Search Results (1,642)

Using Portuguese Food Balance Sheet data for 1990–2024, this study examines whether Mediterranean-classified food availability is associated with an annualised per-capita weighted food-related carbon-pressure proxy (WFCPP). A dynamic HAC–Newey–West model (1998–2024; 27 observations) separates dietary composition from analytical total caloric availability through orthogonalisation, while an exploratory ARDL–UECM assesses conditional level relationships. Because the WFCPP, Mediterranean dietary component, and caloric-availability control share food-group series, orthogonalisation reparameterises rather than removes their structural dependence. The baseline Mediterranean coefficient is negative (β = −0.467; p = 0.018) but becomes insignificant under HC3 inference, exclusion of 2012–2013, the Mediterranean Adequacy Index, and the per-calorie WFCPI; all block-bootstrap 95% intervals contain zero. In the selected ARDL, Mediterranean coefficients are insignificant in both the short and long run. Accordingly, H1 is not robustly supported, and H2 is unsupported; no robust Mediterranean-specific negative association is confirmed at either horizon. The analysis concerns apparent food availability and fixed dimensionless weights, not actual consumption, causal effects, or physical greenhouse-gas emissions. Full article

The building sector faces an urgent challenge in balancing carbon neutrality goals with natural resource conservation. This study constructs a three-dimensional dynamic coupling model integrating Life Cycle Assessment, carbon footprint, and emergy analysis to evaluate the sustainability of a hydrogen–solar–geothermal hybrid energy system for an ecological office building in China’s hot summer and cold winter climate zone over a twenty-year horizon. The model incorporates dynamic factors including grid decarbonization, equipment efficiency degradation, and replacement cycles to overcome the systematic bias inherent in static LCA. Results reveal a significant trade-off: the hybrid system achieves a 29.8% reduction in global warming potential with a seven-year carbon payback period, yet non-renewable resource consumption doubles and resource scarcity damage increases by 173%. The carbon payback trajectory exhibits non-monotonic fluctuation, with electrolyzer replacement in year ten generating 360 tonnes of additional emissions that nearly reset the cumulative net value to zero. Multi-objective optimization identifies photovoltaic capacity as the system baseline (170–210 kW) and electrolyzer capacity as the primary regulating variable (35–62 kW), with the TOPSIS-recommended compromise solution of 200 kW photovoltaic, 50 kW electrolyzer, 30 kW fuel cell, and 32 m³ hydrogen storage achieving annual carbon emissions of 280 tonnes and a 33.3% reduction. Carbon pricing exhibits a nonlinear leverage effect with an incentive threshold of 200 RMB per tonne, substantially above China’s current 60–80 RMB per tonne level. This study concludes that while hydrogen–solar–geothermal hybrid systems offer substantial climate benefits, their comprehensive sustainability depends on proactive management of material scarcity costs, precise planning of equipment replacement cycles, and coordinated multi-level policy instruments. The findings provide methodological foundations for transitioning building carbon neutrality assessment from static LCA to dynamic coupling frameworks and from single carbon metrics to integrated carbon-resource-cost evaluations. All quantitative results presented herein are derived from this specific case study under the stated assumptions and parameter values; generalization to other building types or climate zones requires recalibration. Full article

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development. Full article

Carbon pricing can change manufacturers’ material choices, while the costs of managing the residual waste remain geographically uneven. We formulate a manufacturer–regulator bilevel model for a household plastic-waste closed-loop supply chain with quantity-dependent recycled-bale prices, activity-specific carbon accounts, physical interregional waste routing, and a proportional regional cost-burden standard. The lower level is explicitly a single coordinating-regulator linear program rather than a game among independent regions. Its primal constraints, dual constraints, and strong-duality equality are embedded in the manufacturer problem; binary-continuous products are exactly linearized using the manufacturer’s SOS1 price-grid variables. Thus, every reported policy point is obtained from the same 12-region mixed-integer equilibrium formulation. Across 36 central policy combinations, HiGHS reports a zero mixed-integer programming gap, and the largest feasibility and optimality residual is $5.24\times {10}^{-8}$. Raising the carbon tax from 0 to 10 USD/tCO₂ increases the real recycling rate (RRR) from 15.33% to the bale-capacity limit of 29.85% and reduces physical emissions by 11.64%. Tightening the allowed regional burden deviation from 25% to 5% reduces the standard deviation of normalized residual-waste cost burden by 77.89% and interregional residual-waste transfers by 77.05%, but does not change the RRR. This zero-recycling effect overturns the earlier assumption-driven result: a pure routing-based cost-balancing rule cannot mechanically stimulate the manufacturer’s recycled-input demand. A global analysis of 300 parameter sets and five independent regional samples re-solves 1800 equilibrium models; all have a zero solver gap and pass the residual audit. Carbon-induced RRR increases have a median of 17.03 percentage points, while strict-versus-loose burden-threshold changes in RRR are zero in every set. The results distinguish carbon efficiency, regional cost incidence, and fiscal incidence and show that policy complementarity must be demonstrated through endogenous decision links rather than imposed response functions. Full article

