Search Results (4,691)

In the context of global climate governance and the International Maritime Organization’s (IMO) stringent carbon reduction targets, the transition to green shipping fuels faces systemic challenges in supply chain coordination. This study focuses on the strategic interactions between governments and enterprises in the construction of green fuel supply chains. By constructing a multidimensional scenario framework encompassing time, technological development, social attention, policy intensity, and market competition, and using evolutionary game models and system dynamics simulations, we reveal the dynamic evolution mechanism of government–enterprise decision making. System dynamics simulations reveal that (1) short-term government intervention accelerates infrastructure development but risks subsidy inefficiency; (2) medium-term policy stability and market-driven mechanisms are critical for sustaining enterprise investments; and (3) high social awareness and mature technologies significantly reduce strategic uncertainty. This research advances the application of evolutionary game theory in sustainable supply chains and offers a decision support framework for balancing governmental roles and market forces in maritime decarbonization. Full article

Global tourism activities have become increasingly digitalized, yet the economic and environmental impacts of digitalization on tourism remain underexplored. This study develops a supply-side analytical framework to examine whether and how digitalization decouples tourism economy from carbon emissions by integrating Ghosh input-output analysis, subsystem analysis, and structural decomposition analysis. Our findings reveal that digitalization has largely decoupled China’s tourism economy from carbon emissions, with the increases in economic gains notably outpacing those in emission losses. Specifically, the digital-enabled tourism value-added (DTV) increased by approximately 18 times from 2002 to 2017, while digital-enabled tourism emissions (DTE) only grew by about 11 times. Between 2017 and 2020, due to the impact of the COVID-19 pandemic, the DTV decreased by about 61%, and DTE dropped by approximately 63.5%. The expansion in DTV can be primarily attributed to advancements in software and IT services and electronic components, while the increase in DTE is significantly driven by software and IT services and communication services. The growth in digital supply emerged as the predominant driver for the surging DTV and DTE, with the emission intensity of tourism subsectors acting as a notable constraint. This study offers both a methodological framework and empirical evidence aimed at guiding policy initiatives that target the digitalization and low-carbon transition of the tourism sector. Full article

First-principles calculations were conducted to examine the impact of three sulfonamide-containing molecules (H₄N₂O₂S, CH₈N₄O₃S, and C₂H₂N₆O₄S) adsorbed on the FAPbI₃(001) perovskite surface, aiming to establish a significant positive correlation between the molecular structures and their regulatory effects on the perovskite surface. A systematic comparison was conducted to evaluate the adsorption stability of the three molecules on the two distinct surface terminations. The results show that all three molecules exhibit strong adsorption on the FAPbI₃(001) surface, with C₂H₁₂N₆O₄S demonstrating the most favorable binding stability due to its extended frameworks and multiple electron-donating/withdrawing groups. Simpler molecules lacking carbon skeletons exhibit weaker adsorption and less dependence on surface termination. Ab initio molecular dynamics simulations (AIMD) further corroborated the thermal stability of the stable adsorption configurations at elevated temperatures. Electronic structure analysis reveals that molecular adsorption significantly reconstructs the density of states (DOS) on the PbI₂-terminated surface, inducing shifts in band-edge states and enhancing energy-level coupling between molecular orbitals and surface states. In contrast, the FAI-terminated surface shows weaker interactions. Charge density difference (CDD) analysis indicates that the molecules form multiple coordination bonds (e.g., Pb–O, Pb–S, and Pb–N) with uncoordinated Pb atoms, facilitated by –SO₂–NH₂ groups. Bader charge and work function analyses indicate that the PbI₂-terminated surface exhibits more pronounced electronic coupling and interfacial charge transfer. The C₂H₁₂N₆O₄S adsorption system demonstrates the most substantial reduction in work function. Optical property calculations show a distinct red-shift in the absorption edge along both the XX and YY directions for all adsorption systems, accompanied by enhanced absorption intensity and broadened spectral range. These findings suggest that sulfonamide-containing molecules, particularly C₂H₁₂N₆O₄S with extended carbon skeletons, can effectively stabilize the perovskite interface, optimize charge transport pathways, and enhance light-harvesting performance. Full article

