Search Results (993)

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R²) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R² and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO₂ net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (K_water), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning. 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

County-level administrative areas serve as fundamental components in China’s territorial spatial governance, and the precision and consistency of their carbon emission reduction policies are directly linked to the efficacy of the “dual-carbon” strategy’s execution. However, the spatiotemporal evolution characteristics, future trends, and driving factors of carbon emissions from territorial spatial function (TSF) utilisation at the county level remain unclear, posing a fundamental theoretical issue that local governments urgently need to address when formulating carbon reduction policies. This study developed a framework to simulate the spatial distribution of carbon emissions resulting from land use at the county level. It simulated the carbon emissions in Qionglai City from 2009 to 2023, analysed the spatial-temporal evolution characteristics and future trends using global Moran’s I, the Getis-Ord $G_i^{*}$ index, and the Hurst index, and employed the Geographically and Temporally Weighted Regression (GTWR) model for analysis. The findings indicated the following: (1) From 2009 to 2023, the city’s total carbon emissions increased from 852,300 tonnes to 1,422,500 tonnes, showing a significant phased trend. Among these, rural production spaces (RPSs) were the primary carbon sources, accounting for over 70% of annual carbon emissions each year. (2) County carbon emissions exhibit a pronounced positive geographical correlation and aggregation distribution, characterised by notable regional heterogeneity. (3) From 2009 to 2023, the city’s regional carbon emissions rose dramatically by 65.69%, while 29.66% of the areas experienced negligible increases; 99% of the regions are projected to maintain the historical growth trend, but this continuity exhibits spatial and temporal variations. (4) Economic growth, industrial structure, and development intensity are the core driving factors of carbon emissions at the county level, with spatial variations in their impact. The research findings not only provide a basis for Qionglai City, China, to formulate precise and sustainable carbon reduction policies (such as developing differentiated carbon emission control measures based on the spatiotemporal heterogeneity of carbon emissions and their driving factors), but also offer insights for similar regions worldwide in controlling carbon emissions and addressing global climate change (for example, by optimizing land spatial function utilisation, reducing carbon sources, and maximizing carbon sink capacity). 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

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km²), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km²), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 10⁴ t in 1990 to 169.17 × 10⁴ t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals. Full article

Air pollution critically impacts global health, climate change, and ecosystem balance. In Türkiye, rapid population growth, urban expansion, and industrial activities lead to significant land use and cover changes, negatively affecting air quality. This study examined the relationship between land use and land cover changes and six key pollutants (sulfur dioxide, ozone, aerosol index, carbon dioxide, nitrogen dioxide, and formaldehyde) using TROPOMI/Sentinel-5P and European Space Agency Climate Change Initiative data between 2018 and 2024. Satellite-based remote sensing techniques, MODIS data, land surface temperature, and Normalized Vegetation Index analyses were employed. The findings revealed that nitrogen dioxide and carbon dioxide emissions increase with urban expansion and traffic density in metropolitan areas (Istanbul, Ankara, Izmir), while agriculture and deforestation increase aerosol index levels in inland areas. Additionally, photochemical reactions increased surface ozone in the Mediterranean and Aegean regions. At the same time, sulfur dioxide and formaldehyde concentrations reached high levels in highly industrialized and metropolitan cities such as Istanbul, Ankara, and Izmir. This study highlights the role of green infrastructure in improving air quality and provides data-based recommendations for sustainable land management and urban planning policies. Full article

This study evaluates two approaches for detecting solar photovoltaic (PV) installations across agricultural areas, emphasizing their role in supporting sustainable energy monitoring, land management, and planning. Accurate PV mapping is essential for tracking renewable energy deployment, guiding infrastructure development, assessing land-use impacts, and informing policy decisions aimed at reducing carbon emissions and fostering climate resilience. The first approach applies deep learning-based semantic segmentation to high-resolution RGB orthophotos, using the pretrained “Solar PV Segmentation” model, which achieves an F1-score of 95.27% and an IoU of 91.04%, providing highly reliable PV identification. The second approach employs multitemporal pixel-wise spectral classification using Sentinel-2 imagery, where the best-performing neural network achieved a precision of 99.22%, a recall of 96.69%, and an overall accuracy of 98.22%. Both approaches coincided in detecting 86.67% of the identified parcels, with an average surface difference of less than 6.5 hectares per parcel. The Sentinel-2 method leverages its multispectral bands and frequent revisit rate, enabling timely detection of new or evolving installations. The proposed methodology supports the sustainable management of land resources by enabling automated, scalable, and cost-effective monitoring of solar infrastructures using open-access satellite data. This contributes directly to the goals of climate action and sustainable land-use planning and provides a replicable framework for assessing human-induced changes in land cover at regional and national scales. Full article

