Impacts of unprecedented wood demand for bioenergy in the Southeastern US

Impacts of unprecedented wood demand for bioenergy in the Southeastern US | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impacts of unprecedented wood demand for bioenergy in the Southeastern US Bodie Cabiyo, Richard Manner, Karen Abt, Robert Abt, Erica Belmont, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6839932/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Biomass-based climate solutions, such as bioenergy, are pivotal for meeting climate goals, but they are contingent on sustainable feedstock supply. Early bioenergy development hotspots may offer lessons for broader bioenergy buildout. Here, we examine such a hotspot in the Southeastern US, where planned facilities would increase existing biomass demand by 53%. Using two biophysical forest economics models, we find that market responses, such as shifting wood sourcing, diffuse but do not eliminate land-use change and carbon impacts. New wood demand drives the conversion of 11% of existing natural upland forests into pine plantations. Total forest carbon is persistently lower than without bioenergy buildout, challenging key biogenic carbon accounting assumptions. We find that existing policy frameworks can greatly misrepresent baseline forest carbon accumulation and propose alternative approaches. Combining life cycle emissions and landscape carbon changes, bioenergy deployment achieves net CO 2 removal by 2036, driven by technologies with carbon capture. bioenergy climate change mitigation forestry forest economics lca Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Main Text Biomass-based decarbonization pathways are essential to meet targets set in the Paris Agreement 1 , 2 . Technologies such as bioenergy with carbon capture and storage (BECCS) and biofuels harness biomass to sequester or convert atmospheric carbon dioxide (CO 2 ), offering scalable pathways for net-negative emissions 3 – 6 . The Intergovernmental Panel on Climate Change (IPCC) underscores the importance of biomass-based carbon dioxide removal (CDR), with scenarios limiting warming to 1.5C requiring the removal of hundreds of gigatons (Gt) of CO 2 by 2100, including up to 22.5 Gt annually from BECCS alone 7 , 8 . However, the viability of bioenergy hinges on the sufficient availability of sustainable biomass feedstocks, especially forest biomass, which is expected to supply most feedstock for new bioenergy 9 . Biomass has a wide array of existing economic uses, from bioenergy to paper to structural wood products 10 , 11 . Historically, increased biomass demand has driven the intensification and extensification of land managed for biomass, alongside efficiency gains in biomass processing. But demand for modern bioenergy is expected to triple by 2050 12 . In deep decarbonization scenarios for 2050, biomass demand for bioenergy, CDR, and biomaterials could outstrip sustainable supply by a factor of 11–16, exacerbating risks of ecosystem degradation, market distortions, and socio-economic inequities 13 . The risk of overdrawing sustainable biomass resources may necessitate strategies to mitigate ecological, economic, and social impacts. Due to limited deployment of cellulosic bioenergy to date, there are few cases to inform such strategies. Biomass overdraw is already a localized challenge. Imminent deployment of bioenergy provides a critical test case for scaling biomass demand sustainably. Current and planned projects cluster in regions such as the Nordic countries and the Southeastern United States, where geologic CO 2 storage, supportive policy, and biomass resources coalesce 14 , 15 , 16 . In deployment hotspots, planned facilities already promise to fundamentally restructure markets and land use. These regions thus offer an early stress test for looming global biomass demand. This study investigates the potential impacts of concentrated bioenergy deployment through a case study of unprecedented biomass demand in the Southeastern US (Fig. 1 ). We model nine planned, technically feasible biomass-using facilities in the region, including BECCS, biofuels, and bioplastics plants (collectively referred to as “bioenergy” for simplicity; Table S1). These new facilities collectively represent a 53% increase in biomass processing capacity by 2030, making biomass the dominant forest product in the region, bypassing sawtimber. We find that natural forest conversion is a likely consequence of increased biomass demand. We also find that commonplace carbon-accounting heuristics fail to capture true landscape carbon losses. This case study brings into focus two key indicators of high biomass demand: land-use change and forest carbon stock changes. As one of the first real-world tests of concentrated forest bioenergy build-out, our findings offer early insight into the policy challenges that future biomass pathways will face. Results Markets mitigate impacts We conduct a novel application of two economic models to predict market responses to increased bioenergy deployment in and near the state of Louisiana. These models incorporate biophysical inventory and harvest data, infrastructure, and market dynamics to find market equilibrium conditions. First, we determine explicit spatial redistribution of harvest activity across the region. Subsequently, we project granular biophysical responses to increased harvest activity, in both the core sourcing region and the surrounding region. Markets play an important role reallocating biomass demand across the region. With bioenergy deployment, we find that biomass stumpage prices rise by over 40% with the introduction of new facilities due to price-insensitive biomass supply. This drives two key outcomes: more land conversion to pine plantations and mills expanding their wood procurement zones. Prices remain elevated until new pine plantations mature sufficiently to supply wood in the region (Figure S1). Elevated prices push mills and bioenergy facilities to shift their wood sourcing to regions farther away, even reaching into areas without active forest industries like central Texas (Fig. 1 ). Harvesting increases by only 3.5 million (M) additional green tons (GT) in 2035, representing only 33% of new bioenergy capacity. The remaining two thirds shift outside of the core basin. This shift distributes the market shock widely. To measure the magnitude of this market distribution effect, we ran a scenario with price-insensitive (perfectly inelastic) wood demand. In this scenario, localized