Search Results (694)

Under the constraints of carbon peaking and carbon neutrality targets, corporate emission reduction is shifting from fragmented governance toward integrated governance that aligns pollution control with carbon reduction and long-term sustainable development. New energy transition policies have become a key instrument for restructuring urban energy, environmental, and economic systems, yet it remains unclear how these macro-level policies reshape firms’ marginal abatement cost–benefit structures and under what governance conditions they generate the synergy within corporate pollution reduction, rather than merely shifting burdens. It is valuable to identify whether, how, and under which governance conditions new energy demonstration city policies enhance the synergy between corporate pollution reduction and carbon mitigation. Guided by system synergy theory and a marginal abatement cost perspective, we use panel data on listed firms to construct a synergy index that jointly reflects multiple pollutant emissions and abatement costs, capturing both environmental effectiveness and economic efficiency. A DID model based on the staggered rollout of new energy demonstration cities is then employed to estimate the policy’s impact on the synergy between corporate pollution reduction and carbon mitigation and its contextual conditions. The results show the following: (1) Inclusion in a new energy demonstration city significantly increases the synergy within corporate pollution reduction. (2) Mechanism analysis indicates that higher municipal attention to green and environmental development and higher corporate ESG (environmental, social, and governance) performance strengthen the positive policy influence. (3) Heterogeneous effects are mainly concentrated in non-energy intensive industries, state-owned enterprises, and small firms, which indicates structural divergence in policy incentives across different types of firms. Overall, this study enriches the studies about the synergy between pollution reduction and carbon mitigation to the firm level, embeds a marginal abatement cost perspective into synergy measurement, and provides an evaluative framework that is consistent with how firms balance environmental and financial objectives. The findings contribute to the sustainability literature by informing the design and assessment of energy transition policies and by offering evidence to refine new energy demonstration city programs so that limited governance resources are directed toward more cost-effective joint gains. Full article

Type 2 diabetes (T2D) features insulin resistance that promotes cerebrovascular injury, yet the immune signals linking metabolic stress to vascular dysfunction remain unclear. We tested the hypothesis that insulin resistance and soluble vascular cell adhesion molecule-1 (sVCAM1) act through complementary pathways in mast cells (MCs) to raise circulating histamine levels and impair cerebral vascular function. In a high-fat diet (HFD) plus low-dose streptozotocin (STZ) model, plasma histamine rose sharply after the onset of insulin resistance and remained elevated. Plasma sVCAM1 levels also increased after insulin resistance. In vitro, recombinant sVCAM1 upregulated histidine decarboxylase (HDC) in native MCs in a dose-dependent manner, indicating a shift toward histamine synthesis, but did not enhance degranulation. In contrast, pharmacological inhibition of Akt with MK2206 activated Glycogen Synthase Kinase 3 beta (GSK3β) and increased MC degranulation without affecting HDC expression. Diabetic endothelial cell monolayers exhibited a ~twofold reduction in transendothelial electrical resistance consistent with impaired blood–brain barrier (BBB) integrity. Diabetic cerebral arteries showed receptor remodeling that favored constriction with histamine H1 receptor (H1R) expression increasing in vascular smooth muscle, while endothelial H1R and histamine H2 receptor (H2R) decreased. Functionally, insulin treatment lowered HOMA2-IR in T2D mice but did not restore cerebral artery myogenic tone or improve stroke outcomes after distal middle cerebral artery occlusion (dMCAO). Neutralizing VCAM1 with a monoclonal antibody reduced circulating sVCAM1 and histamine levels, and, together with the GSK3β inhibitor Tideglusib, stabilized MCs, normalized cerebral artery tone, and reduced post-MCAO infarct size and edema. These findings identify two distinct yet complementary mast cell pathways in T2D, highlight an immune-vascular interface that drives cerebrovascular dysfunction, and propose sVCAM1 blockade plus GSK3β inhibition as rational strategies to protect cerebral vascular function in the diabetic brain. Full article

