Search Results (343)

Climate warming is anticipated to significantly alter the distribution and composition of plant species in the Arctic, thereby cascading through food webs and affecting both associated fauna and entire ecosystems. To elucidate the trend in plant distribution in response to climate change, we employed the MaxEnt model to project the future ranges of 25 representative Arctic and Circumpolar plant species (including grasses and shrubs). Species distribution data, in conjunction with bioclimatic variables derived from climate projections of three selected General Circulation Models (GCMs), ESM2, IPSl, and MPIE, were utilized to fit the MaxEnt models. Subsequently, we predicted the potential distributions of these species under three Shared Socioeconomic Pathways (SSPs)—SSP126, SSP245, and SSP585—across a timeline spanning 2010, 2050, 2100, 2200, 2250, and 2300 AD. Range shift indices were applied to quantify changes in plant distribution and range sizes. Our results show that the ranges of nearly all species are projected to diminish progressively over time, with a more pronounced rate of reduction under higher emission scenarios. The species are generally expected to shift northward, with the distances of these shifts positively correlated with both the time intervals from the current state and the intensity of thermal forcing associated with the SSPs. Arctic species (A_Spps) are anticipated to face higher extinction risks compared to Boreal–Arctic species (B_Spps). Additional indices, such as range gain, loss, and overlap, consistently corroborate these patterns. Notably, the peak range shift speeds differ markedly between SSP245 and SSP585, with the latter extending beyond 2100 AD. In conclusion, under all SSPs, A_Spps are generally expected to experience more significant range shifts than B_Spps. In the SSP585 scenario all species are projected to face substantial range reductions, with Arctic species being more severely affected and consequently facing the highest extinction risks. These findings provide valuable insights for developing conservation recommendations for polar plant species and have significant ecological and socioeconomic implications. Full article

In the context of global climate governance and the green transition, digital infrastructure serves as a critical enabler of resource allocation in the digital economy, offering strategic value in tackling synergistic pollution and carbon reduction challenges. Using panel data from 280 prefecture-level cities, this study employs a multiperiod difference-in-differences (DID) approach, leveraging smart city pilot policies as a quasinatural experiment, to assess how digital infrastructure affects urban synergistic pollution-carbon mitigation (SPCM). The empirical results show that digital infrastructure increases the urban SPCM index by 1.5%, indicating statistically significant effects. Compared with energy and income effects, digital infrastructure can influence this synergistic effect through indirect channels such as the energy effect, economic agglomeration effect, and income effect, with the economic agglomeration effect accounting for a larger share of the total effect. Additionally, fixed-asset investment has a nonlinear moderating effect on this relationship, with diminishing marginal returns on emission reduction when investment exceeds a threshold. Heterogeneity tests reveal greater impacts in eastern, nonresource-based, and environmentally regulated cities. This study expands the theory of collaborative environmental governance from the perspective of new infrastructure, providing a theoretical foundation for establishing a long-term digital technology-driven mechanism for SPCM. Full article

This study empirically evaluates the energy and carbon reduction effects of an envelope retrofit applied to an aging public building in South Korea. Unlike previous studies that primarily relied on simulation-based analyses, this work fills the empirical research gap by using actual utility billing data collected over one pre-retrofit year (2019) and two post-retrofit years (2023–2024). The retrofit included improvements to exterior walls, roofs, and windows, aiming to enhance thermal insulation and airtightness. The analysis revealed that monthly electricity consumption was reduced by 14.7% in 2023 and 8.0% in 2024 compared to that in the baseline year, with corresponding decreases in electricity costs and carbon dioxide emissions. Seasonal variations were evident: energy savings were significant in the winter due to reduced heating demand, while cooling energy use slightly increased in the summer, likely due to diminished solar heat gains resulting from improved insulation. By addressing both heating and cooling impacts, this study offers practical insights into the trade-offs of envelope retrofitting. The findings contribute to the body of knowledge by demonstrating the real-world performance of retrofit technologies and providing data-driven evidence that can inform policies and strategies for improving energy efficiency in public buildings. Full article

