Search Results (3,273)

This study focuses on the multi-dimensional uncertainties in synergistic pollution reduction and carbon mitigation pathways for the coal chemical industry in the Yellow River Basin, a region facing extreme water scarcity (only 2% of national water) and fragile ecology, and constructs a Multi-Level Uncertainty Conduction (MLUC) Model integrating data, modeling, and validation. Using 2011–2025 data, Monte Carlo (10,000) simulations quantify the impacts of policy, technology, market, and ecological uncertainties on synergistic benefits. Sobol’ global sensitivity (Saltelli) and Shapley decomposition (14 technologies) identify key drivers and technology contributions. A system dynamics model simulates 2023–2050 pathways under baseline, policy-enhanced, technology breakthrough, and composite uncertainty scenarios. Logarithmic Mean Divisia Index (LMDI-I) decomposition reveals a six-factor driving mechanism for carbon emission changes. Results show that policy uncertainty exerts the largest influence, with a variance contribution of approximately 35%, followed by technology (28%), market (22%), and ecological factors (15%)—the latter primarily reflecting water availability and regional ecological carrying capacity. Critical thresholds are 80 CNY/t CO₂ for carbon capture, utilization and storage (CCUS) viability, and 6.8 CNY/t for green hydrogen substitution. Comprehensive resource utilization is optimal near term, while green hydrogen substitution and CCUS–green hydrogen coupling dominate medium- to- long term. The proposed dynamic threshold response mechanism and technology portfolio strategy could boost synergistic benefits by 22% to 35%. These findings underscore the need for watershed-scale collaborative governance and integrated water–carbon–energy management to ensure robust mitigation under the basin’s constraints. Full article

Against the background of China’s “dual carbon” goals and the continued advancement of the green and low-carbon transformation of resource-based industries, resource-based enterprises urgently need to rely on green innovation to overcome development constraints characterized by high resource dependence, strong environmental pressures, and mounting transformation challenges. Patient capital, with its long-term orientation, stable support, and risk-sharing characteristics, can provide sustained financial backing and governance support for green innovation in resource-based enterprises; however, its underlying mechanism remains to be further explored. Drawing on patient capital theory, this study constructs a “capital–ESG–innovation” analytical framework to examine the impact of patient capital on green innovation in resource-based enterprises and its mechanism of action. Using Chinese A-share listed resource-based enterprises from 2014 to 2023 as the research sample, this study measures patient capital from two dimensions, namely stable equity and relational debt, and conducts empirical analysis through panel regression and multiple robustness tests. The results show that patient capital significantly promotes green innovation in resource-based enterprises, with both relational debt and stable equity playing positive roles. Mechanism tests reveal that ESG performance serves as an important mediating channel through which patient capital promotes green innovation. Further analysis indicates that the level of regional marketization strengthens the green innovation effect of patient capital, and this effect is more pronounced in large enterprises, enterprises subject to stronger media supervision, and enterprises whose executives have higher green cognition. This study enriches the literature on the relationship between patient capital and green innovation and provides empirical evidence for cultivating long-term capital and promoting the green and low-carbon transformation of resource-based enterprises. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

A carbon market is often expected to deliver a stable allowance price, continuous market liquidity and a rigidly binding abatement trajectory, yet these objectives may not be jointly attainable. This paper proposes a sustainability trilemma of carbon markets, arguing that at most two of price stability, market liquidity and abatement rigidity can be achieved simultaneously. We formalise this trade-off in a simple equilibrium framework and examine it using daily price and volume data from China’s national thermal-power emissions trading scheme from 2021 to 2025, together with energy-sector fundamentals proxied by thermal-power generation and power-sector coal consumption. The evidence shows that market liquidity improved substantially over the sample period, while price stability weakened markedly: median daily trading volume rose from near-dormant levels in 2022 to close to one million allowances in 2025, whereas the intra-year price range expanded sharply. Meanwhile, allowance-price changes remained weakly correlated with coal-consumption changes, suggesting that prices were driven more by allocation policy and expectations than by realised emission scarcity. The findings imply that carbon-market design should not pursue an unattainable optimum, but should manage the trade-off among stability, liquidity and abatement rigidity according to policy priorities. Full article