Urban regeneration is an important policy tool for restructuring urban space, but its implications for operational transport-related carbon emissions remain unclear. Using the City Smart Planning System (CitySPS), this study simulates emissions in Shenzhen from 2020 to 2035 under a baseline and five policy scenarios representing temporal modification, land-use type modification and spatial replacement. The analysis uses legally designated regeneration parcels and multi-source spatial data. The accounting boundary covers CitySPS-represented operational transport-related carbon emissions from urban travel and excludes demolition, construction, building operation, embodied emissions, and other life-cycle sources. The baseline emissions rose from 1.840 × 10⁷ t CO₂ in 2020 to 2.269 × 10⁷ t CO₂ in 2035 (approximately 23%). Relative to the same-year baseline, all the policy scenarios produce higher simulated emissions in 2030 (+0.11% to +1.26%) but lower simulated emissions in 2035 (−0.34% to −1.97%). Under the evaluated configurations and the shared CitySPS assumptions, spatial replacement produces the largest simulated reduction in 2035 (−1.97%) despite an increase in 2030 (+0.41%). The results indicate time-dependent and heterogeneous outcomes across the scenario configurations. Full article

AZ91D is a lightweight, representative commercial magnesium alloy known for its excellent castability and specific strength. However, the mechanical properties of as-cast AZ91D remain limited by inherent brittleness, relatively low strength, and microstructural inhomogeneity caused by enrichment of secondary phases at grain boundaries. In this study, AZ91D/Csf (short carbon fiber at 0, 2.5, and 5 wt.%) composites were prepared by gravity casting with mechanical stirring, followed by post-casting T4 heat treatment and equal-channel angular pressing (ECAP). Material characterization included optical microscopy (OM), field-emission scanning electron microscopy (FESEM), energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and microhardness tests. The results demonstrate that T4 treatment reduced intermetallic β-Mg₁₇Al₁₂ segregation and homogenized the microstructure, whereas one-pass ECAP further refined the grains. Mechanically, these two processes enable the (AZ91D/5 wt.% Csf) composite to achieve higher ultimate tensile strength (280.7 MPa by T4/280.2 MPa by T4 + one-pass ECAP), larger maximum strain (12.1% by T4/5.4% by T4 + one-pass ECAP), and higher microhardness (63.9 HV by T4/78.1 HV by T4 + one-pass ECAP). Compared to as-cast AZ91D, these findings demonstrate that T4 treatment provides a better strength–ductility balance via solid solution, whereas one-pass ECAP preferentially enhances surface microhardness by plastic deformation. This study highlights the performance of AZ91D/Csf composites and their potential for lightweight, high-strength-demand applications. Full article

Low-carbon construction of highway projects constitutes a critical pathway toward achieving the carbon peaking target in the transportation sector. Existing studies have been unable to simultaneously address the identification of carbon emission sources across different engineering types and the delineation of responsible entities for implementing management strategies. This study employs the emission factor method to conduct construction-phase carbon emission accounting for a mountainous expressway in Guangdong Province, China, and reveals significant clustering characteristics of highway construction carbon emissions along two dimensions: the proportion of total emissions and the proportion of material-derived carbon emission sources. Based on K-Means cluster analysis, nine engineering categories—temporary works, subgrade works, pavement works, bridge/culvert works, tunnel works, intersection works, traffic engineering works, greening works, and other works—are classified into four types. Accordingly, a dual-factor classification framework is established, comprising “Core–Material Dominant (CMD)”, “Core–Mixed Balanced (CMB)”, “Peripheral–Material Dominant (PMD)”, and “Peripheral–Mixed Balanced (PMB)”. Differentiated carbon abatement strategies are proposed for each engineering type, with explicit definition of implementation stages and primary responsible entities. Application of the proposed framework to the case project achieved a total carbon abatement of 5.2% during the construction phase. This research provides systematic methodological support for differentiated carbon emission mitigation in highway construction. Full article

