Search Results (82)

The significant energy intensity of high-speed railway necessitates integrating renewable technologies to enhance grid resilience and decarbonize transport. This study establishes a coordinated carbon–green certificate market mechanism for railway power systems and develops a tri-source planning model (grid/solar/energy storage) that comprehensively considers the full lifecycle carbon emissions of these assets while minimizing lifecycle costs and CO₂ emissions. The proposed EDMOA algorithm optimizes storage configurations across multiple operational climatic regimes. Benchmark analysis demonstrates superior economic–environmental synergy, achieving a 23.90% cost reduction (USD 923,152 annual savings) and 24.02% lower emissions (693,452.5 kg CO₂ reduction) versus conventional systems. These results validate the synergistic integration of hybrid power systems with the carbon–green certificate market mechanism as a quantifiable pathway towards decarbonization in rail infrastructure. Full article

As an energy-intensive industry, the aviation sector’s carbon emissions have drawn significant attention. Against the backdrop of the “dual carbon” goals, how to enhance the carbon emission efficiency of airlines has become an urgent issue to be addressed for both industry development and low-carbon targets. This paper constructs an evaluation system for the carbon emission efficiency of airlines and uses the SBM-DDF model under the global production possibility set, combined with the bootstrap-DEA method, to calculate the efficiency values. On this basis, the fuzzy-set qualitative comparative analysis method is employed to analyze the synergistic effects of multiple influencing factors in three dimensions: economic benefits, transportation benefits, and energy consumption on improving carbon emission efficiency. The research findings reveal that, first, a single influencing factor does not constitute a necessary condition for achieving high carbon emission efficiency; second, there are four combinations that enhance carbon emission efficiency: “load volume-driven type”, “scale revenue-driven type”, “high ticket price + technology-driven type”, and “passenger and cargo synergy mixed type”. These discoveries are of great significance for promoting the construction of a carbon emission efficiency system by Chinese airlines and achieving high-quality development in the aviation industry. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

The accelerated industrialization in China has precipitated a dramatic surge in solid waste generation, causing severe land resource depletion and posing substantial environmental contamination risks. Simultaneously, the cement industry has become characterized by the intensive consumption of natural resources and high carbon emissions. This review aims to investigate the current technological advances in utilizing industrial solid waste for cement production, with a focus on promoting resource recycling, phase transformations during hydration, and environmental management. The feasibility of incorporating coal-based solid waste, metallurgical slags, tailings, industrial byproduct gypsum, and municipal solid waste incineration into active mixed material for cement is discussed. This waste is utilized by replacing conventional raw materials or serving as active mixed material due to their content of oxygenated salt minerals and oxide minerals. The results indicate that the formation of hydration products can be increased, the mechanical strength of cement can be improved, and a notable reduction in CO₂ emissions can be achieved through the appropriate selection and proportioning of mineral components in industrial solid waste. Further research is recommended to explore the synergistic effects of multi-waste combinations and to develop economically efficient pretreatment methods, with an emphasis on balancing the strength, durability, and environmental performance of cement. This study provides practical insights into the environmentally friendly and efficient recycling of industrial solid waste and supports the realization of carbon peak and carbon neutrality goals. Full article

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting. Full article

In the context of global climate governance and the low-carbon energy transition, virtual power plant (VPP), a key technology for integrating distributed energy resources, is urgently needed to solve the problem of decentralization and lack of synergy in electricity, carbon, and green certificate trading. Existing studies mostly focus on single energy or carbon trading scenarios and lack a multi-market coupling mechanism supported by blockchain technology, resulting in low transaction transparency and a high risk of information tampering. For this reason, this paper proposes a synergistic optimization strategy for electricity/carbon/green certificate virtual power plants based on blockchain cross-chain transactions. First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. The simulation results show that compared with the traditional model, the proposed strategy reduces the carbon emission intensity by 13.3% (1.43 tons/million CNY), increases the rate of new energy consumption to 98.75%, and partially offsets the cost through the carbon trading revenue, which verifies the Pareto improvement of environmental and economic benefits. This study provides theoretical support for the synergistic optimization of multi-energy markets and helps to build a low-carbon power system with a high proportion of renewable energy. Full article

