in the Hulun Lake Basin by integrating additional grassland flux observations from China into a Random Forest model (MCF). We subsequently examined the spatiotemporal dynamics of GPP and identified the dominant climatic drivers and their long-term trends under changing climate conditions. Results show that the MCF model significantly outperformed the model trained solely on FLUXNET 2015 Tier 2 data in both accuracy and trend patterns of grassland GPP. A 23-year MCF simulation in the Hulun Lake Basin showed a clear west–east GPP gradient, averaging 676.36 g C m− 2 yr− 1 and totaling 70.13 Tg C yr− 1. Most of the region exhibited increasing GPP trends, with an average growth rate of 6.85 g C m− 2 yr− 1, resulting in an annual total increase of approximately 0.45 Tg C yr− 1. SHAP analysis demonstrated that shifts in precipitation and temperature are reshaping the dominant factors contributing to GPP increases. Structural equation modeling further revealed that precipitation enhanced GPP through both direct and indirect pathways, with the direct effect accounting for 63.6
Balancing economic growth with emission reductions presents a pivotal challenge under China’s "dual-carbon" strategy. Hunan Province, characterized by its diverse industrial and ecological contexts, offers a quintessential setting for county-level analysis. This study investigates 122 counties within Hunan from 2000 to 2022, employing the Tapio decoupling model, spatial autocorrelation, ordinary least squares (OLS) regression, an augmented STIRPAT framework, and scenario simulations. It analyzes the spatiotemporal dynamics between carbon emissions and GDP, elucidates the principal driving mechanisms, and forecasts peaking trajectories under scenarios of low, medium, and high carbon emissions. The findings reveal a continued rise in emissions, albeit at a reduced pace post-2010, with the proportion of counties achieving strong decoupling escalating from 6
Vegetation productivity is not only determined by current environmental conditions but also reflects the lagged influence of past climate and vegetation states. This temporal dependency, often referred to as ecological memory, arises from both antecedent climate conditions (exogenous lagged climatic effects, LCE) and prior vegetation states (endogenous vegetation growth carryover effects, VGC). However, their spatiotemporal variability, relative importance, and responses to future climate change remain poorly understood at the global scale. Here, we develop a unified analytical framework by integrating vector autoregressive model and impulse response functions to disentangle the roles of LCE and VGC in regulating global gross primary productivity (GPP) across space, time, and future climate scenarios. Both components exhibit rapid initial responses followed by gradual decay within approximately five months, yet differ markedly in magnitude, direction, and persistence. LCE show strong hemispheric asymmetry: in the Northern Hemisphere, increases in temperature exert the strongest positive effect on GPP, whereas increases in atmospheric dryness (vapor pressure deficit) produce the strongest negative effect. In contrast, in the Southern Hemisphere, increased precipitation is the dominant positive driver of GPP, while negative responses exhibit greater spatial heterogeneity and show no clear dominant controlling factor, suggesting more complex and regionally varying climatic influences. In contrast, VGC display globally consistent positive responses with substantially greater intensity (17.35 gC m−2 month−1 at a 1-month lag) and minimal hemispheric differences. Across all lag periods, VGC dominate productivity variability, contributing over 84
Tea plantation soils serve as vital carbon (C) sinks rich in soil organic carbon (SOC), yet existing research primarily focuses on the 0–20 cm topsoil, leaving a lack of systematic study of SOC mineralization characteristics and microbial regulatory mechanisms across the full 0–100 cm soil profile. Therefore, we conducted incubation experiments with typical tea plantation soils to clarify changes in SOC mineralization, the SOC pool, the microbial community, and the functional genes (such as GH48 and cbhI) and enzyme activities related to C decomposition at five soil depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm), and to elucidate their relationships, thereby revealing the mechanisms affecting SOC mineralization in different soil layers. The results revealed significant declines in SOC mineralization rate (from 842 to 431 mg kg⁻¹), particulate organic carbon, water-soluble organic carbon, microbial biomass carbon, β-glucosidase (from 71.6 to 18.2 µg g− 1 h− 1 at the end of incubation), cellobiohydrolase (from 0.229 to 0.091 mg g− 1 3d− 1 at the end of incubation), GH48 and cbhI gene abundances (decreased from 9.2 × 107 to 1.8 × 107 and 7.6 × 107 to 1.4 × 106 copies g− 1 at the end of incubation, respectively), respectively with increasing soil depth. The decrease in the SOC mineralization rate with increasing soil depth was significantly associated with declines in the labile C fraction, C-decomposition-related extracellular enzyme activity, and functional gene abundance. Additionally, increasing soil depth significantly altered the microbial community structure and composition, particularly the relative abundances of dominant taxa such as Alphaproteobacteria, Bacilli, Sordariomycetes, Tremellomycetes, Mortierellomycetes, and Dothideomycetes, thereby further influencing SOC mineralization rate. Our findings demonstrate that increasing soil depth significantly alters soil microbial community characteristics, particularly by reducing the abundance of C-degrading functional genes and enzyme activities, thereby lowering SOC mineralization rate and attenuating soil carbon emissions. These results underscore the importance of deep tillage during fertilization and the incorporation of pruning residue to enhance SOC sequestration in tea plantations.
