Search Results (1,791)

Against the global rollout of Carbon Border Adjustment Mechanisms (CBAMs), carbon tariffs have emerged as a core tool for developed economies to internalize environmental externalities—especially for energy-intensive imports that dominate cross-border carbon flows. However, emission information asymmetry, a critical barrier to implementing cross-border energy and environmental policies, undermines the design of optimal carbon tariffs, as it distorts the link between tariff levels and actual fossil energy-related emissions. This study develops a two-country analytical model to examine how biased assessments of exporters’ carbon intensity influence optimal tariff settings, exporters’ strategic behavior, and aggregate carbon emissions—with a focus on energy-intensive production contexts. The results show that underestimating carbon intensity reduces exporters’ compliance costs, incentivizing emission concealment; this weakens tariffs’ environmental stringency and may raise global emissions. Overestimation, by contrast, inflates exporters’ marginal costs, discouraging green investment and causing emission displacement rather than reduction. The analysis highlights a policy feedback loop wherein misjudged emission information distorts both trade competitiveness and environmental performance. This study concludes that a transparent, accurate, and internationally verifiable carbon accounting system is essential: it not only facilitates the effective implementation of CBAM but also aligns optimal carbon tariffs with CBAM’s dual goals of climate action and trade equity, while supporting global energy transition efforts. Full article

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via $I_{rms}^{2}R$ scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring. Full article

Sustainable energy systems necessitate an equitable distribution of carbon burdens among stakeholders. This paper proposes a Shapley value-based carbon pricing mechanism embedded in a dual-layer Stackelberg framework, where the upper layer optimizes generation schedules and carbon prices, while the lower layer coordinates demand response strategies. The approach introduces several key innovations, including a Shapley allocation method that enhances fairness (achieving a Jain index of 0.94 compared to 0.78 in baselines), multi-dimensional dynamic pricing, and an improved ADMM algorithm that reduces computational demands by 34.2%. Validation on the IEEE 33-node test system yields a 27.5% reduction in operational costs (from USD 1.952 M to 1.415 M), a 17.8% decrease in emissions, and 97.8% integration of renewable energy sources. Overall, this framework promotes the transition to sustainable energy systems while upholding principles of equity. Full article

To address the economic and reliability challenges of high-penetration renewable energy integration in electricity-heat-hydrogen integrated energy systems and support the dual-carbon strategy, this paper proposes an optimal dispatch method integrating Information Gap Decision Theory (IGDT) and Fuzzy Chance-Constrained Programming (FCCP). An IES model coupling multiple energy components was constructed to exploit multi-energy complementarity. A stepped carbon trading mechanism was introduced to quantify emission costs. For interval uncertainties in renewable generation, IGDT-based robust and opportunistic dispatch models were established; for fuzzy load uncertainties, FCCP transformed them into deterministic equivalents, forming a dual-layer “IGDT-FCCP” uncertainty handling framework. Simulation using CPLEX demonstrated that the proposed model dynamically adjusts uncertainty tolerance and confidence levels, effectively balancing economy, robustness, and low-carbon performance under complex uncertainties: reducing total costs by 12.7%, cutting carbon emissions by 28.1%, and lowering renewable curtailment to 1.8%. This study provides an advanced decision-making paradigm for low-carbon resilient IES. Full article

Understanding how climate change will reshape drought dynamics is essential for planning sustainable water and agricultural systems in tropical regions. However, large uncertainties in existing projections limit effective adaptation. To address this, we applied machine learning-enhanced climate projections and satellite-based drought indices to assess drought dynamics in Ethiopia’s Ganale Dawa Basin as a case study. Agricultural and hydrological droughts were analyzed for a historical baseline (1982–2014) and three future periods (2015–2040, 2041–2070, 2071–2100) under SSP2-4.5 (a moderate-emission pathway) and SSP5-8.5 (a high-emission pathway) scenarios. Results show that agricultural droughts occurred 34 times during the historical baseline. Under SSP2-4.5, their frequency declined to 10 in the mid-future, before rising to 16 events in the far future. In contrast, SSP5-8.5 projected increased variability with 33 events in the near future, dropping to 2 in the mid-future, and increasing again to 19 in the far future. Hydrological droughts were more persistent, with a baseline frequency of 31 events, and 26–36 events over future periods under both scenarios. These findings reveal increasing variability in agricultural drought and continued recurrence of hydrological drought. The findings emphasize a dual adaptation approach combining immediate agricultural responses with sustained water management and climate mitigation. Full article