Under the dual constraints of rigid water resource management systems and China’s “dual carbon” national strategy, water resource management authorities face pressing practical demands for the coordinated governance of water conservation and carbon dioxide emission reduction. This study comprehensively compiles nationwide data on water supply/consumption, energy use, water intensity, and CO₂ emissions across Chinese provinces. Employing a non-radial directional distance function (NDDF) model with multiple inputs and outputs, we quantitatively assess provincial water saving and carbon reduction performance during 2000–2021; measure synergistic effects; and systematically examine the spatiotemporal evolution, correlation patterns, and convergence trends of three key indicators: standalone water saving performance, standalone carbon reduction performance, and their synergistic performance—essentially addressing whether “1 + 1 > 2” holds true. Furthermore, we analyze the spatial convergence and clustering characteristics of synergistic effect across regions, delving into the underlying causes of inter-regional disparities in water–carbon synergy. Key findings reveal the following: ① Temporally, standalone water saving and carbon reduction performance generally improved, though the water saving metrics initially declined before stabilizing into sustained growth, ultimately outpacing carbon reduction gains. Synergistic performance consistently surpassed standalone measures, with most regions demonstrating accelerating synergistic enhancement over time. Nationally, water–carbon synergy exhibited early volatile declines followed by steady growth, though the growth rate gradually decelerated. ② Spatially, high-value synergy clusters migrated from the western to eastern regions and the northern to southern zones before stabilizing geographically. The synergy effect demonstrates measurable convergence overall, yet with pronounced regional heterogeneity, manifesting a distinct “high southeast–low northwest” agglomeration pattern. Strategic interventions should prioritize water–carbon nexus domains, leverage spatial convergence trends and clustering intensities, and systematically unlock synergistic potential. Full article

This paper investigates the role of the digital economy in reducing carbon emissions, with a particular focus on the moderating and threshold effects of green finance. An analysis of data from 30 Chinese provinces shows that the digital economy significantly reduces carbon emission intensity by restructuring energy consumption and promoting green technological innovation. Green finance plays a crucial moderating role by alleviating financial barriers to digital transformation and supporting the implementation of emission-reducing technologies. The study reveals a nonlinear relationship, with green finance exhibiting a “strong initial, weak subsequent” threshold effect. At the same time, the digital economy’s impact on carbon reduction strengthens over time as technological development progresses. These findings contribute to understanding how digitalisation and green finance can work synergistically to drive sustainable low-carbon development. Full article

Against the backdrop of intensifying global climate change and deepening sustainable development goals, the low-carbon transformation of agriculture, as a major greenhouse gas emission source, holds significant strategic importance for achieving China’s “carbon peaking and carbon neutrality” (referred to as the “dual carbon”) targets. To reveal the spatiotemporal evolution characteristics and complex driving mechanisms of agricultural carbon emissions (ACEs), this study constructs a comprehensive accounting framework for agricultural carbon emissions based on provincial panel data from China spanning 2000 to 2023. The framework encompasses three major carbon sources—cropland use, rice cultivation, and livestock farming—enabling precise quantification of total agricultural carbon emissions. Furthermore, spatial-temporal distribution patterns are characterized using methodologies including standard deviational ellipse (SDE) and spatial autocorrelation analysis. For driving mechanism identification, the Geodetector and Geographically and Temporally Weighted Regression (GTWR) models are employed. The former quantifies the spatial explanatory power and interaction effects of driving factors, while the latter enables dynamic estimation of factor influence intensities across temporal and spatial dimensions, jointly revealing significant spatiotemporal heterogeneity in driving mechanisms. Key findings: (1) temporally, total ACEs exhibit fluctuating growth, while emission intensity has significantly decreased, indicating the combined effects of policy regulation and technological advancements; (2) spatially, emissions display an “east-high, west-low” pattern, with an increasing number of hotspot areas and a continuous shift of the emission centroid toward the northwest; and (3) mechanistically, agricultural gross output value is the primary driving factor, with its influence fluctuating in response to economic and policy changes. The interactions among multiple factors evolve over time, transitioning from economy-driven to synergistic effects of technology and climate. The GTWR model further reveals the spatial and temporal variations in the impacts of each factor. This study recommends formulating differentiated low-carbon agricultural policies based on regional characteristics, optimizing industrial structures, enhancing modernization levels, strengthening regional collaborative governance, and promoting the synergistic development of climate and agriculture. These measures provide a scientific basis and policy reference for achieving the “dual carbon” goals. Full article