impacts are pronounced. Because the core region bears the full impact of new biomass demand, land-use change is 6 times greater and carbon losses are 5 times greater than under normal market conditions (Fig. 2). This scenario highlights the moderating role that market dynamics play in redistributing impacts. Bioenergy amplifies historic land-use change trends Our simulations indicate that land-use change is a likely consequence of a rapid increase in biomass demand. Increased bioenergy capacity avoids the net loss of 2.5% of plantation forest in the counterfactual scenario. This loss occurs in response to suppressed markets for pulpwood and sawtimber, as well as age class dynamics of pine forests in the region. Natural forests, such as longleaf pine and upland hardwood forests, are likely to be converted into plantations, posing risks to ecological integrity 17 . In the simulations, we see 0.9M acres (ac) of conversion from natural forest types into pine plantations due to new bioenergy facilities by 2035. This represents conversion of 11% of existing natural upland forests, similar to that observed over the previous decade. A large portion of natural forest loss is in the oldest age classes, which harbor more carbon and biodiversity than younger forests (Fig. 3 ) 18 . Some agricultural land (~ 0.1M ac) is converted to pine plantations, as well. Such land conversion has precedent in this region, as with other areas in the Southeastern US with high levels of historic land conversion at both extensive (land-use change) and intensive (forest type change) margins 19 , 20 . Over the past decade, 0.9M ac of natural forest were converted to pine plantations or agriculture (Fig. 3 ). This trend is expected to continue even in the absence of bioenergy deployment. However, bioenergy deployment at scale could increase the conversion rate of natural pine forest relative to the previous decade (Fig. 3 ). Evaluating carbon baseline assumptions Bioenergy accounting often treats biogenic carbon (carbon sequestered by plants) as carbon neutral 21 . This determination rests on choosing a baseline against which biogenic carbon is accounted. Forest biomass requires baselines that accurately account for carbon stock changes in harvested forests. We test three baseline approaches to accounting for biogenic carbon from forests: counterfactual, heuristic, and historical. The counterfactual baseline we model represents a likely outcome—absent new bioenergy facilities—based on a complex set of parameters and assumptions. Counterfactual accounting is common in academic research but less common in policy frameworks. Relative to the counterfactual, 0.3 tonnes of carbon (tC) is lost from the landscape for every tC in biomass feedstock by 2035 (i.e., -0.3 tC/tC, or 30%). In aggregate, this represents a net CO 2 increase of 60 MtCO 2 versus a carbon neutral assumption for biogenic carbon. Heuristic baselines often treat biomass as carbon-neutral if forest carbon stocks are stable or increasing. This approach is common in policy frameworks like the EU Renewable Energy Directive (EU RED), a leading global policy on wood-based bioenergy. In our case study, forest carbon stocks increase by 50% without bioenergy deployment, driven by young, rapidly growing forests (Fig. 4 ). Thus, a heuristic baseline, which assumes no forest carbon accumulation, undercounts the magnitude of potential carbon losses by 530 MtCO 2 by 2035. The EU RED approach would even permit biomass as carbon neutral in the price-insensitive market scenario (All Mills – w/o MM), where 1.6 tC is lost from the landscape for every tC in biomass feedstock by 2035 (i.e., -1.6 tC/tC, or 160%). Historical baselines bridge counterfactual and heuristic approaches by anchoring projections in observed trends. Historical baselines also reduce the subjectivity inherent to counterfactual modeling. In our study, a linear regression based on the most recent 10 yrs of data only slightly underpredicts counterfactual carbon accumulation on the landscape (Fig. 4 ). Relative to the counterfactual, the historical baseline could undercount forest carbon losses by 19 MtCO 2 by 2035 versus a carbon neutral assumption for biogenic carbon. Thus, in this context, a historical baseline is much more accurate than a heuristic approach. Maximizing lifecycle carbon benefits Life-cycle carbon benefits for each planned facility type in this region vary substantially on the feedstock efficiency basis of tC benefit per tC in harvested biomass feedstock (tC/tC). We find that retrofitting existing paper mills with carbon capture and storage (CCS) offers substantial feedstock efficiency (2.13 tC/tC) due to relatively limited biomass required to capture a large pre-existing source of CO 2 . Biopower with CCS is also feedstock efficient (1.14 tC/tC) due to displacement of the relatively carbon intensive local grid. Conversely, bioplastics—the earliest facility constructed—is net emitting (-0.17 tC/tC), mostly due to high production emissions from fossil gas heat. The wide range of feedstock efficiency values highlights the heterogeneity in life cycle climate performance inherent to biomass technologies. Collectively, these facilities will begin to drive net CO 2 removal by 2036 (Fig. 5 ). If all facilities produced BECCS instead, they would achieve net CO 2 removal by 2032. Discussion Our findings underscore the complex interplay between biomass demand, land use and forest management change, and carbon accounting. While market mechanisms help distribute wood sourcing more widely, they do not eliminate the conversion of natural forests to pine plantations—an outcome that diminishes overall forest carbon stocks in the relatively short time window we examine. Baseline assumptions also critically shape whether biomass is deemed “carbon-neutral”; in our scenarios, policy heuristics that assume stable or rising stocks can underestimate the resulting forest carbon losses compared to a counterfactual baseline. Nonetheless, certain pathways (e.g., retrofitting existing pulp and paper mills with CCS) deliver substantial lifecycle carbon benefits more quickly than others. Strategic choices about where and how to deploy bioenergy can shorten the time to achieve net CO 2 reductions, reinforcing the importance of context-specific investments and policy interventions. This case study region presents uniquely favorable conditions for forest bioenergy deployment. Extensive land-use change and intensive forest management have positioned the landscape to produce