Search Results (713)

Against the global backdrop of carbon peaking and carbon neutrality goals, renewable energy subsidy policies worldwide are shifting from universal, tariff-based schemes toward targeted, market-oriented mechanisms. However, existing research remains divided on the incentive effects of subsidies on firm investment and the risk of resource misallocation, and few studies have established a clear micro-macro linkage between firm-level subsidy receipts and regional employment and growth outcomes. Taking China’s 2016 renewable energy subsidy reform—characterized by competitive project bidding and green certificate trading—as a quasi-natural experiment, this study constructs a two-layer panel dataset covering 286 A-share listed renewable energy firms (2010–2023, 2412 firm-year observations) and 30 provincial-level regions in China (2010–2023, 420 region-year observations). Employing difference-in-differences (DID), triple difference-in-differences (DDD), mediation effect models, and threshold regression, combined with instrumental variables and placebo tests to address endogeneity, we empirically examine how subsidy policies transmit from firm behavior to regional employment and economic growth. The results indicate that the 2016 reform significantly boosted regional employment (elasticity = 0.035, p < 0.01) and economic growth (elasticity = 0.031, p < 0.05) in treated provinces. At the firm level, subsidy intensity exhibits an inverted U-shaped relationship with investment efficiency, with an estimated inflection point at 8.3% of operating revenue within the sample. Mechanism analysis shows that easing financing constraints, stimulating technological innovation, and reducing operational risk serve as core transmission channels, with the strongest contribution from financing constraint alleviation. Heterogeneity analysis further finds larger effects for private firms, high-tech enterprises, and coastal regions. This study develops a nonlinear analytical framework of “subsidy intensity–firm behavior–regional outcomes”, identifies context-specific boundaries of subsidy effectiveness, and provides integrated micro-macro empirical evidence for optimizing subsidy policies and advancing the global energy transition. Full article

Pit mud (PM) harbors abundant bacteria and archaea, shaping Baijiu’s flavor and adapting to high-ethanol and acidic conditions. This study integrated 16S rRNA sequencing, physicochemical analysis, and non-targeted metabolomics. The results showed that the 30-year-old Zhuyechun PM had higher levels of humic acid, while the 20-year-old Fengchun and Bitanchun PM exhibited near-neutral pH, which is favorable for Clostridia and methanogens. Canonical correspondence analysis revealed Rummeliibacillus and Caproiciproducen positively correlated with organic carbon, humic acid, and available phosphorus, whereas Methanosarcina showed negative correlations. In Baijiu, 1206 non-volatile signals were identified, with unsaturated fatty acid biosynthesis as a key flavor pathway. Methanogens significantly associated with key metabolites (p < 0.05). These findings reveal the correlation between the PM microecosystem and Baijiu quality, providing insights for the development of high-quality Baijiu. Full article

Under China’s “carbon peaking before 2030 and carbon neutrality before 2060” targets, the low-carbon transformation of the ironmaking stage—which contributes approximately 70% of the CO₂ emitted per ton of steel in the dominant blast furnace–basic oxygen furnace (BF–BOF) route—is decisive for decarbonizing the steel industry. In contrast to earlier reviews that describe individual technologies in isolation, this review provides a structured, cross-technology synthesis: the main routes are classified into short-/medium-term low-carbon blast furnace technologies (hydrogen-rich carbon-recycling oxygen blast furnace, hydrogen-rich injection, biomass char injection and ultimate energy-efficiency measures) and medium-/long-term non-blast furnace technologies (hydrogen-based shaft furnace direct reduction, smelting reduction, rotary hearth furnace, fluidized-bed reduction and electric smelting reduction) and are then compared on a common set of quantitative indicators—CO₂ mitigation, specific energy and hydrogen demand, technology readiness level (TRL) and relative cost. On this basis, emerging hydrogen-based flash ironmaking, developed from the interdisciplinary integration of “iron and steel–non-ferrous metallurgy–hydrogen metallurgy–plasma”, is critically assessed, with a clear separation between laboratory/pilot feasibility and industrial readiness. We conclude that low-carbon blast furnace technologies, owing to low retrofit cost and high maturity, will dominate near-term mitigation, whereas hydrogen-based shaft furnaces and, in the longer term, hydrogen-based flash ironmaking define the pathway toward near-zero-carbon ironmaking—conditional on the availability of green hydrogen and low-carbon electricity and on overcoming key engineering barriers. Full article

