Projecting the Impact of Socioeconomic and Policy Factors on Greenhouse Gas Emissions and Carbon Sequestration in U.S. Forestry and Agriculture.

Understanding greenhouse gas mitigation potential of the U.S. agriculture and forest sectors is critical for evaluating potential pathways to limit global average temperatures from rising more than 2° C. Using the FASOMGHG model, parameterized to reflect varying conditions across shared socioeconomic pathways, we project the greenhouse gas mitigation potential from U.S. agriculture and forestry across a range of carbon price scenarios. Under a moderate price scenario ($20 per ton CO2 with a 3% annual growth rate), cumulative mitigation potential over 2015-2055 varies substantially across SSPs, from 8.3 to 17.7 GtCO2e. Carbon sequestration in forests contributes the majority, 64-71%, of total mitigation across both sectors. We show that under a high income and population growth scenario over 60% of the total projected increase in forest carbon is driven by growth in demand for forest products, while mitigation incentives result in the remainder. This research sheds light on the interactions between alternative socioeconomic narratives and mitigation policy incentives which can help prioritize outreach, investment, and targeted policies for reducing emissions from and storing more carbon in these land use systems.

How the future of the global forest sink depends on timber demand, forest management, and carbon policies.

Deforestation has contributed significantly to net greenhouse gas emissions, but slowing deforestation, regrowing forests and other ecosystem processes have made forests a net sink. Deforestation will still influence future carbon fluxes, but the role of forest growth through aging, management, and other silvicultural inputs on future carbon fluxes are critically important but not always recognized by bookkeeping and integrated assessment models. When projecting the future, it is vital to capture how management processes affect carbon storage in ecosystems and wood products. This study uses multiple global forest sector models to project forest carbon impacts across 81 shared socioeconomic (SSP) and climate mitigation pathway scenarios. We illustrate the importance of modeling management decisions in existing forests in response to changing demands for land resources, wood products and carbon. Although the models vary in key attributes, there is general agreement across a majority of scenarios that the global forest sector could remain a carbon sink in the future, sequestering 1.2-5.8 GtCO2e/yr over the next century. Carbon fluxes in the baseline scenarios that exclude climate mitigation policy ranged from -0.8 to 4.9 GtCO2e/yr, highlighting the strong influence of SSPs on forest sector model estimates. Improved forest management can jointly increase carbon stocks and harvests without expanding forest area, suggesting that carbon fluxes from managed forests systems deserve more careful consideration by the climate policy community.

Projecting U.S. forest management, market, and carbon sequestration responses to a high-impact climate scenario.

The impact of climate change on forest ecosystems remains uncertain, with wide variation in potential climate impacts across different radiative forcing scenarios and global circulation models, as well as potential variation in forest productivity impacts across species and regions. This study uses an empirical forest composition model to estimate the impact of climate factors (temperature and precipitation) and other environmental parameters on forest productivity for 94 forest species across the conterminous United States. The composition model is linked to a dynamic optimization model of the U.S. forestry sector to quantify economic impacts of a high warming scenario (Representative Concentration Pathway 8.5) under six alternative climate projections and two socioeconomic scenarios. Results suggest that forest market impacts and consumer impacts could range from relatively large losses (-$2.6 billion) to moderate gain ($0.2 billion) per year across climate scenarios. Temperature-induced higher mortality and lower productivity for some forest types and scenarios, coupled with increasing economic demands for forest products, result in forest inventory losses by end of century relative to the current climate baseline (3%-23%). Lower inventories and reduced carbon sequestration capacity result in additional economic losses of up to approximately $4.1 billion per year. However, our results also highlight important adaptation mechanisms, such forest type changes and shifts in regional mill capacity that could reduce the impact of high impact climate scenarios.

Importance of Cross-Sector Interactions When Projecting Forest Carbon across Alternative Socioeconomic Futures.