large volumes of biomass, both as pulpwood and sawmill residues. In tandem, regional pulp and paper production capacity is declining, leaving a substantial resource base of small diameter roundwood and sawmill residues underutilized and available for alternative uses. The existing infrastructure for transporting biomass further supports bioenergy suitability by enabling the spatial distribution of harvest pressure. Taken together, these factors represent a favorable context for bioenergy. By the same token, in regions lacking similar characteristics, landscape and market impacts could be much more pronounced. Despite employing two regionally validated forest economics models, sources of uncertainty remain. Long-term projections of forest carbon accumulation and land-use change are sensitive to assumptions about landowner behavior, market elasticity, and policy stability, which may shift substantially over time. The relatively short horizon captured in our analysis may also underestimate long-term carbon dynamics, including the potential for delayed forest regrowth. And although SRTS accounts for demand-induced conversion of certain forest types, it does not fully capture harvesting or management decisions in bottomland hardwood forests, potentially underestimating overall impacts on carbon stocks and ecological function. Further, SRTS is a recursive dynamic framework that does not manage forests with foresight of future market conditions. Contemporaneous markets drive harvest patterns and land use decisions, rather than future expectations, which could bias carbon impacts of bioenergy deployment as single-year harvests are prioritized over long-term planning 22 . Collectively, these limitations highlight the need for continued refinement of modeling frameworks, as well as further empirical validation under diverse environmental and policy contexts. Quantifying these sources of uncertainty will sharpen future estimates. This analysis demonstrates the key role of accounting for market feedbacks when projecting forest carbon change in regions with bioenergy expansion. Ignoring market dynamics can overstate potential carbon impacts of forest harvesting. Models that assume no market feedback (e.g., price responsiveness, product substitution) can erroneously project high net emissions from forest harvest 23 , 24 . Our scenario without market mitigation provides a relevant comparison to regional harvest simulations that are decoupled from markets. This finding is also consistent with recent global-scale economic analyses 25 , 26 . Economic models also highlight potential complementarities between sawtimber markets, bioenergy expansion, and forest carbon 27 . Policy is a critical determinant of bioenergy deployment. Federal incentives, such as the 45Q and 45V tax credits in the US, are key drivers of bioenergy deployment, enhancing the economic viability of these technologies. For example, 45Q offers direct financial incentives of $ 85 per tCO 2 captured and permanently stored. State-level regulations, such as Louisiana’s recent Class VI primacy for underground injections control, streamline the permitting processes for CO 2 storage wells, reducing regulatory uncertainty and accelerating the pace of project deployment. Further, state incentives such as the Louisiana Forest Productivity Program help private forest landowners access cost assistance for establishing and managing forest plantations for timber production 28 . Together, these policy drivers, combined with resource availability and market precedence, help explain the current expansion of bioenergy in this region. Existing policy can also provide caution, as demonstrated by the US Renewable Fuel Standard (RFS) support for corn ethanol 29 , 30 . The RFS increased US corn cultivation by approximately 2.8 million hectares (ha) from 2008–2016 and led to an estimated 320 MtCO₂ e emissions from land-use change 29 . The RFS highlights the need for bioenergy policy frameworks to include sustainability criteria, especially criteria that mitigate land-use change impacts. Our work also highlights the importance of appropriate baseline setting for bioenergy carbon accounting. EU RED is the largest policy driver of forest bioenergy development globally. EU RED operationalizes the requirement for biomass sourcing from areas with “stable or increasing” carbon stocks through alignment with national LULUCF accounting. EU RED offers a straightforward heuristic for avoiding biomass that may be linked to deforestation or degradation. But as a carbon accounting baseline, it is likely to be insufficient in many cases. In forests across the globe, carbon stocks are increasing significantly due to factors like forest expansion and CO 2 fertilization. This is true of our study region: even when we model unrealistically high harvest activity, forest carbon stocks still increase over time. While counterfactual modeling may be impractical for policy applications, use of historical trends may provide more accurate baselines and have precedent in polices such as the US Environmental Protection Agency Air Quality Standards, as well as Reducing Emissions from Deforestation and Degradation (REDD+) programs 31 . Collectively, our results from this early stress test of concentrated biomass demand reveal key policy and accounting challenges. Addressing these can help ensure that bioenergy supports climate and land-use goals as it scales up. Online Methods Facility identification and characterization We compiled planned, technically feasible biomass facilities in the study area from company websites, industry sources, and government permitting databases. Facility attributes (location, capacity, feedstock, status) were standardized, and project readiness was evaluated qualitatively using evidence of financing and technical feasibility. Expert consultation and industry-informed assessment supplemented this study where gaps existed. We excluded multiple additional planned facilities lacking public documentation. The final dataset comprised nine technically feasible planned facilities, informing spatial demand allocation in subsequent modeling. LURA modeling The Land Use and Resource Allocation (LURA) model 32 , 33 , 34 is a spatially explicit framework that simulates biomass supply-demand interactions. The model incorporated detailed transportation logistics and geographic constraints, including transportation networks between biomass supply points (e.g., forest plots), forest product manufacturing facilities, and other