This study introduces a control strategy that integrates a photovoltaic (PV) array reconfiguration approach into a Three-Level Neutral Point Clamped (NPC) inverter with LCL filtering and Space Vector Pulse Width Modulation (SVPWM) control. The control strategy eliminates multiple local Maximum Power Points (MPP) caused by partial shading in PV systems, thereby reducing mismatch losses and preventing the Maximum Power Point Tracking (MPPT) algorithm from becoming stuck at a local maximum. To achieve this, it utilizes an electrical reconfiguration strategy that dynamically shifts the PV array interconnections. Furthermore, this strategy reduces the system’s Total Harmonic Distortion (THD) by adjusting the DC bus voltage. Consequently, simulation evaluations across four different weather conditions have shown that this control strategy achieves significant power improvements: up to 54.8% in Case 1, 39.4% in Case 2 and 3, 21.3% in Case 4. Furthermore, the proposed approach suppressed DC bus voltage changes (<8.8 V) even under the worst conditions and reduced the THD in the grid current from 10.1% to 3.7%. Full article

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, is a destructive forest disease leading to rapid mortality. Although Chongqing is a major epidemic region in China, the population genetic structure of B. xylophilus and the ecological interactions among nematode occurrence, blue stain formation, and microbial community dynamics remain insufficiently clear. This study systematically surveyed nematode incidence and performed morphological and molecular identification, revealing strong correlations between nematode presence, blue stain, and insect infestation (p < 0.0001). Within Monochamus alternatus, nematodes were mainly distributed in the abdomen and thorax (p < 0.0001). High-throughput sequencing showed significantly higher fungal (e.g., Leptographium) and bacterial (e.g., Burkholderia-Caballeronia-Paraburkholderia) diversity in diseased than healthy pinewood, indicating pronounced microbial shifts during disease progression. mtCOI-based genetic analyses of 162 nematodes from 11 populations revealed five haplotypes, with Hap1 shared across all populations. AMOVA indicated that over 80% of genetic variation occurred within populations, and neutrality and mismatch analyses suggested recent expansion in some populations (Beibei, Jiangbei, Rongchang). These findings clarify nematode epidemiology, microbial shifts, and genetic characteristics in Chongqing, providing a scientific basis for precise sampling, rapid detection, and integrated management of PWD, and suggest that microbial community changes may contribute to rapid pine decline. Full article

In order to achieve the carbon peaking and carbon neutrality goals in China’s transportation sector, this paper examines the regional data in transportation carbon emissions across China, investigates the shifting trends of the carbon emission centroid over time, and proposes a novel representation using fuzzy set theory and rough set theory for carbon emission prediction. This paper employs the ESDA model to analyze the regional distribution of carbon emissions in the transportation sector across 30 provinces in China for the years 2005, 2010, 2015, and 2020. Utilizing the economic centroid model and standard deviation ellipse, the trend of carbon emission centroid shifts in China’s transportation sector is examined, revealing that the carbon emission centroid for all four time points is located in Henan Province. Subsequently, focusing on Henan Province, ridge regression analysis is conducted to identify the driving factors influencing carbon emissions in the transportation sector from 2005 to 2020. Lastly, a combined approach integrating scenario analysis and the STIRPAT model is employed to forecast carbon emissions in the transportation sector of Henan Province for the period 2021–2035. The findings suggest that high-carbon-emission regions in China’s transportation sector gradually extend from the eastern coastal areas to the southwestern regions, with an overall trend of the carbon emission centroid shifting northward. The carbon emission centroid for the years 2005, 2010, 2015, and 2020 is consistently located in Henan Province. Ridge regression analysis indicates that population size, transportation energy consumption intensity, energy structure, transportation economic share, and per capita GDP all have promoting effects on carbon emissions in Henan Province’s transportation sector. Based on the combined approach of scenario analysis and the STIRPAT model, it is predicted that the transportation sector in Henan Province may reach its carbon peak between 2027 and 2029. These conclusions facilitate the formulation of region-specific emission reduction policies and measures tailored to the transportation sector. Full article