The transition to clean energy in the United States remains insufficient despite rising environmental concerns and increasing renewable energy adoption. This study investigates whether better institutional quality can effectively drive cleaner energy outcomes by examining the impact of governance alongside key macroeconomic factors. Using quarterly data from 1990 to 2024, the study employs a wavelet quantile regression approach to capture nonlinear and time-varying dynamics across short-, medium-, and long-run horizons. The findings reveal that economic growth, foreign direct investment, and trade openness positively influence renewable energy consumption, particularly over longer time horizons. In contrast, carbon emissions exhibit a negative relationship with renewable energy adoption. Surprisingly, institutional quality shows a predominantly negative effect, suggesting that stronger institutions may reinforce existing fossil fuel-based energy structures rather than accelerate transition. These results highlight the complexity of institutional roles in energy transformation and emphasize the need for targeted regulatory reforms to support renewable energy expansion in the United States.
Rising anthropogenic carbon emissions are a major driver of climate change and pose a critical challenge to global sustainable development. As a rapidly advancing technology, artificial intelligence (AI) has shown strong potential to enhance carbon emissions management. This review provides a critical and comprehensive synthesis of recent advances in AI-enabled approaches for carbon emissions monitoring, prediction, and reduction. For monitoring, it explores the integration of satellite remote sensing, sensor networks, and machine learning (ML) algorithms, which can improve multi-scale, high-resolution, and near-real-time monitoring capabilities. For prediction, it categorizes prediction models into three groups, namely deep learning (DL), ensemble learning, and statistical learning, to facilitate the selection of appropriate technical approaches based on varying data characteristics and prediction requirements. For reduction, it examines the practical effectiveness of AI in industrial process optimization, energy structure transformation, transportation scheduling and management, construction energy efficiency improvement, and carbon capture, utilization, and storage (CCUS). We further reveal core challenges and potential solutions across the data layer, model layer, and application layer in AI deployment, including data availability and quality, model generalization and interpretability, and engineering and governance barriers that hinder the translation of AI methods into real-world applications. Furthermore, future research directions are discussed to promote the development of more reliable and scalable AI methods that can better support decision-making and practical governance in carbon emissions management. Overall, distinct from previous reviews that mainly focus on single tasks, specific model types, or sectoral applications, this review represents, to our knowledge, one of the first review-level attempts to develop a policy-relevant and interdisciplinary AI framework for carbon emissions management across the full process of monitoring, prediction, and reduction. By integrating unified evaluation metrics, evidence matrices, deployment-constraint analysis, and a technology readiness level (TRL)-based assessment, this framework links methodological performance, application readiness, and governance needs. It provides an integrated methodological foundation for fine-grained emissions sensing, predictive analysis, and emissions reduction decision support, while supporting quantifiable, verifiable, and actionable carbon balance and management.
As a sensitive response area to global climate change, the terrestrial ecosystem carbon sink function of the Northwest Arid Region of China is not only a core element in constructing the regional ecological security barrier but also a crucial strategic fulcrum for China to achieve its dual carbon goals. However, the spatial distribution and driving factors of carbon density in this region remain unclear. The research provides a scientific basis for optimizing ecological barrier construction and formulating gradient-based carbon sink management strategies in arid regions. Five types of carbon density data—including aboveground and belowground biomass carbon, soil organic and inorganic carbon, and dead organic matter carbon—were obtained through literature review and field investigations. Environmental drivers were analyzed using generalized dissimilarity modeling (GDM) and structural equation modeling (SEM), and spatial simulations were performed using three machine learning models: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Climatic and soil factors were the primary drivers of carbon density variation. Among the models, XGBoost demonstrated the best performance in simulating all five types of carbon density. Spatially, high carbon density values were mainly concentrated in mountainous and oasis areas, while low values were found in the southern desert regions. Vegetation cover and precipitation were identified as dominant regulating factors. Forest ecosystems play a central role in regional carbon storage. The findings offer a scientific foundation for enhancing ecological barrier construction and developing gradient-based carbon sink management strategies in arid regions.
Against the backdrop of global climate change and sustainable development, cities, as major sources of carbon emissions, play a pivotal role in achieving carbon neutrality goals. This study examines the synergistic impact of innovative city and low-carbon city pilots on carbon intensity, using panel data from 272 Chinese cities. The findings reveal that low-carbon and innovative city construction (LCICC) significantly reduces carbon intensity, primarily through technological advances, industrial upgrading, and green finance. However, the carbon intensity reduction is only significant in central regions and resource-based cities. Conversely, LCICC significantly increases carbon intensity in cities characterized by lower administrative status and non-resource-dependent economic structures. This study not only enriches the theory of urban transformation and provides new perspectives for carbon reduction research but also offers the first empirical evidence of synergistic emission reduction effects from the simultaneous implementation of innovation and low-carbon city pilots. These findings provide a replicable analytical framework for other developing economies pursuing dual policy pathways toward carbon neutrality.