demand points (e.g., international ports). In this study, we apply the LURA model to quantify the spatial redistribution of harvesting activities across the region and explicitly define biomass sourcing boundaries. Specifically, we developed national-scale simulations of market developments, harvest allocation, and biomass sourcing strategies, with and without additional biomass facilities. These boundaries delineate a core sourcing region and a broader secondary effects region, which are subsequently analyzed using two separate SRTS simulations. SRTS modeling We employed the SubRegional Timber Supply (SRTS) model 35 , 36 , 19 , 37 to simulate forest market responses and carbon outcomes under increased biomass demand. SRTS is a dynamic model that integrates forest inventory, growth, and harvesting behavior with economic responses to wood prices and landowner decision-making. The model is calibrated to the US South using observed behavior of private timberland owners, and reflects regional forest management practices, land ownership, and market conditions. Simulations were initialized with USDA Forest Service Forest Inventory and Analysis (FIA) and Timber Products Output (TPO) data, capturing forest conditions by species, age class, and ownership group. The model tracks changes in aboveground biomass and projects annual removals and growing stock by product class (pine sawtimber, pine pulpwood, hardwood sawtimber, and hardwood pulpwood). Soil organic carbon is not included, consistent with prior work showing limited sensitivity to management scenarios. We use a static baseline for pulpwood and sawtimber demand due to its alignment with recent market trends, but alternative baseline scenarios are detailed in the SI Appendix. Life cycle analysis of biomass products We use published values to quantify cradle-to-grave carbon impacts across four key categories: harvest and transport emissions, fossil process emissions, carbon removal volumes, and displaced emissions due to substitution of carbon-intensive products. Our unit of analysis is one tonne of harvested carbon. We rely on data from life-cycle assessment studies that employ feedstocks and system boundaries closely matching those of the facilities modeled here. When available, data is sourced from studies employing the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model, and harvest and transport emissions are normalized across all product pathways using GREET-derived values. We assume grid carbon intensity of 336 g CO 2 e /kWh across all products. Assumptions and facility-specific parameters are detailed in the SI Appendix. To project net CO 2 outcomes in Fig. 5 , we take the mean of total life cycle carbon efficiency values for all products with and without CCS. We then apply those mean values to all facilities. Declarations Competing interest The views expressed in this paper are those of the authors and do not necessarily reflect the views or positions of any organization with which they are affiliated. Multiple authors are employed by Carbon Direct Inc. The authors do not stand to gain financially from any specific outcome of this paper. Acknowledgements We are grateful for the study design and interpretation contributions of Rafael Broze and TJ Considine, the data visualization contributions of Jasper Croome and Peter Tittmann, and modeling parameterization contributions of Raju Pokharel. 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Policy Econ 156:103059 Henderson JD, Abt RC, Abt KL, Baker J, Sheffield R (2022) Impacts of hurricanes on forest markets and economic welfare: The case of hurricane Michael. Policy Econ 140:102735 Cheng F et al (2025) Assessing Carbon Emission Impacts of Forest-Based Bioenergy in the Southern U.S. Environ Sci Technol. 10.1021/acs.est.4c06272 Additional Declarations The authors declare potential competing interests as follows: Multiple authors are employed by Carbon Direct Inc. The authors do not stand to gain financially from any specific outcome of this paper. Supplementary Files SIBiomassOverdraw662025.docx Supplemental Materials: Impacts of unprecedented wood demand for bioenergy in the Southeastern US Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6839932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467764402,"identity":"bcfaf81a-99fd-4344-9e91-32eeb7cde679","order_by":0,"name":"Bodie Cabiyo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACAwYGNoYEBgY5BhADCBjbiNVijKrlACEtQJDYANPSQEiLOfvhZw8ettmk97efffbg4w4G2T7pwwc/f2Cwk9NtwK7FsifN3CCxLS13xpl0c8OZZxiM2/jSkiUOMCQbm+GwyuAGg5lEYtvh3A0MaWzSvG3/E9t4eMyADjuQuA2nFvZvIC3pBvzPQFoYgFr4vxHQwgO2JcFAIg2mhYcNrxbLnpxyg4RzaYYzbjxjN5zZBvQLD5uxxBkD3H4xZz++7eGPMht5/v40tgcf2xhk5/cwP/xQUWEnh0sLGDCyYToYj3Iw+ENIwSgYBaNgFIxoAACUJFhoT9Zh5wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4321-7253","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Bodie","middleName":"","lastName":"Cabiyo","suffix":""},{"id":467764403,"identity":"e8768076-f78d-41df-bdca-becaec7f989d","order_by":1,"name":"Richard Manner","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Manner","suffix":""},{"id":467764404,"identity":"bc77f330-acd0-4510-9759-407a8c4e24b1","order_by":2,"name":"Karen Abt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Abt","suffix":""},{"id":467764405,"identity":"77f44ce1-0caf-4fd5-a150-1f6b489bbcf7","order_by":3,"name":"Robert Abt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Abt","suffix":""},{"id":467764406,"identity":"4499a81c-42c7-4ae7-b48c-0a611f32c8c0","order_by":4,"name":"Erica Belmont","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Erica","middleName":"","lastName":"Belmont","suffix":""},{"id":467764407,"identity":"6c954a92-2355-4a4d-ac8c-f214d57ec4ee","order_by":5,"name":"Louisa Brotherson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Louisa","middleName":"","lastName":"Brotherson","suffix":""},{"id":467764408,"identity":"2f0d9d86-45e1-4af5-895b-0f0feff6d031","order_by":6,"name":"Gregory Latta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Latta","suffix":""},{"id":467764409,"identity":"8661bddd-d19c-4822-9504-cde274a59e32","order_by":7,"name":"Shirin Mavandad","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shirin","middleName":"","lastName":"Mavandad","suffix":""},{"id":467764410,"identity":"c5762862-96e9-4fbc-9926-ce6b965003ba","order_by":8,"name":"Justin Baker","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"","lastName":"Baker","suffix":""}],"badges":[],"createdAt":"2025-06-07 01:09:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6839932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6839932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84372307,"identity":"a89205b6-2259-4ba3-820b-9a2622396506","added_by":"auto","created_at":"2025-06-11 07:38:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":571058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMarket-mediated distribution of biomass procurement for (A) pre-existing paper mills in 2024 and (B) bioenergy and paper mills in 2035, as well as (C) cumulative biomass procurement locations over time with all bioenergy deployment. Green dots indicate new bioenergy facility sites.