Greenhouse gases, such as carbon dioxide (CO₂), released from excessive fossil fuel consumption, are major contributors to global warming. Understanding the spatial distribution of CO₂ emissions on a refined scale is crucial for promoting green economic development. Xi’an, a key central city in China, serves as the case study for this research. Using nighttime light data from Black Marble, combined with energy statistics and socio-economic information, this study employed spatial analysis to simulate CO₂ emissions on the district and county levels in Xi’an for the years 2012 and 2022. The results indicated that nighttime light data were significantly correlated with CO₂ emissions (linear function; coefficients of determination: 0.7838 and 0.7941 for 2012 and 2022, respectively). The spatial distribution analysis revealed a clear pattern in CO₂ emissions, with higher emissions concentrated in central urban areas and lower emissions in peripheral regions. Additionally, a comparative analysis of carbon emissions and carbon emission intensity across districts and counties between 2012 and 2022 showed that CO₂ emissions in central urban areas had continued to grow and expand, while emission intensity had declined. These findings suggest that the socio-economic development, policy interventions, and industrial structure in Xi’an influence the spatial distribution of CO₂ emissions. Full article

Fujian Province launched a talent recruitment policy in 2012 to integrate top university graduates into grassroots government roles, aiming to support green development. This study investigates the impact of recruiting “three-high” talents—those who are highly educated, skilled, and specialized—on reducing county-level carbon emissions. Using panel data from 134 counties between 2007 and 2021, we apply a time-varying difference-in-differences model. Robustness checks, including propensity score matching estimation, placebo tests, and fixed-effect controls, confirm the reliability of our results. We find that the policy significantly reduces carbon emission intensity, primarily by enhancing green technological innovation. The effect is more pronounced in urban, economically developed, and non-resource-based regions, especially where public awareness of green practices is higher. These findings suggest that localized talent policies can play a critical role in advancing low-carbon development. Our results offer new evidence for integrating human capital strategies into environmental policy design and highlight the importance of aligning recruitment efforts with regional development needs to support China’s carbon neutrality goals. Full article

n-Octanol and related ether alcohols are studied via molecular dynamics (MD) simulations using the two classical all-atom force fields OPLS-AA and CHARMM. The ether alcohols studied possess one ether functionality separated by varying n carbon atoms from the hydroxy group to elucidate how the positioning of the ether functionality affects intra- and intermolecular hydrogen bonding and, in turn, the physical properties of the studied alcohols. Important general trends observed from simulations with both force fields include the following: Intramolecular hydrogen bonding is majorly present in 3-butoxypropanol and 4-propoxybutanol (n = 3 and 4) while being only marginally present for 5-ethoxypentanol and 6-methoxyhexanol (n = 5 and 6) and absent in 1-hexyloxymethanol and 2-pentyloxyethanol (n = 1 and 2). The intramolecular hydrogen bonds formed by 3-butoxypropanol and 4-propoxybutanol are among the most stable ones of all present hydrogen bonds. Intermolecular hydrogen bonding is stronger between hydroxy groups (OH-OH) than between hydroxy and ether groups (OH-OE). An increased temperature causes a reduction in intermolecular OH-OH and OH-OE hydrogen bonding but a slight increase in intramolecular hydrogen bonding. A reduction in end-to-end distances at a higher temperature is also observed for all studied alcohols, which is likely a reflection of increased dihedral bond rotations. Hydrogen bonding extends mostly between just two molecules while hydrogen bonding networks are rare but do exist, involving, in some instances, up to 30 hydrogen bonds. Regardless of force field and temperature, the obtained radial distribution functions (RDFs) mostly show the same features at same distances that only vary in their intensity. 1-hexyloxymethanol forms a very specific and stable intermolecular double OH-OE hydrogen-bonded dimer. Similar double-hydrogen-bonded dimers can be found for the ether alcohols but are only significantly present for 2-pentyloxyethanol. Overall, the main difference between OPLS-AA and CHARMM is their quantitative prediction of the present hydrogen bonding speciation largely due to the stiffer dihedral potentials in OPLS-AA compared to the CHARMM force field. The simulations indicate that (a) the variations in densities are correlated to the reduced packing efficiency caused by intramolecular hydrogen bonding, (b) self-diffusion correlates with the stability of the intermolecular hydrogen bonds, and (c) the presence of hydrogen-bonded networks, although small in numbers, affect the viscosity. Full article

