Search Results (922)

Wastewater treatment plants (WWTPs) function as engineered ecosystems in which microbial consortia mediate nutrient cycling, xenobiotic degradation, and heavy metal detoxification. This review discusses a forward-looking roadmap that integrates microbial ecology, multi-omics diagnostics, and artificial intelligence (AI) for next-generation treatments. Meta-analyses suggest that a globally conserved core microbiome indicates sludge functions, with high predictive value for treatment stability. Multi-omics approaches, including metagenomics, metatranscriptomics, and environmental DNA (eDNA) profiling, have integrated microbial composition with greenhouse gas (GHG) emissions, showing that WWTPs contribute 2–5% of anthropogenic nitrous oxide (N₂O) emissions. Emerging AI-enhanced eDNA models have achieved >90% predictive accuracy for effluent quality and antibiotic resistance gene (ARG) prevalence, facilitating near-real-time monitoring and adaptive control of effluent quality. Key advances include microbial strategies for degrading organic pollutants, pesticides, and heavy metals and monitoring industrial effluents. This review highlights both translational opportunities, including engineered microbial consortia, AI-driven digital twins and molecular indices, and persistent barriers, including ARG dissemination, resilience under environmental stress and regulatory integration. Future WWTPs are envisioned as adaptive, climate-conscious biorefineries that recover resources, mitigate ecological risks, and reduce their carbon footprint. Full article

Higher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in Türkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO₂e (1.19 tCO₂e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions. Full article

Grassland ecosystems, a major component of the global carbon (C) and nitrogen (N) cycles, are increasingly impacted by anthropogenic N addition. However, a comprehensive, integrated assessment of all three major greenhouse gas (GHG) responses in grasslands is lacking. Here, we present the first global meta-analysis to evaluate the effects of N addition on all three major GHGs (i.e., nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂) fluxes) in grasslands. Our results show that N addition significantly and consistently stimulates N₂O emissions, a response primarily modulated by key drivers such as grassland type, management, N addition rate and forms, humidity index (HI), and soil pH, clay, and total nitrogen (TN) content. In contrast, N addition has a minimal and non-significant overall effect on soil CO₂ fluxes. For CH₄, N addition causes a context-dependent reduction in uptake, an effect that is exacerbated by high mean annual precipitation (MAP) and soil bulk density (BD) but alleviated by high soil organic carbon (SOC) content. Notably, both CO₂ and N₂O showed a dose-dependent effect, while soil CO₂ fluxes were unexpectedly suppressed by nitrate nitrogen (NO₃⁻) addition. Our findings indicate that the pronounced and consistent increase in N₂O emissions is the dominant factor in GHG-related impacts in grasslands, implying a net positive climate forcing in grasslands from N enrichment, even if there is insufficient data to calculate net climate forcing directly. Our study highlights the heterogeneous nature of grassland GHG responses and provides critical insights for developing sustainable N management strategies to mitigate climate change. Full article

Fungi in tropical ecosystems remain an understudied yet critical component of climate change mitigation, particularly within the Land Use, Land-Use Change, and Forestry (LULUCF) sector. This review highlights their dual role in reducing greenhouse gas (GHG) emissions by regulating carbon dioxide (CO₂), methane (CH₄), and nitrous oxides (N₂O) while enhancing long-term carbon sequestration. Mycorrhizal fungi are pivotal in maintaining soil integrity, facilitating nutrient cycling, and amplifying carbon storage capacity through symbiotic mechanisms. We synthesize how fungal symbiotic systems under LULUCF shape ecosystem networks and note that, in pristine ecosystems, these networks are resilient. We introduce the concept of Nature-based Culture (NbC) to describe symbiotic self-cultures sustaining ecosystem stability, biodiversity, and carbon sequestration. Case studies demonstrate how the NbC concept is applied in reforestation strategies such as AeroHydro Culture (AHC), the Integrated Mangrove Sowing System (IMSS), and the 4N approach (No Plastic, No Burning, No Chemical Fertilizer, Native Species). These approaches leverage mycorrhizal networks to improve restoration outcomes in peatlands, mangroves, and semi-arid regions while minimizing land disturbance and chemical inputs. Therefore, by bridging fungal ecology with LULUCF policy, this review advocates for a paradigm shift in forest management that integrates fungal symbioses to strengthen carbon storage, ecosystem resilience, and human well-being. Full article