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/26e557e2711048b824ff50e9.png"},{"id":84371671,"identity":"13df3e97-550b-4a33-aca8-7301d1b0fa64","added_by":"auto","created_at":"2025-06-11 07:30:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMarkets mitigate landscape impacts relative to the counterfactual baseline. The All Mills scenario shows expected impacts under dynamic market conditions. The All Mills without market mitigation (“w/o MM”) scenario shows impacts that would occur if markets did not redistribute biomass demand. The grey shaded area shows the mitigating role markets play in reducing landscape impacts.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/3475183e6ccc42d2ea7bd079.png"},{"id":84371673,"identity":"e2bba54c-64d2-4daf-bf1e-e04be2d6cb15","added_by":"auto","created_at":"2025-06-11 07:30:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLand-use change under increased biomass demand, by (A) forest management type and (B) forest age class. The vertical dashed line in (A) divides historical and simulated data, and solid lines indicate simulations for the All Mills scenario.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/cc28d92a28498db67a00f846.png"},{"id":84371674,"identity":"0a7cb58e-a635-440e-a38d-f23d57f938e7","added_by":"auto","created_at":"2025-06-11 07:30:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThree approaches to baseline setting for biogenic carbon accounting. The counterfactual baseline is a dynamically modeled estimate of forest carbon. The historical baseline is a regression on historical data (10 yrs, see SI Methods). The heuristic baseline represents stable carbon stocks calibrated to 2024. The grey shaded area indicates the range of these baseline outcomes. The All Mills scenario shows expected impacts under dynamic market conditions. The All Mills without market mitigation (“w/o MM”) scenario shows impacts that would occur if markets did not redistribute biomass demand.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/d2b60fe697c3da69153f9713.png"},{"id":84371675,"identity":"0a69ee96-aa37-4d46-a30a-0fbe7ac5ab4e","added_by":"auto","created_at":"2025-06-11 07:30:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":190487,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLife cycle carbon impacts of biomass utilization at the landscape (A) and product (B) level. Black dots (B) and lines (A) show net results. In A, dotted lines show net results if all new biomass facilities were built with (upper) or without (lower) BECCS (see Methods). P+P is pulp and paper. SAF is sustainable aviation fuel.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/ea598ab2422f3dcc2b847a3a.png"},{"id":84373910,"identity":"a1153e44-bca5-4687-bf77-2d68bbe1af1b","added_by":"auto","created_at":"2025-06-11 08:02:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1559953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/67bc7e2c-2c6d-463b-a869-8dab77779e1a.pdf"},{"id":84371676,"identity":"99812cd8-b1f0-42ab-8a9e-41bb7704d7e2","added_by":"auto","created_at":"2025-06-11 07:30:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":662801,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Materials: Impacts of unprecedented wood demand for bioenergy in the Southeastern US\u003c/p\u003e","description":"","filename":"SIBiomassOverdraw662025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6839932/v1/e9e1f80ad0731a53b2ce231b.docx"}],"financialInterests":"The authors declare potential competing interests as follows: Multiple authors are employed by Carbon Direct Inc. The authors do not stand to gain financially from any specific outcome of this paper.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eImpacts of unprecedented wood demand for bioenergy in the Southeastern US\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Main Text","content":"\u003cp\u003eBiomass-based decarbonization pathways are essential to meet targets set in the Paris Agreement\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Technologies such as bioenergy with carbon capture and storage (BECCS) and biofuels harness biomass to sequester or convert atmospheric carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), offering scalable pathways for net-negative emissions\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The Intergovernmental Panel on Climate Change (IPCC) underscores the importance of biomass-based carbon dioxide removal (CDR), with scenarios limiting warming to 1.5C requiring the removal of hundreds of gigatons (Gt) of CO\u003csub\u003e2\u003c/sub\u003e by 2100, including up to 22.5 Gt annually from BECCS alone\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the viability of bioenergy hinges on the sufficient availability of sustainable biomass feedstocks, especially forest biomass, which is expected to supply most feedstock for new bioenergy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Biomass has a wide array of existing economic uses, from bioenergy to paper to structural wood products\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Historically, increased biomass demand has driven the intensification and extensification of land managed for biomass, alongside efficiency gains in biomass processing. But demand for modern bioenergy is expected to triple by 2050\u003csup\u003e12\u003c/sup\u003e. In deep decarbonization scenarios for 2050, biomass demand for bioenergy, CDR, and biomaterials could outstrip sustainable supply by a factor of 11\u0026ndash;16, exacerbating risks of ecosystem degradation, market distortions, and socio-economic inequities\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The risk of overdrawing sustainable biomass resources may necessitate strategies to mitigate ecological, economic, and social impacts. Due to limited deployment of cellulosic bioenergy to date, there are few cases to inform such strategies.