Against the backdrop of China’s “dual carbon” strategy, investigating the carbon emission characteristics and low-carbon operational status of wastewater treatment plants (WWTPs) across regions is pivotal for achieving synergistic pollution reduction and carbon mitigation. Leveraging 2024 operational data from 98 WWTPs in eastern China—encompassing treatment volume, energy consumption, sludge production, and chemical dosages—this study refined the Assessment Standard for Carbon Mitigation in Municipal WWTPs and Technical Specification for Low-Carbon Operation of WWTPs. A novel carbon accounting framework and low-carbon performance evaluation system were subsequently developed to analyze the impacts of treatment scale, technological configuration, and load rate on carbon footprints. Key findings revealed an average carbon intensity of 0.399 kg CO₂-eq/m³ for the region, with small-scale facilities (0.582 kg CO₂-eq/m³) exhibiting significantly higher emissions compared to their large-scale counterparts (0.392 kg CO₂-eq/m³). Indirect emissions constituted 62.1% of the total footprint, while chemical dosing contributed 14.2%, primarily driven by carbon sources and phosphorus removal agents. Fossil-derived CO₂ accounted for 4.6% of emissions. Notably, the AAO process demonstrated the lowest carbon intensity (0.370 kg CO₂-eq/m³), whereas SBR systems registered the highest (0.617 kg CO₂-eq/m³). Furthermore, 25% of the assessed facilities were classified as high-emission plants. Strategic recommendations are proposed, including prioritizing AAO process optimization, implementing intelligent chemical dosing control, utilizing food wastewater as an alternative carbon source, and enhancing operational load rates, to advance synergistic environmental and carbon mitigation goals in eastern China’s wastewater sector. Full article

This study investigates the annual carbon emission disparities between privately-owned electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) by developing a usage-phase life cycle assessment (LCA) model, with a focus on the synergistic impacts of grid carbon intensity, driving intensity (e.g., annual mileage), and vehicle energy efficiency. Through scenario analyses and empirical case studies in four Chinese megacities, three key findings are obtained: (1) Grid carbon intensity is the primary factor affecting the emission advantages of EVs. EVs demonstrate significant carbon reduction benefits in regions with low-carbon power grids, even when the annual mileage is doubled. However, in coal-dependent grids under intensive usage scenarios, high-energy-consuming EVs may experience emission reversals, where their emissions exceed those of ICEVs. (2) Higher annual mileage among EV owners (1.5–2 times that of ICEV owners) accelerates carbon accumulation, particularly diminishing per-kilometer emission advantages in regions where electricity grids are heavily reliant on fossil fuels. (3) Vehicle energy efficiency heterogeneity plays a critical role: compact, low-energy EVs (e.g., A0-class sedans/SUVs) maintain emission advantages across all scenarios, while high-energy models (e.g., C-class sedans/SUVs) may exceed ICEV emissions even in regions with low-carbon power grids under specific conditions. The study proposes a differentiated policy framework that emphasizes the synergistic optimization of grid decarbonization, vehicle-class-specific management, and user behavior guidance to maximize the carbon reduction potential of EVs. These insights provide a scientific foundation for refining EV adoption strategies and achieving sustainable transportation transitions. Full article

Green technology innovation (GTI) is crucial for achieving synergistic development in reducing pollution and carbon emissions (CEs). The spatio-temporal evolutionary aspects of carbon emission intensity (CEI) in resource-based cities (RBCs) and the heterogeneity of the carbon emission reduction effects of GTI from zoning, grading, and classification perspectives are investigated using kernel density estimation, Markov chains, and panel regression models. Our results are as follows: the CEI of RBCs displays a fluctuating downwards trend from 2006 to 2022. Spatially, the main feature is that the north is higher than the south. Second, GTI has significantly reduced the CEI of RBCs through structural optimization, energy savings, and efficiency improvement, as verified in different development stages and dominant resource types. In addition, national high-tech zones (NHTZs) have significantly contributed to reducing CEI in RBCs. The proposed countermeasures include increasing investment in GTI, establishing an exchange platform for GTI, and implementing differentiated policies according to local conditions, which are important for constructing an ecological civilization in RBCs. Full article