The decarbonization of maritime transportation requires shipboard energy systems to coordinate conventional generators, renewable energy sources, energy storage devices, and thermal energy units under voyage-dependent operating constraints. This paper develops a configurable hybrid multienergy ship system for coordinated electrical and thermal energy scheduling. The proposed framework functionally separates the propulsion subsystem from the service and thermal subsystem while retaining system-level coordination among photovoltaic generation, wind generation, diesel generators, micro gas turbines, energy storage batteries, and thermal energy units. A convolutional neural network is employed to provide short-term photovoltaic power forecasts for day-ahead scheduling. The resulting scheduling problem simultaneously considers voyage completion, power balance, equipment operating limits, ramp-rate constraints, battery charging and discharging restrictions, operating costs, and pollutant emission treatment costs. The nonlinear operating logic is reformulated as a mixed-integer optimization problem and solved using CPLEX. A representative coastal voyage case study is used to evaluate the proposed framework. The results demonstrate that the method can coordinate multiple shipboard energy sources, satisfy the prescribed electrical and thermal demands, and provide a set of Pareto-optimal solutions describing the trade-off between operating cost and emission-related cost. The proposed framework provides a system-level scheduling approach for supporting the economic and low-carbon operation of hybrid multienergy ships under increasingly stringent maritime emission reduction requirements. 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

The interplay between water-saving irrigation and potassium (K) management in regulating paddy methane (CH₄) emissions remains poorly understood. It remains unclear whether carbon pool enhancement induced by K fertilization could counteract the oxidation effects under non-flooded irrigation. Here, we conducted a field-based trial over two years to explore how irrigation regimes (continuous flooding, I_F; intermittent irrigation, I_I) and K application rates (K₀, K₇₅, K₁₅₀ kg ha⁻¹) modify soil redox status, carbon pools, crop growth, CH₄ emissions and economic benefits. Intermittent irrigation markedly elevated soil redox potential (Eh), suppressed dissolved organic carbon (DOC), and substantially reduced two-year average CH₄ emissions by 75.1–76.9%, regardless of K supply. Under I_F, high-rate K fertilization (K₁₅₀) stimulated cumulative CH₄ emissions; nevertheless, such K-driven CH₄ stimulation was completely offset under intermittent irrigation. Soil Eh, DOC and microbial biomass carbon (MBC) dominated CH₄ variation, among which Eh exerted the primary control. Intermittent irrigation combined with K fertilization improved rice grain yield. A comprehensive TOPSIS-Entropy multi-criteria evaluation identified the I_IK₇₅ treatment as the optimal option balancing environmental benefits and economic returns. Collectively, intermittent irrigation can mitigate the methanogenic risk from high potassium input, providing a promising redox-regulated strategy for low-CH₄ emission and high rice production. Full article

Under China’s dual-carbon targets, mountainous areas are critical ecological barriers with substantial carbon-sink potential. This study investigated the effects of land-use and land-cover change (LUCC) on carbon emission (CE), carbon storage (CS), and carbon balance in Longhui, Dongkou, and Suining counties in the central Xuefeng Mountains. Using LUCC data from 2000 to 2020, we combined the InVEST, PLUS, and Gray Models with an emission-coefficient method to reconstruct historical LUCC and analyze the spatiotemporal patterns of CE and CS. Future carbon balance was then modeled for 2040 under natural development (ND), ecological protection (EP), and economic development (ED) scenarios. The results indicated that: (1) from 2000 to 2020, construction land doubled, whereas cultivated land and forest land decreased significantly; (2) net CE increased by 3.5-fold, with construction land acting as the dominant carbon source and forest land serving as the primary carbon sink. CS declined by 3.7 × 10⁵ t and exhibited a spatial pattern of high in the west, low in the east. The carbon supply-demand ratio (CSDR) showed a continuous downward trend, with central towns approaching carbon deficits. (3) Projections for 2040 indicated that Taohong Town will face a carbon deficit under the ED, while nearing the critical threshold under the ND. Although the EP prevents a deficit, the town remains vulnerable near the tipping point; in contrast, western ecological townships maintain substantial carbon surpluses. Accordingly, four carbon-balance regulation zones are identified, and differentiated governance strategies are proposed. These findings provide a scientific basis for carbon-balance regulation and low-carbon territorial planning in mountainous counties. Full article

