Search Results (2,670)

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial for achieving carbon budget balance. This article synthesizes the impact of farmland management practices on GHG emissions, evaluates prevalent accounting methods and their applicable scenarios, and proposes mitigation strategies based on systematic analysis. The present review (2000-2025) indicates that fertilizer management dominates research focus (accounting for over 50%), followed by water management (approximately 18%) and tillage practices (approximately 14%). Critically, the effects of these practices extend beyond GHG emissions, necessitating concurrent consideration of crop yields, soil health, and ecosystem resilience. Therefore, it is necessary to conduct joint research by integrating multiple approaches such as water-saving irrigation, conservation tillage and intercropping of leguminous crops, so as to enhance productivity and soil quality while reducing emissions. The GHG accounting framework and three primary accounting methods (In situ measurement, Satellite remote sensing, and Model simulation) each exhibit distinct advantages and limitations, requiring scenario-specific selection. Further refinement of these methodologies is imperative to optimize agricultural practices and achieve meaningful GHG reductions. Full article

As the global climate discourse shifts from mitigation to achieving net-negative emissions, there is a critical need for replicable, real-world models of climate-positive development at a regional scale, particularly in the Global South. This study addresses this gap by conducting a detailed greenhouse gas (GHG) inventory of four diverse provinces in Thailand and analyzing the results through the newly proposed Climate-Positive Pathways Framework (CPPF). Our findings reveal that all four provinces function as significant net-negative GHG sinks. They achieve this status through three distinct archetypes: a Conservation-Dependent pathway, an Agricultural Frontier pathway, and a novel Agro-Sink pathway. Most significantly, in the Agro-Sink model, we find that in specific economic contexts, managed agricultural landscapes can surpass natural forests as the primary driver of regional carbon removal. This typology provides a new, landscape-scale paradigm for cleaner production, proposing these three archetypes as transferable, evidence-based models for regional policymakers. This underscores that effective climate action requires context-specific regional planning that strategically leverages both natural and agricultural capital. Full article

Air conditioners are a critical adaptation measure against heat- and cold-related risks under climate change. However, their electricity use and refrigerant leakage increase greenhouse gas (GHG) emissions. This study developed a data-driven life-cycle assessment (LCA) framework for residential room air conditioners in Japan by integrating large-scale field operation data with life-cycle climate performance (LCCP) modeling. We aggregated 1 min records for approximately 4100 wall-mounted split units and evaluated the 10-year LCCP across nine climate regions. Using the annual operating hours and electricity consumption, we classified the units into four behavioral quadrants and quantified the life-cycle GHG emissions and parameter sensitivities for each. The results show that the use-phase electricity dominated the total emissions, and that even under the same climate and capacity class, the 10-year per-unit emissions differed by roughly a factor of two between the high- and low-load quadrants. The sensitivity analysis identified the heating hours and the setpoint–indoor temperature difference as the most influential drivers, whereas the grid CO₂ intensity, equipment lifetime, and refrigerant assumptions were of secondary importance. By replacing a single assumed use scenario with empirical profiles and behavior-based clusters, the proposed framework improves the representativeness of the LCA for air conditioners. This enabled the design of cluster-specific mitigation strategies. Full article

Carbon farming represents a strategic approach to enhancing agricultural sustainability while reducing greenhouse gas (GHG) emissions. In Türkiye, agriculture accounted for approximately 14.9% of national GHG emissions in 2023, dominated by methane (CH₄) and nitrous oxide (N₂O). By increasing carbon storage in soils and vegetation, carbon farming can improve soil health, water retention, and climate resilience, thereby contributing to mitigation efforts and sustainable rural development. This study reviews and synthesizes international and national evidence on carbon farming mechanisms, practices, payment models, and adoption enablers and barriers, situating these insights within Türkiye’s agroecological and institutional context. The analysis draws on a systematic review of peer-reviewed literature, institutional reports, and policy documents published between 2015 and 2025. The findings indicate substantial mitigation potential from soil-based practices and livestock- and manure-related measures, yet limited uptake due to low awareness, capacity constraints, financial and administrative barriers, and regulatory gaps, highlighting the need for region-specific approaches. To support implementation and scaling, the study proposes a policy-oriented, regionally differentiated and digitally enabled MRV framework and an associated implementation pathway designed to reduce transaction costs, enhance farmer participation, and enable integration with emerging carbon market mechanisms. Full article

