ABSTRACT This study uses the directional distance function parameter estimation method to measure the MAC of carbon dioxide of the logistics industry in China’s 30 provinces from 2006 to 2021 and analyses its evolution characteristics. A panel data model is further constructed to examine its influencing factors. The results show that: (1) The MAC is generally showing a continuous growth trend, rising from 8971.29 yuan/ton in 2006 to 9496.18 yuan/ton in 2021. At the regional level, the MAC is highest in the central region, followed by the eastern and northeast regions, and lowest but fastest growing in the western region. At the provincial level, the MAC is relatively higher in economically developed regions, while those in inland regions are relatively lower. With the exception of Inner Mongolia and Shandong, which exhibit relatively pronounced peak characteristics, most provinces generally show an upward trend, although the growth patterns vary. (2) There is an inverted ‘U’ shaped relationship between the MAC and carbon emission intensity, with a critical value of 6.0634 tons/10,000 yuan for the carbon emission intensity. There is a negative correlation between the MAC and technological progress. Energy structure has a negative impact on the MAC, although this effect is not significant.

Since the discovery of anaerobic ammonium oxidation bacteria, commonly known as AnAOB in the early 1990s, more than a quarter century has passed and partial nitrification/anammox process for sewage treatment is still mainly in lab and pilot-scale research phase with few plants in operation. The main challenges for that are enrichment, grow and how to keep AnAOB in the reactor on low-strength wastewater treatment, such as in anaerobically treated domestic sewage. Another important aspect is need for continuous supply of nitrite and how to minimize nitrite consumption by others than anammox. In addition to that other minor control parameters play an important role, such as hydraulic and sludge retention time, dissolved oxygen, temperature, pH, etc. This paper presents a detailed review of essential process parameters and identifies gaps and solutions for effective implementation of the anammox process highlighting the different factors that suppress AnAOB growth, along with the aspects favouring activity and immobilization. Reactor start-up and operation, bacteria inhibition and conversion of emerging-pollutants is also investigated, with their effect on AnAOB and their removal. The main conclusions are the sustainability evaluation, which found that the process reduce the overall GHG emissions compared to conventional nitrogen removal processes; a possible microbial pathway that could be involved for simultaneous organics, nutrients and emerging-pollutants removal; and, finally, a novel concept of a three-stage treatment process in two up-flow anaerobic sludge blanket-based system is proposed.

ABSTRACT This study examines how spatial context influences the relationship between environmental performance (lower carbon emission intensity) and financial performance (higher profitability) in real estate investment trusts (REITs). Analyzing 375 REITs across 21 economies during 2017-2023, we identify a negative, non-linear relationship between carbon emission intensity and profitability. However, this relationship is significantly weaker for REITs headquarted in major urban agglomerations. We attribute this to three mechanisms: reduced environmental differentiation in dense markets, elevated operational costs, and systemic environmental externalities. In contrast, REITs outside agglomerations show a stronger negative relationship, driven largely by Scope 2 emissions. Our findings reveal the importance of geographical context in shaping green financial outcomes and highlight the need for spatially sensitive policy design. We also discuss how AI can enhance emission monitoring, hotspot identification, and resource optimization, strengthening the environmental-financial link in future real estate management.

As a core metric for climate policy, the scientific estimation of carbon social costs is crucial for formulating mitigation strategies. However, traditional integrated assessment models predominantly focus on the global aggregate, failing to adequately account for regional heterogeneity, sectoral characteristics, and strategic interactions between regions. They also lack systematic integration of ESG principles. To address this, this paper examines regional and sectoral carbon social costs driven by ESG development. Through cooperative and non-cooperative games, we improve the integrated economic-environmental-climate development model, take the eight economic regions in China as an example, get the carbon social cost of each economic region and typical important industries, and obtain the key parameters and the evolution law of carbon social cost. The model categorizes the carbon emissions after the implementation of emission reduction policies under the ESG perspective into direct and indirect emissions. It studies the economic impacts of the two types of emissions before and after the implementation of emission reduction policies, and conducts research on the top four typical important industries (industry, construction, transportation, and power) that rank among the top four global CO2 emitters, to obtain the analytical solution of the social cost of carbon in the region and the typical important industries. In addition, this paper numerically simulates the social cost of carbon for the four industries under the baseline scenario, cooperative game scenario, non-cooperative game scenario, and temperature limitation scenario. The study shows that the social cost of carbon in the northern, southern and eastern coastal economic regions is higher than that in other economic regions, the social cost of carbon in the industrial and electric power industries in each economic region is higher than that in the building and transportation industries, and the more stringent the temperature limit is, the higher the social cost of carbon is in the economic regions.