This study investigates the environmental and operational performance of X-DF and ME-GI propulsion systems in large LNG carriers, focusing on key emission and transport efficiency metrics—CO₂, the EEOI, and the CII—and their relationship with operational factors such as shaft power, vessel speed, propeller slip, and specific fuel oil consumption. Statistical methods including correlation analysis, regression modeling, outlier detection, and clustering are employed to evaluate engine behavior across the ship’s fuel gas steaming envelope and to identify critical efficiency trends. The results show that ME-GI engines deliver lower CO₂ emissions and consistent efficiency under steady-load conditions, due to their higher thermal efficiency and precise control characteristics. In contrast, X-DF engines demonstrate greater adaptability, leveraging LNG combustion to achieve cleaner emissions and optimal performance in specific operational clusters. Clustering analysis highlights distinct patterns: ME-GI engines excel with optimized shaft power and RPM, while X-DF engines achieve peak efficiency through adaptive load and fuel management. These findings provide actionable insights for integrating performance indicators into SEEMP strategies, enabling targeted emission reductions and fuel optimization across diverse operating scenarios—thus supporting more sustainable maritime transport. Full article

As the climate crisis intensifies, corporate operations face unprecedented challenges from increasing climate risks, necessitating rigorous investigation into their resultant economic ramifications. This study employs text analysis and machine learning methods to construct climate risk perception indicators for a sample of China’s A-share listed energy sector firms (2014–2023). A two-way fixed effects panel model is then applied to study the impact of climate risk perception on corporate performance in the energy industry. The empirical results demonstrate that in China’s energy sector, a 1% rise in climate risk perception corresponds to a 0.104% decline in ROE, mediated through diminished financial flexibility (β = −0.075 **) and elevated R&D intensity (β = 0.649 ***). Moderating effect testing indicates that firms with higher levels of administrative spending effectively buffer against the adverse effects of heightened climate risk perception. Furthermore, this study shows that climate risk perception has more pronounced negative effects on corporate performance in state-owned enterprises (β = −0.113 **), heavily polluting enterprises (β = −0.131 *), carbon-intensive industries, and non-carbon trading pilot regions (β = −0.119 ***). These findings empirically demonstrate how climate risk perception reshapes corporate resource allocation and management, ultimately affecting performance. This study also proposes policy recommendations to enhance corporate climate risk responsiveness, promote technological innovation, accelerate the energy sector’s green transition, optimize corporate capital structure, and advance sustainable development goals. Full article

Above-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). Incorporating fourteen multi-source features (seven vegetation indices, e.g., Modified Vegetation Index (MVI) and Green Chlorophyll Index (CIg); four meteorological variables; three soil properties), all data were standardized via z-score normalization before training. The MLP model, optimized via six-fold cross-validation, achieved an R² of 0.765 and RMSE of 38.066 g/m², outperforming XGBoost (R² = 0.723, RMSE = 41.354 g/m²) with a statistically significant 5.8% accuracy improvement (p < 0.05). Spatial analysis revealed a north-to-south AGB gradient, strongly correlated with precipitation gradients (250–350 mm/year) and soil organic carbon (R = 0.428). These findings provide a robust framework for climate-adaptive grassland management and carbon assessment in semi-arid regions. Full article