Global warming, driven by greenhouse gas (GHG) emissions, is accelerating globally and highlights the need for effective mitigation strategies. This study assesses the economic feasibility of rainbow trout aquaculture by incorporating GHG emissions into its analysis, thereby contributing to mitigation efforts in the fisheries sector. Focusing on two farming systems—recirculating aquaculture systems (RAS) and flow-through systems (FTS)—we estimated GHG emissions and conducted an economic evaluation using data collected through field surveys. The average GHG emission was 7.14 kg CO₂ eq per kilogram of trout produced, with RAS showing lower emissions than FTS. Electricity and feed were identified as the primary emission sources. The economic analysis revealed an average net present value (NPV) of USD 987,609 and an internal rate of return (IRR) of 18%, with RAS outperforming FTS in profitability. A sensitivity analysis under carbon pricing showed that economic feasibility was maintained, but the NPV declined by about 24% under the carbon tax scenario. Overall, these findings underscore the importance of balancing profitability and emission reduction for sustainable aquaculture management. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the multimodel ensemble from the Couple Model Intercomparison Project 6 and a temperature threshold method to investigate the EAWC changes during the period 1979–2100. The results show that the EAWC has been undergoing widespread and robust changes in response to global warming. The winter length in East Asia has shortened and will continue shortening owing to later onsets and earlier withdrawals, leading to a drastic contraction in length from 100 days in 1979 to 43 days (27 days) in 2100 under SSP2-4.5 (SSP5-8.5). While most regions of the East Asian continent are projected to become warmer in winter, the Japan and marginal seas of northeastern Asia will face the risks from colder winters with more frequent extreme cold events, accompanied by less precipitation. Meanwhile, the Tibetan Plateau is very likely to have colder winters in the future, though its surface snow amounts will significantly decline. Greenhouse gas (GHG) emissions are found to be responsible for the EAWC changes. GHG traps heat inside the Earth’s atmosphere and notably increases the air temperature; moreover, its force modulates large-scale atmospheric circulation, facilitating an enhanced and northward-positioned Aleutian low together with a weakened Siberian high, East Asian trough, and East Asian jet stream. These two effects work together, resulting in a contracted winter with robust and uneven regional changes in the EAWC. This finding highlights the urgency of curbing GHG emissions and improving forecasts of the EAWC, which are crucial for mitigating their major ecological and social impacts. Full article

This study explores the convergence in greenhouse gas emissions (GHGs) and its determinants across 38 OECD countries during the period 1996–2022, employing the novel approach which combined club convergence method with supervised machine learning algorithm Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) method. The findings reveal the presence of three distinct convergence clubs shaped by structural economic and institutional characteristics. Club 1 exhibits low energy efficiency, high fossil fuel dependence, and weak governance structures; Club 2 features strong institutional quality, advanced human capital, and effective environmental taxation; and Club 3 displays heterogeneous energy profiles but converges through socio-economic foundations. While traditional growth-related drivers such as technological innovation, foreign direct investments, and GDP growth play a limited role in explaining emission convergence, energy structures, institutional and policy-related factors emerge as key determinants. These findings highlight the limitations of one-size-fits-all climate policy frameworks and call for a more nuanced, club-specific approach to emission mitigation strategies. By combining convergence theory with interpretable machine learning, this study contributes a novel empirical framework to assess the differentiated effectiveness of environmental policies across heterogeneous country groups, offering actionable insights for international climate governance and targeted policy design. Full article

