Carbon Balance and Management最新文献

Background

Mountain cloud forests (MCF) are vulnerable ecosystems that harbor considerable biodiversity and are essential carbon regulators. However, information is scarce on the carbon storage potential and its patterns of variability across the conservation gradient in these forests. This study determined the carbon storage potential, the contribution of different pools, and their relationship with the degree of forest and soil conservation.

Results

The organic carbon storage of the communities ranged from 145.9 to 279 Mg C ha⁻¹. Soil was the primary pool (68.08–198.1 Mg C ha⁻¹), followed by above-ground biomass (42.87 – 116.74 Mg C ha⁻¹), while the contribution of litter and roots was less. The contribution of above-ground biomass to the carbon stock was low due to the level of timber and fuelwood extraction present in these communities. The high carbon storage potential of the soil pool is determined by the presence of the O horizon, with a thickness of 8–10 cm, forming mull-type humus and a deep organo-mineral surface horizon with a high carbon content > 10 g kg⁻¹, and with varying degrees of humification. The formation of clay-humus complexes maintains carbon stabilization and the formation of deep surface horizons (between 20 and 38 cm deep).

Conclusion

The results show that the carbon sequestration potential of the MCF is found in the soil associated with the organic horizons that develop at the surface and the presence of deep A horizons with high carbon content. The conservation of these layers, despite forest management, reflected in the aerial biomass, demonstrates the resilience of the soil due to carbon stabilization, attributed to the composition of resistant organic compounds and the formation of clay-humus complexes, which reduce the impact of degradation from erosion. This indicates that the conditions of the MCF still sustain the ecological and biogeochemical processes that support carbon sequestration and are regulated by the conservation policies of the Sierra Gorda Biosphere Reserve, Querétaro, Mexico.

Despite the rapid and well-documented surge in global atmospheric CO₂ levels, predominantly driven by fossil fuel combustion and industrialization, the characterization of CO₂ variations at regional scales remains notably sparse. This study integrates satellite remote sensing (RS) and ground-based measurements to examine the spatiotemporal distributions and drivers of CO₂ in China’s Shaanxi Province from 2013 to 2022. Although Shaanxi has experienced rapid development, its CO₂ trends have remained unclear. By integrating CO₂ observations from satellite sources, specifically the Orbiting Carbon Observatory-2 (OCO-2) and Fourier Transform Spectrometer (FTS), with data from the World Data Centre for Greenhouse Gases (WDCGG) Hong Kong ground station, we have synthesized a uniquely comprehensive dataset that enables enhanced resolution in exploring intra-annual, interannual, and spatial CO₂ variations across the province. The results reveal pronounced seasonal CO₂ cycles and a consistent upward trend over the past decade. The monthly concentrations exhibited a sinusoidal pattern, oscillating between a minimum of 399.68 ± 6.58 ppm in August and peaking at 407.48 ± 6.58 ppm in April. High CO₂ regions within Shaanxi are predominantly found in its southern subtropical and temperate areas, reaching 418.4 ppm in 2022. From 2013 to 2022, the annual average CO₂ increased by 4.12% from 396 to 412.34 ppm, with a higher growth rate in southern compared to northern Shaanxi. This study elucidates the distinct spatiotemporal variations in CO₂ levels across Shaanxi Province, revealing prominent seasonal cycles and a discernible upward trend over the past decade. The results offer new insights into CO₂ characteristics and dynamics in this rapidly developing region of China, and further investigation into the factors underlying the observed variations is warranted.

Purpose

To achieve Chengdu's dual-carbon goals and support high-quality development, it is essential to analyze the impact mechanisms of land use patterns on carbon storage in Chengdu. This study aims to provide evidence-based land use planning strategies that synergistically balance ecological security and economic growth for the Chengdu-Chongqing Economic Circle's dual-carbon objectives.

Methods

This study uses the MCCA - InVEST model to simulate the impact of land use changes on carbon storage in Chengdu City from 2025 to 2035, setting up three scenarios: natural development (ND), ecological conservation (EC), and sustainable development (SD). The MCCA model is used to simulate land use dynamics by integrating 11 driving factors. The InVEST model is then used to assess changes in carbon storage, with a focus on the area evolution and carbon storage response mechanisms of cropland , forests, and impervious land.

