Carbon Balance and Management最新文献

Background

Cropland agroforestry practices are widely adopted over various land ecosystems in Bangladesh, offering the potential to capture carbon (C) and safeguard biodiversity. Lack of accurate assessments of biomass carbon and the diversity of woody perennials in cropland agroforestry practices across different land ecosystems presents a hurdle for the efficient execution of initiatives such as REDD + and comparable mechanisms. The present research sought to estimate biomass carbon stocks and diversity of woody species, exploring the influence of stand structure and diversity indices on these C stocks. We conducted woody perennials’ inventory in 180 sampling quadrates (10 m × 10 m) from cropland agroforestry practices in forest, plains land, and char land ecosystems.

Results

Altogether, we identified 42 woody species; however, the predominant species in three land ecosystems were Acacia auriculiformis, Gmelina arborea, and Tectona grandis. Swietenia macrophylla and Swietenia mahogany contributed the greatest amount of carbon stocks. Carbon stocks in woody perennials were 30–44% higher in plains land and forest land ecosystems compared to the char land ecosystem, attributable to significantly increased stand density, basal area and diameter. The significantly highest Shannon–Wiener index (2.75) and Margalef’s richness index (3.37) were found in forest land compared to other ecosystems. The highest total carbon stocks (131.27 Mg C ha⁻¹) of cropland agroforestry were found in the forest land ecosystem, which had the greatest soil organic carbon, density, and richness of woody perennials. A rise in the richness and diversity index of woody species by one unit led to a concurrent increase of 12 and 8 Mg C ha⁻¹ in carbon stocks, respectively.

Conclusions

Cropland agroforestry practices in the forest land ecosystem are more diverse and could sequester more carbon stock than in the other two land ecosystems in Bangladesh. The biomass C stocks of woody species were positively correlated with stand structure and diversity, having the potential to contribute to biodiversity conservation in Bangladesh and other similar countries.

Forests have the potential to contribute significantly to global climate policy efforts through enhanced carbon sequestration and storage in terrestrial systems and wood products. Projections models simulate changes future in forest carbon fluxes under different environmental, economic, and policy conditions and can inform landowners and policymakers on how to best utilize global forests for mitigating climate change. However, forest carbon modeling frameworks are often developed and applied in a highly disciplinary manner, e.g., with ecological and economic modeling communities typically operating in silos or through soft model linkages through input–output parametric relationships. Recent disciplinary divides between economic and ecological research communities confound policy guidance on levers to increase forest carbon sinks and enhance ecosystem resilience to global change. This paper reviews and summarizes the expansive literature on forest carbon modeling within economic and ecological disciplines, discusses the benefits and limitations of commonly used models, and proposes a convergence approach to better integrating ecological and economic systems frameworks. More specifically, we highlight the critical feedback loops that exist when economic and ecological carbon models operate independently and discuss the benefits of a more integrated approach. We then describe an iterative approach that involves the sharing of methodology, perspectives, and data between the regimented model types. An integrated approach can reduce the limitations or disciplinary bias of forest carbon models by exploiting and merging their relative strengths.

Territorial pattern plays an important role in regional ecosystem management and service provision. It is significant to demonstrate the coordination relationships between the territorial space evolutions and ecosystem services for sustainable regional development. This study focused on quantifying the impacts of production-living-ecological space change on carbon sequestration and water yield in the upper and middle-lower reaches of the Yangtze River Basin. Our results indicated that the production-living-ecological space variation trends are similar between the upper and middle-lower reaches during 2000–2020, while their impacts on ecosystem services are different in their respective regions. In the upper reaches, the changes in production and ecological space had a direct positive impact on NPP while the changes of living space had a negative impact on the NPP. However, the changes of production-living-ecological space had no significant effects on the water yield. In contrast, the changes of production and ecological space had no significant effect on the NPP in the middle-lower reaches, while the changes of ecological space had a positive effect on the water yield. Additionally, we also found that social-economic factors had no significant effects on the changes of ecological space in the middle-lower reaches of the Basin. We suggested that policy makers need to optimize the distribution of territorial space in order to maintain sustainable development.

Background

Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.

Results

We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.

Conclusions

By applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.