To achieve low-carbon economic dispatch and collaborative optimization of carbon capture efficiency in power systems, this paper proposes a flexible carbon capture power plant and generalized energy storage collaborative operation model under a dynamic carbon quota mechanism. First, adjustable carbon capture devices are integrated into high-emission thermal power units to construct carbon–electricity coupled operation modules, enabling a dynamic reduction of carbon emission intensity and enhancing low-carbon performance. Second, a time-varying carbon quota allocation mechanism and a dynamic correction model for carbon emission factors are designed to improve the regulation capability of carbon capture units during peak demand periods. Furthermore, pumped storage systems and price-guided demand response are integrated to form a generalized energy storage system, establishing a “source–load–storage” coordinated peak-shaving framework that alleviates the regulation burden on carbon capture units. Finally, a multi-timescale optimization scheduling model is developed and solved using the GUROBI algorithm to ensure the economic efficiency and operational synergy of system resources. Simulation results demonstrate that, compared with the traditional static quota mode, the proposed dynamic carbon quota mechanism reduces wind curtailment cost by 9.6%, the loss of load cost by 48.8%, and carbon emission cost by 15%. Moreover, the inclusion of generalized energy storage—including pumped storage and demand response—further decreases coal consumption cost by 9% and carbon emission cost by 17%, validating the effectiveness of the proposed approach in achieving both economic and environmental benefits. Full article

This study examines the causal and dynamic effects of carbon information disclosure on firm value, using a policy-driven setting in China’s carbon-intensive industries. In 2018, the Ministry of Ecology and Environment implemented a regulatory policy requiring internal carbon accounting and third-party verification for carbon-intensive enterprises, without mandating public disclosure. This exogenous policy shock offers a quasi-natural experiment to investigate how firms in carbon-intensive industries respond to environmental mandates through voluntary disclosure and how such disclosure affects their market valuation. Employing a difference-in-differences framework combined with two-stage least squares estimation, we identify a significant increase in carbon information disclosure following the policy intervention. This disclosure leads to a positive and growing effect on firm value, particularly when sustained over multiple years. Moreover, the valuation effect is moderated by regional environmental regulation: firms in areas with lower enforcement intensity benefit more from disclosure, as the signal is perceived to be more voluntary and credible. These findings provide robust causal evidence on the role of carbon information disclosure in shaping market outcomes under regulatory pressure. The study contributes to the literature on environmental regulation and corporate financial behavior in emerging markets. Full article

Transportation infrastructure systems sit at the nexus of urban economic development and emission mitigation. The primary objective is to identify and quantify the key factors influencing CI, with a focus on both the conventional and emerging indicators through an innovative MLP neural network developed using the data of 20 Chinese transportation enterprises that have a business focus on the construction and operation sector from 2018 to 2022. The hypothesis is that integrating unconventional indicators—such as business model entropy and green revenue share—alongside traditional metrics can significantly enhance the predictive accuracy for CI. The results show that business model entropy explains 42.6% of carbon intensity (Cl) variability through green revenue diversification pathways, while emissions trading system (ETS) exposure accounts for 51.83% of decarbonization outcomes via price-signaling effects. The analysis reveals that a critical operational threshold–renewable energy capacity below 75% fails to significantly reduce Cl, and capex/revenue ratios exceeding 73.58% indicate carbon lock-in risks. These findings enable policymakers to prioritize industries with sub-75% renewable adoption while targeting capex-intensive sectors for circular economy interventions. The novelty of this work lies in the application of advanced machine-learning techniques to a comprehensive, multi-source dataset, enabling a nuanced analysis of CI drivers and offering actionable insights for policymakers and industry stakeholders aiming to decarbonize transport infrastructure. Full article