Reducing carbon emissions while ensuring economic growth has become a realistic demand in China. The ideal scenario would be to realize the coupling and coordination of the economic and energy systems. This research constructs a coupling coordination evaluation system that objectively reflects the low-carbon energy system (LCES) and socioeconomic system of China. The LCES level has increased to varying degrees in all provinces, with significant differences across regions. The coupling degree of the 30 provinces is between 0.5955 and 0.9999, belonging to the running-in stage and high-coupling stage. Moreover, the average coupling coordination degree (CCD) is 0.3–0.4, belonging to moderate incoordination. In terms of sub-provinces, the CCDs in all provinces indicate high coupling with varying degrees of coordination. Only Qinghai falls into the running-in low-incoordination category. Reaching the 2030 carbon intensity reduction target would be challenging under the baseline scenario. However, this target is expected to be achieved under two scenarios in which the policy constraints of each province are realized. Based on these conclusions, this research proposes a regionally differentiated low-carbon synergistic development strategy to provide a targeted regional synergistic path for the realization of carbon emission reduction and dual-carbon goals in China during the stage of high-quality development. Full article

Although algae possess a high capacity for carbon sequestration, the recalcitrant multilayered cell wall structure and residual microcystin toxicity associated with Microcystis aeruginosa significantly hinder the efficient recovery of algal biomass resources. This study developed a synergistic ozone-ultrasonication (O₃-US) pretreatment strategy, systematically comparing its cell-disruption efficacy with standalone O₃ or US, using harvested algal biomass from natural aquatic systems dominated by Microcystis aeruginosa. The synergistic effects revealed were: (1) O₃-mediated oxidation of extracellular polymeric substances and cell wall matrices, (2) the release of ultrasound-induced cavitation-enhancing intracellular components, and (3) an improvement in the O₃ mass transfer by hydrodynamic shear forces. Through response surface methodology optimization, the O₃-US process achieved maximal performance at 0.14 gO₃/gTSS, with a 4 W/mL ultrasonic intensity, and a 20 min duration. Remarkably, the released protein was 289.2 mg/gTSS, which was 4.3-fold and 1.9-fold, respectively, more than that released in O₃ pretreatment and US pretreatment, while the polysaccharide was 87.5 mg/gTSS, increased by 2.4-fold and 3.1-fold respectively, compared to O₃ alone and US alone. The released solubilized chemical oxygen demand (SCOD) was 1037.1 mg/gTSS, increased by 43.3% and 216.1%, respectively, relative to O₃ alone and US alone. DNA quantification further validated the synergistic cell disruption caused by O₃-US. Fluorescence excitation-emission matrix (EEM) spectroscopy identified biodegradable aromatic proteins (Regions I-II) and soluble microbial byproducts (Region IV) as dominant organic fractions, demonstrating enhanced bioavailability. The hybrid process reduced energy consumption by 33.3% in ultrasonic intensity and 60% in duration versus US alone, while achieving 94.5% microcystin-LR (MC-LR) degradation, which showed a 96.6% risk reduction compared to ultrasonic treatment. This work establishes an efficient, low-energy, and safe pretreatment technology for algal resource recovery, synergistically enhancing intracellular resource release while mitigating cyanotoxin hazards in algal biomass valorization. Full article

As an economically active region, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) faces dual challenges of carbon and air pollution reduction. Existing studies predominantly focus on single pollutants or engineering pathways, lacking systematic analyses of multi-scale synergistic effects. This study investigates the spatiotemporal distributions, driving factors, and synergistic effects of CO₂ and volatile organic compounds (VOCs) at the multi-scale of urban agglomerations, cities, and industries, using global Moran’s index, standard deviational ellipse, logarithmic mean divisa index decomposition model, and Tapio decoupling model. The results show that the average annual growth rate of CO₂ (7.4%) was significantly higher than that of VOCs (4.5%) from 2000 to 2020, and the industrial sector contributed more than 70% of CO₂ and VOC emissions, with the center of gravity of emissions migrating to Dongguan. Industrial energy intensity improvement emerged as the primary mitigation driver, with Guangzhou and Shenzhen demonstrating the highest contribution rates. Additionally, CO₂ and VOC reduction show two-way positive synergy, and the path of “energy intensity enhancement–carbon and pollution reduction” in the industrial sector is effective. Notably, the number of strong decouplings of the economy from CO₂ (11 times) is much higher than the number of strong decouplings of VOCs (3 times), suggesting that the synergy between VOC management and economic transformation needs to be strengthened. This study provides scientific foundations for phased co-reduction targets and energy–industrial structure optimization, proposing regional joint prevention and control policy frameworks. Full article