The spatial organization of public service facilities within transit-oriented development TOD blocks is closely related to residents’ daily travel conditions and travel-related carbon emissions. This study examined all 172 operating metro station areas in Qingdao using public service facility POIs, buildings, pedestrian road network and population data, field observations, and a resident travel survey. K-means clustering, spatial syntax analysis, Global Moran’s I, spatial regression, FDR-adjusted Pearson correlation analysis, and scenario simulation were jointly applied. Four facility layout types were identified: Spatially Balanced Type, Main-Road-Concentrated Type, Point-Concentrated Type, and Scattered-and-Disordered Type. The survey included 240 valid respondents distributed across all 41 station areas along Qingdao Metro Line 1. The Spatially Balanced Type had the lowest mean weekly per capita travel carbon emissions, followed by the Main-Road-Concentrated Type, whereas the Point-Concentrated and Scattered-and-Disordered types had similarly higher emission levels. The density and accessibility of Commercial and Entertainment facilities, Medical and Health facilities, and total facilities remained negatively associated with travel carbon emissions after FDR correction. Spatial syntax analysis showed that higher road network integration and connectivity were associated with stronger facility agglomeration. Facility density, road density, and population density exhibited significant positive network-based spatial autocorrelation, and the spatial error model provided the best fit, identifying positive associations of facility density with road density and population density. Based on these findings and field observations, three differentiated renewal pathways—node embedding, proximity coordination, and intensive integration—were proposed. Under the specified scenario, a 20% increase in facilities was associated with modeled reductions in aggregate weekly carbon emissions of 32.3% in Li Village, 28.1% in the University of Petroleum station area, and 33.0% in Jinggangshan Road. These results suggest that improving overall facility coverage may support lower-carbon travel across different facility layout contexts. This study connects facility layout typology, spatial structure, travel carbon emission associations, and differentiated renewal strategies at the TOD-block scale. 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

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated with renewable energy systems. The system (7 × 3 × 5 m) uses polyurethane sandwich panels and requires a maximum cooling load of 6.14 kW with peak power consumption of 7.93 kW. The estimated capital cost is approximately USD 34,100, while the operational cost is about USD 198 per cycle. A comparative analysis using the Levelized Cost of Energy (LCOE) method indicates that diesel generators provide the lowest cost at approximately USD 0.56/kWh, whereas standalone photovoltaic (PV) systems exhibit the highest cost at around USD 0.89/kWh. However, hybrid PV systems offer the best balance between cost efficiency and environmental performance by reducing carbon emissions. The results demonstrate that integrating hybrid renewable energy into modular cold storage enhances cold chain reliability, reduces fish losses, and supports sustainable coastal development. This approach contributes to low-carbon fisheries infrastructure and aligns with global sustainability and renewable energy transition goals. Full article

In today’s volatile global environment, designing sustainable and resilient supply chain networks is essential for balancing economic efficiency, environmental responsibility, and social equity. This study presents a multi-objective mathematical model for sustainable supply chain network design under facility disruption risks and demand uncertainty. A fuzzy robust optimization approach, incorporating triangular fuzzy numbers, is employed to handle uncertain demand while balancing model optimality and feasibility. The proposed network includes production centers, disruption-prone retailers, and customers, addressing both strategic retailer selection and tactical product allocation. The model optimizes three core sustainability objectives: minimizing total operational costs, reducing carbon emissions, and mitigating product shortages. Small-scale instances (five test problems) are validated using the exact $ϵ$-constraint method, while larger-scale problems are solved using three multi-objective metaheuristic algorithms: NSGA-II, MOPSO, and MOEA/D. A comparative analysis based on standard performance metrics and supported by Analysis of Variance (ANOVA) indicates that while MOEA/D offers superior computational speed, MOPSO and NSGA-II exhibit higher solution quality and diversity, with MOPSO demonstrating an overall well-balanced performance. Furthermore, comprehensive sensitivity analyses highlight the model’s responsiveness to disruption probabilities, warehouse capacities, product perishability rates, and demand fluctuations. The results demonstrate that the proposed approach effectively reduces costs and shortages while maintaining environmental targets, providing decision-makers with a practical and scalable framework for resilient supply chain design under real-world uncertainties. Full article