Marine fisheries play a dual role in global warming as both a “carbon source” and “carbon sink.” This study analyzed carbon emissions from marine fisheries in Shandong Province from 2010 to 2022 by integrating carbon accounting, extended Kaya-LMDI decomposition, and System Dynamics (SD) modeling. The results reveal a distinct temporal trend characterized by an initial increase followed by a gradual decline in net carbon emissions, while marine carbon sinks increased steadily over the study period. Marine capture fisheries consistently remained the dominant source of total carbon emissions. Decomposition analysis reveals that economic scale and population were the primary drivers of carbon emission growth, while carbon intensity exerted a smaller but positive effect, whereas improvements in energy intensity and industrial structure contribute to emission reduction, highlighting the importance of energy efficiency improvement and industrial structural adjustment. Using a validated SD model to project trends from 2023 to 2035, we simulated three scenarios: Baseline (BS), High-Growth (HG), and Low-Carbon Development (LD) scenarios. The results show that the low-carbon development scenario achieves the most pronounced reduction in net carbon emissions, driven by simultaneous declines in capture emissions and a strong enhancement of carbon sink capacity from shellfish and algae aquaculture. In contrast, the baseline and high-growth scenarios exhibit relatively weaker mitigation effects. Overall, this study provides quantitative evidence and a strategic roadmap for advancing the green, sustainable transition of marine fisheries in Shandong Province, China.

Understanding the spatiotemporal impacts of land use transition on carbon emissions is crucial for achieving regional carbon neutrality. This study presents an integrated analytical framework that combines dynamic land use modeling, the Geo-detector method (GDM), and Geographically and Temporally Weighted Regression (GTWR) to analyze land use transition and carbon emission dynamics in China's Pearl River Delta (PRD) from 2000 to 2020. Key findings include: (1) Construction land expansion was the dominant explicit transition, with land conversion sources shifting from cropland-centric patterns to diverse transfers involving woodland and water bodies. (2) The implicit land use transition index exhibited an annual growth rate of 15.6%, progressing through three phases-rapid development (2000-2010), structural adjustment (2010-2015), and high-quality transition (2015-2020). (3) Regional carbon emissions increased by 186.96%, exhibiting spatial disparities between core and peripheral regions. Construction land expansion and GDP density were primary drivers. This research advances the theoretical integration of land system science and low-carbon governance, offering actionable insights for spatially differentiated emission reduction strategies in megacity clusters.

In the context of globalization, variations in carbon emission intensity and economic growth rates exhibit not only direct, reciprocal effects within individual countries but also indirect transmission mechanisms across regions and nations. Employing the Global Vector Autoregressive (GVAR) model and utilizing quarterly data from 33 countries—including 8 Eurozone members—spanning from the first quarter of 1990 to the fourth quarter of 2019, this paper demonstrates that major global economies continue to exhibit salient features of low-carbon economic development. Specifically, reductions in carbon emission intensity in developed countries frequently exert adverse spillover effects on the economic growth rates of other nations. In response to negative economic shocks, numerous countries increase their carbon emission intensity as a countercyclical measure, while others reduce emissions—potentially reflecting that they have surpassed the turning point posited by the Environmental Kuznets Curve (EKC). These findings highlight that advancing low-carbon economic development requires sustained improvements in production technologies and energy efficiency, alongside strengthened international cooperation in carbon emissions management, in order to alleviate the additional costs arising from the asynchronous progression of global carbon reduction efforts.

