Search Results (2,356)

To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation. Full article

Climate risks drive dynamic adjustments to global climate policies, creating significant climate policy uncertainty (CPU). This uncertainty profoundly affects enterprises’ survival and sustainable development. A key question emerges: how do enterprises’ perception of CPU influence their resilience? To answer this, this study adopts a capability perspective and empirically examines the impact of climate policy uncertainty perception (CPUP) on enterprise resilience (RESI) and the underlying mechanism. Using panel data on Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2023, the study defines CPUP as the interaction between a news-based provincial CPU index and the frequency of climate risk words in annual report texts, and measures RESI with the entropy weight method across four dimensions (business volatility, long-term growth, short-term performance, and enterprise survival). Panel regression with fixed effects indicates that CPUP significantly enhances RESI. A one-standard-deviation increase in CPUP raises RESI by approximately 0.0019 index units, equivalent to about 2.2% of the standard deviation of RESI. This effect is more pronounced for enterprises in the eastern and central regions and in high-carbon industries. Mechanism tests confirm that CPUP boosts RESI by optimizing management capabilities and strengthening development capabilities, revealing a capability-driven path between CPUP and RESI. This study enriches the theoretical understanding of CPU’s economic consequences and RESI antecedents from a capability perspective. It also provides empirical references for enterprises to build resilience amid policy fluctuations and for policymakers to formulate regionally differentiated climate policies. Full article

Against the backdrop of China’s 14th Five-Year Plan, digital economy strategy, and dual-carbon goals, this study draws on panel data from Chinese A-share-listed firms over 2008–2024 to construct a provincial digital intelligence (DI) index using the entropy-weighting method, measure corporate green total factor productivity (GTFP) using the SBM–GML model, and examine the effect of DI on GTFP and the mechanisms underlying this effect. The results show that regional DI is significantly and positively associated with GTFP, and this association remains robust across alternative specifications and endogeneity treatments. Mechanism tests indicate that lower financing constraints and lower ownership concentration may serve as potential channels linking DI to GTFP. Human capital strengthens this positive effect, whereas total asset turnover weakens it. Heterogeneity analysis further shows that the productivity gains from DI are more pronounced among large firms. Although subgroup estimates are larger for firms without executives who have overseas experience, the between-group difference is not statistically robust and is therefore interpreted as exploratory rather than causal. These findings highlight the importance of integrating DI with green transformation, improving firms’ access to finance and human capital, and adopting differentiated support policies. Full article

Against China’s dual carbon peaking and carbon neutrality strategic goals, nationwide digital transformation brings both carbon abatement dividends and potential energy rebound risks, and its full low-carbon potential is constrained by local industrial foundations and regional economic development stages. Most existing studies merely treat industrial upgrading as an intermediate transmission channel, with little discussion of its moderating influence. Moreover, few threshold analyses take the comprehensive level of regional economic development as the core threshold variable to capture the boundary conditions of digital decarbonization effects. Based on balanced panel data covering 30 provincial-level regions of China from 2011 to 2023, this paper constructs a multi-dimensional digital economy index via the entropy weight method. Prior to formal regression, we conduct Pearson correlation analysis and mean-centered VIF multicollinearity diagnostics to avoid biased estimation. Two-way fixed-effects regression, moderation tests, Bootstrap-based regional heterogeneity comparison and Hansen’s single threshold model are adopted for empirical analysis. The results show that digital economy development significantly curbs carbon emission intensity; a one-standard-deviation increase in the digital economy composite index is associated with an approximately 9.7% decline in carbon emission intensity. The mean-centered interaction term DIG × UIS is significantly negative at the 1% level, proving that service-oriented industrial upgrading strengthens the carbon reduction effect of digitalization. The mitigation effect displays distinct spatial divergence: the estimated coefficient equals −2.638 for eastern provinces, −3.585 for central regions and −1.700 for western areas. Bootstrap inter-group coefficient tests confirm statistically significant gaps between east–west and central–western subgroups. Threshold regression identifies a single threshold of logarithmic per capita GDP at 11.94. After crossing this economic development threshold, the inhibitory coefficient of the digital economy rises markedly from −0.844 to −1.473. This study enriches the theoretical system of digital low-carbon transition by jointly uncovering the moderating role of industrial upgrading and the stage threshold constraint of economic development and offers differentiated digital low-carbon policy guidance for provincial governments. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article

