Carbon Balance and Management最新文献

Background

Wood Harvesting and Storage (WHS) is a form of Biomass Carbon Removal and Storage (BiCRS) that utilizes a combined natural and engineered process to harvest woody biomass and put it into long term storage, most frequently in the form of subterranean burial. This paper aims to quantify the availability of woody biomass for the purposes of WHS in the continental United States using a carbon cycle modeling approach. Using a regional version of the VEGAS terrestrial carbon cycle model at 10 km resolution, this paper calculates the annual woody net primary production in the continental United States. It then applies a series of constraints to exclude woody biomass that is unavailable for WHS. These constraints include fine woody biomass, current land use, current wood utilization, land conservation, and topographical limitations. These results were then split into state by state and regional totals.

Results

In total, the model projects the continental United States could produce 1,274 MtCO₂e (CO₂ equivalent) worth of coarse woody biomass annually in a scenario with no anthropogenic land use or constraints. In a scenario with anthropogenic land use and constraints on wood availability, the model projects that 415 MtCO₂e of coarse woody biomass is available for WHS annually. This is enough to offset 8.5% of the United States’ 2020 greenhouse gas emissions. Of this potential, 20 MtCO₂e is from the Pacific region, 77 MtCO₂e is from the Western Interior, 91 MtCO₂e is from the Northeast region, and 228 MtCO₂e is from the Southeast region.

Conclusion

There is enough coarse woody biomass available in the continental United States to make WHS a viable form of carbon removal and storage in the country. There is coarse woody biomass available across the continental United States. All four primary regions analyzed have enough coarse woody biomass available to justify investment in WHS projects.

Background

Ecosystem models are valuable tools to make climate-related assessments of change when ground-based measurements of water and carbon fluxes are not adequately detailed to realistically capture geographic variability. The Carnegie-Ames-Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems.

Results

CASA model predictions from 2015 to 2022 for Western Europe revealed several notable high and low periods in growing season NPP totals in most countries of the region. For the total land coverage of France, Greece, Italy, Portugal, and Spain, 2018 was the year with the highest terrestrial plant growth, whereas 2017 and 2019 were the years with the highest summed NPP across the UK, Germany, and Croatia. For most of Western Europe, 2022 was the year predicted with the lowest summed plant growth. Annual precipitation in most countries of Western Europe gradually declined from a high average rate in 2018 to a low average precipitation level in 2022.

Conclusions

The CASA model predicted decreased growing season NPP of between − 25 and − 60% across all of Spain, southern France, and northern Italy from 2021 to 2022, and much of that plant production loss was detected in the important cropland regions of these nations.

Background

Southeast Asian (SEA) mangroves are globally recognized as blue carbon hotspots. Methodologies that measure mangrove soil carbon stock (SCS) are either accurate but costly (i.e., elemental analyzers), or economical but less accurate (i.e., loss-on-ignition [LOI]). Most SEA countries estimate SCS by measuring soil organic matter (OM) through the LOI method then converting it into organic carbon (OC) using a conventional conversion equation (%C_org = 0.415 * % LOI + 2.89, R² = 0.59, n = 78) developed from Palau mangroves. The local site conditions in Palau does not reflect the wide range of environmental settings and disturbances in the Philippines. Consequently, the conventional conversion equation possibly compounds the inaccuracies of converting OM to OC causing over- or under-estimated SCS. Here, we generated a localized OM-OC conversion equation and tested its accuracy in computing SCS against the conventional equation. The localized equation was generated by plotting % OC (from elemental analyzer) against the % OM (from LOI). The study was conducted in different mangrove stands (natural, restored, and mangrove-recolonized fishponds) in Oriental Mindoro and Sorsogon, Philippines from the West and North Philippine Sea biogeographic regions, respectively. The OM:OC ratios were also statistically tested based on (a) stand types, (b) among natural stands, and (c) across different ages of the restored and recolonized stands. Increasing the accuracy of OM-OC conversion equations will improve SCS estimates that will yield reasonable C emission reduction targets for the country’s commitments on Nationally Determined Contributions (NDC) under the Paris Agreement.

Results

The localized conversion equation is %OC = 0.36 * % LOI + 2.40 (R² = 0.67; n = 458). The SOM:OC ratios showed significant differences based on stand types (x² = 19.24; P = 6.63 × 10^–05), among natural stands (F = 23.22; p = 1.17 × 10^–08), and among ages of restored (F = 5.14; P = 0.03) and recolonized stands (F = 3.4; P = 0.02). SCS estimates using the localized (5%) and stand-specific equations (7%) were similar with the values derived from an elemental analyzer. In contrast, the conventional equation overestimates SCS by 20%.

Conclusions

The calculated SCS improves as the conversion equation becomes more reflective of localized site conditions. Both localized and stand-specific conversion equations yielded more accurate SCS compared to the conventional equation. While our study explored only two out of the six marine biogeographic regions in the Philippines, we proved that having a localized conversion equation leads to improved SCS measurements. Using our proposed equations will make more realistic SCS targets (and therefore GHG reductions) in designing mangrove restoration programs to achieve the country’s NDC commitments.