Increasing global awareness of environmental issues has encouraged the development of sustainable and eco-friendly methods for extracting phenolic compounds from Cinnamomum burmannii. This study integrates Microwave-Assisted Extraction (MAE) with Natural Deep Eutectic Solvents (NaDES) as a green extraction approach. The objectives were to evaluate thermal efficiency, energy consumption, carbon emissions, and extraction kinetics at microwave powers of 640, 720, and 800 W using citric acid–sucrose as the solvent. The highest thermal efficiency (69.82%) was achieved at 640 W, with an energy consumption of 540.86 kJ and carbon emissions of 0.131 kg CO₂e. Extraction kinetics were described using the Peleg model, which accurately represented changes in phenolic concentration during extraction. The highest extraction rate constant (B₀ = 0.2117 mg/mL·min) was obtained at 640 W, while the highest equilibrium capacity constant (Ce = 0.6982 mg/mL) and total phenolic content (6.42 ± 0.046 mg GAE/mL) were achieved at 800 W. These findings indicate that increasing microwave power enhances both extraction rate and phenolic yield. Compared with conventional methods, MAE combined with NaDES demonstrated lower energy consumption and reduced carbon emissions, highlighting its potential as a sustainable extraction technology.

The transition to green hydrogen is critical for achieving sustainable energy systems and climate goals. This study presents MODERHydrogen-H2, a comprehensive framework for assessing solar- and wind-based green hydrogen production, fossil fuel substitution, and greenhouse gas (GHG) reduction. The method integrates Geographic Information Systems (GIS) to optimize renewable energy resource allocation while adhering to sustainability criteria. Applied to four solar sites (2000 MW) in Colombia’s Magdalena–Cauca Basin and three wind projects (1700 MW) in the Caribbean Basin, the model estimates an annual production of 211,074 tons of green hydrogen by 2030. This output could displace 37,221 terajoules of fossil fuels, contributing 2.5% to the national energy matrix and reducing CO2 emissions by 10.09 million tons. MODERHydrogen-H2 demonstrates scalability and adaptability, offering a decision-support tool for global energy transition strategies. Its implementation supports affordable, reliable, and low-carbon energy systems, aligning with Sustainable Development Goals (SDGs) targets. The model offers a single platform from which to simulate renewable energy potential in a sustainable manner within a given geographical area, develop scenarios for modifying the energy matrix of a country or region, simulate rational and efficient water supply and demand for energy uses, including aspects of climate change, calculate green hydrogen production in a sustainable manner, and finally calculate greenhouse gas emissions.

This study investigates the spatial variability of forest fire intensity, burn indices, ecosystem productivity, and Greenhouse Gas (GHG) emissions in Pakistan from 2001 to 2023. Using satellite-derived burn indices such as SAVI, LST, NMDI, LSWI, NBR, and MSAVI2, the study examines the relationship between forest fires and net primary productivity (NPP) across diverse ecological regions. The analysis reveals that northern Pakistan, particularly Khyber Pakhtunkhwa and Gilgit-Baltistan, experiences high fire intensity, resulting in significant reductions in NPP and increased emissions of COx, NOx, and CH₄. Central and southern Pakistan, including the arid regions of Balochistan and Sindh, exhibit lower fire intensity but remain vulnerable due to climate-driven dry conditions. The study also applies the ΔNPP/ΔBurn approach to evaluate how changes in burn indices correspond to shifts in NPP, revealing that small increases in fire intensity can lead to substantial ecosystem productivity loss. Additionally, a comparative analysis of Random Forest (RF) and XGBoost machine learning models for fire prediction found RF to be the more accurate model, achieving 88.0% accuracy and a 93.8% AUC score. These findings underscore the importance of developing region-specific fire management strategies to mitigate the ecological and environmental impacts of wildfires. The study highlights the critical need for improved fire prediction, early warning systems, and long-term monitoring of post-fire ecosystem recovery. By drawing comparisons with global research, this study contributes to understanding the broader implications of forest fires on carbon dynamics and ecosystem productivity, providing valuable insights for future fire management policies in Pakistan.