A computational fluid dynamics (CFD) numerical simulation methodology was implemented to model transient heating processes in steel industry reheating furnaces, targeting combustion efficiency optimization and carbon emission reduction. The effects of oxygen concentration (O₂%) and different fuel types on the flow and heat transfer characteristics were investigated under both oxygen-enriched combustion and MILD oxy-fuel combustion. The results indicate that MILD oxy-fuel combustion promotes flue gas entrainment via high-velocity oxygen jets, leading to a substantial improvement in the uniformity of the furnace temperature field. The effect is most obvious at O₂% = 31%. MILD oxy-fuel combustion significantly reduces NOx emissions, achieving levels that are one to two orders of magnitude lower than those under oxygen-enriched combustion. Under MILD conditions, the oxygen mass fraction in flue gas remains below 0.001 when O₂% ≤ 81%, indicating effective dilution. In contrast, oxygen-enriched combustion leads to a sharp rise in flame temperature with an increasing oxygen concentration, resulting in a significant increase in NOx emissions. Elevating the oxygen concentration enhances both thermal efficiency and the energy-saving rate for both combustion modes; however, the rate of improvement diminishes when O₂% exceeds 51%. Based on these findings, MILD oxy-fuel combustion using mixed gas or natural gas is recommended for reheating furnaces operating at O₂% = 51–71%, while coke oven gas is not. Full article

The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the farm level, with a particular focus on technological, environmental and organisational factors affecting emission levels. The analysis is based on a review of, inter alia, 46 peer-reviewed publications and 11 environmental reports, legal acts and databases concerning the CF in different regions and under various production systems. This study identifies the main sources of emissions, including enteric fermentation, manure management, and the production and use of feed and fertiliser. It also demonstrates the significant variability of the CF values, which range, on average, from 0.78 to 3.20 kg CO₂ eq kg⁻¹ of milk, determined by the farm scale, nutritional strategies, local environmental and economic determinants, and the methodology applied. Moreover, this study stresses that higher production efficiency and integrated farm management could reduce the CF per milk unit, with further intensification having, however, diminishing effects. The application of life cycle assessment (LCA) methods is essential for a reliable assessment and comparison of the CF between systems. Ultimately, an effective CF reduction requires a comprehensive approach that combines improved nutritional practices, efficient use of resources, and implementation of technological innovations adjusted to regional and farm-specific determinants. The solutions presented in this paper may serve as guidelines for practitioners and decision-makers with regard to reducing GHG emissions. Full article

This study focusses on the transmission of carbon pricing mechanisms in shaping trade dynamics between the Eurozone and key partners: the USA and China. Using Bayesian variable selection methods and a Time-Varying Structural Vector Autoregressions (TV-SVAR) model, the research identifies the key variables impacting EU carbon emissions over time. The results reveal that manufactured products from the US have a diminishing positive impact on EU carbon emissions, suggesting potential exemption from future regulations. In contrast, manufactured goods from the US and petroleum products from China are expected to increase emissions, indicating a need for stricter trade policies. These findings provide strategic insights for policymakers aiming to balance trade and environmental objectives. Full article

This study presents a novel approach to address these challenges by introducing automobile platinum honeycomb catalysts into biomass combustion systems. The study employed a dual methodology, combining experimental investigations and a Life Cycle Assessment (LCA) case study, to comprehensively evaluate the catalyst’s performance and environmental impacts. The catalyst’s ability to facilitate combustion without open flame formation and its operational efficiency throughout combustion phases position it as a promising avenue for reducing gaseous and particulate matter emissions. The LCA considers multiple impact categories, employing the ReCiPe 2008 Hierarchist midpoint and endpoint perspective to assess environmental effects. The experimental results show that the catalyst effectively reduced CO, SO₂, and particulate emissions. Temperatures below 400 °C diminished the catalyst’s performance. The catalyst achieved a 100% CO conversion rate at specific temperatures of 427.4–490.3 °C. The findings highlight the potential for a 34% reduction in environmental impacts when replacing conventional rice husk combustion with the catalyst-integrated system. Notably, the study emphasizes the significance of sustainable catalyst manufacturing processes and cleaner electricity sources in maximizing environmental benefits. In conclusion, the integration of platinum honeycomb catalysts into biomass combustion systems, exemplified by rice husk combustion, emerges as a promising strategy for achieving more sustainable and environmentally friendly bioenergy production. Full article

