Search Results (1,552)

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

Carbon capture, utilization, and storage (CCUS) play an increasingly important role in climate mitigation strategies by addressing industrial emissions and enabling pathways toward net-negative emissions. A key challenge lies in determining the pathway of captured CO₂, whether through permanent geological storage or conversion into value-added products to enhance system viability. As hard-to-abate sectors and the power industry remain major sources of emissions, a comprehensive assessment of the technical, environmental, and economic performance of CCUS pathways is essential. This study evaluates bioenergy with carbon capture and storage/utilization (BECCUS) in the context of the Polish energy sector. Techno-environmental performance was assessed across three pathways: CO₂ storage in saline formations, CO₂ mineralization, and methanol synthesis. The results show levelized costs of 59.9 EUR/tCO_2,in for storage, 109.7 EUR/tCO_2,in for mineralization, and 631.1 EUR/tCO_2,in for methanol production. Corresponding carbon footprints (including full chain emissions) were −936.4 kgCO_2-eq/tCO_2,in for storage, −460.6 kgCO_2-eq/tCO_2,in in for mineralization, and 3963.4 kgCO_2-eq/tCO_2,in for methanol synthesis. These values highlight the trade-offs between economic viability and climate performance across utilization and storage options. The analysis underscores the potential of BECCS to deliver net-negative emissions and supports strategic planning for CCUS deployment in Poland. Full article

With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial for achieving economical and low-carbon operation of IES. To this end, this paper unveils a comprehensive modeling and optimization framework: Firstly, a Spatio-Temporal Diffusion Model (STDM) is proposed, which generates high-quality wind power forecasting data by accurately capturing its spatio-temporal correlations, thereby providing reliable input for IES dispatch. Subsequently, a stochastic optimal scheduling model for electricity–heat–carbon coupled IES is established, comprehensively considering carbon capture equipment and a carbon quota mechanism. Finally, a multi-IES Nash bargaining cooperative game model is developed, encompassing bilateral energy trading and bilateral carbon trading, to equitably distribute cooperative benefits. Simulation results demonstrate that the STDM model significantly outperforms baseline models in both forecasting accuracy and scenario quality, while the designed bilateral market mechanism enhances system economics by reducing the total operating cost by 19.63% and lowering the total carbon emissions by 4.09%. Full article

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

Coastal shipping plays a critical role in meeting maritime decarbonization targets under the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII) and the European Union Emissions Trading System (EU ETS); however, operators currently lack robust tools to forecast route-specific carbon intensity and evaluate the causal benefits of fuel switching. This study developed a distribution-free causal forecasting framework for voyage-level Carbon Dioxide (CO₂) intensity using an enriched panel of 1440 real-world voyages across four Nigerian coastal routes (2022–2024). We employed a physics-informed monotonic Light Gradient Boosting Machine (LightGBM) model trained under a strict leave-one-route-out (LORO) protocol, integrated with split-conformal prediction for uncertainty quantification and Causal Forests for estimating heterogeneous treatment effects. The model predicted emission intensity on completely unseen corridors with a Mean Absolute Error (MAE) of 40.7 kg CO₂/nm, while 90% conformal prediction intervals achieved 100% empirical coverage. While the global average effect of switching from heavy fuel oil to diesel was negligible (≈−0.07 kg CO₂/nm), Causal Forests revealed significant heterogeneity, with effects ranging from −74 g to +29 g CO₂/nm depending on route conditions. Economically, targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO₂. These findings demonstrate that effective coastal decarbonization requires moving beyond static baselines to uncertainty-aware planning and targeted, route-specific fuel strategies rather than uniform fleet-wide policies. Full article

To address climate change and advance environmental sustainability in the context of the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), China has actively promoted digital trade development under its carbon peaking and carbon neutrality goals. However, whether digital trade contributes to environmental improvement, and through which mechanisms it does this, remains an open empirical question. This study examines whether and how digital trade development affects environmental pollution in China, with particular emphasis on spatial spillover effects and underlying mechanisms. Using provincial panel data from 2009 to 2023, we employ a spatial Durbin model combined with a mediation analysis framework. The results show that digital trade development has increased steadily in China and significantly reduces local environmental pollution, indicating a clear green effect. The spatial Durbin model shows that the environmental benefits of digital trade are unevenly distributed across space, with pollution reductions in core regions accompanied by increased emissions in neighboring areas. Further mechanism analysis indicates that industrial structure upgrading and consumption structure transformation are key channels through which digital trade development improves environmental sustainability. These findings provide important insights for coordinating digital trade expansion with regional environmental governance and low-carbon transition strategies. Full article