The transition toward carbon neutrality demands not only an understanding of the complex dynamics within energy–economy–environment (3E) systems but also the ability to strategically enhance their resilience against external shocks. Here, we introduce a bidirectional coupled framework that integrates macro-level supernetwork topology with micro-level agent-based modeling (ABM) to diagnose structural vulnerabilities and optimize systemic performance. Applied to 41 cities in the Yangtze River Delta (YRD) from 2010 to 2023, our framework reveals a persistent core–periphery spatial disparity in coupling coordination, underpinned by four distinct network layers—energy flow, economic linkage, environmental impact, and policy synergy—whose densities vary by an order of magnitude. Through coupled evolutionary simulations, we quantify system resilience as a tripartite metric of robustness, adaptability, and recovery, identifying a systemic structural weakness: a robust recovery capacity is offset by substantially lower robustness. To address this, we deploy a genetic algorithm to solve for optimal investment allocation under a budget constraint, demonstrating that a targeted, hub-centric strategy yields a higher marginal resilience gain than uniform distribution. Furthermore, embedding multi-agent reinforcement learning (MARL) and social learning into policy scenario simulations shows that unified environmental standards and a carbon-inclusive mechanism effectively eliminate regulatory arbitrage and accelerate low-carbon behavioral diffusion, improving system-wide robustness by 35% under extreme climate shocks. This work delivers a closed-loop, transferable analytical framework that transforms structural diagnosis into actionable optimization, offering a scientific basis for coordinated regional decarbonization strategies. Full article

Environmental pollution is becoming an urgent global issue. Countries are implementing various solutions and establishing legal frameworks and institutional policies to promote the use of green energy sources and thereby minimize the adverse environmental impact of economic growth, investment, and consumption. The goal of economies worldwide, including in Asia, is to implement policies that promote sustainable development while optimizing economic performance. The purpose of this study is to evaluate the impact of green energy consumption and institutional quality on environmental quality. Using data from 29 selected Asian countries over the period 1970–2021, the empirical results indicate that green energy consumption does not have a statistically significant impact on environmental quality across the full sample. Improvements in institutional quality can enhance environmental quality and accelerate progress toward carbon neutrality and net-zero emissions targets. The Climate Change Conferences (COPs) have helped raise global awareness of the dangers of climate change, fostering greater international cooperation to protect the planet and promote sustainable development. To capture the influence of international climate commitments, this study employs a dummy variable for the COP period (coded 0 before COP2 in 1996 and 1 thereafter). This variable is interacted with green energy consumption and institutional quality to examine whether the implementation of COP commitments moderates their effects on CO₂ emissions. The empirical results show that greater political stability, particularly during the implementation of carbon emission reduction commitments, contributes to reducing greenhouse gas emissions and promoting sustainable development. The study also confirms that institutional quality plays a significant role in improving environmental quality and accelerating countries’ progress toward carbon neutrality and net-zero emission goals. Full article