Background

Carbon dioxide removal from the atmosphere (CDR) is a critical component of strategies for restricting global warming to 1.5°C and is expected to come largely from the sequestration of carbon in vegetation. Because CDR rates have been declining in the United States, in part due to land use changes, policy proposals are focused on altering land uses, through afforestation, avoided deforestation, and no-net-loss strategies. Estimating policy effects requires a careful assessment of how land uses interact with forest conditions to determine future CDR.

Results

We evaluate how alternative specifications of land use-forest condition interactions in the United States affect projections of CDR using a model that mirrors land sector net emission inventories generated by the US government (EPA). Without land use change, CDR declines from 0.826 GT/yr in 2017 to 0.596 GT/yr in 2062 (28%) due to forest aging and disturbances. For a land use scenario that extends recent rates of change, we compare CDR estimated based on net changes in land use (Net Change model) and estimates that separately account for the distinct CDR implications of forest losses and forest gains (Component Change model). The Net Change model, a common specification, underestimates the CDR losses of land use by about 56% when compared with the Component Change models. We also estimate per hectare CDR losses from deforestation and gains from afforestation and find that afforestation gains lag deforestation losses in every ecological province in the US.

Conclusions

Net Change approaches substantially underestimate the impact of land use change on CDR and should be avoided. Component Change models highlight that avoided deforestation may provide up to twice the CDR benefits as increased afforestation—though preference for one policy over the other would require a cost assessment. The disparities in the CDR impacts of afforestation and deforestation indicate that no-net-loss policies could mitigate some CDR losses but would lead to overall declines in CDR for our 45-year time horizon. Over a much longer period afforestation could capture more of the losses from deforestation but at a timeframe inconsistent with most climate change policy efforts.

Background

Given the increasing commitment of numerous nations to achieving future carbon neutrality, urban development planning that integrating carbon storage considerations plays a crucial role in enhancing urban carbon efficiency and promoting regional sustainable development. Previous studies have indicated that optimizing land use structure and quality is essential for regional carbon storage management. Taking the core area of Taihu Bay as study area, this study innovatively combined high-precision urban 3D data to account for the whole urban carbon pools of buildings, vegetation, soils, water. Then, multi-objective linear programming model and PLUS (Patch-generating Land Use Simulation) model were applied at patch scale to assess and compare carbon storage in various scenarios, considering both carbon storage maximization and urban development requirements.

Results

The results were presented as follows. (1) Urban woodland carbon pool accounts for only a fraction of total carbon pool, and the role of soil and building carbon pools cannot be ignored. (2) Compared with the current situation, the carbon-growth optimized scenario will lead to the increase of total carbon storage by 38,568.31 tons. (3) Carbon-growth optimized scenario has reduced carbon storage in Woodland, Cropland, Village, Water compared to the Natural growth scenario, but has increased carbon storage in Garden plots, Street, Urban district, Town and other areas.

Conclusions

Therefore, we find that for fast-growing cities, rationally planning built-up areas and woodland areas can achieve the twin goals of economic development and maximizing regional carbon storage. Furthermore, the implementation of new energy policies and projects such as green roofs can help to achieve regional carbon neutrality. The study provides new insights into the accounting of carbon pools within cities and the simulation of fine-grained land use planning based on the dual objectives of carbon stock maximization and urban development.

Estimating emissions and removals from forest degradation is important, yet challenging, for many countries. This paper reports results from analysis of country reporting (to the United Nations Framework Convention on Climate Change and also to several climate finance initiatives) and key take-aways from a south-south exchange workshop among 17 countries with forest mitigation programmes. During the workshop discussions it became clear that, where forest degradation is a major source of emissions, governments want to include it when reporting on their mitigation efforts. However, challenges to accurately estimating emissions from degradation relate to defining forest degradation and setting the scope for estimating carbon stock changes; to detecting and monitoring degradation using earth observation data; and to estimating associated emissions and removals from field observation results. The paper concludes that recent and ongoing investments into data and analysis methods have helped improve forest degradation estimation, but further methodological work and continued effort will be needed.