Background

Dry Afromontane forests play a vital role in mitigating climate change by sequestering and storing carbon, as well as reducing greenhouse gas emissions. Despite previous research highlighting the importance of carbon stocks in these ecosystems, the influence of canopy cover and environmental factors on carbon storage in dry Afromontane forests has been barely assessed. This study addresses this knowledge gap by investigating the effects of environmental factors and vegetation cover on carbon stocks in Desa’a forest, a unique and threatened Afromontane dry forest ecosystem in northern Ethiopia. Data on woody vegetation, dead litter, grass biomass, and soil samples were collected from 57 plots. A one-way analysis of variance (ANOVA) was performed at a 95% confidence level (α = 0.05) to examine the influence of canopy cover and environmental factors on the carbon stocks of various pools.

Results

Among the 35 woody species identified, Juniperus procera was the most dominant, while Carissa edulis Vahl and Eucalyptus globulus were the least dominant. The average total carbon stock was 92.89 Mg ha⁻¹, with contributions from aboveground carbon, below-ground carbon, litter carbon, grass carbon, and soil organic carbon. Among the carbon pools, soil organic carbon had the highest carbon stock, accounting for 76.8% of the total, followed by above-ground biomass carbon at 17.7%. Significant variations in carbon stocks were found across altitude class and canopy level but not slope and aspect factors.

Conclusions

In summary, altitude and canopy level were found to significantly influence carbon stocks in Desa’a forest, providing valuable insights for conservation and climate change mitigation efforts in dry Afromontane forests. Forest intervention planning and management strategies should consider the influence of different environmental variables and tree canopy levels.

Background

Thinning practices are useful measures in forest management and play an essential role in maintaining ecological stability. However, the effects of thinning on the soil properties and microbial community in large Chinese fir timber plantations remain unknown. The purpose of this study was to investigate the changes in soil physicochemical properties and microbial community composition in topsoil (0–20 cm) under six different intensities (i.e., 300 (R300), 450 (R450), 600 (R600), 750 (R750) and 900 (R900) trees per hectare and 1650 (R1650) as a control) in a large Chinese fir timber plantation.

Results

Compared with the CK treatment, thinning significantly altered the contents of soil organic carbon (SOC) and its fractions but not in a linear fashion; these indicators were highest in R900. In addition, thinning did not significantly affect the soil microbial community diversity indices but significantly affected the relative abundance of the core microbial community. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant bacterial phyla; the relative abundances of Proteobacteria and Acidobacteria were highest in R900, and that of Actinobacteria was lowest in R900. The dominant fungal phyla were Ascomycota, Basidiomycota and Mucoromycota; the relative abundance of Ascomycota was lowest in R900, and that of Mucoromycota was highest in R900. The fungal microbial community composition was more sensitive than the bacterial community composition. The activity of the carbon-cycling genes was not linearly correlated with thinning, and the abundance of C-cycle genes was highest in R900.

Conclusions

These findings are important because they show that SOC and its fractions and the abundance of the soil microorganism community in large Chinese fir timber plantations can be significantly altered by thinning, thus affecting the capacity for carbon storage. These results may advance our understanding of how the density of large timber plantations could be modified to promote soil carbon storage.

Forest conversion to agricultural land has been shown to deplete soil organic carbon (SOC) and soil total nitrogen (STN) stocks. However, research on how soil properties respond to forest conversion to shifting cultivation has produced conflicting results. The conflicting findings suggest that the agricultural system may influence the response of SOC and STN to forest conversion to agriculture, depending on the presence of vegetative cover throughout the year. Due to the unique characteristics of montane evergreen forests (MEF) and banana plantations (BP), SOC and STN response to MEF conversion to BP may differ from existing models. Nevertheless, research on how soil properties are affected by MEF conversion to BP is scarce globally. In order to fill this research gap, the goal of this study was to evaluate how much deforestation for BP affects SOC, STN, and soil quality by analysing these soil parameters in MEF and BP fields down to 1-m depth, using standard profile-based procedures. Contrary to the specified hypothesis that SOC and STN losses would be restricted to the upper 20-cm soil layer, SOC losses were extended to the 40-cm depth layer and STN losses to the 60-cm depth layer. The soils lost 18.56 Mg ha ^{– 1} (37%) of SOC from the upper 20 cm and 33.15 Mg ha ^{– 1} (37%) from the upper 40 cm, following MEF conversion to BP. In terms of STN, the upper 20, 40, and 60 cm lost 2.98 (43%), 6.62 (47%), and 8.30 Mg ha ^{– 1} (44%), respectively. Following MEF conversion to BP, the SOC stratification ratio decreased by 49%, implying a decline in soil quality. Massive exportation of nutrients, reduced C inputs due to complete removal of the arboreal component and crop residues, the erodibility of the soils on the study area’s steep hillslopes, and the potential for banana plantations to increase throughfall kinetic energy, and splash erosion through canopy dripping are thought to be the leading causes of SOC and STN losses. More research is needed to identify the extent to which each cause influences SOC and STN losses.