Climate risk has become a defining challenge for businesses, influencing strategic decision-making, organisational resilience, and long-term performance. Despite the rapid growth of research in this area, the intellectual development and thematic evolution of climate-related corporate studies remain fragmented. This study provides a comprehensive synthesis of the literature through a bibliometric analysis and systematic review of 643 Scopus-indexed, peer-reviewed articles published between 1993 and 2025, with a systematic thematic synthesis of 23 empirical studies. Using Biblioshiny and VOSviewer, science-mapping techniques, including co-citation analysis and bibliographic coupling, were employed to examine publication trends, intellectual foundations, and major research themes. The findings indicate a shift from environmental measurement and compliance toward climate-risk management, carbon disclosure, and sustainable finance. Financial outcomes are heterogeneous, and context-dependent carbon exposure is generally associated with valuation penalties and downside risk, while the relevance of disclosure and climate strategies depends on credibility, substantive implementation, and organisational and institutional conditions. The integrated review shows that the financial implications of climate-related corporate actions are contingent upon climate-risk exposure, disclosure credibility, organisational capabilities, and institutional context. It further explains the coexistence of mixed empirical findings and identifies priorities for future research and policy. The findings offer valuable implications for researchers, corporate managers, investors, and policymakers seeking to strengthen sustainable business practices under an evolving climate risk landscape. Full article

Ag-Ti₃SiC₂ composites are promising electrical contact materials, yet the atomic-scale interfacial behaviour between Ti₃SiC₂ and Ag remains poorly understood. Here, first-principles calculations were performed to investigate the interfacial stability, electronic structure, and alloying effects at the Ti₃SiC₂(0001)/Ag(111) interface. Surface-energy calculations for six terminations of Ti₃SiC₂(0001) show that the TiC(TiC) termination is preferred at low carbon chemical potential, whereas the TiC(TiSi) termination becomes the most stable once ΔμC exceeds −1.50 eV. Eighteen interface models combining the six terminations with three stacking sequences (OT, MT, and HCP) were constructed, and their work of adhesion (Wad) and equilibrium spacing (d₀) were determined by the Universal Binding Energy Relation and full structural relaxation. The HCP stacking is preferred for all terminations, and the C(TiC)-terminated HCP interface exhibits the highest work of adhesion among all configurations, with Wad = 9.25 J/m² at d₀ = 1.2 Å; relaxation enhances Wad by 10%–75%. Charge density, charge density difference, and partial density of states analyses reveal that the interfacial bonding is dominated by C 2p-Ag 4d hybridization accompanied by electron transfer from Ag and Ti atoms to the interfacial C atoms, which accounts for the adhesion hierarchy. Substitutional alloying with Cu, Ni, Zn, and Cr preferentially segregates into the interfacial Ag layer, where the defect formation energies, although positive, are the lowest, and Wad increases in the order Cu < Zn < Ni < Cr, reaching 11.0 J/m² for interfacial Cr, an enhancement of 19% over the pristine interface. The strengthening correlates directly with the filling of the dopant 3d band. These results provide theoretical guidance for the interfacial design of high-performance Ag-Ti₃SiC₂ electrical contact composites. Full article

Industrial steel property prediction is often constrained by limited labelled data, reducing the effectiveness of conventional machine learning models. This study investigated whether metallurgy-informed feature engineering enhances predictive performance under small-data conditions. A representative set of 300 samples from an industrial low-carbon hot-rolled steel strip dataset (C: 0.02–0.06 wt%; Mn: 0.17–0.38 wt%) was used to derive five physically meaningful descriptors: carbon equivalent (CE), nitrogen-to-aluminum ratio (N/Al), microalloying efficiency index (MEI), thermal processing parameter (TPP), and solid solution strengthening index (SSSI). These descriptors were combined with the original 17 compositional and processing variables to create a 22-feature dataset. Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models were evaluated on an independent 60-sample test set using 5-fold cross-validation. Feature engineering improved the prediction accuracy, with the greatest gain observed for elongation. For XGBoost, the mean percentage error decreased from 3.23% to 3.05%, whereas the test-set R² increased from 0.4935 to 0.5444, representing a 10.3% improvement in the explained variance. For the yield strength, the Random Forest method increased the R² from 0.4744 to 0.4861. Permutation importance and partial dependence analyses identified MEI and TPP as the six most influential predictors across all targets, confirming that the engineered descriptors provide complementary metallurgical information. Learning curve analysis showed slightly higher cross-validation R² values at intermediate training sizes (n = 125–175), indicating modestly improved sample efficiency. These findings establish domain-informed feature engineering as an interpretable and practical strategy for improving machine learning in data-limited steel manufacturing processes. Full article

