Monitoring Network Confirms Land Use Change is a Substantial Component of the Forest Carbon Sink in the eastern United States

Impacts of Cross-Sectoral Climate Policy on Forest Carbon Sinks and Their Spatial Spillover: Evidence from Chinese Provincial Panel Data

This paper examines the impact of cross-sectoral climate policy on forest carbon sinks. Due to the complexity of the climate change issue and the professional division of labor among government departments, cross-sectoral cooperation in formulating climate policy is a desirable strategy. Forest carbon sinks play an important role in addressing climate change, but there are few studies focusing on forest carbon sinks and cross-sectoral climate policies. Thus, based on the panel data of 30 provinces and cities in China from 2007 to 2020, this paper establishes a benchmark regression model and a spatial panel model to analyze the impact of cross-sectoral climate policies on forest carbon sinks. We find that cross-sectoral climate policies positively impact forest carbon sinks. Under the influence of the "demonstration effect", we find that cross-sectoral climate policies have a positive impact not only on the forest carbon sinks in the region but also on those in the neighboring region. Further analysis shows that for provinces with less developed forestry industry and small forest areas, the positive effect of cross-sectoral climate policies on forest carbon sinks is more obvious. Overall, this paper can serve as an important reference for local governments to formulate climate policies and increase the capacity of forest carbon sinks.

Comparing methods that quantify forest disturbances in the United States' national forest inventory

Forest disturbances play a critical role in ecosystem dynamics. However, the methods for quantifying these disturbances at broad scales may underestimate disturbances that affect individual trees. Utilizing individual tree variables may provide early disturbance detection that directly affects tree demographics and forest dynamics. The goals of this study were to (1) describe different methods for quantifying disturbances at individual tree and condition-level scales, (2) compare the differences between disturbance variables, and (3) provide a methodology for selecting an appropriate disturbance variable from national forest inventories for diverse applications depending on user needs. To achieve these goals, we used all the remeasurements available from the USDA Forest Inventory and Analysis (FIA) database since the start of the annual inventory for the lower 48 US states. Variables used included disturbance code, treatment code, agent of mortality, and damage code. Chi-square tests of independence were used to verify how the choice of the variable that represents disturbance affects its magnitude. Disturbed plots, as classified by each disturbance variable, were mapped to observe their spatial distribution. We found that the Chi-square tests were significant when using all the states and comparing each state individually, indicating that different results exist depending on which variable is used to represent disturbance. Our results will be a useful tool to help researchers measure the magnitude and scale of disturbance since the manner in which disturbances are categorized will impact forest management plans, national and international reports of forest carbon stocks, and sequestration potential under future global change scenarios.

Modeling land use change and forest carbon stock changes in temperate forests in the United States

BACKGROUND

Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine.

RESULTS

During the study period (2000-2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change.

CONCLUSIONS

Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.

Zones of influence for soil organic matter dynamics: A conceptual framework for data and models

Soil organic matter (SOM) is an indicator of sustainable land management as stated in the global indicator framework of the United Nations Sustainable Development Goals (SDG Indicator 15.3.1). Improved forecasting of future changes in SOM is needed to support the development of more sustainable land management under a changing climate. Current models fail to reproduce historical trends in SOM both within and during transition between ecosystems. More realistic spatio-temporal SOM dynamics require inclusion of the recent paradigm shift from SOM recalcitrance as an 'intrinsic property' to SOM persistence as an 'ecosystem interaction'. We present a soil profile, or pedon-explicit, ecosystem-scale framework for data and models of SOM distribution and dynamics which can better represent land use transitions. Ecosystem-scale drivers are integrated with pedon-scale processes in two zones of influence. In the upper vegetation zone, SOM is affected primarily by plant inputs (above- and belowground), climate, microbial activity and physical aggregation and is prone to destabilization. In the lower mineral matrix zone, SOM inputs from the vegetation zone are controlled primarily by mineral phase and chemical interactions, resulting in more favourable conditions for SOM persistence. Vegetation zone boundary conditions vary spatially at landscape scales (vegetation cover) and temporally at decadal scales (climate). Mineral matrix zone boundary conditions vary spatially at landscape scales (geology, topography) but change only slowly. The thicknesses of the two zones and their transport connectivity are dynamic and affected by plant cover, land use practices, climate and feedbacks from current SOM stock in each layer. Using this framework, we identify several areas where greater knowledge is needed to advance the emerging paradigm of SOM dynamics-improved representation of plant-derived carbon inputs, contributions of soil biota to SOM storage and effect of dynamic soil structure on SOM storage-and how this can be combined with robust and efficient soil monitoring.

