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.

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.

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.

The Influence of Parametric Uncertainty on Projections of Forest Land Use, Carbon, and Markets.

This paper uses Monte Carlo methods and regression analysis to assess the role of uncertainty in yield function and land supply elasticity parameters on land use, carbon, and market outcomes in a long-term dynamic model of the global forest sector. The results suggest that parametric uncertainty has little influence on projected future timber prices and global output, but it does have important implications for regional projections of outputs. A wide range of outcomes are possible for timber outputs, depending on growth and elasticity parameters. Timber output in the U.S., for instance, could change by -67 to +98 million m3 per year by 2060. Despite uncertainty in the parameters, our analysis suggests that the temperate zone may sequester +30 to +79 Pg C by 2060 and +58 to +114 Pg C by 2090 while the tropics are projected to store -35 to +70 Pg C and -33 to +73 Pg C for the same time periods, respectively. Attributional analysis shows that uncertainty in the parameters regulating forest growth has a more important impact on projections of future carbon storage than uncertainty in the land supply elasticity parameters. Moreover, the results suggest that understanding growth parameters in regions with large current carbon stocks is most important for making future projections of carbon storage.

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.

Logging residue supply and costs for electricity generation: Potential variability and policy considerations.

This paper applies a spatial allocation optimization model to evaluate logging residue supply potential and costs for bioelectricity generation within the conterminous United States. Simulations are developed to estimate a range in supply potential and costs across a broad range of sensitivity scenarios, including (1) different biomass availability rates based on observed roundwood removals, (2) renewable energy targets set nationally or at a state-level, (3) with and without biomass sourcing restrictions within a state, (4) with and without access to public lands, and (5) policy restrictions on eligible facility types. Under the least restrictive policy scenario (a hypothetical national mandate), total supply is 8.8 million dry tons (MDT) at $20/DT and increases to 32.5 MDT at $80/DT. Results fall within the range of previous logging residue supply studies in the U.S., including the last two Billion Ton reports. Results from this paper offer important policy insight into the potential cost efficiency of a flexible policy design. Sensitivity scenarios show potential supply cost increases that could result from policies imposing regional restrictions, limiting access to public lands, and restricting eligible facilities. Restricting biomass supply sources within state boundaries reduces total supply up to 10% relative to an unrestricted national policy.

A Land Use and Resource Allocation (LURA) modeling system for projecting localized forest CO2 effects of alternative macroeconomic futures.

The United States has recently set ambitious national goals for greenhouse gas (GHG) reductions over the coming decades. A portion of these reductions are based on expected sequestration and storage contributions from land use, land use change, and forestry (LULUCF). Significant uncertainty exists in future forest markets and thus the potential LULUCF contribution to US GHG reduction goals. This study seeks to inform the discussion by modeling US forest GHG accounts per different simulated demand scenarios across a grid of over 130,000 USDA Forest Service Forest Inventory and Analysis (FIA) forestland plots over the conterminous United States. This spatially disaggregated future supply is based on empirical yield functions for log volume, biomass and carbon. Demand data is based on a spatial database of over 2300 forest product manufacturing facilities representing 11 intermediate and 13 final solid and pulpwood products. Transportation costs are derived from fuel prices and the locations of FIA plot from which a log is harvested and mill or port destination. Trade between mills in intermediate products such as sawmill residues or planer shavings is also captured within the model formulation. The resulting partial spatial equilibrium model of the US forest sector is solved annually for the period 2015-2035 with demand shifted by energy prices and macroeconomic indicators from the US EIA's Annual Energy Outlook for a Reference, Low Economic Growth, and High Economic Growth case. For each macroeconomic scenario simulated, figures showing historic and scenario-specific live tree carnon emissions and sequestration are generated. Maps of the spatial allocation of both forest harvesting and related carbon fluxes are presented at the National level and detail is given for both regions and ownerships.

Modeling wood product carbon flows in southern us pine plantations: implications for carbon storage.

BACKGROUND: Wood products continue to store carbon sequestered in forests after harvest and therefore play an important role in the total carbon storage associated with the forest sector. Trade-offs between carbon sequestration/storage in wood product pools and managed forest systems exist, and in order for forest sector carbon modeling to be meaningful, it must link wood product carbon with the specific forest system from which the products originate and have the ability to incorporate in situ and ex situ carbon synchronously over time. RESULTS: This study uses elements of a life cycle assessment approach, tracing carbon from US southern pine timber harvests to emission, to create a decision support tool that practitioners can use to inform policy design around land- and bioproduct-based mitigation strategies. We estimate that wood products from annual loblolly and shortleaf pine timber harvests across the southern US store 29.7 MtC in the year they enter the market, and 11.4 MtC remain stored after 120 years. We estimate fossil fuel emissions from the procurement, transportation, and manufacturing of these wood products to be 43.3 MtCO2e year-1. We found that composite logs, used to manufacture oriented strand board (OSB), were the most efficient log type for storing carbon, storing around 1.8 times as much carbon as saw logs per tonne of log over 120 years. CONCLUSIONS: Results from our analysis suggest that adjusting rotation length based on individual site productivity, reducing methane emissions from landfills, and extending the storage of carbon in key products, such as corrugated boxes, through longer lifespans, higher recycling rates, and less landfill decomposition could result in significant carbon gains. Our results also highlight the benefits of high site productivity to store more carbon in both in situ and ex situ pools and suggest that shorter rotations could be used to optimize carbon storage on sites when productivity is high.

Forested watersheds provide the highest water quality among all land cover types, but the benefit of this ecosystem service depends on landscape context.