The global energy transition is pushing the development of advanced biofuels to reduce greenhouse gas (GHG) emissions in the aviation industry. This study thoroughly evaluates the potential of the Colombian crude palm oil (CPO) sector to support sustainable aviation fuel (SAF) production. Extensive primary data from 53 palm oil mills and 269 palm plantations were examined. The methodology included a carbon footprint analysis of SAF produced from Colombian CPO through the HEFA pathway, an economic aspects analysis, a review of renewable fuel standards, and an assessment of market access for low-CO₂-emitting feedstocks. The results show that the carbon footprint of the Colombian palm oil-SAF is 16.11 g CO₂eq MJ⁻¹ SAF, which is significantly lower than the 89.2 g CO₂eq MJ⁻¹ reference value for traditional jet fuel. This figure considers current direct Land Use-Change (DLUC) emissions and existing methane capture practices within the Colombian palm oil agro-industry. A sensitivity analysis indicated that this SAF’s carbon footprint could decrease to negative values of −4.58 g CO₂eq MJ⁻¹ if all surveyed palm oil mills implement methane capture. Conversely, excluding DLUC emissions from the assessment raised the values to 47.46 g CO₂eq MJ⁻¹, highlighting Colombia’s favorable DLUC profile as a major factor in its low overall CPO carbon footprint. These findings also emphasize that methane capture is a key low-carbon practice for reducing the environmental impact of sustainable fuel production, as outlined by the CORSIA methodology. Based on the economic analysis, investing in Colombian CPO-based SAF production is a financially sound decision. However, the project’s profitability is highly susceptible to the volatility of SAF sales prices and raw material costs, underscoring the need for meticulous risk management. Overall, these results demonstrate the strong potential of Colombian palm oil for producing sustainable aviation fuels that comply with CORSIA requirements. Full article

This study examines the long-run relationship between carbon dioxide (CO₂) emissions and key macroeconomic and sectoral drivers in ten ASEAN economies from 1995 to 2023. Employing Driscoll–Kraay standard errors, Prais–Winsten regression, heteroskedastic panel-corrected standard errors, Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) estimators, the analysis accounts for cross-sectional dependence, slope heterogeneity, and endogeneity. Results indicate that GDP exerts a more-than-unitary positive effect on emissions, with a negative GDP-squared term supporting the Environmental Kuznets Curve. Agriculture raises emissions through land-use change and high-emission cultivation practices, while tourism shows a negative association likely reflecting territorial accounting effects. Trade openness increases emissions, highlighting the carbon intensity of export structures, whereas foreign direct investment exerts no significant net effect. These results suggest that ASEAN economies must accelerate renewable energy adoption, promote climate-smart agriculture, embed enforceable environmental provisions in trade policy, and implement rigorous sustainability screening for FDI to achieve low-carbon growth trajectories. Full article

Arizona has committed to reducing emissions by 50–52% by 2030 and achieving net-zero emissions by 2050, requiring major changes to its electricity infrastructure. This study develops a MATLAB model with hourly electricity load and solar insolation data to determine the solar PV and energy storage infrastructure required to replace all utility-scale non-renewable generation. Whereas PV tilt angle is typically optimized to maximize solar capture, this study instead links tilt and tracking configuration to land use, storage requirements, and total system cost to identify the optimal configuration. Results show that a 76 GW_DC 0° fixed-tilt system requires ~0.15% (438 km²) of Arizona’s land to achieve a carbon-neutral grid. Increasing tilt decreases the land required to 287 km² at 54° for fixed-tilt systems and 221 km² at 65° for single-axis tracking systems. A minimum of 320 GWh of annual energy storage is required based on TMY solar insolation data, which increases to 430 GWh for the 2022 time synchronized analysis. A 0° fixed-tilt angle system with energy storage is the cheapest configuration at USD 218 billion. At this tilt, PV generation produces ~80,000 GWh of excess electricity annually, 47% of which could achieve 80% decarbonization across all sectors of the economy. 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

Soils play an important role in the global carbon cycle by storing organic carbon and releasing carbon dioxide (CO₂) through biological processes. Land use management practices influence soil CO₂ emissions by changing physical, chemical, and biological soil properties. Seasonal soil C-CO₂ emissions (soil CO₂ efflux expressed as C-CO₂ in kg ha⁻¹ day⁻¹) were analyzed under cropland, orchard, grassland, forest, and abandoned land, in a peri-urban area in central Croatia in 2021 and 2023. Emissions were measured using the static method in a closed chamber, accompanied by measurements of soil temperature, moisture, and total porosity. In both years, grassland and orchards had the highest average soil C-CO₂ emissions, whereas cropland showed consistently lower values. However, total soil C-CO₂ emissions were significantly lower in 2023, probably influenced by higher precipitation and changes in soil moisture. The seasonal trends differed from year to year, with the highest emissions recorded in fall 2021 and spring 2023. In both years, there was a positive correlation between average soil C-CO₂ emissions and soil temperature/moisture, while soil porosity also contributed to the observed emission variations. The results show the significant influence of land use types on soil C-CO₂ emissions and emphasize the importance of seasonal and environmental factors in assessing soil carbon cycling. This research enhances understanding of soil contributions to climate change and supports the development of sustainable land management practices aimed at reducing greenhouse gas emissions. Full article