Accurate characterization of multi-band solar radiation is essential for optimizing photovoltaic (PV) site selection and supporting carbon neutrality targets. Shandong Province, a major economic and energy-consuming province in eastern China, possesses abundant solar resources but exhibits pronounced spatiotemporal heterogeneity driven by complex terrain, rapid urbanization, and variable cloud cover. Based on high-resolution CARE (Cloud Remote Sensing, Atmospheric Radiation and Renewable Energy Application) satellite products (0.1°, hourly, 2016–2020) combined with SRTM DEM, CLCD land use, and ERA5 cloud data, this study systematically analyzes the spatiotemporal distribution and driving factors of four solar radiation components—shortwave radiation (SWR), photosynthetically active radiation (PAR), UVA, and UVB—across Shandong Province. Key findings are as follows: (1) All four radiation components exhibit a consistent spatial pattern characterized by higher radiation intensities in the eastern coastal and northern plain regions, which gradually decrease toward the western inland and southern mountainous areas. Provincial five-year means are SWR 186.6 W/m², PAR 86.3 W/m², UVA 11.4 W/m², and UVB 0.3 W/m², with high-value zones concentrated in the Jiaodong Peninsula coast and the North Shandong Plain. (2) During 2016–2020, short-term increasing tendencies were observed across 80.4% (SWR), 78.0% (PAR), 85.1% (UVA), and 91.3% (UVB) of the province, while all components declined in winter. (3) STL decomposition reveals a “down-up-down” multi-year trend, a unimodal annual seasonal cycle peaking in May, and residuals closely associated with extreme weather events. (4) Geodetector analysis identifies cloud cover as the dominant factor (q = 0.332), followed by elevation (q = 0.100); and nonlinear enhancement characterizes all factor interactions, especially cloud cover × elevation (q = 0.393) and cloud cover × land-use (q = 0.347), revealing a “climate–topography–human activity” multi-level coupling mechanism. Built-up land records the lowest SWR (172.4 W/m²) and spatially coincides with radiation low-value zones. These results provide a scientific basis for PV site optimization and the realization of carbon neutrality goals in Shandong Province. Full article

Under the “dual carbon” goals (carbon peak and carbon neutrality), the new energy vehicle (NEV) industry has become a strategic focus of great-power competition, and the resilience of its international supply chain is critical to industrial security and development initiatives. As a traditional automobile manufacturing hub in China, Hubei Province faces increasingly prominent global risks in its supply chain during the transition to NEVs; scientifically evaluating and enhancing its international supply chain resilience is therefore of great practical significance. Drawing on supply chain resilience theory, this paper constructs an evaluation index system comprising 18 specific indicators across four dimensions: robustness, redundancy, agility, and innovativeness. To overcome the limitations of a single weighting method, a combined subjective and objective weighting approach that integrates the Analytic Hierarchy Process (AHP) and the entropy weight method was employed to determine indicator weights. Subsequently, the TOPSIS model was applied to measure the supply chain resilience level of Hubei Province from 2018 to 2025, with horizontal comparisons conducted against Shanghai and Guangdong. Finally, an obstacle degree model was introduced to quantitatively diagnose the key factors constraining resilience improvement. The results indicate that the international supply chain resilience of Hubei’s NEV industry has shown a continuous upward trend. By 2025, it ranks in the first tier alongside Guangdong (with closeness coefficients of 0.8180 and 0.8181, respectively), approaching the level of Shanghai. Weaknesses are concentrated primarily in the agility dimension, while upstream resource dependence remains a salient issue within the robustness dimension. “External dependence on key raw materials,” “average recovery time from logistics disruptions,” and “level of supply chain information sharing” are still the top three obstacle factors. Fuzzy-set qualitative comparative analysis (fsQCA) further reveals that low resource autonomy and slow logistics recovery are core conditions leading to low resilience, and the coupling of multiple obstacle factors amplifies the risk transmission effect. Based on this, this study proposes optimization recommendations focusing on foundation strengthening and chain consolidation, digital chain connectivity, and innovation–chain integration, in order to enhance the resilience of the international supply chain for new energy vehicles in Hubei. This research provides a methodological reference for evaluating the supply chain resilience of regionally distinctive industries and offers a quantitative basis for Hubei Province and related enterprises to formulate targeted improvement strategies. Full article