Conversion of natural land cover can degrade water quality in water supply watersheds and increase treatment costs for Public Water Systems (PWSs), but there are few studies that have fully evaluated land cover and water quality relationships in mixed use watersheds across broad hydroclimatic settings. We related upstream land cover (forest, other natural land covers, development, and agriculture) to observed and modeled water quality across the southeastern US and specifically at 1746 PWS drinking water intake facilities. While there was considerable complexity and variability in the relationship between land cover and water quality, results suggest that Total Nitrogen (TN), Total Phosphorus (TP) and Suspended Sediment (SS) concentrations decrease significantly with increasing forest cover, and increase with increasing developed or agricultural cover. Catchments with dominant (>90 %) agricultural land cover had the greatest export rates for TN, TP, and SS based on SPARROW model estimates, followed by developed-dominant, then forest- and other-natural-dominant catchments. Variability in modeled TN, TP, and SS export rates by land cover type was driven by variability in natural background sources and catchment characteristics that affected water quality even in forest-dominated catchments. Both intake setting (i.e., run-of-river or reservoir) and upstream land cover were important determinants of water quality at PWS intakes. Of all PWS intakes, 15 % had high raw water quality, and 85 % of those were on reservoirs. Of the run-of-river intakes with high raw water quality, 75 % had at least 50 % forest land cover upstream. In addition, PWS intakes obtaining surface water supply from smaller upstream catchments may experience the largest losses of natural land cover based on projections of land cover in 2070. These results illustrate the complexity and variability in the relationship between land cover and water quality at broad scales, but also suggest that forest conservation can enhance the resilience of drinking water supplies.

Analysis of the production and transaction costs of forest carbon offset projects in the USA.

Forest carbon offset project implementation costs, comprised of both production and transaction costs, could present an important barrier to private landowner participation in carbon offset markets. These costs likewise represent a largely undocumented component of forest carbon offset potential. Using a custom spreadsheet model and accounting tool, this study examines the implementation costs of different forest offset project types operating in different forest types under different accounting and sampling methodologies. Sensitivity results are summarized concisely through response surface regression analysis to illustrate the relative effect of project-specific variables on total implementation costs. Results suggest that transaction costs may represent a relatively small percentage of total project implementation costs - generally less than 25% of the total. Results also show that carbon accounting methods, specifically the method used to establish project baseline, may be among the most important factors in driving implementation costs on a per-ton-of-carbon-sequestered basis, dramatically increasing variability in both transaction and production costs. This suggests that accounting could be a large driver in the financial viability of forest offset projects, with transaction costs likely being of largest concern to those projects at the margin.

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).

Cumulative global forest carbon implications of regional bioenergy expansion policies.

Several previous studies have evaluated the potential greenhouse gas (GHG) benefits of forest biomass energy relative to fossil fuel equivalents over different spatial scales and time frames and applying a variety of methodologies. This paper contributes to this literature through an analysis of multiple projected sources of biomass demand growth in different regions of the world using a detailed intertemporal optimization model of the global forest sector. Given the range of current policies incentivizing bioenergy expansion globally, evaluating the combined global implications of regional bioenergy expansion efforts is critical for understanding the extent to which renewable energy supplied from forest biomass can contribute to various policy goals (including GHG emissions mitigation). Unlike previous studies that have been more regionally focused, this study provides a global perspective, illustrating how large potential demand increases for forest biomass in one or multiple regions can alter future forest management trends, markets, and forest carbon sequestration in key timber supply regions. Results show that potential near term (2015-2030) biomass demand growth in the U.S., Europe, and elsewhere can drive forest resource investment at the intensive and extensive margins, resulting in a net increase in forest carbon stocks for most regions of the world. When the reallocation of biomass away from traditional pulp and sawtimber markets is accounted for, net forest carbon sequestration increases (that stored on the land and in wood products) by 9.4 billion tons CO2 over the near term and 15.4 billion tons CO2 by 2095. Even if most of the increased forest biomass demand arises from one region (e.g., Europe) due to a particularly strong promotion of forest bioenergy expansion, changes in forest management globally in anticipation of this demand increase could result in carbon beneficial outcomes that can be shared by most regions.

Evaluating the effects of climate change on US agricultural systems: sensitivity to regional impact and trade expansion scenarios.

Agriculture is one of the sectors that is expected to be most significantly impacted by climate change. There has been considerable interest in assessing these impacts and many recent studies investigating agricultural impacts for individual countries and regions using an array of models. However, the great majority of existing studies explore impacts on a country or region of interest without explicitly accounting for impacts on the rest of the world. This approach can bias the results of impact assessments for agriculture given the importance of global trade in this sector. Due to potential impacts on relative competitiveness, international trade, global supply, and prices, the net impacts of climate change on the agricultural sector in each region depend not only on productivity impacts within that region, but on how climate change impacts agricultural productivity throughout the world. In this study, we apply a global model of agriculture and forestry to evaluate climate change impacts on US agriculture with and without accounting for climate change impacts in the rest of the world. In addition, we examine scenarios where trade is expanded to explore the implications for regional allocation of production, trade volumes, and prices. To our knowledge, this is one of the only attempts to explicitly quantify the relative importance of accounting for global climate change when conducting regional assessments of climate change impacts. The results of our analyses reveal substantial differences in estimated impacts on the US agricultural sector when accounting for global impacts vs. US-only impacts, particularly for commodities where the United States has a smaller share of global production. In addition, we find that freer trade can play an important role in helping to buffer regional productivity shocks.

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.