This paper explores the intersection of digital technologies, sustainable agriculture, and biodiversity conservation within the framework of the European Green Deal. The study investigates how intelligent agricultural practices—enabled by digital tools such as sensors, AI, and IoT—can enhance soil health and conserve agrobiodiversity. A systematic literature review was conducted to map out current research trajectories, identify the taxonomic focus areas in biodiversity monitoring, and assess the integration of digital tools. Results show a significant upward trend in publications linking digitalization and sustainability in agriculture. Findings highlight that pollinators and soil biota dominate monitoring focus, while technologies like remote sensing and AI show increasing adoption. The study concludes that intelligent agriculture offers a path toward ecological and economic resilience in rural landscapes, aligning with the EU’s green transition agenda. Full article

Background: Healthcare is a large contributor to greenhouse gas (GHG) emissions, contributing to climate change and health impairments. However, the magnitude of health and climate benefits of local and regional GHG mitigation strategies has not been well quantified. Few studies have demonstrated the use of public tools for this purpose in healthcare facilities. Methods: We evaluated several renewable energy and energy efficiency scenarios focused on one academic medical center in New York State. We used the Environmental Protection Agency’s (EPA) publicly available AVoided Emissions and geneRation Tool to estimate avoided GHG and health-harmful air pollutant emissions. The economic value of the resulting avoided health and climate damages was quantified using EPA’s CO-Benefits Risk Assessment screening tool. Results: Transitioning one healthcare institution to 100% solar energy and improving energy efficiency by 25% could yield approximately $807,000 to $1.5 million in annual health savings, with an additional $2.3 million benefits in avoided climate damages. There is an approximate $108.5–$196.6 million in annual climate and health benefits when extrapolating these energy solutions to hospitals across the same state. Conclusions: There are significant health savings from healthcare GHG mitigation strategies. This application of publicly available and accessible tools demonstrates ways to integrate climate and health benefits into local decision-making around climate change mitigation and sustainability efforts. Full article

Wind-assisted ship propulsion (WASP) has gained considerable interest as a means of reducing fuel consumption and Greenhouse Gas (GHG) emissions, with further benefits when combined with weather-optimized routing. This study employs and extends a National Technical University of Athens (NTUA) weather-routing optimization tool to more realistically assess WASP performance through integrated modeling. The original tool minimized fuel consumption using forecasted weather data and a physics-based performance model. A previous extension to account for the WASP effect introduced a 1-Degree Of Freedom (DOF) model that accounted only for longitudinal hydrodynamic and aerodynamic forces, estimating the reduced main-engine power required to maintain speed in given conditions. The current study incorporates a 3-DOF model that includes side forces and yaw moments, capturing resulting drift and rudder deflection effects. A Kamsarmax bulk carrier equipped with suction sails served as the case study. Initial simulations across various operating and weather conditions compared the two models. The 1-DOF model predicted fuel-saving potential up to 26% for the tested apparent wind speed and the range of possible headings, whereas the 3-DOF model indicated that transverse effects reduce WASP benefits by 2–7%. Differences in Main Engine (ME) power estimates between the two models reached up to 7% Maximum Continuous Rating (MCR) depending on the speed of wind. The study then applied both models within a weather-routing optimization framework to assess whether the optimal routes produced by each model differ and to quantify performance losses. It was found that the revised optimal route derived from the 3-DOF model improved total Fuel Oil Consumption (FOC) savings by 1.25% compared with the route optimized using the 1-DOF model when both were evaluated with the 3-DOF model. Full article

The dairy cattle sector is a critical source of anthropogenic greenhouse gas (GHG) emissions and must transition to low-carbon farming to meet global climate goals. However, a systematic synthesis of the evolution and future trajectories of GHG emissions research in this field is still lacking. This study aims to address this gap by conducting a bibliometric analysis to elucidate the research evolution, hotspots, and future trends in GHG emissions from dairy cattle farming. The results showed a steady linear increase in publications (R² = 0.80), with the highest average annual growth rate of approximately 45.9% (2009–2014). The United States (91), Italy (68), the Netherlands (58), Germany (51), and Ireland (45) were the most productive countries, accounting for 60.2% of the global total. Both institutional (0.0347) and author (0.0069) collaboration densities in the global network are low, indicating a lack of a tightly integrated collaborative framework. The research hotspots evolved from foundational themes (e.g., agriculture, grasslands; 2005–2010) to environmental pressures and mitigation (2010–2020). A recent thematic shift (2020–2025) is evident towards specific mitigation strategies like rumen fermentation, sustainability, and fertility, indicating a field oriented toward low-carbon, high-efficiency transformation. The analysis underscores the critical perspective provided by life cycle assessment for this transition. This study provides a comprehensive map of the research landscape, highlighting future priorities. Grounded in a holistic life cycle assessment framework, future work should integrate technology, management, and policy to steer the global dairy industry towards a sustainable future that balances environmental health with economic viability. Full article