Capturing CO₂ emitted by coal chemical enterprises and injecting it into oil reservoirs not only effectively improves the recovery rate and development efficiency of tight oil reservoirs in the Ordos Basin but also addresses the carbon emission problem constraining the development of the region. Since initiating field experiments in 2012, the Ordos Basin has become a significant base for CCUS (Carbon capture, Utilization, and Storage) technology application and demonstration in China. However, over the years, projects have primarily focused on enhancing the recovery rate of CO₂ flooding, while issues such as potential reservoir damage and its extent have received insufficient attention. This oversight hinder the long-term development and promotion of CO₂ flooding technology in the region. Experimental results were comprehensively analyzed using techniques including nuclear magnetic resonance (NMR), X-ray diffraction (XRD), scanning electron microscopy (SEM), inductively coupled plasma (ICP), and ion chromography (IG). The findings indicate that under current reservoir temperature and pressure conditions, significant asphaltene deposition and calcium carbonate precipitation do not occur during CO₂ flooding. The reservoir’s characteristics-high feldspar content, low carbon mineral content, and low clay mineral content determine that the primary mechanism affecting physical properties under CO₂ flooding in the Chang 4 + 5 tight sandstone reservoir is not, as traditional understand, carbon mineral dissolution or primary clay mineral expansion and migration. Instead, feldspar corrosion and secondary particles migration are the fundamental reasons for the changes in reservoir properties. As permeability increases, micro pore blockage decreases, and the damaging effect of CO₂ flooding on reservoir permeability diminishes. Permeability and micro pore structure are therefore significant factors determining the damage degree of CO₂ flooding inflicts on tight reservoirs. In addition, temperature and pressure have a significant impact on the extent of reservoir damage caused by CO₂ flooding in the study region. At a given reservoir temperature, increasing CO₂ injection pressure can mitigate reservoir damage. It is recommended to avoid conducting CO₂ flooding projects in reservoirs with severe pressure attenuation, low permeability, and narrow pore throats as much as possible to prevent serious damage to the reservoir. At the same time, the production pressure difference should be reasonably controlled during the production process to reduce the risk and degree of calcium carbonate precipitation near oil production wells. Full article

Selective catalytic reduction filter (SDPF) technology constitutes a critical methodology for controlling nitrogen oxide (NOx) and particulate matter emissions from light-duty diesel vehicles. A series of SDPFs with different sulfur poisoning times and concentrations were prepared using the worldwide harmonized light vehicles test cycle (WLTC). Bench testing revealed that sulfur poisoning diminished the catalyst’s NH₃ storage capacity, impaired the transient NOx reduction efficiency, and induced premature ammonia leakage. After multiple sulfur poisoning incidents, the NOx reduction performance stabilized. Higher SO₂ concentrations accelerated catalyst deactivation and hastened the attainment of this equilibrium state. The characterization results for the catalyst indicate that the catalyst accumulated the same sulfur content after tail gas poisoning with different sulfur concentrations and that sulfur existed in the form of SO₄²⁻. The sulfur species in low-sulfur-poisoning-concentration catalysts mainly included sulfur ammonia and sulfur copper species, while high-sulfur-poisoning-concentration catalysts contained a higher proportion of sulfur copper species. Neither species type significantly altered the zeolite coating’s crystalline structure. Sulfur ammonia species could easily lead to a significant decrease in the specific surface area of the catalyst, which could be decomposed at 500 °C to achieve NOx reduction performance regeneration. In contrast, sulfur copper species required higher decomposition temperatures (600 °C), achieving only partial regeneration. Full article