ABSTRACT A comprehensive understanding of long‐term trends in near‐surface wind speed (SWS) and their underlying physical mechanisms is imperative for progress in atmospheric science, climatology, and energy‐related fields. Utilising observational data and simulations from 10 models, this study investigates the role of anthropogenic activities in the observed decline of SWS over the Tibetan Plateau (TP) from 1961 to 2014. The results show a widespread and statistically significant decline in the annual mean SWS across the TP. The models qualitatively captured this decreasing trend under all‐forcing scenarios. Detection and attribution analysis attributes the observed wind speed decline primarily to anthropogenic forcings, which account for most of the reduction, while natural forcings show no detectable influence. Among these anthropogenic factors, greenhouse gas (GHG) emissions were responsible for the greatest decrease in SWS over the TP. In comparison, the contributions from aerosol forcing and land use change were marginal; their negative regression coefficients indicate that they partially offset the overall weakening trend. The underlying mechanism involves GHG‐induced asymmetric warming. This warming weakened the pressure gradient over the TP by causing a greater increase in geopotential height over the mid‐high latitudes north of the plateau than over the regions to its south. These findings highlight the dominant influence of human activities on wind speed changes over the TP, with important implications for wind resource planning and ecological management.

Tourism's link to the Sustainable Development Goals has been a continuing emphasis, adding momentum to long-standing efforts to ensure tourism's sustainability. Tourism transport is one of the largest sources of anthropogenic carbon emissions, driving global ecological change with profound consequences for ecosystem functioning and biodiversity. Large-scale infrastructure projects such as railway expansion are increasingly promoted for their potential to reduce tourism-related carbon dioxide emissions, yet their spatial ecological impacts on regional carbon cycles and ecosystem services remain poorly understood. This study introduces the concept of Tourism Transport Ecological Efficiency (TTEE) to assess the relationship between human infrastructure, carbon emissions, and ecological sustainability. Using panel data from China's railway expansion between 2011 and 2018, the study provides spatially explicit evidence of how transport infrastructure shapes tourism's ecological footprint. Results show that non-Eastern regions experienced a greater increase in TTEE (8.7%) compared to Eastern regions (5.5%), highlighting regional disparities in tourism transport ecological sustainability. Railway density had a significant positive direct effect on TTEE, particularly pronounced in non-Eastern regions. Additionally, a significant indirect effect of railway density in nearby regions was identified. These findings reveal the interconnected ecological impacts of transport systems and underscore the importance of regionally targeted railway investment strategies. By bridging infrastructure development with ecological processes, this study advances understanding of how tourism transport can be aligned with global carbon reduction goals and ecosystem protection.

The sustainable transformation of electronics supply chains (ESCs) increasingly relies on effective green supply chain planning under carbon pricing and demand uncertainty. However, prior studies often lack an integrated framework that jointly considers carbon taxation, green technology investment, and profitability—environment trade-offs in forward and reverse supply chains. To address this gap, this study proposes a fuzzy multi-goal optimization model using linear goal programming under progressive carbon taxation. The model incorporates fuzzy demand (triangular fuzzy numbers), carbon emissions, carbon taxes, and green investment costs and is converted into a solvable linear form via a defuzzification-based procedure to simultaneously achieve multiple aspiration levels for economic and environmental objectives. A real-world ESC case validates the model. The results show that carbon taxation and green investments can reduce emissions while maintaining profitability, with total cost and emission sensitivity of ±10–20% across different policies and demand uncertainty settings. The findings support adaptive, policy-aware planning by guiding green investment intensity and forward–reverse logistics decisions to balance cost efficiency and emissions reduction and provide actionable insights for managers facing progressive carbon pricing regulations.