Worldwide the adoption of electric vehicles (EVs) is recognized as a key strategy for reducing transport-related greenhouse gas (GHG) emissions, a major contributor to global warming and climate change. The objective of this pilot study is to examine the key variables that might have influenced electric vehicle (EV) purchase decisions among current EV owners and how they are aligned or different for the prospective EV owners in North Carolina (NC). By adopting a web-based survey for data collection, the study specifically aims to identify economic, demographic, environmental, and commuting behaviors, along with existing government policies and incentives that might motivate consumer choices regarding EV adoption. Most existing EV owners who participated in the survey identified themselves as college-educated White men with USD 100 K or higher income, have more than two cars, commute more than 30 min, and live in single-family homes with EV charging. In contrast, among non-EV owners who plan to adopt an EV within the next three years, a significant proportion are non-White, women, and earn USD 50,000 or less annually. While home charging is important to both current EV owners and non-EV owners, EV incentive policies and proximity to public changing stations are found to be more important to non-EV owners’ decision to adopt EVs. A reasonable conclusion from this research is that expanding EV-friendly policies, incentives, and infrastructure will encourage first-time EV ownership in NC while providing deeper insights into the dynamics of sociodemographic among both EV owners and non-EV owners. 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

The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO₂) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit for 55’ emissions-reduction target. Türkiye faces significant challenges in achieving net-zero greenhouse gas (GHG) emissions, particularly as a developing country confronting the impacts of climate change and in the market situation, such as the effects of the ongoing Russia-Ukraine conflict, limited access to affordable raw materials, and rising operational costs. This study serves as a guideline for the Turkish steel sector’s roadmap towards modernization and eventual compliance with net-zero targets. The consideration and integration of new technologies planned for the Turkish steel industry, in both electric arc furnace (EAF) and blast furnace-basic oxygen furnace (BF-BOF) facilities, have been outlined in conjunction with green hydrogen and with Carbon Capture and Storage (CCS) technologies. Four different scenarios were analysed to understand the reduction in CO₂ emissions: (1) In a Business-As-Usual (BAU) scenario without any reduction, (2) 39.9% CO₂ emission reduction with the Moderate scenario, (3) 59.6% reduction with the Advanced scenario, and (4) 82.9% reduction in CO₂ emissions from the Turkish steel sector with the Net-Zero scenario. To quantify the uncertainty in these long-term projections, a Monte Carlo simulation was conducted, generating probabilistic confidence intervals that reinforce the robustness and credibility of the net-zero pathway. The official roadmap for the sector is not available as of today; however, an in-depth discussion with a policy innovation leading to it is the objective of this study. Full article

This study presents an integrated Life Cycle Sustainability Assessment (LCSA) of the Natural gas-to-methanol (NGTM) and methanol-to-gasoline (MTG) pathways using Aspen HYSYS process modeling, Environmental Life Cycle Assessment (LCA), Social Life Cycle Assessment (SLCA), and Life Cycle Costing (LCC). The results reveal significant variability in sustainability performance across process units. The DME and MTG Reactors Section generates the highest direct greenhouse gas (GHG) emissions at 0.86 million tons CO₂-eq, representing 54.9% of total global warming potential, while the Compression Section consumes 2717.5 TJ/year of energy, making it the dominant source of electricity-related indirect emissions. Distillation and Purification withdraws 31,100 Mm³/year of water—approximately 99% of total demand—yet delivers 86.6% of the overall economic surplus despite high operating costs. Social impacts concentrate in the Methanol Reactor Looping and DME and MTG Reactors Sections, with human health burdens of 305.79 and 804.22 DALYs, respectively, due to catalyst handling and high-pressure operations. Sensitivity results show that methanol purity rises from 0.9993 to 0.9994 with increasing methane content, while gasoline output decreases from 3780 to 3520 kg/h as natural gas flow increases. The findings provide process-level evidence to support sustainable development of natural gas-based fuel conversion industries, aligning with Qatar National Vision 2030 objectives for industrial diversification and lower-carbon energy systems. Full article