Natural gas represents a pivotal transitional clean energy resource, and biogenic coalbed methane (CBM) is ubiquitously distributed in coal reservoirs worldwide. In the context of carbon neutrality targets and the growing demand for large-scale commercial CBM exploitation, innovative technological solutions are urgently required. CBM bioengineering aims to substantially enhance CBM production by stimulating biomethane generation, promoting gas desorption, and improving reservoir permeability, while simultaneously enabling effective CO₂ sequestration. The potential for biomethane generation is largely governed by the intrinsic physicochemical characteristics of coal, including aromatic structures, maceral composition, and pore–fracture architecture. In addition, hydrogeological conditions—such as geothermal gradients, pH variability, and redox potential—play critical roles in regulating microbial functional gene expression and metabolic enzyme synthesis. Core pretreatment strategies in coalbed gas bioengineering can be broadly classified into approaches that enhance coal bioconversion potential and those that optimize functional microbial consortia. Electric fields and conductive materials can influence microbial community structure by enriching electroactive microorganisms and facilitating interspecies electron transfer. In addition to engineered conductive interventions, reservoir environmental conditions also play an important role in shaping methanogenic community structure. Experimental observations under reservoir-relevant CO₂ pressure and temperature conditions indicate that deep coalbed environments are associated with shifts in methanogenic community composition, including an increased relative abundance of hydrogenotrophic methanogens. These observations suggest that physicochemical conditions in deep coal seams may favor hydrogen-dependent CO₂ reduction pathways, thereby supporting hydrogenotrophic methanogenesis and contributing to biomethane generation. The integration of supercritical CO₂ with microbially acclimated stimulation fluids as an innovative reservoir fracturing strategy offers multiple advantages, including effective reservoir stimulation, permanent carbon sequestration, and sustainable biomethane generation. Future research should focus on modulating coal matrix bioavailability, optimizing microbial consortia, enhancing interspecies metabolic synergies, and advancing carbon fixation bioprocesses to facilitate the large-scale implementation of coalbed gas bioengineering systems. This review synthesizes recent advances in microbially mediated CBM enhancement and CO₂ sequestration, with a particular focus on field-scale evidence and the key challenges that must be addressed for large-scale implementation. Full article

Microbial volatile organic compounds (VOCs) mediate rhizosphere plant-microbe interactions, yet their integrated effects on plant microbiome assembly and host transcriptional regulation remain unresolved. Here we address this gap by investigating how two common VOCs, acetoin (AC) and 2,3-butanediol (BD), influence growth, rhizosphere communities, and root gene expression in the medicinal plant Pseudostellaria heterophylla using a split-pot system. Bacterial and fungal communities were monitored across three developmental stages via amplicon sequencing, alongside root transcriptome profiling during tuber enlargement. Contrasting with widely reported growth-promoting effects of microbial VOCs, both compounds significantly reduced tuber number and biomass. Bacterial communities remained taxonomically stable, shaped primarily by species replacement, with modest VOC responses but clear shifts across developmental stages. Fungal communities exhibited marked compositional restructuring and greater treatment sensitivity, particularly under BD. Neutral community modeling indicated predominantly stochastic bacterial assembly, while fungal assembly—especially under BD—showed stronger influence of deterministic processes. BD associated with broader transcriptional reprogramming than AC, including downregulation of photosynthesis, specialized metabolism, and defense pathways. Cross-omics network analysis revealed discriminant genera (e.g., Granulicella, Harposporium) that correlated strongly with host genes involved in stress response, development, and epigenetic regulation, with fungal taxa showing tighter associations with host expression than bacteria. Together, these findings establish a mechanistic framework for how microbial VOCs shape rhizosphere communities and host responses, with implications for microbiome-based strategies in medicinal plant cultivation. Full article

Large-scale pretrained foundation models are increasingly essential for affective analysis in user-generated videos. However, current approaches typically reuse generic multi-modal representations directly with task-specific adapters learned from scratch, and their performance is limited by the large affective domain gap and scarce emotion annotations. To address these issues, we introduce a novel paradigm that leverages auxiliary cross-modal priors to enhance unimodal emotion modeling, effectively exploiting modality-shared semantics and modality-specific inductive biases. Specifically, we propose a progressive prototype evolution framework that gradually transforms a neutral prototype into discriminative emotional representations through fine-grained cross-modal interactions with visual cues. The auxiliary prior serves as a structural constraint, reframing the adaptation challenge from a difficult domain shift problem into a more tractable prototype shift within the affective space. To ensure robust prototype construction and guided evolution, we further design category-aggregated prompting and bidirectional supervision mechanisms. Extensive experiments on VideoEmotion-8, Ekman-6, and MusicVideo-6 validate the superiority of our approach, achieving state-of-the-art results and demonstrating the effectiveness of leveraging auxiliary modality priors for foundation-model-based emotion recognition. Full article