Urban areas, where diverse human activities take place, generate significant amounts of carbon dioxide. This study proposes a simplified method for estimating the reduction effects of carbon dioxide emissions from passenger transportation resulting from compact city measures, which are expected to contribute to reducing emissions in urban areas. In particular, this study contributes by introducing a distance-band-based estimation framework, enabling scenario-based analysis with reduced data requirements while retaining key spatial characteristics. This method enables planners to quickly assess whether the required CO₂ reduction levels can be achieved under given policy settings, thereby supporting evidence-based policy formulation. As an example of how this method could be applied in the policy formulation stage, it was applied to Toyohashi City, which faces the challenge of car dependency, a common problem in many regional cities. It was demonstrated that the scenario assuming the achievement of the targets for land use and transport measures outlined in municipal plans from 2022 to 2050 could reduce carbon dioxide emissions by over 40%. This research supports integrated consideration of urban and transportation measures at the planning and policy formulation stages, contributing to highlighting pathways toward sustainable urban development aimed at achieving carbon neutrality. Full article

Against the backdrop of optimizing national energy mix and advancing industrial low-carbon transformation to achieve sustainable socioeconomic development, this study adopts prefecture-level panel data covering 2005–2020 to reveal the spatiotemporal evolution law of carbon emissions generated by urban energy consumption. We systematically characterize emission disparities from three dimensions: total carbon output, per capita carbon emissions, and carbon emission intensity, and further adopt regression analysis to quantitatively identify core socioeconomic and industrial drivers behind energy-related carbon flows. The results indicate that China’s total urban energy carbon emissions kept rising over the research window with decelerating growth momentum. Driven by cross-regional industrial transfer and uneven energy resource endowments, high-emission zones gradually spread from eastern coastal agglomerations to northern and western inland territories, forming a stable spatial layout of high emissions in the east and north, and low emissions in the west and south. Per capita carbon emissions present striking regional differentiation: northwest resource-abundant provinces become concentrated high-value clusters, while populous southeast regions maintain relatively low levels, with inter-regional per capita emission gaps continuously widening. Nationwide carbon emission intensity maintained a persistent downward trend; high-intensity zones shrank markedly while low-carbon areas expanded continuously, and inter-regional efficiency gradients gradually converged, reflecting tangible achievements in nationwide energy conservation and low-carbon industrial transition. Overall, the gravity center of energy carbon emissions shifted northwestward, with Inner Mongolia, Xinjiang, and Ningxia evolving into major high-emission hotspots relying on fossil energy exploitation and heavy industrial layout. Statistical regression associations suggest that urban construction land expansion, economic expansion, foreign capital agglomeration, and industrial energy carbon outputs are positively correlated with urban carbon emissions; by contrast, commercial housing scale and domestic enterprise development present significant negative correlational links with emission levels. The differentiated spatiotemporal carbon landscape arises from the joint interplay of regional resource endowment, coal-dominated energy structure, industrial layout restructuring, and tiered low-carbon policy implementation, demonstrating China’s overall shift from high-carbon extensive industrial growth toward energy-efficient, low-carbon intensive sustainable development. This research delivers empirical evidence for formulating zoned carbon abatement schemes, optimizing regional energy allocation and industrial layouts, and advancing long-term low-carbon sustainable development to fulfill China’s carbon peaking and carbon neutrality targets. Full article