Potential efficiency gains in payment programs from resolving spatial and temporal heterogeneity in the cost of supplying forest carbon

The environmental benefits and costs of conservation policies often vary over space and through time. Accounting for this spatial and temporal heterogeneity has important implications for the potential cost effectiveness of different payment program designs. In this study, we examine the cost efficiency gain from spatial and temporal targeting in payment designs for forest carbon storage in the Central and Southern Appalachian Mountains in the Eastern United States. We run a forest land change model and a carbon simulation model utilizing a panel data on forest land and its competing uses, economic returns, and spatial characteristics for each 1 km² grid cells in 1992, 2001, 2006 and 2011. A time- and space-specific carbon cost for each individual 1 km² grid cell is calculated that captures the spatial and temporal heterogeneity in carbon cost efficiency. From there, we compare carbon cost efficiency levels of various payment designs that allow for different degrees of spatial and temporal flexibility. We find that 1) spatial targeting improves carbon cost efficiency, and this efficiency gain is larger as payments become more narrowly targeted, 2) this carbon efficiency gain is present in all market conditions, but is largest in a moderately growing market and smallest in a downturn market, 3) accounting for temporal heterogeneity results in even larger carbon efficiency gains, almost double those from spatial targeting. Just as policies that enable spatial targeting (e.g., auctions) increase cost efficiency savings, so too will policy mechanisms that emphasize budget flexibility through time. These could include utilizing loans or flexible conservation financing, or allowing movement across budgeting categories within a given time period.

Land Use Changes, Disturbances, and Their Interactions on Future Forest Aboveground Biomass Dynamics in the Northern US

Land use change (LUC), disturbances, and their interactions play an important role in regional forest carbon (C) dynamics. Here we quantified how these activities and events may influence future aboveground biomass (AGB) dynamics in forests using national forest inventory (NFI) and Landsat time series data in the Northern United States (US). Total forest AGB predictions were based on simulations of diameter growth, mortality, and recruitment using matrix growth models under varying levels of LUC and disturbance severity (low (L), medium (M), and high (H)) every five years from 2018 to 2098. Land use change included the integrated effects of deforestation and reforestation/afforestation (forest [F]→agriculture [A], settlements [S, urbanization/other], and A&S→F), specifically, conversion from F→A, F→S, F→A&S, A→F, S→F, and A&S→F. Disturbances included natural and anthropogenic disturbances such as wildfire, weather, insects and disease, and forest harvesting. Results revealed that, when simultaneously considering both medium LUC and disturbances, total forest AGB predictions of LUC + fire, LUC + weather, LUC + insect & disease, and LUC + harvest indicated substantial increases in regional C stocks (± standard deviation) from 1.88 (±0.13) to 3.29 (±0.28), 3.10 (±0.24), 2.91 (±0.19), and 2.68 (±0.17) Pg C, respectively, from 2018 to 2098. An uncertainty analysis with fuzzy sets suggested that medium LUC under disturbances would lead to greater forest AGB C uptake than undisturbed forest C uptake with high certainty, except for LUC + harvest. The matrix models in this study were parameterized using NFI and Landsat data from the past few decades. Thus, our results imply that if recent trends persist, LUC will remain an important driver of forest C uptake, while disturbances may result in C emissions rather than undisturbed forest C uptake by 2098. The combined effects of LUC and disturbances may serve as an important driver of C uptake and emissions in the Northern US well into the 21st century.

Spatial-temporal dynamics of carbon emissions and carbon sinks in economically developed areas of China: a case study of Guangdong Province

This study analysed spatial-temporal dynamics of carbon emissions and carbon sinks in Guangdong Province, South China. The methodology was based on land use/land cover data interpreted from continuous high-resolution satellite images and energy consumption statistics, using carbon emission/sink factor method. The results indicated that: (1) From 2005 to 2013, different land use/land cover types in Guangdong experienced varying degrees of change in area, primarily the expansion of built-up land and shrinkage of forest land and grassland; (2) Total carbon emissions increased sharply, from 76.11 to 140.19 TgC yr⁻¹ at the provincial level, with an average annual growth rate of 10.52%, while vegetation carbon sinks declined slightly, from 54.52 to 53.20 TgC yr⁻¹. Both factors showed significant regional differences, with Pearl River Delta and North Guangdong contributing over 50% to provincial carbon emissions and carbon sinks, respectively; (3) Correlation analysis showed social-economic factors (GDP per capita and permanent resident population) have significant positive impacts on carbon emissions at the provincial and city levels; (4) The relationship between economic growth and carbon emission intensity suggests that carbon emission efficiency in Guangdong improves with economic growth. This study provides new insight for Guangdong to achieve carbon reduction goals and realize low-carbon development.

Influence of different geographical factors on carbon sink functions in the Pearl River Delta

This study analyzed carbon fixation across different land use types in the Pearl River Delta to identify the influence of different geographical factors on carbon fixation ability. The methodology was based on interpreting land use data from TM imagery, MODIS13Q1 data, and climate data, using the improved CASA and GeogDetector models. The results show that: (1) From 2000 to 2013, the total carbon sink increased slightly, from 15.58 × 10⁶ t to 17.52 × 10⁶ t, being spatially low at the center and increasing outwards; (2) Proxy variables (topography and landform characteristics), influencing urbanization, significantly affect the carbon sink function of the Pearl River Delta region. The proportion of urban and other construction land showed increasing effect on the regional carbon sink each year. However, the spatial structure of land in the study area changed from complex to simple, with enhanced stability; consequently, the influence of landscape characteristics (landscape dominance and landscape perimeter area fractal dimension) on the regional carbon sink gradually decreased; (3) The influence of the same factors differed with different land use types. Slope and altitude were found to have the greatest influence on the carbon sink of cultivated land, while landscape perimeter area fractal dimension more significantly affected the forest carbon sink.