In the context of China’s pursuit of high-quality economic development, enhancing agricultural productivity is crucial for ensuring food security and promoting common prosperity. This paper constructs a systematic IV-LP-ACF-SAR econometric framework to analyze agricultural Total Factor Productivity (TFP) growth using panel data from 31 Chinese provinces spanning 2014 to 2023 (n = 341 observations). The framework employs the instrumental variable (IV)-based Levinsohn–Petrin (LP) proxy variable method under the Ackerberg–Caves–Frazer (ACF) system to estimate a Translog production function while addressing endogeneity using multiple spatial weight matrices. TFP growth is decomposed into technical change (TC), technical efficiency (EC), and scale efficiency (SC). A Spatial Autoregressive (SAR) model with Dynamic Common Correlated Effects (DCCE) explores spatial spillover effects and regional heterogeneity. Results show that China’s agricultural TFP remained largely stagnant from 2014 to 2023 with an average annual growth rate of −0.18%, where technical efficiency decline (−0.33% annually) was the main constraint. Technical change remained neutral, while scale efficiency contributed positively (+0.15% annually). Mechanization showed the highest output elasticity (0.99), while fertilizers, pesticides, and labor exhibited negative marginal returns. Spatial analysis revealed significant negative scale efficiency spillovers with regional patterns of “scale synergy in the Northeast/Northwest” and “efficiency synergy in East/North China.” These findings suggest that productivity policy should shift toward a dual-driver model combining efficiency enhancement and optimal scaling, with differentiated regional policies and inter-provincial coordination mechanisms necessary to mitigate negative spillovers and enhance sustainable agricultural growth quality. Full article

Decarbonizing the transportation sector is critical for China to achieve its ambitious “dual-carbon” goals of peaking carbon dioxide emissions before 2030 and attaining carbon neutrality by 2060. Guided by the overarching philosophy of “ecological civilization,” this paper systematically reviews the historical evolution, technological pathways, and global implications of China’s transport transition. We analyze the institutional trajectory as governance shifts from early administrative mandates focused on energy conservation to a sophisticated, market-oriented framework incorporating carbon trading and green finance. The study identifies a synergistic technical pathway centered on the widespread adoption of new-energy vehicles (NEVs), the deep integration of renewable energy, and the deployment of intelligent transportation systems (ITSs) to enhance operational efficiency. Beyond domestic progress, the review highlights significant global spillover effects: China’s massive deployment scale and manufacturing capabilities have accelerated technological learning, driving down costs for batteries and clean technologies, thereby lowering adoption barriers worldwide. Furthermore, by reshaping green industrial value chains and actively engaging in global climate governance, China plays a pivotal role in fostering international technology diffusion. Ultimately, this review offers valuable insights into the complexity of systemic decarbonization, demonstrating how the coordination of policy guidance, technological innovation, and market mechanisms can advance sustainable development and effective emission reductions on a global scale. Full article