This study creates and tests a machine learning model that can predict fuel use and emissions (NO_x, CO₂, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R² > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO_x exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Climate change risk has significant impacts on corporate financial activities. Using firm-level data from A-share listed companies in China from 2010 to 2022, we examine how climate risk affects corporate debt financing capacity. We find that climate change risk significantly weakens firms’ ability to raise debt, leading to lower leverage and higher financing costs. These results remain robust across various checks for endogeneity and alternative specifications. We also show that reducing corporate carbon emission intensity can mitigate the negative impact of climate risk on debt financing, suggesting that supply-side credit policies are more effective than demand-side capital structure choices. Furthermore, we identify three channels through which climate risk impairs debt capacity: reduced competitiveness, increased default risk, and diminished resilience. Our heterogeneity analysis reveals that these adverse effects are more pronounced for non-state-owned firms, firms with weaker internal controls, and companies in highly financialized regions, and during periods of heightened environmental uncertainty. We also apply textual analysis and machine learning to the measurement of climate change risks, partially mitigating the geographic biases and single-dimensional shortcomings inherent in macro-level indicators, thus enriching the quantitative research on climate change risks. These findings provide valuable insights for policymakers and financial institutions in promoting corporate green transition, guiding capital allocation, and supporting sustainable development. Full article

Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. All three models exhibit strong predictive performance, with R² values exceeding 0.9 for both EUI and cost. XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m², while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m². In terms of computational efficiency, the surrogate models significantly outperform a physics-based simulation model, with MLP running approximately 340 times faster and XGBoost and RF achieving over 200 times speedup. This study also examines the effect of training dataset size on model performance, identifying a point of diminishing returns where further increases in data size yield minimal accuracy gains but substantially higher training times. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. Overall, the findings demonstrate that machine learning-based surrogate models can serve as fast, accurate, and interpretable alternatives to traditional simulation methods, supporting efficient decision-making during early-stage building design. Full article

Amidst escalating concerns about climate change, manufacturers are increasingly pressured to adopt a low-carbon supply chain (LCSC). Financial constraints deter numerous companies from embracing low-carbon initiatives in a competitive landscape. Inclusive internal financing (IIF) provides operational funds from capital-abundant members to capital-constrained members, resolving funding shortages internally within the system. However, when dominant members cannot support all such enterprises, selective internal financing (SIF) or hybrid financing (HF) becomes necessary. This paper studies the operation and financing strategies of a competitive LCSC. Within the framework of an LCSC where two capital-constrained retailers compete, using Stackelberg game theory and the backward induction method, three game-theoretical models are developed under IIF, SIF, and HF. The results indicate that increased competition intensity reduces product sales price, the manufacturer’s carbon emission reduction level, and profit. When competition intensity is high, SIF more effectively enhances carbon emission reduction level, product sales quantity, and profit acquisition. HF reduces profits for the allied retailer and diminishes its competitiveness, yet enhances the competitive strength of the rival retailer. Numerical analysis demonstrates that when equity financing in HF exceeds 0.546, the allied retailer becomes unprofitable and is driven out of the market. This study complements LCSC finance research and provides references for supply chain operations and financing strategy formulation. Full article

Currently, the cement industry stands as one of the sectors with the most significant environmental impact, primarily due to its substantial greenhouse gas emissions and energy consumption. To mitigate this impact, a roadmap has been followed in recent years, outlining a set of objectives aimed at diminishing the environmental footprint of the construction industry. This research focuses on the development of mortars with different water/cement ratios employing an alternative cement, specifically magnesium phosphate cement (MPC) formulated with secondary sources. The goal of this research relays in developing mortars based on MPC by using waste from the metallurgical industry, named tundish deskulling waste (TUN), as an MgO source. The results revealed the optimal water/cement (W/C) ratio for MPC-TUN mortars production through the assessment of various characterization techniques, which was 0.55. This ratio resulted in the highest compressive strength after 28 days of curing and the formation of a stable K-struvite matrix. Furthermore, it demonstrated the effectiveness of aluminum sulphate in preventing efflorescence caused by carbonates. The development of alternative masonry mortars for application in building materials represents a significant stride towards advancing the principles of a circular economy, in alignment with the objectives laid out in the 2030 roadmap. Full article