Temperate forest soils are considered significant methane (CH₄) sinks, but other methane sources and sinks within these forests, such as trees, litter, deadwood, and the production of volatile organic compounds are not well understood. Improved understanding of all CH₄ fluxes in temperate forests could help mitigate CH₄ emissions from other sources and improve the accuracy of global greenhouse gas budgets. This review highlights the characteristics of temperate forests that influence CH₄ flux and assesses the current understanding of the CH₄ cycle in temperate forests, with a focus on those managed for specific purposes. Methane fluxes from trees, litter, deadwood, and soil, as well as the interaction of canopy-released volatile organic compounds on atmospheric methane chemistry are quantified, the processes involved and factors (biological, climatic, management) affecting the magnitude and variance of these fluxes are discussed. Temperate forests are unique in that they are extremely variable due to strong seasonality and significant human intervention. These features control CH₄ flux and need to be considered in CH₄ budgets. The literature confirmed that temperate planted forest soils are a significant CH₄ sink, but tree stems are a small CH₄ source. CH₄ fluxes from foliage and deadwood vary, and litter fluxes are negligible. The production of volatile organic compounds could increase CH₄’s lifetime in the atmosphere, but current in-forest measurements are insufficient to determine the magnitude of any effect. For all sources and sinks more research is required into the mechanisms and microbial community driving CH₄ fluxes. The variability in CH₄ fluxes within each component of the forest, is also not well understood and has led to overestimation of CH₄ fluxes when scaling up measurements to a forest or global scale. A roadmap for sampling and scaling is required to ensure that all CH₄ sinks and sources within temperate forests are accurately accounted for and able to be included in CH₄ budgets and models to ensure accurate estimates of the contribution of temperate planted forests to the global CH₄ cycle.

Background

Understanding the drivers of variations in carbon stocks is essential for developing the effective management strategies that contribute to mitigating climate change. Although a positive relationship between biodiversity and the aboveground carbon (AGC) has been widely reported for various Brazilian forest types, representing a win–win scenario for climate change mitigation, this association has not been commonly found in Brazilian subtropical forests. Therefore, in the present study, we aimed to evaluate the effects of Araucaria angustifolia, stand structure and species diversity in shaping AGC stocks in Brazilian subtropical mixed forest. We hypothesized that the effects on the AGC of stand structure and diversity would be mediated by A. angustifolia. We also evaluated the expectation of higher carbon stocks in protected forest as a result of their positive correlation with biodiversity conservation.

Results

We found that stand structure, followed by A. angustifolia, played the most important role in shaping the AGC stock. Our hypothesis was partially confirmed, the indirect effects of A. angustifolia on stand structure being found to have shaped the AGC. Similarly, our expectation was partially supported, with the higher AGC in the protected area being related not to diversity, but rather to the presence of larger trees, denser stands, and a greater abundance of A. angustifolia.

Conclusion

Although the win–win strategy between diversity conservation and carbon storage is not a peculiarity of Araucaria forests, we highlight the potential of these forests as a nature-based climate solution, maintaining high levels of carbon storage in harmony with the provision of keystone socio-economic resources.

In this review, we discuss current research on forest carbon risk from natural disturbance under climate change for the United States, with emphasis on advancements in analytical mapping and modeling tools that have potential to drive research for managing future long-term stability of forest carbon. As a natural mechanism for carbon storage, forests are a critical component of meeting climate mitigation strategies designed to combat anthropogenic emissions. Forests consist of long-lived organisms (trees) that can store carbon for centuries or more. However, trees have finite lifespans, and disturbances such as wildfire, insect and disease outbreaks, and drought can hasten tree mortality or reduce tree growth, thereby slowing carbon sequestration, driving carbon emissions, and reducing forest carbon storage in stable pools, particularly the live and standing dead portions that are counted in many carbon offset programs. Many forests have natural disturbance regimes, but climate change and human activities disrupt the frequency and severity of disturbances in ways that are likely to have consequences for the long-term stability of forest carbon. To minimize negative effects and maximize resilience of forest carbon, disturbance risks must be accounted for in carbon offset protocols, carbon management practices, and carbon mapping and modeling techniques. This requires detailed mapping and modeling of the quantities and distribution of forest carbon across the United States and hopefully one day globally; the frequency, severity, and timing of disturbances; the mechanisms by which disturbances affect carbon storage; and how climate change may alter each of these elements. Several tools (e.g. fire spread models, imputed forest inventory models, and forest growth simulators) exist to address one or more of the aforementioned items and can help inform management strategies that reduce forest carbon risk, maintain long-term stability of forest carbon, and further explore challenges, uncertainties, and opportunities for evaluating the continued potential of, and threats to, forests as viable mechanisms for forest carbon storage, including carbon offsets. A growing collective body of research and technological improvements have advanced the science, but we highlight and discuss key limitations, uncertainties, and gaps that remain.