The European Union’s enhanced greenhouse gas (GHG) reduction targets for 2030 make the large-scale deployment of carbon capture and storage (CCS) technologies essential to achieve deep decarbonization goals. Within this context, this study aims to advance CCS research by developing and testing a pilot-scale system that integrates gasification for syngas and power production with CO₂ absorption and solvent regeneration. The work focuses on improving and validating the operability of a pilot plant section designed for CO₂ capture, capable of processing up to 40 kg CO₂ per day through a 6 m absorber and stripper column. Experimental campaigns were carried out using different amine-based absorbents under varied operating conditions and liquid-to-gas (L/G) ratios to evaluate capture efficiency, stability, and regeneration performance. The physical properties of regenerated and CO₂-saturated solvents (density, viscosity, pH, and CO₂ loading) were analyzed as potential indicators for monitoring solvent absorption capacity. In parallel, a process simulation and optimization study was developed in Aspen Plus, implementing a split-flow configuration to enhance energy efficiency. The combined experimental and modeling results provide insights into the optimization of solvent-based CO₂ capture processes at pilot scale, supporting the development of next-generation capture systems for low-carbon energy applications. Full article

Global warming has become a top priority on the international environmental policy agenda. The recent rise in CO₂ emissions observed in Türkiye has further emphasized the country’s critical role in addressing climate change. This study aims to estimate Türkiye’s CO₂ emissions through 2030 and identify the key socioeconomic and environmental factors driving these emissions, using multiple linear regression (MLR) and time series analysis methods. Six primary variables are examined: population, gross domestic product (GDP), CO₂ intensity, per capita energy consumption, total greenhouse gas (GHG) emissions, and forest area. This study introduces a new multivariate forecasting framework that integrates time series projections with multiple linear regression and elasticity-based sensitivity analysis, providing novel insight into the relative influence of key emission drivers compared to prior research. The results suggest that, if current policy trends persist, Türkiye’s CO₂ emissions will increase substantially by 2030. Variables such as GHG emissions, energy consumption, and population growth are found to have an increasing effect on emissions, while the limited expansion of forest areas is insufficient to offset this trend. In contrast, the negative correlation between GDP and CO₂ emissions suggests that economic growth can occur in alignment with environmental sustainability. The model’s validity is supported by a high R² (0.99) value and low error rates. The findings indicate that Türkiye must reassess its current strategies and strengthen policies targeting renewable energy, energy efficiency, and carbon sinks to achieve its climate goals. The proposed framework provides a transparent basis for climate planning and policy prioritization in Türkiye. Full article

Reconciling agricultural productivity with greenhouse gas (GHG) mitigation remains a pivotal challenge for achieving climate-smart food systems. This study evaluates the capacity of legume-based crop rotations to balance economic viability, yield stability, and GHG reduction in the North China Plain. A two-year randomized complete block field experiment compared six cropping systems: conventional wheat–maize (WM) rotations and legume-integrated systems (wheat–soybean, WS; wheat–soybean–maize, WSM), under fertilized and unfertilized regimes. Results revealed that nitrogen fertilization increased cumulative N₂O emissions and global warming potential (GWP), with seasonal peaks occurring post-fertilization. Legume systems enhanced CH₄ uptake but showed no significant effect on N₂O emissions compared to conventional systems. N₂O fluxes correlated positively with soil moisture and soil temperature, while CH₄ uptake increased with soil moisture alone. Soybean phases reduced short-term yields by 32–52% relative to the maize yield of conventional systems, but boosted subsequent wheat/maize productivity by 2–47% through hydraulic redistribution and N priming. The wheat–soybean rotation with 200 kg N ha⁻¹ (WS200) achieved optimal sustainability, delivering the highest net profit (8061.56 USD ha⁻¹) alongside a 9% reduction in global warming potential (3980.21 kg CO_2-eq ha⁻¹) versus conventional systems. These findings provide actionable insights for sustainable intensification in global cereal systems, demonstrating that strategic legume integration can advance both food security and climate goals. Full article