Nowadays, for energy-based targets, investigations on the thermal characteristics of building materials are becoming increasingly common. Foam concrete is one of them. Foam concrete, which is already a very popular building material in terms of thermal insulation, needs to simultaneously improve its mechanical and thermal characteristics. Therefore, in the present study, we address the effects on foam mortars of blended cements containing zeolites. The replacement ratios of blended cements containing two different zeolites were 0, 10, 30, and 50%. This study aims to encourage the use of alternative additives to achieve objectives such as sustainability, energy efficiency and lower carbon emissions and to obtain optimum design data for the foam concrete market. The parameters examined in 28-day-old samples were basic physical characteristics, water absorption, ultrasonic pulse velocity (UPV), compressive strength, thermal characteristics and microstructure analysis. Based on the test results, for foam mortars containing blended cement with analcime and clinoptilolite, a 10% replacement ratio is optimal in terms of strength, whereas a 30% ratio is required for a significant improvement in thermal insulation. The foam mortars with a 10% analcime replacement ratio demonstrated the highest specific heat capacity. Full article

Carbon pricing is central to China’s low-carbon transition, and its effectiveness is tied to the carbon market’s links with commodities. This paper examines state-dependent return connectedness between China’s national carbon emission allowance (CEA) market and 20 representative commodity futures. Using daily data from July 2021 to February 2026, we combined quantile vector autoregression (QVAR) connectedness, the Baruník–Křehlík frequency decomposition, and wavelet-based coherence and quantile-based correlation methods to characterize return transmission across market states and frequencies. We obtained four findings. First, total connectedness is almost identical at the lower and upper tails (around 92%) and far higher than at the median (around 59%)—tail symmetry with median heterogeneity—and is dominated by the short-term band. Second, the CEA is largely decoupled from the commodity system under normal conditions and is drawn in only at the tails as a net receiver. Third, the two tails exhibit distinct event contexts, with downside episodes associated with external financial shocks and upside episodes associated with domestic policy expectations. Fourth, the CEA tends to precede high-emission commodities at long horizons. These results suggest that institutional and policy factors continue to play an important role in shaping CEA price dynamics, with implications for carbon market regulation and cross-market hedging. Full article

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

Beyond nutrition, taste quality is a key quality trait driving the global popularity of Agaricus bisporus. This study systematically investigated non-volatile taste-related metabolite dynamics in caps and stipes during fruiting body development using non-targeted metabolomics. Among 1358 identified metabolites (974 in caps, 997 in stipes), 328 taste-related metabolites were screened. Applying screening criteria of VIP > 1, p < 0.01, and fold change ≥ 2 or ≤ 0.5, 492 and 446 differentially accumulated metabolites (DAMs) were identified in cap and stipe during fruiting body development, respectively. Cross-tissue comparison revealed 975 tissue-specific DAMs between cap and stipe across all developmental stages. Notably, 127 and 116 taste-related DAMs in cap and stipe, respectively, exhibited seven distinct accumulation profiles. Key umami-related compounds, aroma precursors, and antioxidants peaked in cap tissue at stage 3 (closed cup stage), suggesting a preliminary optimal harvest timing for market-quality mushrooms based on metabolic profiling of non-volatile taste-active compounds. Organic acids and nucleotides were more abundant in immature stages, while phosphorylated six-carbon sugars showed stipe-dominant accumulation at middle–late stages. Notably, all taste-related conclusions are inferred from non-volatile metabolite characterization rather than direct sensory measurements. KEGG pathway enrichment highlighted that taste-related metabolites primarily shaped taste via amino acid biosynthesis, cofactor metabolism, lysine biosynthesis, and nucleotide pathways. These insights provide a metabolic foundation for optimizing cultivation strategies and enhancing product quality in Agaricus bisporus. Full article

Vehicles with electrified powertrains carry the promise of significant reductions in greenhouse gas (GHG) emissions from a lifecycle analysis (LCA) standpoint compared to conventional internal combustion engine (CICE) vehicles. However, trade-offs exist between different types of electrified powertrains in terms of cost, consumer acceptance, and GHG reduction efficacy for different operating conditions. The open-source tool CarGHG was developed with an aim to enable the exploration of a plethora of parametric study scenarios, including the cost of electrification technologies, different driving patterns and charging habits, and the cost and carbon intensity of electricity and fuel blends. This paper introduces the framework of CarGHG, then showcases total cost of ownership (TCO) and LCA GHG results for select models of light-duty vehicles. Another capability of CarGHG, which is the ability to estimate the performance of “virtual” vehicle models (perceived vehicle design specifications not yet on the market), is utilized to explore future scenarios of electrification and low-carbon fuel blends for Small Sports Utility Vehicles (SUVs), a popular light-duty vehicle segment in North America. With opportunities, but also uncertainties, in future scenarios, it is likely wise to continue pursuing multiple ways towards the reduction of LCA GHG. Full article