\u003c/p\u003e \u003cp\u003eBiomass overdraw is already a localized challenge. Imminent deployment of bioenergy provides a critical test case for scaling biomass demand sustainably. Current and planned projects cluster in regions such as the Nordic countries and the Southeastern United States, where geologic CO\u003csub\u003e2\u003c/sub\u003e storage, supportive policy, and biomass resources coalesce\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In deployment hotspots, planned facilities already promise to fundamentally restructure markets and land use. These regions thus offer an early stress test for looming global biomass demand.\u003c/p\u003e \u003cp\u003eThis study investigates the potential impacts of concentrated bioenergy deployment through a case study of unprecedented biomass demand in the Southeastern US (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We model nine planned, technically feasible biomass-using facilities in the region, including BECCS, biofuels, and bioplastics plants (collectively referred to as \u0026ldquo;bioenergy\u0026rdquo; for simplicity; Table S1). These new facilities collectively represent a 53% increase in biomass processing capacity by 2030, making biomass the dominant forest product in the region, bypassing sawtimber. We find that natural forest conversion is a likely consequence of increased biomass demand. We also find that commonplace carbon-accounting heuristics fail to capture true landscape carbon losses. This case study brings into focus two key indicators of high biomass demand: land-use change and forest carbon stock changes. As one of the first real-world tests of concentrated forest bioenergy build-out, our findings offer early insight into the policy challenges that future biomass pathways will face.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMarkets mitigate impacts\u003c/h2\u003e \u003cp\u003eWe conduct a novel application of two economic models to predict market responses to increased bioenergy deployment in and near the state of Louisiana. These models incorporate biophysical inventory and harvest data, infrastructure, and market dynamics to find market equilibrium conditions. First, we determine explicit spatial redistribution of harvest activity across the region. Subsequently, we project granular biophysical responses to increased harvest activity, in both the core sourcing region and the surrounding region.\u003c/p\u003e \u003cp\u003eMarkets play an important role reallocating biomass demand across the region. With bioenergy deployment, we find that biomass stumpage prices rise by over 40% with the introduction of new facilities due to price-insensitive biomass supply. This drives two key outcomes: more land conversion to pine plantations and mills expanding their wood procurement zones. Prices remain elevated until new pine plantations mature sufficiently to supply wood in the region (Figure S1). Elevated prices push mills and bioenergy facilities to shift their wood sourcing to regions farther away, even reaching into areas without active forest industries like central Texas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Harvesting increases by only 3.5\u0026nbsp;million (M) additional green tons (GT) in 2035, representing only 33% of new bioenergy capacity. The remaining two thirds shift outside of the core basin. This shift distributes the market shock widely.\u003c/p\u003e \u003cp\u003eTo measure the magnitude of this market distribution effect, we ran a scenario with price-insensitive (perfectly inelastic) wood demand. In this scenario, localized impacts are pronounced. Because the core region bears the full impact of new biomass demand, land-use change is 6 times greater and carbon losses are 5 times greater than under normal market conditions (Fig.\u0026nbsp;2). This scenario highlights the moderating role that market dynamics play in redistributing impacts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioenergy amplifies historic land-use change trends\u003c/h3\u003e\n\u003cp\u003eOur simulations indicate that land-use change is a likely consequence of a rapid increase in biomass demand. Increased bioenergy capacity avoids the net loss of 2.5% of plantation forest in the counterfactual scenario. This loss occurs in response to suppressed markets for pulpwood and sawtimber, as well as age class dynamics of pine forests in the region. Natural forests, such as longleaf pine and upland hardwood forests, are likely to be converted into plantations, posing risks to ecological integrity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In the simulations, we see 0.9M acres (ac) of conversion from natural forest types into pine plantations due to new bioenergy facilities by 2035. This represents conversion of 11% of existing natural upland forests, similar to that observed over the previous decade. A large portion of natural forest loss is in the oldest age classes, which harbor more carbon and biodiversity than younger forests (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Some agricultural land (~\u0026thinsp;0.1M ac) is converted to pine plantations, as well.\u003c/p\u003e \u003cp\u003eSuch land conversion has precedent in this region, as with other areas in the Southeastern US with high levels of historic land conversion at both extensive (land-use change) and intensive (forest type change) margins\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Over the past decade, 0.9M ac of natural forest were converted to pine plantations or agriculture (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This trend is expected to continue even in the absence of bioenergy deployment. However, bioenergy deployment at scale could increase the conversion rate of natural pine forest relative to the previous decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvaluating carbon baseline assumptions\u003c/h3\u003e\n\u003cp\u003eBioenergy accounting often treats biogenic carbon (carbon sequestered by plants) as carbon neutral\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This determination rests on choosing a baseline against which biogenic carbon is accounted. Forest biomass requires baselines that accurately account for carbon stock changes in harvested forests. We test three baseline approaches to accounting for biogenic carbon from forests: counterfactual, heuristic, and historical.