Results

Land use changes show that under the ND scenario, cropland decreases by an average of 0.82 million hectares per year, impervious land expand by 38.1%, and forest area increases by 20.8%. Under the EC scenario, the rate of cropland reduction slows, with forests growing at an average annual rate of 2.6%, and impervious land expanding by 28.1%. Under the SD scenario, which lies between the two, the reduction in cropland is 0.5 million hectares less than in the ND scenario, forests increase by 25.0%, and impervious land expand by 32.4%, achieving a balance between loss and gain. In terms of carbon storage, the ND scenario results in a net decrease of 9.6×10⁶ tons, the EC scenario results in a net increase of 13.2×10⁶ tons, and the SD scenario remains largely stable. In terms of spatial distribution, high carbon storage areas are concentrated in the Longquan Mountains and the western mountainous regions, with NDVI being the primary driving factor (q=0.7264).

Conclusion

The SD scenario can balance ecological conservation and economic development. It is recommended to enhance forest construction in the Longquan Mountains and the western mountainous regions, and to promote 'compact' expansion in the urban core area. The MCCA-InVEST coupling model provides an effective tool for evaluating carbon storage across multiple scenarios. This study offers a scientific basis for Chengdu City to achieve its 'dual carbon' goals and serves as a reference for other cities conducting carbon storage assessments and land use planning

The contradiction between rapid urbanization and ecological protection leads to the aggravation of carbon sink function differentiation in coastal areas. In this study, a framework of "historical assessment-future prediction-mechanism analysis" was constructed, and InVEST, PLUS, SEM, and OPGD models were integratedto study the dynamics of carbon storage and its driving mechanisms in three rapidly urbanizing coastal areas (Beidaihe, Caofeidian and Huanghua) in the Bohai Rim region of China. The main results showed that: (1) carbon storage showed the characteristics of periodic change—the expansion of low-carbon land led to the decline of carbon storage (2000–2010), and then Caofeidian achieved 1.50 × 10⁶ tons of carbon storage restoration through ecological restoration (2010–2020). (2) Carbon stocks are expected to increase in all four scenarios in 2030, with the largest increase in the Cultivated Land Protection (CP) scenario, with a peak of 3.00 × 10⁷ tons. (3)The analysis of the driving mechanism showed that population density was the main driving factor (q = 0.2126–0.2129), and there was a significant nonlinear interaction with socio-economic factors, which jointly shaped the spatial pattern. This analysis reveals the spatiotemporal heterogeneity and core driving factors of carbon storage changes in coastal areas, emphasizes the key role of human activities under urbanization and ecological conflicts, and provides a scientific basis for the implementation of differentiated low-carbon spatial planning in rapidly developing coastal areas.

The estimation of carbon emission reduction potential in existing regions often faces the problem of missing data, so scenario analysis based estimation research is carried out. Under the constraints of emission standards, three emission limit scenarios are set: maintaining, ultra-low, and tightening. Based on the SBM model, a carbon emission reduction potential index model is constructed using the full factor carbon emission efficiency measurement method. Build a model that considers the impact of industrial output value and estimate carbon emission rights from 2018 to 2030. After analysis and calculation of allocation weights, experiments show that carbon emission performance is less than 0.05, efficiency is improved, weight is about 4.64%, and industrial carbon emissions contribute nearly zero.

Background

In this study, we used a generalised linear mixed-effects model (GLMER) to establish a predictive pedotransfer function defining the relationship between forest soil bulk density and total organic carbon. More than 950 soil samples were obtained from four forested areas with a wide range of bedrock (limestone, loess, crystalline volcanic, sandstone, alluvial loam, polygenic loam and transported materials rich in organic carbon) and soil types (Leptosols, Cambisols, Fluvisols, Podzols and Technosols). Model validation was performed by testing against 10% of the data randomly selected from the original dataset (10% dataset) and an independent dataset from the Czech national forest inventory (NFI2 dataset).

Result

The GLMER including sample origin locality as random effect displayed a highly accurate predictive capacity. Subsequent analysis avoided model simplification by excluding sample origin and retaining the global GLMER only. For all samples, the final model covered a range from 0.16 to 27.70% for total organic carbon and from 0.27 to 1.94 g cm^− 3 for bulk density. Model residuals based on laboratory values were symmetrical with a median value just 0.09 g cm^− 3 higher. While validation with the 10% dataset confirmed model parameter validity with high accuracy, validation using the NFI2 dataset indicated slight discrepancies, possibly due to differences in sampling method used. Individual GLMs fitted both validation datasets better than the global GLMER; however, Wilcoxon tests showed better consistency in the original model on the 10% validation data. Consequently, we suggest the global GLMER may prove more suitable for direct use in expressing bulk density from total organic carbon.

Conclusion

The pedotransfer functions produced, particularly that based on global GLMER, can be used to express bulk density via total organic carbon content, or vice versa, with high accuracy. While based on a wide range of bedrock/soil types, further studies may be needed in other regions to validate the model for general application.