Under the background of the steady advancement of the dual-carbon goal and the increasing improvement of the green financial system, green supply chain finance is like a bridge that closely links the capital of the financial market and the low-carbon transformation of the real economy. The following article chooses A-shares traded enterprises from 2014 to 2024 as the study sample, adopts multi-dimensional empirical methods to study the association in green supply chain finance along with corporate emission levels, and analyzes its transmission mechanisms and heterogeneity. The findings demonstrate that green supply chain finance has a substantial inhibitory impact with enterprise emission levels, a finding that remains robust across a series of tests, including parallel trend tests, placebo tests, and propensity score matching (PSM). Mechanism analysis demonstrates that green supply chain finance can indirectly reduce carbon emission intensity by improving both financing levels and supply chain stability. Looking at heterogeneity, we find that the emission-reducing effect tends to be stronger among state-owned firms, non-heavy polluters, enterprises with higher total factor productivity, and enterprises that are more financially oriented. Our theoretical value lies in clarifying the direct relationship between green supply chain finance and micro-enterprise carbon emissions, identifying two differentiated intermediary transmission paths, and defining the boundary conditions of the policy role across multiple dimensions, thereby better coordinating and promoting the digital and low-carbon transformation of enterprises. Full article

Nickel has become a strategic mineral for the energy transition, yet its supply chain is increasingly shaped by a compound risk regime involving resource nationalism, processing concentration, geopolitical compliance rules, carbon-footprint requirements, and commodity-market volatility. This study develops NiChain-LLM-ABM, a large-language-model-empowered agent-based model for simulating nickel supply chain resilience under semantically rich policy shocks. The framework uses a policy semantic parsing module to transform official policy texts into structured shock parameters, a multi-agent strategy generation module to represent adaptive decisions by seven agent classes, a calibrated supply chain network module to simulate material, financial, and information flows, and a four-dimensional resilience assessment module. The model is anchored in observed nickel production, price, trade, and technology data from USGS, IEA, UN Comtrade, LME, and official legal sources, and its scenario outputs are generated through 100 Monte Carlo replications over 2025–2035. Results show that the baseline Comprehensive Resilience Index (CRI) declines from 0.620 in 2025 to 0.547 in 2035. Indonesian policy tightening causes the sharpest near-term deterioration, with CRI falling to 0.445 in 2028 and the simulated supply deficit reaching 24.5 kt Ni equivalent. A geopolitical compliance shock produces the lowest terminal resilience (CRI = 0.472 in 2035). A green-compliance scenario is disruptive in the short run but exceeds the baseline by 2035, while a coordinated policy portfolio raises the terminal CRI to 0.744, a 36.0% improvement over the baseline. Compared with a conventional rule-based ABM, the LLM-ABM reduces extreme-event backcasting error by 57%, improves policy-response fidelity by 53%, and more than doubles agent heterogeneity differentiation. The results support portfolio-based critical-mineral governance combining strategic reserves, overseas equity investment, recycling, technology substitution, and international cooperation. Full article

This study provides a structured operational-to-financial roadmap for soil organic carbon (SOC) sequestration in mineral soils as a specific carbon-farming pathway. It integrates SOC management; Monitoring, Reporting, and Verification (MRV) execution; financial recognition; and farmer adoption barriers. A comparison of carbon farming pathways is first presented to investigate their strengths and limitations, highlighting the specific importance of SOC management in mineral soils. For high-integrity carbon accounting, SOC gains should be assessed not only for quantity, but also for additionality, permanence, uncertainty, leakage, lifecycle emissions, and transparent verification. Credible MRV frameworks operationalize this logic: monitoring quantifies SOC changes, reporting ensures transparency, and verification provides independent assurance for carbon credit issuance and financial recognition. However, MRV execution faces several challenges, including high spatial variability of SOC, slow accumulation rates, methodological uncertainty, and high costs that limit scalability and reduce trust among stakeholders. Financial incentives are available from both public and private sources, supporting long-term soil carbon stabilization, verified carbon removals, and corporate insetting projects. Yet, adoption remains constrained by uncertain payments, poor transparency, contract and permanence concerns, as well as learning and operational costs for farmers. Addressing these bottlenecks is essential for transforming mineral-soil SOC sequestration into a scalable, high-integrity climate and economic opportunity. Full article