Graphene-based nanosheets (GNS), including graphene, graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs), have attracted increasing attention for developing high-performance and sustainable cementitious composites. Compared with conventional dispersion strategies, graphene-based coating strategies enable the targeted localization of GNS at critical interfacial transition zones (ITZs), thereby maximizing their reinforcing efficiency while mitigating agglomeration issues. This review systematically summarizes recent advances in GNS coating technologies for cementitious composites, including physical adsorption, chemical assembly, electrophoretic deposition, and in situ growth. The effects of GNS coatings on interfacial engineering, mechanical performance, durability enhancement, and smart functionalities are critically discussed. Existing studies indicate that GNS coatings can improve strength, crack resistance, impermeability, and resistance to chloride ingress, freeze–thaw cycles, and other degradation processes mainly through ITZ densification and microstructure refinement. However, these benefits are strongly dependent on the coating method, substrate type, and stability of the graphene–substrate interface in calcium-rich alkaline pore solutions. In particular, physically adsorbed GO coatings may suffer from desorption or Ca²⁺-induced aggregation, chemically assembled coatings require further validation beyond laboratory-scale systems, and electrophoretic deposition is mainly applicable to electrically conductive substrates. In addition, localized conductive networks created by GNS coatings facilitate multifunctional properties such as self-sensing, electromagnetic shielding, and electrothermal performance. From a carbon-neutrality perspective, the improvements in mechanical properties and durability provide opportunities to reduce material consumption, extend service life, and lower life-cycle carbon emissions. Nevertheless, their carbon-neutral contribution should be verified through quantitative life-cycle assessment rather than inferred directly from strength or durability enhancement alone. Finally, the remaining challenges associated with large-scale implementation, long-term stability, cost-effectiveness, and field-scale validation are discussed. Particular attention is given to the fact that most existing evidence is derived from laboratory-scale specimens rather than real structural elements exposed to service environments. Full article

Quantifying mountain forest carbon stocks and elucidating their spatially heterogeneous driving mechanisms are both critical for terrestrial carbon management under the global carbon neutrality agenda. Conventional single-source remote sensing approaches can neither fully exploit multi-source data synergies nor adequately resolve spatial heterogeneity in complex terrains. This study develops an integrated framework combining multi-source remote sensing classification, InVEST-based carbon estimation, and multiscale geographically weighted regression (MGWR) and applies it to Zixi County, a subtropical mountainous bamboo-abundant region in southeastern China. Sentinel-2 imagery, PlanetScope data, and DEM derivatives were fused with an optimized Random Forest classifier, achieving an overall accuracy of 0.8565 (Kappa = 0.7065). Carbon stocks were then estimated via the InVEST model. MGWR analysis (adjusted R² = 0.930, AICc = 594.032) substantially outperformed the global OLS model (adjusted R² = 0.795, AICc = 1717.450), confirming strong spatial non-stationarity across all drivers. Canopy density exhibited the strongest positive local effect (coefficient range: 0.343–0.768); slope position showed predominantly negative regulation with localized positive reversals (−0.778 to 0.270); elevation displayed a broad-scale positive gradient (0.133–0.140); and total vegetation cover exhibited bidirectional effects (−0.134 to 0.208) with pronounced east–west divergence. This framework not only provides a robust methodological reference for carbon stock assessment in complex mountain landscapes but also supports targeted forest management and carbon sequestration strategies through spatially explicit driver identification. Full article

Against the backdrop of China’s “Dual Carbon” strategy—the national strategic goal of achieving carbon peaking by 2030 and carbon neutrality by 2060—and the accelerated improvement of the solid waste governance system specified in the 15th Five-Year Plan, the low-carbon recycling of construction waste has become a core research topic for the sustainable development of the construction industry. To systematically reveal the evolutionary laws, research hotspots and frontier trends regarding the life-cycle carbon emissions of recycled concrete (this study defines its accounting scope clearly for the first time, covering three parts: ① direct carbon emissions generated in the stages of recycled aggregate recovery, processing, transportation and concrete mixing; ② indirect carbon emissions reduced by replacing the exploitation of natural aggregates with recycled aggregates; ③ potential carbon sequestration benefits brought by carbonation curing during the service phase of recycled concrete), the literature published from 2015 to 2025 retrieved from the CNKI and Web of Science Core Collection databases was selected as the research sample. Standardized data preprocessing was carried out: intra-database duplicate literatures were removed based on titles, authors and publication years, cross-database duplicate records were manually eliminated, and non-academic documents including news, editorial notes and conference abstracts were screened out. A total of 1340 valid publications were finally obtained to form the analysis dataset. Bibliometric tools CiteSpace and VOSviewer were adopted to quantitatively analyze the annual publication trends, national and institutional distribution, keyword co-occurrence clustering and temporal evolution characteristics in this research field. The results show that the annual publication volume concerning the life-cycle carbon emissions of recycled concrete presents a continuous upward trend, and the research development can be divided into three stages: initial exploration, rapid expansion and steady growth. China, the United States and Australia act as the core research forces in this field. Current research hotspots mainly focus on recycled aggregate modification, life-cycle assessment, carbon emission accounting, durability performance optimization and low-carbon preparation technologies, while research frontiers are gradually shifting toward multi-source data fusion, machine learning-based carbon emission prediction and low-carbon path optimization. Based on the quantitative bibliometric results, this study puts forward targeted priorities for future research: establishing a unified localized specification system for life-cycle carbon accounting, developing data-driven optimization models for the low-carbon mix proportion design of recycled concrete, and promoting the large-scale engineering demonstration and application of high-value resource utilization technologies, to facilitate the full life-cycle low-carbon transformation of construction waste recycling. Full article