\u003c/p\u003e \u003cp\u003eThe counterfactual baseline we model represents a likely outcome\u0026mdash;absent new bioenergy facilities\u0026mdash;based on a complex set of parameters and assumptions. Counterfactual accounting is common in academic research but less common in policy frameworks. Relative to the counterfactual, 0.3 tonnes of carbon (tC) is lost from the landscape for every tC in biomass feedstock by 2035 (i.e., -0.3 tC/tC, or 30%). In aggregate, this represents a net CO\u003csub\u003e2\u003c/sub\u003e increase of 60 MtCO\u003csub\u003e2\u003c/sub\u003e versus a carbon neutral assumption for biogenic carbon.\u003c/p\u003e \u003cp\u003eHeuristic baselines often treat biomass as carbon-neutral if forest carbon stocks are stable or increasing. This approach is common in policy frameworks like the EU Renewable Energy Directive (EU RED), a leading global policy on wood-based bioenergy. In our case study, forest carbon stocks increase by 50% without bioenergy deployment, driven by young, rapidly growing forests (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, a heuristic baseline, which assumes no forest carbon accumulation, undercounts the magnitude of potential carbon losses by 530 MtCO\u003csub\u003e2\u003c/sub\u003e by 2035. The EU RED approach would even permit biomass as carbon neutral in the price-insensitive market scenario (All Mills \u0026ndash; w/o MM), where 1.6 tC is lost from the landscape for every tC in biomass feedstock by 2035 (i.e., -1.6 tC/tC, or 160%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHistorical baselines bridge counterfactual and heuristic approaches by anchoring projections in observed trends. Historical baselines also reduce the subjectivity inherent to counterfactual modeling. In our study, a linear regression based on the most recent 10 yrs of data only slightly underpredicts counterfactual carbon accumulation on the landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Relative to the counterfactual, the historical baseline could undercount forest carbon losses by 19 MtCO\u003csub\u003e2\u003c/sub\u003e by 2035 versus a carbon neutral assumption for biogenic carbon. Thus, in this context, a historical baseline is much more accurate than a heuristic approach.\u003c/p\u003e\n\u003ch3\u003eMaximizing lifecycle carbon benefits\u003c/h3\u003e\n\u003cp\u003eLife-cycle carbon benefits for each planned facility type in this region vary substantially on the feedstock efficiency basis of tC benefit per tC in harvested biomass feedstock (tC/tC). We find that retrofitting existing paper mills with carbon capture and storage (CCS) offers substantial feedstock efficiency (2.13 tC/tC) due to relatively limited biomass required to capture a large pre-existing source of CO\u003csub\u003e2\u003c/sub\u003e. Biopower with CCS is also feedstock efficient (1.14 tC/tC) due to displacement of the relatively carbon intensive local grid. Conversely, bioplastics\u0026mdash;the earliest facility constructed\u0026mdash;is net emitting (-0.17 tC/tC), mostly due to high production emissions from fossil gas heat. The wide range of feedstock efficiency values highlights the heterogeneity in life cycle climate performance inherent to biomass technologies. Collectively, these facilities will begin to drive net CO\u003csub\u003e2\u003c/sub\u003e removal by 2036 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). If all facilities produced BECCS instead, they would achieve net CO\u003csub\u003e2\u003c/sub\u003e removal by 2032.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings underscore the complex interplay between biomass demand, land use and forest management change, and carbon accounting. While market mechanisms help distribute wood sourcing more widely, they do not eliminate the conversion of natural forests to pine plantations\u0026mdash;an outcome that diminishes overall forest carbon stocks in the relatively short time window we examine. Baseline assumptions also critically shape whether biomass is deemed \u0026ldquo;carbon-neutral\u0026rdquo;; in our scenarios, policy heuristics that assume stable or rising stocks can underestimate the resulting forest carbon losses compared to a counterfactual baseline. Nonetheless, certain pathways (e.g., retrofitting existing pulp and paper mills with CCS) deliver substantial lifecycle carbon benefits more quickly than others. Strategic choices about where and how to deploy bioenergy can shorten the time to achieve net CO\u003csub\u003e2\u003c/sub\u003e reductions, reinforcing the importance of context-specific investments and policy interventions.\u003c/p\u003e \u003cp\u003eThis case study region presents uniquely favorable conditions for forest bioenergy deployment. Extensive land-use change and intensive forest management have positioned the landscape to produce large volumes of biomass, both as pulpwood and sawmill residues. In tandem, regional pulp and paper production capacity is declining, leaving a substantial resource base of small diameter roundwood and sawmill residues underutilized and available for alternative uses. The existing infrastructure for transporting biomass further supports bioenergy suitability by enabling the spatial distribution of harvest pressure. Taken together, these factors represent a favorable context for bioenergy. By the same token, in regions lacking similar characteristics, landscape and market impacts could be much more pronounced.\u003c/p\u003e \u003cp\u003eDespite employing two regionally validated forest economics models, sources of uncertainty remain. Long-term projections of forest carbon accumulation and land-use change are sensitive to assumptions about landowner behavior, market elasticity, and policy stability, which may shift substantially over time. The relatively short horizon captured in our analysis may also underestimate long-term carbon dynamics, including the potential for delayed forest regrowth. And although SRTS accounts for demand-induced conversion of certain forest types, it does not fully capture harvesting or management decisions in bottomland hardwood forests, potentially underestimating overall impacts on carbon stocks and ecological function. Further, SRTS is a recursive dynamic framework that does not manage forests with foresight of future market conditions. Contemporaneous markets drive harvest patterns and land use decisions, rather than future expectations, which could bias carbon impacts of bioenergy deployment as single-year harvests are prioritized over long-term planning\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Collectively, these limitations highlight the need for continued refinement of modeling frameworks, as well as further empirical validation under diverse environmental and policy contexts. Quantifying these sources of uncertainty will sharpen future estimates.