Background

The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU) is a new joint project by Japan Aerospace Exploration Agency (JAXA) and ANA HOLDING INC. (ANAHD), which operates ANA flights. GOBLEU aims to visualizes our climate mitigation effort progress in support of subnational climate mitigation by collecting greenhouse gas (GHG) data as well as relevant data for emissions (nitrous dioxide, NO₂) and removals (Solar-Induced Fluorescence, SIF) from regular passenger flights. We developed a luggage-sized instrument based on the space remote-sensing techniques that JAXA has developed for Japan’s Greenhouse gas Observing SATellite (GOSAT). The instrument can be conveniently installed on a coach-class passenger seat without modifying the seat or the aircraft.

Results

The first GOBLEU observation was made on the flight from the Tokyo Haneda Airport to the Fukuoka Airport, with only the NO₂ module activated. The collected high-spatial-resolution NO₂ data were compared to that from the TROPOspheric Monitoring Instrument (TROPOMI) satellite and surface NO₂ data from ground-based air quality monitoring stations. While GOBLEU and TROPOMI data shared the major concentration patterns largely driven by cities and large point sources, regardless of different observation times, we found fine-scale concentration pattern differences, which might be an indication of potential room for GOBLEU to bring in new emission information and thus is worth further examination. We also characterized the levels of NO₂ spatial correlation that change over time. The quickly degrading correlation level of GOBLEU and TROPOMI suggests a potentially significant impact of the time difference between CO₂ and NO₂ as an emission marker and, thus, the significance of co-located observations planned by future space missions.

Conclusions

GOBLEU proposes aircraft-based, cost-effective, frequent monitoring of greenhouse emissions by GOBLEU instruments carried on regular passenger aircraft. Theoretically, the GOBLEU instrument can be installed and operated in most commercially used passenger aircraft without modifications. JAXA and ANAHD wish to promote the observation technique by expanding the observation coverage and partnership to other countries by enhancing international cooperation under the Paris Agreement.

Background

Wood carbon fractions (CFs)—the proportion of dry woody biomass comprised of elemental carbon (C)—are a key component of forest C estimation protocols and studies. Traditionally, a wood CF of 50% has been assumed in forest C estimation protocols, but recent studies have specifically quantified differences in wood CFs across several different forest biomes and taxonomic divisions, negating the need for generic wood CF assumptions. The Intergovernmental Panel on Climate Change (IPCC), in its 2006 “Guidelines for National Greenhouse Gas Inventories”, published its own multitiered system of protocols for estimating forest C stocks, which included wood CFs that (1) were based on the best available literature (at the time) and (2) represented a significant improvement over the generic 50% wood CF assumption. However, a considerable number of new studies on wood CFs have been published since 2006, providing more accurate, robust, and spatially- and taxonomically- specific wood CFs for use in forest C estimation.

Main text

We argue that the IPCC’s recommended wood CFs and those in many other forest C estimation models and protocols (1) differ substantially from, and are less robust than, wood CFs derived from recently published data-rich studies; and (2) may lead to nontrivial errors in forest C estimates, particularly for countries that rely heavily on Tier 1 forest C methods and protocols (e.g., countries of the Global South with large expanses of tropical forests). Based on previous studies on this topic, we propose an alternative set of refined wood CFs for use in multiscale forest C estimation, and propose a novel decision-making framework for integrating species- and location-specific wood CFs into forest C estimation models.

Conclusion

The refined wood CFs that we present in this commentary may be used by the IPCC to update its recommended wood CFs for use in forest C estimation. Additionally, we propose a novel decision-making framework for integrating data-driven wood CFs into a wider suite of multitiered forest C estimation protocols, models, and studies.

Background

Forests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO₂ reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks.

Results

The summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO₂ emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates.

Conclusions

The WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Background

Implementing large-scale carbon sink afforestation may contribute to carbon neutrality targets and increase the economic benefits of forests in rural areas. However, how to manage planted forests in China to maximize the joint benefits of timber production and carbon sequestration is still unclear. Therefore, the present study quantified the effects of different rotation lengths, thinning treatments, site quality (SCI), stand density (SDI), and management costs on the joint benefits of carbon sequestration and timber production based on a stand-level model system developed for larch plantations in northeast China.

Results

The performances of the different scenarios on carbon stocks were satisfactory, where the variations in the outcomes of final carbon stocks could be explained by up to 90%. The joint benefits increased significantly with the increases of SDIs and SCIs, regardless of which rotation length and thinning treatments were evaluated. Early thinning treatments decreased the joint benefits significantly by approximately 131.53% and 32.16% of middle- and higher-SDIs, however longer rotations (60 years) could enlarge it by approximately 71.39% and 80.27% in scenarios with and without thinning when compared with a shorter rotation length (40 years). Discount rates and timber prices were the two most important variables affecting joint benefits, while the effects of carbon prices were not as significant as expected in the current trading market in China.

Conclusions

The management plans that promote longer rotations, higher stand densities, and no thinning treatments can maximize the joint benefits of carbon sequestration afforestation and timber production from larch plantations located in northeast China.

Graphical Abstract