In the context of global climate change and carbon-neutrality goals, carbon taxes and carbon tariffs have become key policy tools for regulating corporate emissions. However, most existing studies examine these policies in isolation and overlook firms’ behavioral responses under their joint implementation, especially with product heterogeneity. This study analyzes production and emission-reduction decisions of two-country manufacturers under carbon taxation and further investigates corporate behavior and social welfare outcomes when both carbon taxes and carbon tariffs are imposed. The results show that carbon taxes enhance emission-reduction efforts, though with diminishing marginal effects. Moderate carbon tariffs further motivate exporting firms to reduce emissions, while overly high tariffs may induce market exit, particularly for high-quality manufacturers. Consumer preferences also interact with policy effects: stronger preferences for high-quality products encourage firms to expand domestic markets and increase green investments, whereas weaker preferences shift focus toward exports. Social welfare responds asymmetrically, moderate tariffs improve environmental performance, while excessive tariffs lead to trade distortions and welfare losses. Overall, this study highlights nonlinear and heterogeneous firm responses under combined carbon policies, offering insights for policy design and corporate strategy. Full article

This study provides a comprehensive analysis of the impact of different marine fuels such as heavy fuel oil (HFO), methane, methanol, ammonia, or hydrogen, on energy efficiency and pollutant emissions in maritime transport, using a combined application of the Energy Efficiency Design Index (EEDI), Energy Efficiency Operational Indicator (EEOI), and Carbon Intensity Indicator (CII). The results show that methane offers the most balanced alternative, reducing CO₂ by more than 30% and improving energy efficiency, while methanol provides an intermediate performance, eliminating sulfur and partially reducing emissions. Ammonia and hydrogen eliminate CO₂ but generate NO_x (nitrogen oxides) emissions that require mitigation, demonstrating that their environmental impact is not negligible. Unlike previous studies that focus on a single fuel or only on CO₂, this work considers multiple pollutants, including SO_x (sulfur oxides), H₂O, and N₂, and evaluates the economic cost of emissions under the European Union Emissions Trading System (EU ETS). Using a representative model ship, the study highlights regulatory gaps and limitations within current standards, emphasizing the need for a global system for monitoring and enforcing emissions rules to ensure a truly sustainable and decarbonized maritime sector. This integrated approach, combining energy efficiency, emissions, and economic evaluation, provides novel insights for the scientific community, regulators, and maritime operators, distinguishing itself from previous multicriteria studies by simultaneously addressing operational performance, environmental impact, and regulatory gaps such as unaccounted NO_x emissions. Full article

Against the backdrop of global climate governance and China’s “dual carbon” goals, carbon emissions trading (CET) has become a core policy instrument for promoting low-carbon transformation. However, it remains unclear whether CET policies can effectively improve corporate carbon performance and, more importantly, through which micro-level mechanisms such effects operate within firms. To address these gaps, this study applies a difference-in-differences (DID) approach to examine the impact of CET policy on corporate carbon performance and its transmission pathways. The results show that CET policy significantly enhances corporate carbon performance. Heterogeneity analysis further reveals that this positive effect is more pronounced in regions with lower environmental governance intensity, and that the policy’s effectiveness strengthens over time. Mechanism tests indicate that financing constraints and R&D investment serve as chain mediators: CET policy alleviates financing constraints, stimulates R&D investment, and thereby improves carbon performance. Moreover, the moderating effect analysis shows that executives’ green backgrounds reinforce the policy’s effectiveness by further easing financing constraints and mitigating their negative impact on R&D investment. Overall, these findings deepen the micro-level understanding of market-based environmental regulation and provide policy implications for optimizing CET policy design, improving resource allocation efficiency, and fostering low-carbon transformation and sustainable competitive advantages for enterprises. Full article

The previous studies have suggested that the cap-and-trade carbon emissions trading scheme (ETS) was effective in reducing greenhouse gas emissions and atmospheric pollution. Are there other environmental benefits of this policy? This research question remains unanswered in the literature. Our study reports that China’s carbon ETS significantly improved the land surface ecological quality (LSEQ). The study analyzes the data of 328 Chinese cities during 2005–2020. A difference-in-differences (DID) regression model is used for quantitative policy evaluation. The land surface ecological quality is measured by a synthetic indicator of the remote sensing ecological index (RSEI). There are three main findings. (1) On average, the carbon ETS improved the land surface ecological quality index by 0.0113, which contributed 51% of the ecological quality improvement in ETS-implementing regions in the post-policy period. The positive effect of the policy increased over time. (2) The implementation of the carbon ETS reduced pollution emissions, promoted green innovation, and expanded the share of land with natural vegetation coverage. These phenomena provide explanations for why the policy improved the land surface ecological quality. (3) The policy effect exhibited some heterogeneities contingent on local climatic conditions. The effect was stronger in regions with more precipitation, shorter sunlight duration, and higher temperature. Full article

Using panel data from 273 prefecture-level cities in China from 2008 to 2020, this study employs the Entropy Weight Method -Technique for Order Performance by Similarity to Ideal Solution (EWM-TOPSIS) model to measure people’s well-being and applies a staggered Difference-in-Differences (DID) model to evaluate the impact of the carbon emissions trading system on people’s well-being. The findings indicate that the carbon emissions trading system generally improves people’s well-being. The mechanism analysis reveals that the primary channel through which the carbon emissions trading system improves people’s well-being is the stimulation of green technology innovation. Additionally, fiscal expenditure decentralization negatively moderates the carbon emissions trading system’s impact on people’s well-being, whereas marketization degree does not exert a moderating effect. Further research reveals that fiscal expenditure decentralization exhibits a double threshold effect, while the degree of marketization displays a single threshold effect. The carbon emissions trading system exhibits heterogeneous impacts on people’s well-being. From a regional perspective, the carbon emissions trading system enhances people’s well-being in non-Yangtze River Economic Belt (YREB) regions, whereas it dampens people’s well-being in YREB cities. Regarding resource endowment, the carbon emissions trading system positively influences people’s well-being in non-resource-based cities, but its impact remains statistically insignificant in resource-based cities. Full article