Under the framework of the Paris Agreement, carbon trading has emerged as a pivotal market-based instrument for achieving carbon neutrality. Following years of pilot programs, China has taken a critical step toward establishing a unified national carbon market. Consequently, accurate carbon price forecasting is essential for constructing a stable and effective carbon pricing mechanism. However, the 2017 reform of the EU Emissions Trading System (EU ETS) significantly altered the carbon price formation mechanism, exacerbating price volatility and uncertainty. This shift further underscores the urgent need for research into high-precision carbon price forecasting.Existing deep learning models struggle to simultaneously capture short-term high-frequency fluctuations and long-term evolutionary trends within complex carbon market data, a limitation that compromises their prediction accuracy and stability. To address these challenges, this paper proposes a Transformer-based carbon price forecasting model that incorporates an sLSTM structure. By enhancing sequence memory and state update mechanisms, this model effectively improves the capability to model both short-term volatility characteristics and long-term evolutionary patterns of carbon prices. In the data preprocessing phase, Variational Mode Decomposition (VMD) is employed to perform multi-scale decomposition of carbon price sequences, effectively mitigating the issue of overlapping fluctuations across different time scales. Furthermore, the Whale Optimization Algorithm (WOA) is utilized to optimize the number of decomposition modes and the penalty factor, thereby resolving the parameter sensitivity issues inherent in modal decomposition. Experimental results on real-world carbon price datasets demonstrate that the model achieves an average coefficient of determination ($R^2$) of 0.9862 and a Mean Absolute Percentage Error (MAPE) of only 0.5607%. These findings indicate that the proposed method possesses significant advantages in characterizing the complex dynamic features of time series, thereby effectively enhancing prediction accuracy.The proposed model can serve as a supportive tool for carbon-market risk monitoring and policy evaluation by identifying abnormal fluctuations and mitigating market inefficiencies caused by information asymmetry. This enhances the stability and predictability of carbon price signals as incentives for emissions reduction, enabling firms to plan abatement pathways and low-carbon investments, and strengthening the sustainable role of carbon markets in achieving carbon neutrality. Full article

Utilizing bamboo residues for the cultivation of Stropharia rugosoannulata is an ecological practice grounded in the concept of agricultural waste recycling, aiming to improve soil microecology and enhance nutrient cycling in bamboo forests. However, a comprehensive and systematic evaluation of the ecological effects of using bamboo residues as cultivation substrates is lacking. To evaluate soil responses following the cultivation of S. rugosoannulata, a field experiment was conducted using bamboo residues pre-fermented with 4% rapeseed cake. The results showed that cultivating S. rugosoannulata with rapeseed cake-fermented bamboo residues significantly enhanced soil nutrient levels and enzyme activities. Notable increases were observed in soil organic carbon, total nitrogen, available nitrogen, and total potassium, as well as in the activities of sucrase, urease, peroxidase, polyphenol oxidase, and neutral protease. Both bacterial and fungal α-diversity were significantly enhanced, and substantial shifts occurred in the community structure and composition of soil microbiota. Metabolomic analysis revealed that significantly differential metabolites were primarily enriched in five key pathways, including purine metabolism, glycerolipid metabolism, biosynthesis of plant secondary metabolites, and starch and sucrose metabolism. Correlation analyses further revealed that specific microbial taxa (four bacterial genera and seven fungal genera) exhibited strong correlations with soil nutrient indicators, whereas another group of taxa (six bacterial phyla and eight fungal genera) was closely linked to soil enzyme activities. Furthermore, bacterial communities were significantly correlated with metabolite variations after substrate addition. Specifically, Firmicutes showed strong positive correlations with multiple metabolites, whereas Planctomycetes exhibited negative correlations with some of the same metabolites, indicating potential competitive interactions. Based on these findings, this study proposes a preliminary “Microbe–Enzyme–Metabolite–Nutrient” coupling cycle, driven by the synergistic interplay among bamboo residues, hypha–microbiome complex, soil enzymes, and functional metabolites. This mechanism provides a scientific explanation for the soil health improvements observed during S. rugosoannulata cultivation and offers theoretical support for the efficient utilization of bamboo waste and maintenance of forest ecosystem stability. Full article