The need to mitigate climate change has increased interest in Waste-to-Energy (WtE) plants, which reduce landfill use while generating power, but remain significant sources of carbon dioxide emissions. Carbon Capture, Utilization, and Storage (CCUS) represents a promising pathway to substantially reduce emissions from WtE facilities and, when applied to WtE, it can enable net-zero or carbon-negative systems by capturing both non-biogenic and biogenic CO2. This review systematically analyzes existing Waste-to-Energy plants implementing carbon capture technologies. By collecting and critically assessing the available technical and operational information, this work provides a comprehensive synthesis that is currently lacking in the literature. Based on the reported data, only a limited number of WtE plants with CCUS are operating worldwide. Among these, facilities with the most detailed publicly available information are located in Saga City (Japan), Twence (The Netherlands), Klemetsrud (Norway), Duiven (The Netherlands), and Copenhagen (Denmark). This review highlights the current deployment status of WtE + CCUS systems and identifies key insights to support future research and large-scale implementation.

The rapid urbanization of cities has exacerbated traffic congestion, resulting in significant environmental impacts, including elevated greenhouse gas emissions and deteriorating air quality. Traffic management systems, while effective in many contexts, often fail to consider the ecological and dynamic complexities of modern urban environments. This paper introduces MM-STMAP, a framework for urban traffic management that integrates multi modal perception with deep reinforcement learning. The approach utilizes a spatio temporal graph convolutional network to model intricate traffic patterns across diverse urban environments, while incorporating real-time environmental data, including meteorological factors, to address the ecological limitations of traditional traffic systems. A linear attention mechanism is employed to optimize computational efficiency in processing large-scale, dynamic traffic data, thereby enhancing both operational performance and energy consumption. The multi agent reinforcement learning structure governs the coordination of traffic signals across intersections, achieving a dual optimization of reduced vehicular delays and minimized emissions. Empirical evaluations on major metropolitan datasets demonstrate that MM-STMAP outperforms existing traffic management methods and significantly enhances traffic flow efficiency. The model's ability to integrate heterogeneous data streams spanning traffic sensors and environmental reports enables a comprehensive and adaptive approach to urban mobility, supporting the development of sustainable smart city infrastructure.

Intensive swine production contributes significantly to the global protein supply but generates considerable environmental pressure, particularly through greenhouse gas emissions and surplus slurry management. Anaerobic digestion (AD), especially (co-AD), has been widely investigated as a mitigation strategy to enhance renewable energy generation and nutrient recovery. This systematic review synthesizes life cycle assessment (LCA) studies published between 2019 and 2025 that evaluated AD systems treating swine slurry, following the PRISMA 2020 guidelines. Across diverse methodological approaches and regional contexts, the literature consistently shows that AD can reduce global warming potential compared with conventional slurry management, with stronger environmental benefits when biogas is efficiently valorized and when swine slurry is co-digested with complementary organic substrat. Co-AD emerges as a key mitigation option by improving biogas yields, process stability, and overall environmental performance while also enabling better utilization of external organic waste. However, the results remain highly sensitive to operational factors such as methane leakage, digestate management, energy efficiency, and substrate selection. This review highlights the methodological inconsistencies among LCA studies and underscores the need for harmonized assessment frameworks and improved emission data. Overall, co-AD represents a promising pathway for enhancing the environmental sustainability of swine production systems when integrated into optimized, context-specific management strategies.