This study provides a multi-faceted and detailed perspective on the relationships between logistics performance, environmental degradation, and economic growth in 38 OECD countries, using each as an individual target variable. In the Analysis section, the relationship between logistics and environment is examined within a broader context, taking economic indicators into account. This examination utilizes the machine learning algorithms Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). For each algorithm, the dataset is split into training and testing sets using three different ratios: 90:10, 80:20, and 70:30. A comprehensive performance evaluation is conducted on each of these splits by applying 5-fold and 10-fold cross-validation (CV). Considering economic indicators, the analysis section examines how the logistics-environment relationship is shaped in a broader context using the machine learning algorithms RF, XGBoost, and LightGBM. MSE, MAE, RMSE, MAPE, and R² metrics are utilized to evaluate model performance, while MDA and SHAP are employed to assess feature importance. Furthermore, a bee swarm plot is leveraged for visualizing the results. The XGBoost algorithm can successfully predict carbon dioxide (CO₂) emissions from transport and economic growth with high accuracy. However, the logistics performance model achieves high performance only with the LightGBM algorithm using a 90% train, 10% test split, and 5-fold CV setup. Based on the variable importance levels of the best-performing algorithm for each of the three target variables separately, the prediction of logistics performance is largely dependent on the economic growth predictor, and secondly, on the trade openness predictor. In predicting CO₂ emissions from transport, economic growth is identified as the most effective predictor, while logistics performance and trade openness contribute the least to the prediction. The findings also reveal that transport-related emissions and environmental indicators are prominent in the prediction of economic growth, whereas logistics performance and trade openness play a supportive, yet secondary role. Full article

This study presents a geospatial assessment and modeling of the water–energy–food (WEF) nexus to enrich the sustainable paddy cultivation of the North Central Province (NCP) of Sri Lanka in the Dry Zone. Increasing climatic variability and limited resources have raised concerns about the need for efficient resource management to restore food security globally. The study analyzed the three components of the WEF nexus for their synergies and trade-offs using GIS and remote sensing applications. The food productivity potential was derived using the Normalized Difference Vegetation Index (NDVI), Soil Organic Carbon (SOC), soil type, and land use, whereas water availability was assessed using the Normalized Difference Water Index (NDWI), Soil Moisture Index (SMI), and rainfall data. Energy potential was mapped using WorldClim 2.1 datasets on solar radiation and wind speed and the proximity to the national grid. Scenario modeling was conducted through raster overlay analysis to identify zones of WEF constraints and synergies such as low food–low water areas and high energy–low productivity areas. To ensure the accuracy of the created model, Pearson correlation analysis was used to internally validate between hotspot layers (representing extracted data) and scenario layers (representing modeled outputs). The results revealed a strong positive correlation (r = 0.737), a moderate positive correlation for energy (r = 0.582), and a positive correlation for food (r = 0.273). Those values were statistically significant at p > 0.001. These results confirm the internal validity and accuracy of the model. This study further calculated the total greenhouse gas (GHG) emissions from paddy cultivation in NCP as 1,070,800 tCO_2eq yr⁻¹, which results in an emission intensity of 5.35 tCO_2eq ha⁻¹ yr⁻¹, with CH₄ contributing around 89% and N₂O 11%. This highlights the importance of sustainable cultivation in mitigating agricultural emissions that contribute to climate change. Overall, this study demonstrates a robust framework for identifying areas of resource stress or potential synergy under the WEF nexus for policy implementation, to promote climate resilience and sustainable paddy cultivation, to enhance the food security of the country. This model can be adapted to implement similar research work in the future as well. Full article

Cross-border e-commerce logistics has long prioritized delivery speed; however, the trade-offs between cost-effectiveness, carbon emissions, risk, and financial performance have received relatively little attention. To address this deficiency, this study constructs a fuzzy nonlinear multi-objective optimization framework that integrates the particle swarm optimization (PSO) algorithm and the Sobol sensitivity analysis to capture the uncertainty and nonlinear dynamics of logistics systems. Using operational data from a Taiwanese cross-border e-commerce exporter from 2023 to 2024, empirical results show that extending the standard 25-day delivery time to an acceptable maximum of 32–37 days (i.e., an extension of 7–12 days) can reduce logistics costs per order by 22–38%, carbon emissions by 18–31%, and increase financial returns. Sobol sensitivity analysis further demonstrates that extended delivery time (T) is a significant controllable factor ($S_1=0.62, {ST}_1=0.75$). This study empirically verifies the profitability and sustainability of moderately T, challenges the current “speed-first” model, and provides a transparent, replicable, and scalable decision-making framework for promoting low-carbon, economically viable cross-border e-commerce supply chains. Full article

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios. Full article