High-speed rail is a crucial transportation infrastructure in China and has a significant impact on the location choice of renewable energy enterprises. However, it is unclear how substantial is this impact, and how can it be measured. Research on these topics is quite limited. This study collects the data on various prefecture-level cities in China and their renewable energy enterprises from 2006 to 2020. Using high-speed rail openings as a dummy variable and a multi-period Difference-in-Differences (DID) approach, this study empirically examines the effect of high-speed rail openings on the site selection of renewable energy enterprises. The findings indicate that: (1) The coefficient for the impact of high-speed rail openings on the site selection of renewable energy enterprises is significantly positive, suggesting that high-speed rail policies facilitate cites’ acceptance of these enterprises; (2) There is a regional imbalance in the site selection for renewable energy enterprises, with economically developed cities showing greater receptivity; (3) Cities with higher waste treatment rates tend to attract more renewable energy enterprises.

Background

This study evaluated the effects of grazing management practices, topographic position, and land cover types on mineral-associated organic carbon (MAOC) and particulate organic carbon (POC) in a semi-arid rangeland of Kenya. Research was conducted at Mpala Research Centre (controlled grazing) and Ilmotiok Community Group Ranch (continuous grazing) in Laikipia County. A factorial experimental design with a split-plot arrangement was used in this study. Grazing management practices (controlled and continuous grazing) and topographic positions (midslope, foot slope, and bottomland) were assigned to the main plots, while land cover types (bare ground, grass patches, and tree mosaics) were designated as subplots. Soil samples were collected at 10 cm intervals, 0–10 cm, 10–20 cm, and 20–30 cm depth for MAOC and POC analysis. Data analysis was done using R software, where nonparametric tests were done when the assumptions of normality and homogeneity of variance were violated.

Results

Controlled grazing resulted in higher MAOC (0.361%) and POC (0.683%) compared to continuous grazing (0.352% and 0.548%, respectively), indicating an increase of 2.56% in MAOC and 24.64% in POC under controlled grazing. This can largely be attributed to improved vegetation recovery, especially in midslope areas. The highest MAOC (0.367%) was found in the bottomland, likely due to reduced erosion and improved water retention. The midslope and foot slope positions had lower MAOC means of 0.358% and 0.344%, respectively. Depth analysis showed peak MAOC at 20 cm (0.390%), with controlled grazing resulting in better carbon retention at 30 cm. Similarly, controlled grazing yielded a mean POC of 0.683% versus 0.548% for continuous grazing, with bottomland having the highest POC of 0.754%. A Kruskal‒Wallis tests showed significant differences in MAOC and POC across land cover types (χ² = 42.701, p < 0.001 for MAOC, and χ² = 83.53, p < 0.001 for POC), with tree mosaics and bare land contributing most to POC and MAOC, respectively.

Conclusions

These findings highlight the beneficial role of controlled grazing and diverse land cover in enhancing soil carbon storage. To promote sustainable rangeland management, it is recommended that rangeland managers adopt controlled grazing practices and allow diverse land cover types, such as tree mosaics, to increase carbon sequestration and ecosystem resilience.

As the global greenhouse effect intensifies, the emission and balance of greenhouse gases, particularly carbon dioxide (CO₂), have become crucial for achieving global carbon neutrality. Volcanic geothermal regions, as major natural sources of carbon emissions, release substantial volume of greenhouse gases into the atmosphere in various ways including volcanic eruptions, soil microseepages, vents, and hot springs. Among these, soil microseepages are particularly important due to their widespread and persistent nature. However, the geochemical dynamics of CO₂ release from soil microseepage in volcanic regions remain poorly understood. In this study, we propose a novel CO₂ release model employing computational fluid dynamics (CFD) to model CO₂ emissions from soil microseepage in volcanic regions. Our results provide important insights as follows: (1) Low porosity in subsurface strata inhibits CO₂ penetration, while well-developed underground cracks and channels enhance release rates. (2) Favorable gas pathways enable CO₂ to penetrate dense layers, and migrate upward, with migration patterns influenced by gas source pressure, temperature, and soil permeability. Slowing vertical migration increases horizontal diffusion and expands the effective surface release area. (3) Surface release is also influenced by external factors like wind speed, though these do not significantly affect underground seepage. (4) To improve the accuracy of CO₂ flux measurements using the closed chamber method, it is recommended to reverse the initial slope of the CO₂ concentration-time curve. This study provides critical data to enhance global carbon budget assessments and support efforts towards carbon neutrality.