Plant traits could help in designing feasible strategies to mitigate global change in inland wetlands, but the correlations between plant traits and carbon emissions in coastal wetlands remain unclear. Here, we investigated the plant traits (including nutrient, structural, and biomass traits) and environmental conditions (including climate and soil properties) and determined the soil carbon emissions (methane (CH₄), carbon dioxide (CO₂), and their temperature sensitivities (Q₁₀ value)) from the soils of 90 coastal herbaceous wetlands differing in land use types along China’s coastline. We further tested how environmental conditions affected plant traits and how these traits then altered carbon emissions. We found that plant traits had a greater effect on CH₄ and CO₂ emissions than on their Q₁₀ values, with nutrient traits being the key drivers in coastal herbaceous wetlands in China. In general, coastal herbaceous wetlands with larger leaf C and N contents combined with a lower leaf N:P ratio tended to have higher CH₄ emission; those with larger leaf C and P contents combined with a lower leaf N:P ratio tended to have higher CO₂ emission; and those with higher leaf N content and N:P ratio combined with a lower leaf C:P ratio tended to have higher Q₁₀ values of both CH₄ and CO₂. Notably, the predictive power of plant traits in coastal herbaceous wetlands varied significantly across heterogeneous environments influenced by climate and land use. Our results highlight the critical role of plant nutrient traits in driving soil carbon emissions and provide practical insights into understanding coastal carbon dynamics under pressures from climate and land use changes (e.g., coastal reclamation and plant invasion). Full article

Methane fluxes in disturbed peatlands can exhibit significant heterogeneity with regard to land cover composition on abandoned peat extraction areas. The temporal and spatial variability of CH₄ fluxes is considered in this paper in the context of a detailed vegetation classification on a typical milled peatland in the Baltic region of Russia (Kaliningrad oblast, Rossyanka Carbon Supersite). The findings are derived from the analysis of 12,000 air samples obtained by the opaque emission chamber method at 10 peatland sites with different environmental characteristics during regular measurement campaigns of 2022–2024. The emission data have been mapped using a multilevel B-spline interpolation procedure. The mean cumulative methane flux was found to be 18.7–28.8 kg ha⁻¹yr⁻¹, which is close to the IPCC conventional value of 32.9 kg ha⁻¹yr⁻¹ estimated for boreal and temperate zones. However, environmental distinctions across the peatland sites result in considerable emission heterogeneity ranging from −0.02 to 11.5 kg ha⁻¹month⁻¹. Temperature is considered a principal factor responsible for the baseline CH₄ emission level in seasonal scale, while hydrology defines emission rate during the warm period of the year and in the inter-annual scales. Five peatland site types have been defined according to a level of methane emissions. Full article

In recent years, the frequent occurrence of extreme weather events has prompted increased global attention to greenhouse gas emissions. This study analyzes the spatio-temporal evolution of carbon emission intensity (CEI) across land use types in China’s 30 provinces from 2009 to 2022. Based on the data from China Rural Statistical Yearbook, China City Statistical Yearbook, China Energy Statistical Yearbook, China Natural Resources Statistical Yearbook, and China Statistical Yearbook, this study aims to reveal the spatio-temporal differentiation patterns of CEI, analyze the decoupling status between development mode and carbon emissions, and establish a three-dimensional collaborative emission reduction framework. Firstly, employing the carbon emission factor method, provincial carbon emissions, sinks, and net emissions are calculated, with intensity levels derived from gross domestic product (GDP). Secondly, spatio-temporal trends and inter-provincial disparities are analyzed using the decoupling index. The spatial effects among the provinces are investigated based on Moran’s I index. The results show that while the overall CEI has declined since 2009, significant regional disparities persist, with the southern provinces showing lower carbon emission intensities compared to the northern and western regions. The spatial analysis reveals a strong aggregation effect, with provinces clustering into high-high (HH) and low-low (LL) regions regarding CEI. This study concludes with policy recommendations for emission reduction and climate change mitigation, emphasizing industrial structure adjustment, enhanced regional coordination, and optimized land use planning. Full article