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the gasoline production and minimize the coke yield—the latter being directly linked to $\mathrm{C}{\mathrm{O}}_2$ emissions in FCC. A data-driven optimization model leveraging a dual Long Short-Term Memory architecture is developed to capture complex relationships between operating variables and product yields. To efficiently solve the model, an Improved Multi-Objective Whale Optimization Algorithm (IMOWOA) is proposed, integrating problem-specific adaptive multi-neighborhood search and dynamic restart mechanisms. Extensive experimental evaluations demonstrate that IMOWOA achieves superior convergence characteristics and comprehensive performance compared to established multi-objective algorithms. Relative to the yields before optimization, the proposed methodology increases the gasoline yield by 0.32% on average, coupled with an average reduction of 0.11% in the coke yield. For the studied FCC unit with an annual processing capacity of 2.6 million tons, the coke reduction corresponds to an annual $\mathrm{C}{\mathrm{O}}_2$ emission reduction of approximately 10,277 tons, delivering benefits to sustainable FCC operations. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

Dy³⁺:InF₃ fiber shows promise for 4.4 μm mid-infrared lasing, but the much shorter lifetime of its upper laser level compared to the lower level causes inevitable self-termination. While cascade 4.4 μm/3 μm lasing has been proposed as a potential solution, this method faces complex configuration and an extremely high pump threshold (>30 W under continuous-wave operation), rendering it impractical for high-power use, especially given InF₃’s soft-glass nature. To address the self-termination challenge and enable the low-threshold, high-efficiency lasing, this study proposes, for the first time to our knowledge, a dual-wavelength cascade-pumping scheme utilizing 2.8 μm and 2.4 μm pumps. Numerical simulations demonstrate that the dual-wavelength cascade-pumped Dy³⁺:InF₃ fiber laser exhibits an optical-to-optical efficiency of up to 18.4% and a maximum slope efficiency of 38.5%. The total pump threshold is as low as 5.4 W, remarkably lower than that required by the cascade lasing approach. This work provides a viable solution and design guidelines for the development of 4 μm-class mid-infrared fiber lasers. Full article

Biochar and organic fertilizer applications are widely recognized as effective strategies for mitigating greenhouse gas emissions and controlling agricultural non-point source pollution in agroecosystems. However, the combined effects of these two approaches on greenhouse gas emissions and agricultural non-point source pollution remain insufficiently understood. Through consecutive field-based positioning plot trials, this study systematically examined the individual and combined effects of biochar and organic fertilizer amendments on N runoff loss (WTN) and gaseous emissions (N₂O and NH₃), N-cycling functional microbial communities, and soil physicochemical properties. Results demonstrated that conventional chemical fertilization resulted in 20.70% total N loss (4.48% gaseous emissions, 15.22% runoff losses). Biochar and organic fertilizer applications significantly reduced WTN losses by 8.06% and 7.43%, respectively, and decreased gaseous losses by 2.01% and 1.88%, while concurrently enhancing plant N uptake and soil residual N. Random forest analysis combined with partial least squares structural equation modeling revealed that soil organic carbon directly modulated nitrogen runoff losses and indirectly influenced aggregate stability and macroaggregate formation. Dissolved organic carbon (DOC) and recalcitrant organic carbon (ROC) exhibited dual regulatory effects on NH₃ volatilization through both direct pathways and indirect mediation via aggregate stability. Notably, biochar and organic fertilizer amendments induced significant compositional shifts in nirS- and nirK-type denitrifying microbial communities. pH, cation exchange capacity (CEC), and iron oxide–carbon complexes (IOCS) were identified as key factors suppressing N₂O emissions through inhibitory effects on Azoarcus and Bosea genera. Our findings demonstrate that biochar and organic fertilizers differentially modulate soil physicochemical properties and denitrifier community structure, with emission reduction disparities attributable to distinct mechanisms’ enhanced aggregate stability and modified denitrification potential through genus-level microbial community restructuring, particularly affecting Azoarcus and Bosea populations. This study offers valuable insights into the regulation of carbon sources for nitrogen management strategies within sustainable acidic soil vegetable production systems. Full article