China’s pursuit of its dual-carbon targets amid rapid artificial intelligence (AI) development raises an important question: do the environmental implications of AI innovation change as regional innovation advances? Using a balanced panel of 30 Chinese provinces from 2011 to 2024, this study measured patent-based AI innovation intensity using applications identified through a strict AI patent classification. Linear and quadratic models with province and year fixed effects were estimated, and the Lind–Mehlum test was used to assess the shape of the relationship within the observed range. The preferred specification indicates an inverted-U-shaped association: carbon emission intensity initially increases with patent-based AI innovation but declines beyond an interior turning point. The negative quadratic coefficient remains stable when the emissions data source, patent classification, sample period, treatment of outliers, and timing of the AI terms are varied. Supplementary Bartik and copula-control analyses preserve the negative curvature, although their identification limitations preclude a definitive causal interpretation. A Kaya-based exact decomposition shows that the estimated curvature is concentrated in energy intensity rather than the carbonization factor. Human capital strengthens the estimated concavity, while the clearest regional contrast is observed between central and eastern China, with the strongest curvature in the central provinces. These findings suggest that greater AI patenting does not automatically reduce emissions. Its environmental implications depend on the stage of regional innovation and its interaction with energy efficiency and absorptive capacity. Policies promoting AI innovation should therefore be coordinated with cleaner energy supply, efficiency improvements, and human capital investment. More broadly, the study provides a stage-sensitive basis for evaluating the sustainability implications of patent-based AI innovation through measurable changes in carbon emission intensity. Full article

Biochar has traditionally been investigated as a soil amendment for improving fertility, carbon sequestration, and nutrient retention. However, recent advances in fine milling, colloidal stabilization, and nanotechnology have enabled the development of biochar-derived materials for foliar application. Unlike conventional soil application, foliar delivery enables direct interaction with leaf tissues, potentially providing faster physiological responses, improved resource-use efficiency, and complementary functions to existing plant biostimulants. This review critically evaluates the scientific basis, agronomic performance, and regulatory implications of foliar biochar applications across diverse crop systems. We synthesize and compare major formulation types, including finely milled suspensions, aqueous extracts, nano-biochar dispersions, and biochar-based composite carriers, based on their formulation characteristics, application methods, and reported biological effects. Across multiple crops, foliar biochar has been associated with enhanced chlorophyll content, improved gas exchange, strengthened antioxidant systems, better osmotic adjustment, and increased nutrient uptake, particularly under abiotic stresses such as salinity, drought, and heat. Mechanistically, these responses are linked to surface deposition effects, redox-active functional groups, modulation of leaf microclimate, and delivery of soluble bioactive compounds. Nevertheless, outcomes remain highly context-dependent, influenced by feedstock origin, pyrolysis conditions, particle size, formulation chemistry, dose, and crop species. Potential risks including phytotoxicity, nanoparticle exposure, environmental fate, and regulatory ambiguity especially for nano-scale formulations pose additional challenges for large-scale adoption. By integrating physiological, agronomic, environmental, and legislative perspectives, this review also highlights key barriers to commercialization, including formulation stability, limited field-scale validation, environmental safety, and regulatory uncertainty, while identifying research priorities needed to determine whether foliar biochar can become a scalable and scientifically validated biostimulant for sustainable agriculture. Full article

Titanium alloys are widely used for implants, yet corrosion, bacterial colonization and incomplete osseointegration remain important causes of interfacial failure. This review critically analyzes major biomedical functional coating fabrication techniques employed to enhance the surface properties of titanium alloys, including micro-arc oxidation, anodic oxidation, magnetron sputtering, electrochemical deposition, electrophoretic deposition, plasma spraying, physical vapor deposition, plasma immersion ion implantation, laser surface treatment, and hybrid (composite) approaches. For each method, key operational principles, structural and functional characteristics, performance advantages and limitations, and representative application domains are critically analyzed. Across these routes, biological performance depends on coating continuity, pore or nanotube geometry, interfacial bonding, phase composition and ion release. Calcium- and phosphorus-rich oxides and hydroxyapatite deposits generally promote cell adhesion, proliferation, alkaline phosphatase activity, mineralization and osteogenic differentiation. Dense oxide, nitride, tantalum and carbon-based films strengthen corrosion barriers, whereas Mn, Zn, Cu and Ag containing surfaces can inhibit bacterial adhesion and biofilm formation. Excessive ion release, however, may compromise cytocompatibility. Reported outcomes also vary with test medium, exposure time, bacterial strain and cell model. Standardized quantitative endpoints and longer-term corrosion, biofilm and osseointegration studies are required to guide clinically reliable multifunctional coatings. Full article