Wastewater treatment plants (WWTPs) are crucial for environmental protection and public health; however, they are among the most energy-intensive facilities in the water sector. This study examines the correlation between energy consumption and treatment efficiency at the Metamorphosis WWTP (MWWTP) in Attica, Greece, during the years 2022 and 2023. By analyzing influent and effluent characteristics, energy consumption patterns, and the removal efficiencies of key pollutants—Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD₅), and Suspended Solids (SS)—this research provides valuable insights into optimizing wastewater treatment operations. The findings reveal that, despite seasonal variations and fluctuations in influent composition, the facility consistently achieved high pollutant removal rates while maintaining stable energy consumption. The influent BOD₅ increased from 992.8 mg L⁻¹ in 2022 to 1122.3 mg L⁻¹ in 2023. COD rose from 1925.4 mg L⁻¹ to 2594.4 mg L⁻¹, SS from 1280.8 mg L⁻¹ to 1421.2 mg L⁻¹, and total phosphorus from 14.2 mg L⁻¹ to 17.0 mg L⁻¹. Effluent concentrations remained consistently low, with BOD₅ at 6.1 mg L⁻¹ in 2022 and 4.7 mg L⁻¹ in 2023; COD at 23.8 mg L⁻¹ and 25.2 mg L⁻¹, respectively; total nitrogen at 20.2 mg L⁻¹ and 16.7 mg L⁻¹; total phosphorus at 2.4 mg L⁻¹ and 2.6 mg L⁻¹; and SS at 2.4 mg L⁻¹ and 3.5 mg L⁻¹. These results indicate removal efficiencies exceeding 90%. Energy consumption remained stable, recorded at 13,044.9 kWh (0.593 kWh m⁻³ influent) in 2022 and 13,126.1 kWh (0.598 kWh m⁻³ influent) in 2023. These results highlight the importance of integrating energy-efficient strategies and renewable energy solutions to enhance wastewater treatment plant (WWTP) sustainability. This study contributes to ongoing efforts to improve energy optimization in wastewater treatment, supporting global initiatives for carbon footprint reduction and advancing the principles of a circular economy. Full article

The identification and prediction of effective reservoir rocks are important for evaluating the energy production potential of ultra-deep tight sandstone reservoirs. Taking the Keshen gas field, Tarim Basin, as an example, three distinct petrofacies are divided according to petrology, pores, and diagenesis. Petrofacies, well logs, and factor analysis are combined to predict effective reservoir rocks. We find that petrofacies A has a relatively coarse grain size, moderate mechanical compaction, diverse but low-abundance authigenic minerals, and well-developed primary and secondary pores. It is an effective reservoir rock. Petrofacies B and petrofacies C are tight sandstones with a poorly developed pore system and almost no dissolution. Petrofacies B features abundant compaction-susceptible ductile grains, intense mechanical compaction, and underdeveloped authigenic minerals, while petrofacies C features pervasive carbonate cementation with a poikilotopic texture. We combine well logging with gamma ray, acoustic, bulk density, neutron porosity, resistivity, and factor analyses to facilitate the development of petrofacies prediction models. The models reveal interbedded architecture where effective reservoir rocks are interbedded with tight sandstone, resulting in the restricted connectivity and pronounced reservoir heterogeneity. Classifying and combining well logs with a factor analysis to predict petrofacies provide an effective means for evaluating the energy potential of ultra-deep reservoirs. Full article

This study investigates the annual carbon emission disparities between privately-owned electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) by developing a usage-phase life cycle assessment (LCA) model, with a focus on the synergistic impacts of grid carbon intensity, driving intensity (e.g., annual mileage), and vehicle energy efficiency. Through scenario analyses and empirical case studies in four Chinese megacities, three key findings are obtained: (1) Grid carbon intensity is the primary factor affecting the emission advantages of EVs. EVs demonstrate significant carbon reduction benefits in regions with low-carbon power grids, even when the annual mileage is doubled. However, in coal-dependent grids under intensive usage scenarios, high-energy-consuming EVs may experience emission reversals, where their emissions exceed those of ICEVs. (2) Higher annual mileage among EV owners (1.5–2 times that of ICEV owners) accelerates carbon accumulation, particularly diminishing per-kilometer emission advantages in regions where electricity grids are heavily reliant on fossil fuels. (3) Vehicle energy efficiency heterogeneity plays a critical role: compact, low-energy EVs (e.g., A0-class sedans/SUVs) maintain emission advantages across all scenarios, while high-energy models (e.g., C-class sedans/SUVs) may exceed ICEV emissions even in regions with low-carbon power grids under specific conditions. The study proposes a differentiated policy framework that emphasizes the synergistic optimization of grid decarbonization, vehicle-class-specific management, and user behavior guidance to maximize the carbon reduction potential of EVs. These insights provide a scientific foundation for refining EV adoption strategies and achieving sustainable transportation transitions. Full article