Cashew nut shell liquid (CNSL) is a byproduct of cashew processing that has largely been overlooked as a biomass resource for biodiesel production. While some research has been conducted on CNSL in diesel engines, there remains a lack of studies on using processed CNSL with industrial waste catalysts for diesel engines. This study focuses on the performance and emissions of catalytically cracked CNSL (CC-CNSL) created with fly ash as a catalyst. Blends of 25%, 50%, 75%, and 100% CC-CNSL-diesel were used as a fuel in a single-cylinder diesel engine under different load conditions. The CC-CNSL25 blend, which contains 25% CC-CNSL, outperformed the others with a 2% increase in brake thermal efficiency. Additionally, it showed substantial reductions in emissions, i.e., 11.76% less carbon monoxide, 9.09% reduced smoke density, 8.57% lower hydrocarbon emissions, and 5.27% decreased specific fuel consumption compared to conventional diesel at full load. This research highlights fly ash-catalyzed CNSL processing as an effective method for converting agricultural waste into high-quality biodiesel. It offers a dual advantage as a sustainable fuel source while addressing waste management challenges. Full article

Cereal-based complementary foods are widely consumed by children, yet limited data exist on their elemental composition and potential health risks. This study quantified As, Cd, Co, Cr, Cu, Fe, K, Mn, Mg, Mo, Ni, P, Pb, Se, Si, V, and Zn in eight commercial cereal-based products collected in Campo Grande, Brazil, using inductively coupled plasma optical emission spectrometry (ICP OES). Arsenic, cadmium, cobalt, and chromium were consistently below the detection limit. Phosphorus and potassium were the predominant elements across brands, followed by Fe, Mg, and Zn, with significant inter-brand variability (Kruskal–Wallis, p < 0.05). Lead was detected in Brands 1–5 (0.11–0.41 mg/kg), but it was below the limit of detection (LOD = 0.003 mg/L) in the other samples. Estimated daily intake (ID) values at 30 g/day and 90 g/day showed that Fe, Zn, Mn, and Se frequently met or exceeded dietary reference intakes for children aged 1–3 years, while Cu, Ni, and P remained below tolerable levels. Comparison with tolerable upper intake levels and ATSDR minimal risk levels indicated that higher consumption (90 g/day) could result in excess intake of Mn, Zn, and Se, with Pb contributing to cumulative hazard indices above the safety threshold (HI > 1). These findings emphasize the dual role of cereal-based foods as important nutrient sources and potential contributors to excessive trace element exposure in young children. Full article

Carbon emission efficiency plays a vital role in the realization of the “dual carbon” goals. Taking land resource allocation as the entry point, this paper explores how land resource misallocation (LRM) affects carbon emission efficiency (CEE) to support the enhancement of CEE and the optimal allocation of land resources. Using 108 cities in the Yangtze River Economic Belt from 2003 to 2021 as an example, this paper constructs a panel smooth transition model (PSTR), with industrial structure as the transition variable, to examine the nonlinear impact effects of LRM on CEF and its regional heterogeneity. The research results show that the LRM index as a whole presents a fluctuating downward trend, while CEF shows a fluctuating but slow upward trend, and the regional differences in both LRM and CEF continue to expand. There exists a significant nonlinear relationship between LRM and CEF. When the advancement of industrial structure index shifts from the low regime to the high regime, the impact of LRM on CEF presents an inverted “U”-shaped curve characteristic. The nonlinear impact of LRM on CEF exhibits regional heterogeneity, and the threshold effect of industrial structure is the main reason for the regional differences in the nonlinear impact. Therefore, it is necessary to accelerate the market-oriented reform of land factor allocation, and to formulate phased and differentiated land resource allocation policies adapted to the stages of industrial structure development, so as to effectively serve the goals of green, low-carbon, and high-quality development. Full article