Enhancing the resilience of renewable energy enterprises is crucial for advancing the energy revolution and building a clean, low-carbon, safe, and efficient energy system. Against the background of an accelerating energy transition and rising external uncertainty, renewable energy enterprises face multiple challenges, including technological iteration, policy adjustments, and market fluctuations. Thus, enhancing enterprise resilience through digital transformation has become a key issue for promoting high-quality development in the energy industry. Using a sample of Chinese A-share listed renewable energy companies covering the period 2014–2024, this study empirically examines the impact of digital transformation on the resilience of these enterprises and the underlying mechanisms. The results show that digital transformation can significantly enhance enterprise resilience, a finding that remains robust after a series of robustness tests. Mechanism analysis indicates that digital transformation enhances resilience mainly by alleviating financing constraints, optimizing resource allocation, and improving innovation quality. Heterogeneity analysis further reveals that the resilience-enhancing effect of digital transformation varies across enterprises of different sizes and life-cycle stages. This paper provides empirical evidence that renewable energy enterprises can rely on digital technologies to strengthen their risk resilience and improve their sustainable development. Full article

This paper examines how the body of research and innovation on electric cooking for low- and middle-income countries has evolved to the extent that electric cooking can now be argued to have the potential to influence energy system performance. Methods: The paper synthesises recent evidence on electric cooking from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly argued to be acting as a system-strengthening source of demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption can improve utility and mini-grid viability and support further investment, and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which has great potential to improve load factors and align demand with generation. However, a persistent planning blind spot remains, with growth in electric cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward tangible system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should increasingly be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential. Full article

Promoting urban green and low-carbon transformation is essential for achieving carbon peaking and carbon neutrality, yet cities continue to face financing constraints, insufficient project identification, and weak incentives for green innovation. This study treats China’s climate investment and financing pilot program as a quasi-natural experiment. It uses panel data for 242 prefecture-level and above cities from 2016 to 2023 to estimate its effect on urban green transformation with a difference-in-differences (DID) specification. The results show that the pilot significantly promotes urban green transformation, and the policy effect is stronger in coastal cities, large and medium-sized cities, major urban agglomerations, and non-resource-based cities. Mechanism analysis shows that the pilot promotes urban green transformation by increasing local governments’ focus on carbon reduction, advancing green finance development, and stimulating green technological innovation. These findings support improving pilot evaluation and implementation, strengthening climate-project development and green financial instruments, and tailoring policy support to local conditions. Full article

Against the background of global production-network restructuring, low-carbon transition, and rising external uncertainty, this study examines the effect of green infrastructure investment on urban industrial chain resilience. Using panel data for 285 Chinese prefecture-level cities from 2012 to 2024, industrial chain resilience is measured from the dimensions of industrial diversification and urban innovation capacity. Double machine learning is employed for baseline estimation, supplemented by mediation analysis, threshold regression, spatial econometric analysis, and a series of robustness tests. The results show that green infrastructure investment significantly enhances industrial chain resilience, and the finding remains robust to alternative model specifications, cross-fitting settings, generalized propensity score weighting, continuous-treatment entropy balancing, winsorization, and the exclusion of pandemic-period observations. Resource allocation efficiency plays a partial mediating role in this relationship. The threshold analysis identifies a significant nonlinear effect associated with energy consumption intensity, with the positive effect of green infrastructure investment being stronger below the estimated threshold and weakening above it. Spatial analysis further shows significant spatial dependence in both green infrastructure investment and industrial chain resilience, together with positive spillover effects on neighboring cities. These findings highlight the importance of improving green infrastructure investment efficiency, strengthening factor allocation, and promoting regional coordination in enhancing urban industrial chain resilience. Full article