In recent decades, the carbon sink provided by the U.S. forest sector has offset a sizable portion of domestic greenhouse gas (GHG) emissions. In the future, the magnitude of this sink has important implications not only for projected U.S. net GHG emissions under a reference case but also for the cost of achieving a given mitigation target. The larger the contribution of the forest sector towards reducing net GHG emissions, the less mitigation is needed from other sectors. Conversely, if the forest sector begins to contribute a smaller sink, or even becomes a net source, mitigation requirements from other sectors may need to become more stringent and costlier to achieve economy wide emissions targets. There is acknowledged uncertainty in estimates of the carbon sink provided by the U.S. forest sector, attributable to large ranges in the projections of, among other things, future economic conditions, population growth, policy implementation, and technological advancement. We examined these drivers in the context of an economic model of the agricultural and forestry sectors, to demonstrate the importance of cross-sector interactions on projections of emissions and carbon sequestration. Using this model, we compared detailed scenarios that differ in their assumptions of demand for agriculture and forestry products, trade, rates of (sub)urbanization, and limits on timber harvest on protected lands. We found that a scenario assuming higher demand and more trade for forest products resulted in increased forest growth and larger net GHG sequestration, while a scenario featuring higher agricultural demand, ceteris paribus led to forest land conversion and increased anthropogenic emissions. Importantly, when high demand scenarios are implemented conjunctively, agricultural sector emissions under a high income-growth world with increased livestock-product demand are fully displaced by substantial GHG sequestration from the forest sector with increased forest product demand. This finding highlights the potential limitations of single-sector modeling approaches that ignore important interaction effects between sectors.

Projecting the Spatial Distribution of Possible Planted Forest Expansion in the United States.

As the demand for forest products and carbon storage in standing timbers increases, intensive planting of forest resources is expected to increase. With the increased use of plantation practices, it is important to understand the influence that forest plot characteristics have on the likelihood of where these practices are occurring. Depending on the goals of a policy or program, increasing forest planting could be a desirable outcome or something to avoid. This study estimates a spatially explicit logistical regression function to assess the likelihood that forest plots will be planted based on physical, climate, and economic factors. The empirical results are used to project the potential spatial distribution of forest planting, at the intensive and extensive land-use margins, across illustrative future scenarios. Results from this analysis offer insight into the factors that have driven forest planting in the United States historically and the potential distribution of new forest planting in the coming decades under policy or market scenarios that incentivize improved forest productivity or certain ecosystem services provided by intensively managed systems (e.g., carbon sequestration).

Evaluating Potential Sources of Aggregation Bias with a Structural Optimization Model of the U.S. Forest Sector.

Structural economic optimization models of the forestry and land use sectors can be used to develop baseline projections of future forest carbon stocks and annual fluxes, which inform policy dialog and investment in programs that maintain or enhance forest carbon stocks. Such analyses vary in terms of the degree of spatial, temporal, and activity-level aggregation used to represent forest resources, land cover, and markets. While the statistical and econometric modeling communities widely discuss the effects of aggregation bias and have developed correction techniques, there is limited prior research investigating how aggregation bias may affect structural optimization models. This paper explores potential aggregation bias using the Land Use and Resource Allocation model (LURA), a detailed spatial allocation partial equilibrium model of the U.S. forest sector. We ran a series of projections representing alternative aggregation approaches including averaging forest stocks at plot, county, state, and regional levels, across one-, five, or ten-year age classes, and by two or fourteen forest types. We compared the resulting projections of forest carbon stocks and harvesting activities across each aggregation scenario. This allows us to isolate the effect of aggregation on key variables of interest (e.g., GHG emissions and supply costs), while holding all other structural characteristics of the modeling framework constant. We find that age-class and forest type aggregations have the greatest impact on modeling results, with the potential to substantially impact market and greenhouse gas projections. On the other hand, spatial aggregation has a small impact on national carbon stock projections. Importantly, regional results are greatly impacted by different aggregation approaches, with projected regional cumulative carbon stocks differing by more than 25% across scenarios.

Implications of Alternative Land Conversion Cost Specifications on Projected Afforestation Potential in the United States.

The Forestry and Agriculture Sector Optimization Model with Greenhouse Gases (FASOMGHG) has historically relied on regional average costs of land conversion to simulate land use change across cropland, pasture, rangeland, and forestry. This assumption limits the accuracy of the land conversion estimates by not recognizing spatial heterogeneity in land quality and conversion costs. Using data from Nielsen et al. (2014), we obtained the afforestation cost per county, then estimated nonparametric regional marginal cost functions for land converting to forestry. These afforestation costs were then incorporated into FASOMGHG. Three different assumptions for land moving into the forest sector (constant average conversion cost, static rising marginal costs, and dynamic rising marginal cost) were run in order to assess the implications of alternative land conversion cost assumptions on key outcomes, such as projected forest area and cropland use, carbon sequestration, and forest product output.