Carbon capture and storage (CCS) represents a critical technology for achieving climate neutrality targets, particularly for regions with significant industrial CO₂ emissions. This study presents a comprehensive numerical simulation assessment of CO₂ geological storage potential in the Sarmatian formations of the Getic Platform, Romania, located near the Turceni power plant—one of Europe’s largest thermal power facilities. Using ECLIPSE 300 compositional simulator with the CO2STORE option, we developed reservoir dynamic models incorporating geological properties, fluid characteristics, and pressure–volume–temperature (PVT) data specific to the Sarmatian aquifer system. Multiple injection scenarios were evaluated, including configurations with 3, 4, and 5 injection wells at varying inter-well distances (2000–10,000 m). The simulations covered a 20-year injection period followed by 300 years of monitoring. While previous assessments have provided static capacity estimates for Sarmatian formations, this study presents the first dynamic simulation-based evaluation of multi-well injection scenarios and long-term CO₂ trapping behavior in this geological setting, directly linked to the Turceni Power Plant emissions profile. Results demonstrate that the study area (Zone V) can accommodate the target CO₂ injection rate of 2.07 × 10⁶ Sm³/day using five injection wells, with final reservoir pressure increasing only 7–9 bar above initial conditions, well below fracture pressure thresholds (~280 bar). Long-term simulations reveal favorable CO₂ trapping behavior, with significant portions immobilized through residual and dissolution trapping mechanisms. The static storage capacity was estimated at 2.44 × 10¹⁴ kg CO₂. These findings support the technical feasibility of large-scale CO₂ storage in Romanian Sarmatian formations, providing quantitative evidence for CCS implementation strategies in the region. Full article

Carbon storage dynamics in dryland and semi-arid ecosystems remain a major uncertainty in global carbon cycle assessments, particularly in regions like the Yellow River Basin (YRB). Using the Arid Ecosystem Model (AEM), we simulated the spatiotemporal evolution of four major carbon pools—total carbon (TOTC), vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC)—from 1981 to 2060 under factorial climate scenarios. During 1981–2020, TOTC increased by 0.09 Pg C (+3.54%), driven by gains in VEGC (+0.03 Pg C, +21.43%) and SOC (+0.06 Pg C, +2.78%). LTRC showed minimal net change but was highly sensitive to interannual variability. From 2021 to 2060, under the high-emission SSP5 scenario, TOTC is projected to increase by 0.114 Pg C (+4.81%), with VEGC contributing most of the gain (+23.87%). CO₂_only simulations showed similar increases, underscoring the dominant role of CO₂ fertilization. In contrast, warming and precipitation alone produced weaker and more variable effects. Spatially, upper YRB regions are expected to maintain strong sink capacity, while the Loess Plateau and central-western subregions remain vulnerable to warming and moisture decline. LTRC exhibited the highest variability across scenarios (−18% to +22%), highlighting its role as a sensitive indicator of sink stability. These findings emphasize the need to account for nonlinear climate–carbon interactions and regional heterogeneity. Region-specific, adaptive strategies that integrate ecological restoration and climate adaptation will be critical to enhancing carbon sinks and supporting China’s carbon neutrality targets in the Yellow River Basin. Full article