Different strategies are used in organic and conventional cultivation, which can significantly influence crop yield, greenhouse gas (GHG) emissions, and soil quality. However, the relative efficiency of these fertilization practices has not been systematically compared. The objective of this study was to evaluate the impacts of organic, conventional, and semi-organic fertilization systems on soil properties, crop productivity, and GHG emissions through a comprehensive meta-analysis. The analysis showed that conventional systems had the highest increase in nitrous oxide (N₂O) emissions (+62%), followed by semi-organic (+55%) and organic (+21%). Soil texture strongly influenced methane (CH₄) and carbon dioxide (CO₂) fluxes, with clay soils showing the highest CH₄ response (+50%). Cropping practices such as intercropping and crop rotation enhanced soil nitrate availability (+18%), while vegetable and cereal systems improved crop yield by +29% and +19%, respectively. Importantly, semi-organic systems increased yield (+25%) while reducing greenhouse gas intensity (+13%), especially in cereals under intercropping. Integrating organic inputs into semi-organic systems, especially in cereal cultivation under intercropping practices, appears to reduce the carbon intensity per unit yield while maintaining productivity. These findings underscore the importance of context-specific management strategies to optimize agronomic performance and mitigate environmental impacts. Full article

This study evaluated soil–atmosphere greenhouse gas (GHG) fluxes—including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—in cacao agroecosystems on São Tomé Island, Central Africa. The field campaign was conducted between April and May 2025, coinciding with the transition from the short rainy season to the onset of the dry period. The sampling design comprised two system types (biodiverse and conventional), two crop development stages (growing and productive), and two climatic zones (wet and dry). Gas fluxes were measured using the static chamber method and analyzed in relation to climatic, topographic, and edaphic variables. CO₂ fluxes were the dominant contributor to total emissions, accounting for approximately 97.4% of the global warming potential (GWP), while CH₄ and N₂O together contributed less than 3%. The highest CO₂ emissions occurred in conventional systems during the growing stage in the wet zone (125.5 ± 11.41 mg C m⁻² h⁻¹). CH₄ generally acted as a sink, particularly in conventional systems in the dry zone (−12.58 ± 2.35 μg C m⁻² h⁻¹), although net emissions were detected in biodiverse systems in the wet zone (5.08 ± 1.50 μg C m⁻² h⁻¹). The highest N₂O fluxes were observed in conventional growing systems (32.28 ± 5.76 μg N m⁻² h⁻¹). GHG dynamics were mainly regulated by climatic factors—such as air temperature, relative humidity, and precipitation—and by key edaphic properties, including soil pH, soil organic carbon, soil temperature, and clay content. Projected GWP values ranged from 9.05 ± 2.77 to 40.9 ± 6.23 Mg CO₂-eq ha⁻¹ year⁻¹, with the highest values recorded in conventional systems in the growing stage. Overall, our findings underscore the potential of biodiversity-based agroforestry as a climate-smart practice to mitigate net GHG emissions in tropical cacao landscapes. Full article

This study analyzes the effects on global greenhouse gas (GHG) emissions of various combinations of lifecycle refrigerant management (LRM) practices and refrigerant substitutions in residential air conditioners (ACs) until the year 2070. Six scenarios involving three refrigerant types with different levels of global warming potential (GWP)—high, medium, and ultralow—and three levels of LRM involving leakage reduction during operation and end-of-life refrigerant recovery are examined. The findings reveal that combining medium GWP refrigerants (e.g., R32) with the highest level of LRM could achieve as much as a 95% reduction in emissions (933 MtCO₂eq) by 2050 and a 97% reduction (1660 MtCO₂eq) by 2070, when compared to using high GWP refrigerants (e.g., R410A). The substitution of ultralow GWP refrigerants (e.g., R290) is projected to achieve up to a 97% emissions reduction (954 MtCO₂eq) by 2050 and a 100% (1709 MtCO₂eq) reduction by 2070. Global mean temperature decreases in 2070 are nearly identical under scenarios in which either medium GWP refrigerants or ultralow GWP refrigerants are combined with the highest level of LRM (0.067 °C versus 0.069 °C). The implication is that combining medium GWP refrigerants, already underway in some regions, with the highest level of LRM offering an effective and pragmatic strategy for mitigating the climate impacts of refrigerant emission from residential ACs. Full article