This paper develops a Sustainable Artificial Intelligence-Driven Wind Power Forecasting System (SAI-WPFS) to enhance the integration of renewable energy while minimizing the environmental footprint of deep learning computations. Although deep learning models such as CNN, LSTM, and GRU have achieved high accuracy in wind power forecasting, existing research rarely considers the computational energy cost and associated carbon emissions, creating a gap between predictive performance and sustainability objectives. Moreover, limited studies have addressed the need for a balanced framework that jointly evaluates forecast precision and eco-efficiency in the context of large-scale renewable deployment. Using real-time data from the Dumat Al-Jandal Wind Farm, Saudi Arabia’s first utility-scale wind project, this study evaluates multiple deep learning architectures, including CNN-LSTM-AM and GRU, under a dual assessment framework combining accuracy metrics (MAE, RMSE, R²) and carbon efficiency indicators (CO₂ emissions per computational hour). Results show that the CNN-LSTM-AM model achieves the highest forecasting accuracy (MAE = 29.37, RMSE = 144.99, R² = 0.74), while the GRU model offers the best trade-off between performance and emissions (320 g CO₂/h). These findings demonstrate the feasibility of integrating sustainable AI into wind energy forecasting, aligning technical innovation with Saudi Vision 2030 goals for zero-carbon cities and carbon-efficient energy systems. Full article

Achieving carbon neutrality is a global priority, and China’s “dual-carbon” goals place urgent demands on emission reduction. In this context, the digital economy and industrial structure transformation are key drivers of synergistic carbon mitigation and sustainable development. This study constructs an integrated analytical framework, combining an improved three-system coupling coordination model, exploratory spatial data analysis, and panel vector autoregression, using panel data from 30 Chinese provinces between 2013 and 2022. The results reveal three main findings: (1) Spatial heterogeneity: The digital economy follows an “advanced East—catching-up Central—lagging West” pattern, while carbon emissions show a “higher North—lower South” gradient. (2) Improving coordination with regional disparities: Overall coupling coordination has steadily increased, but Eastern provinces exhibit stronger synergistic capabilities than Central and Western regions. (3) Bidirectional interactions and self-reinforcing effects: Digital economy development drives industrial structure upgrading, which in turn promotes long-term carbon reduction; all three systems display self-reinforcing dynamics. These findings provide robust empirical evidence on the complex co-evolution of digital economy, industrial transformation, and carbon emissions, offering actionable insights for policymakers to design region-specific strategies for coordinated low-carbon development. Full article

Sustainable development, which integrates economic progress with environmental stewardship to serve societal needs, seeks a balanced approach to resource utilization and intergenerational equity. Implementing carbon policies to limit emissions in production is an effective measure that also puts pressure on the supply chain’s profitability. Meanwhile, the emergence of the price reference effect affects consumers’ behavior and the decisions of supply chain members. This study constructs a dual-channel supply chain model under three carbon policy scenarios within a manufacturer-led Stackelberg game framework. The model is solved analytically to examine equilibrium outcomes and investigate the influence of channel competition, the price reference effect, and carbon policies on profitability and carbon emissions across different scenarios. The results are as follows. (1) As consumers’ online channel preference increases, manufacturers’ profits turn from falling to rising, especially under a lower carbon tax (higher carbon quota), with profit growing earlier. (2) A stronger price reference effect encourages higher emission reduction efforts, selling prices, and profits in smaller markets. However, this effect can reduce prices and profits due to increased competition and pricing pressure in larger markets. (3) The influence of carbon tax and emission quota on emission reduction and price depends on the initial carbon emission of the product, and their interaction has different impacts on total profits at different initial emission levels. (4) Within the mixed policy, the supply chain can obtain better economic and environmental benefits at a specific range of basic market demand. This study provides valuable references for formulating tactics to cope with low-carbon demand and price reference effects, as well as for developing effective environmental protection policies. Full article