The large-scale integration of electric vehicles (EVs) introduces significant operational challenges for power systems, particularly when grid-favourable operating periods coincide with high marginal carbon emissions. This paper proposes a carbon-aware rolling-horizon energy management framework for EV fleets coordinated through virtual power plants (VPPs), explicitly addressing such carbon–grid conflict conditions. The proposed framework prioritises grid-friendly scheduling through power and ramp constraints while enforcing energy-service equivalence and a policy-level carbon budget consistent with carbon peak and carbon neutrality objectives. Carbon awareness is incorporated as a secondary steering term within the rolling-horizon optimisation, enabling temporal shifting of EV charging toward low-carbon periods without compromising grid stability. A Pareto-based trade-off analysis is conducted to characterise the relationship between grid stress mitigation and carbon reduction, and a knee point is identified to select a balanced operating regime. Simulation results using real EV charging demand combined with a conflict-driven carbon intensity signal demonstrate that grid-oriented scheduling alone can increase emissions under carbon–grid mismatch. In the evaluated conflict scenario, the proposed carbon-aware rolling-horizon strategy achieves a 17.35% reduction in total CO₂ emissions relative to RH-NoCarbon scheduling while maintaining peak–valley load variation below 11.03 kW compared with 43.65 kW under uncontrolled charging. These results confirm that explicit carbon-aware coordination can significantly mitigate emissions without compromising grid operational stability. All control strategies are evaluated in a simulation environment using real EV charging demand data as exogenous inputs, ensuring realistic demand representation while enabling controlled assessment of operational performance. These findings highlight the necessity of embedding carbon considerations directly into operational EV scheduling and establish VPP-based rolling-horizon coordination as a practical mechanism for low-carbon power system operation. Full article

Under the vision of carbon neutrality, the global energy system urgently requires storable, transportable, and tradable zero-carbon carriers. Iron, due to its high crustal abundance, low cost, environmentally friendly reaction products, and ease of closed-loop cycling, is being reconsidered as a potential “metallic energy” alternative to fossil fuels. This paper systematically reviews the conceptual evolution, scientific lineage, and paradigm shift logic of iron-based energy within the framework of dual pathways: combustion and electrochemistry. On the combustion front, a multi-level understanding has been established—ranging from microscopic reaction mechanisms to macroscopic flame propagation, and from unit combustors to diversified thermal power systems—laying a methodological foundation for an integrated “solid fuel–thermal–power” approach. In parallel, the electrochemical pathway has developed both liquid and solid routes, integrating energy storage, pollution control, and resource recovery within a single device through multi-valent redox reversibility, thereby expanding the concept of generalized energy storage under the “battery-as-factory” paradigm. Current research is shifting its focus from single performance metrics toward synergistic optimization of efficiency, lifespan, cost, safety, and environmental impact, marking a transition in technological paradigm from “material trial-and-error” to “mechanism design.” Looking forward, to advance iron energy beyond the experimental validation stage, it is imperative to establish a cross-scale, closed-loop scientific characterization system, develop recycling strategies with low entropy and low energy consumption, and deeply integrate with renewable electricity, hydrogen, and high-temperature heat sources to form spatiotemporally transferable zero-carbon energy systems. In this way, iron may integrate into global energy trade as a “metallic energy in specific scenarios like ports/islands,” offering a scalable, hydrocarbon-independent technological option for achieving carbon neutrality. Full article

Ecological network optimization can enhance ecological connectivity, regional ecological stability, and carbon sink capacity. Current research on ecological networks employs single-perspective optimization, which overlooks the synergistic requirements between network topological characteristics and the dual carbon goals. It lacks a comprehensive, systemic optimization framework. Focusing on the Beijing–Tianjin–Hebei region, the work constructs an ecological network by integrating ecosystem services, morphological spatial pattern analysis (MSPA), and circuit theory. A framework integrating barrier removal, structural robustness, and carbon sink enhancement is proposed, incorporating ecological barrier identification, complex network theory, and carbon offset patterns for multi-objective structural and functional optimization. The optimized network is evaluated using structural metrics, robustness analysis, and carbon sequestration validation. The network comprises 41 ecological sources and 102 corridors, exhibiting a dense northwest and sparse southeast distribution. Ecological barriers totaling 565.56 km² are removed to improve connectivity in the region. An edge-addition strategy introduces 12 nodes and 49 edges, enhancing connectivity, stability, and carbon sink capacity. Restoration priorities are set with the phased objectives of removing barriers, connecting topological weak points, and optimizing low-value carbon offset areas. Shifting the focus from structural connectivity to integrated function, the work contributes a methodological framework for advancing ecological security and carbon neutrality in urban agglomerations. Full article