Stable interactions among electricity, carbon allowance, and fossil fuel markets are essential for sustainable energy transition, because excessive cross-market risk transmission may affect energy affordability, carbon-price credibility, and low-carbon investment signals. This study provides comparative evidence on dynamic connectedness, tail-state shock responses, and return-based complexity in representative Chinese and European benchmark markets. Using daily market data from the Wind database for November 2021–January 2026, the empirical framework combines time-varying parameter vector autoregression (TVP-VAR), quantile vector autoregression and quantile impulse response functions (QVAR/QIRFs), and rolling multifractal detrended fluctuation analysis (MFDFA). The results show that the European benchmark system has a higher absolute connectedness level than the Chinese benchmark system: the full-sample mean total connectedness index (TCI) is 18.75 in Europe and 5.63 in China, while the crisis-period mean TCIs are 25.19 and 12.12, respectively. Post-peak adjustment depends on the reversion metric used: China shows a faster initial half-life decline from the crisis peak, whereas reversion to lower region-specific connectedness thresholds depends on the selected benchmark. Natural-gas-shock QIRFs indicate stronger upper-tail persistence in Europe, whereas China is characterized mainly by short-run directional divergence; supplementary coal-, oil-, and carbon-shock checks show that response patterns are shock-source-dependent. Electricity-return multifractal spectrum width (MFW) does not show stable full-sample explanatory power for TCI, but it provides stage-dependent auxiliary diagnostic information. These findings provide a comparative diagnostic framework for monitoring cross-market systemic risk and supporting sustainability-oriented energy-market governance under low-carbon transition. Full article

Carbon emission reduction decisions are subject to risks for shipping carriers. These include policy uncertainty (an upcoming policy may be stringent or lenient) and cost uncertainty (the operation cost may increase or decrease in the future). This paper develops a two-period game model to study the carbon emission reduction strategy choices of two risk-averse shipping carriers facing both policy uncertainty and cost uncertainty, with the goal of advancing sustainable maritime transport. They can choose a high- or low-carbon emission reduction strategy in period 1. Whether they need to upgrade in period 2 depends on the strategy they choose in period 1 and the policy implemented in period 2. The results show that in a deterministic environment, a high-cost strategy translates directly into a high-price strategy. However, in period 2, when the policy is lenient, adopting a high-carbon emission reduction strategy does not always result in a higher price than adopting a low-carbon emission reduction strategy. This result is counterintuitive. In addition, the carrier adopting a high-carbon emission reduction strategy does not necessarily set a higher price than the competitor who adopts a low-carbon emission reduction strategy. The market share plays an important role in shaping the equilibrium. When the possibility of a stringent policy is extremely low or extremely high, both carriers will choose an identical strategy. However, when the possibility is medium, they will choose differentiated strategies. The carrier with a bigger market share can tolerate a higher possibility of an upcoming stringent policy than the competitor. The degree of cost volatility also has a significant impact on the equilibrium. Its influence is particularly pronounced under a moderate probability of a stringent policy. Shippers’ carbon emission sensitivity also has a positive effect on encouraging carriers to choose a greener strategy. Our findings provide actionable insights for policymakers and industry stakeholders to facilitate the sustainability transition of the shipping sector through appropriate policy design. Full article

At present, photovoltaic (PV) systems are becoming the core of low-carbon power systems, but their large-scale integration is still limited by weather-driven intermittency, heterogeneous data, equipment failures, operational uncertainty, and life-cycle sustainability requirements. Unlike specific task reviews that only focus on photovoltaic forecasting, fault diagnosis, or general artificial intelligence applications in renewable energy, this review develops an integrated data-driven perspective for machine learning and artificial intelligence in photovoltaic power generation systems. It links data governance, feature engineering, prediction, and uncertainty quantification, fault diagnosis and predictive maintenance, energy management, market participation, and carbon-aware optimization within a framework for photovoltaic systems. This review indicates that traditional machine learning, deep learning, graph learning, reinforcement learning, generative artificial intelligence, and physics-based artificial intelligence are suitable for different photovoltaic tasks based on data structure, time range, operational constraints, and deployment maturity. The main contribution is cross-task integration, which links the output of artificial intelligence models, including scheduling, storage scheduling, maintenance planning, virtual power plant operation, and low-carbon management, with actual decision-making. The review further identified the most critical deployment barriers, such as incomplete benchmarks, weak cross-site generalization, insufficient uncertainty calibration, limited interpretability, network security risks, and computational costs. The resulting methodological approach emphasizes data management, uncertainty awareness, physical constraints, decision orientation, and sustainability-driven photovoltaic intelligence. Full article