Context Methane emissions from ruminant livestock are a significant global greenhouse gas source, with 80% of New Zealand's methane emissions from livestock production. There is increasing pressure to reduce this to meet reduction targets set by the Paris agreement. There are approximately 1 million farmed deer in New Zealand. While breeding for lower methane has proven successful in sheep, direct methane measurements in deer are prohibitively difficult and expensive. Proxies from sheep may enable selection of lower-emitting animals. High and low methane sheep differ in rumen size and microbial profiles. Aims This study investigated individual variation in deer rumen size and whether size differences were associated with specific microbial profiles. Associations between production phenotypes (eye muscle area, carcass fat and lean proportions estimated by CT) and microbial profiles were also examined. Methods Ten-month-old animals (n=127) were CT-scanned to calculate rumen size, eye muscle area, and carcass fat/lean proportions. Rumen samples were processed into operational taxonomic units with 97% homology. Relationships between phenotypes and microbial profiles were estimated using linear mixed models in ASReml 3.0 and R software. Key Results No association was found between deer rumen size and ruminotype profiles linked to low methane in sheep. However, three individual taxonomic groups showed associations with deer rumen size and eye muscle area: Methanobrevibacter gottschalkii and Methanobrevibacter ruminantium, Fibrobacter, and Anoplodinium and Diplodinium. Conclusions While rumen size in deer does not currently show a clear link to low-methane ruminotypes identified in sheep, microbial composition exhibits notable associations with rumen size and production traits. Importantly, differences in taxonomic groups revealed that Methanobrevibacter gottschalkii and Methanobrevibacter ruminantium—previously thought to be competing species where one clade replaced the other—may not be in direct competition. The relationship between rumen size and methane-related microbial profiles warrants further study. Implications Understanding microbial associations in deer could inform breeding strategies aimed at reducing methane emissions. Identifying key taxa linked to rumen size and production traits may provide practical proxies for selecting low-emission animals without direct methane measurement.

Food systems are major drivers of global environmental change, accounting for about one-third of global greenhouse gas (GHG) emissions and contributing to land degradation, freshwater depletion, and biodiversity loss. Within this system, post-retail activities generate an estimated 18–20% of total food-related GHG emissions. In Europe, food service is responsible for roughly 12% of total food waste, making collective catering a strategic sector for sustainability interventions. Objective: Through menu design and composition, collective catering services can influence the environmental performance of thousands of meals served daily. This study introduces a novel meal-level scoring system—the App for the Environmental Impact Assessment of Dishes in Collective Catering (EcoRistApp, ERA)—designed to assess and communicate the environmental performance of institutional canteen dishes. Methods: EcoRistApp was developed and applied to a representative selection of first courses, second courses, and side dishes. Environmental impacts were quantified using Life Cycle Assessment (LCA) with SimaPro 9.5.5 software and the ReCiPe Midpoint (H) method. Normalized and weighted impact results were aggregated into a composite Environmental Impact Index (EII), which was then translated into a five-color interpretative scale to enhance usability and comprehension. Results: The analysis highlighted marked differences in environmental performance among dishes, largely driven by ingredient type and origin. Plant-based meals, such as lentil soup, consistently achieved lower impact scores, while dishes containing animal-derived ingredients, particularly beef and fish, showed higher impacts across multiple categories. Recipes combining high- and low-impact ingredients demonstrated potential for reducing overall environmental burdens. Conclusions: By converting complex LCA outcomes into an intuitive scoring system, EcoRistApp supports informed decision-making by catering operators and consumers, encourages plant-forward menu strategies, and contributes to the environmental transition of food service systems.

Life cycle assessment of greenhouse gas emissions and carbon balance of bread winter wheat cultivars: Varietal differentiation and straw management effects in the Ukraine

Carbon dioxide (CO2) and methane (CH4) are major greenhouse gases, and their increasing emissions contribute significantly to global warming. Dry reforming of methane (DRM) offers a promising route to mitigate these emissions by simultaneously utilizing both CO2 and CH4 and converting them into syngas, a valuable intermediate for producing fuels and chemicals. Nickel-based catalysts are widely used in DRM due to their high activity and cost-effectiveness. However, their performance depends strongly on metal loading and support properties. This study aims to investigate the effect of different NiO loadings (40, 50, and 60 wt%) on the structural and morphological characteristics of NiO-YSZ and NiO-SDC catalysts synthesized via the impregnation method. In this method, yttria-stabilized zirconia (YSZ) and samarium-doped ceria (SDC) powders were dispersed into a nickel precursor solution to form supported catalysts, which were then characterized to evaluate their structural integrity, crystallinity, and surface morphology. The results showed that higher NiO loadings generally improved the structural and morphological features, with NiO-SDC demonstrating better characteristics than NiO-YSZ. These findings provide essential insights that will guide future work on fabricating membranes using these catalysts for the CO2-CH4 dry reforming process.