In the current context of the international climate agenda, understanding both the sources of greenhouse gas (GHG) emissions and the mechanisms for their mitigation is a fundamental requirement for low-carbon development strategies. Kazakhstan has pledged to reduce its GHG emissions by 15–25% by 2030, relative to 1990 levels, and to achieve carbon neutrality by 2060. However, there is no unified methodology for comprehensively assessing the national carbon balance, particularly at the regional scale. This study addresses this gap by analyzing GHG emissions and carbon sequestration capacities across Kazakhstan’s regions using a sectoral disaggregation approach and scenario-based modeling aligned with IPCC methods. Emission hotspots were identified in the energy sector (328 MtCO₂-eq), agriculture (118 MtCO₂-eq—primarily from pasturelands), and transport (7 MtCO₂-eq). In contrast, current carbon sinks—mainly forest ecosystems and abandoned pasturelands—account for only 3.97 and 13.9 MtCO₂-eq, respectively. The research evaluates the technical potential for emissions reduction through the best available technologies (BAT), livestock management, partial transition to gas-powered vehicles, and reforestation. A geoengineering scenario combining all measures suggests that Kazakhstan could meet its 2030 climate targets, although full carbon neutrality by 2060 would remain out of reach under current policy trajectories. The Akmola region is examined as a representative case study, demonstrating a possible 35% reduction in net emissions by 2035. This work contributes a regionally nuanced, data-driven framework for integrating ecosystem services into national climate policy and identifies nature-based solutions—especially forest management—as essential components of Kazakhstan’s decarbonization pathway, offering insights for other carbon-intensive economies. Full article

To mitigate climate change, achieving net-zero carbon dioxide (CO₂) emissions across all sectors is essential. In the floricultural and landscaping industries, a key concern is whether the production and use of landscape plants contribute to CO₂ reduction. However, few studies have assessed the greenhouse gas (GHG) budgets of landscape plant production. This study quantified all major components of GHG budgets to determine whether herbaceous plant production acts as a GHG sink or source. Kentucky bluegrass sod and three herbaceous plants (Hedera canariensis, Liriope muscari, and Tagetes patula) were investigated for their GHG (CO₂, CH₄, and N₂O) budgets. For Kentucky bluegrass sod production, the total GHG budget was calculated as −17.764 t-CO₂e ha⁻¹ year⁻¹, comprising carbon sequestration (23.014 t-CO₂/ha), GHG fluxes (0.049 t-CO₂e/ha), and GHG emissions from energy and resource consumption (5.201 t-CO₂e/ha). These results indicate that Kentucky bluegrass sod production functions as a GHG sink. In contrast, the total GHG budgets for potting production of the three herbaceous plants were positive, primarily due to higher GHG emissions from the use of potting soil and granular pesticides. To reduce net CO₂ emissions in herbaceous plant production, using biochar as a growth medium and minimizing granular pesticides is an effective approach. Full article

With agriculture being the second biggest contributor to greenhouse gas (GHG) emissions through the excessive use of fertilizers, machinery, and inefficient farming practices, global efforts to reduce emissions have been intensified, opting for smarter, data-driven solutions. However, while machine learning (ML) offers powerful predictive capabilities, its black-box nature presents a challenge for trust and adoption, particularly when integrated with auditable financial technology (FinTech) principles. To address this gap, this work introduces a novel, explanation-focused GHG emission optimization framework for IoT-enabled smart agriculture that is both transparent and prescriptive, distinguishing itself from macro-level land-use solutions by focusing on optimizable management practices while aligning with core FinTech principles and pollutant stock market mechanisms. The framework employs a two-stage statistical methodology that first identifies distinct agricultural emission profiles from macro-level data, and then models these emissions by developing a cluster-oriented principal component regression (PCR) model, which outperforms simpler variants by approximately 35% on average across all clusters. This interpretable model then serves as the core of a FinTech-aligned optimization framework that combines cluster-oriented modeling knowledge with a sequential least squares quadratic programming (SLSQP) algorithm to minimize emission-related costs under a carbon pricing mechanism, showcasing forecasted cost reductions as high as 43.55%. Full article