\u003c/p\u003e \u003cp\u003eThis analysis demonstrates the key role of accounting for market feedbacks when projecting forest carbon change in regions with bioenergy expansion. Ignoring market dynamics can overstate potential carbon impacts of forest harvesting. Models that assume no market feedback (e.g., price responsiveness, product substitution) can erroneously project high net emissions from forest harvest\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our scenario without market mitigation provides a relevant comparison to regional harvest simulations that are decoupled from markets. This finding is also consistent with recent global-scale economic analyses\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Economic models also highlight potential complementarities between sawtimber markets, bioenergy expansion, and forest carbon\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePolicy is a critical determinant of bioenergy deployment. Federal incentives, such as the 45Q and 45V tax credits in the US, are key drivers of bioenergy deployment, enhancing the economic viability of these technologies. For example, 45Q offers direct financial incentives of \u003cspan\u003e$\u003c/span\u003e85 per tCO\u003csub\u003e2\u003c/sub\u003e captured and permanently stored. State-level regulations, such as Louisiana\u0026rsquo;s recent Class VI primacy for underground injections control, streamline the permitting processes for CO\u003csub\u003e2\u003c/sub\u003e storage wells, reducing regulatory uncertainty and accelerating the pace of project deployment. Further, state incentives such as the Louisiana Forest Productivity Program help private forest landowners access cost assistance for establishing and managing forest plantations for timber production\u003csup\u003e \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e \u003c/sup\u003e. Together, these policy drivers, combined with resource availability and market precedence, help explain the current expansion of bioenergy in this region. Existing policy can also provide caution, as demonstrated by the US Renewable Fuel Standard (RFS) support for corn ethanol\u003csup\u003e \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e \u003c/sup\u003e. The RFS increased US corn cultivation by approximately 2.8\u0026nbsp;million hectares (ha) from 2008\u0026ndash;2016 and led to an estimated 320 MtCO₂\u003cem\u003ee\u003c/em\u003e emissions from land-use change\u003csup\u003e \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e \u003c/sup\u003e. The RFS highlights the need for bioenergy policy frameworks to include sustainability criteria, especially criteria that mitigate land-use change impacts.\u003c/p\u003e \u003cp\u003eOur work also highlights the importance of appropriate baseline setting for bioenergy carbon accounting. EU RED is the largest policy driver of forest bioenergy development globally. EU RED operationalizes the requirement for biomass sourcing from areas with \u003cem\u003e\u0026ldquo;stable or increasing\u0026rdquo;\u003c/em\u003e carbon stocks through alignment with national LULUCF accounting. EU RED offers a straightforward heuristic for avoiding biomass that may be linked to deforestation or degradation. But as a carbon accounting baseline, it is likely to be insufficient in many cases. In forests across the globe, carbon stocks are increasing significantly due to factors like forest expansion and CO\u003csub\u003e2\u003c/sub\u003e fertilization. This is true of our study region: even when we model unrealistically high harvest activity, forest carbon stocks still increase over time. While counterfactual modeling may be impractical for policy applications, use of historical trends may provide more accurate baselines and have precedent in polices such as the US Environmental Protection Agency Air Quality Standards, as well as Reducing Emissions from Deforestation and Degradation (REDD+) programs\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Collectively, our results from this early stress test of concentrated biomass demand reveal key policy and accounting challenges. Addressing these can help ensure that bioenergy supports climate and land-use goals as it scales up.\u003c/p\u003e \u003c/div\u003e"},{"header":"Online Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eFacility identification and characterization\u003c/h2\u003e \u003cp\u003eWe compiled planned, technically feasible biomass facilities in the study area from company websites, industry sources, and government permitting databases. Facility attributes (location, capacity, feedstock, status) were standardized, and project readiness was evaluated qualitatively using evidence of financing and technical feasibility. Expert consultation and industry-informed assessment supplemented this study where gaps existed. We excluded multiple additional planned facilities lacking public documentation. The final dataset comprised nine technically feasible planned facilities, informing spatial demand allocation in subsequent modeling.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLURA modeling\u003c/h3\u003e\n\u003cp\u003eThe Land Use and Resource Allocation (LURA) model\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e is a spatially explicit framework that simulates biomass supply-demand interactions. The model incorporated detailed transportation logistics and geographic constraints, including transportation networks between biomass supply points (e.g., forest plots), forest product manufacturing facilities, and other demand points (e.g., international ports). In this study, we apply the LURA model to quantify the spatial redistribution of harvesting activities across the region and explicitly define biomass sourcing boundaries. Specifically, we developed national-scale simulations of market developments, harvest allocation, and biomass sourcing strategies, with and without additional biomass facilities. These boundaries delineate a core sourcing region and a broader secondary effects region, which are subsequently analyzed using two separate SRTS simulations.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSRTS modeling\u003c/h2\u003e \u003cp\u003eWe employed the SubRegional Timber Supply (SRTS) model\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e to simulate forest market responses and carbon outcomes under increased biomass demand. SRTS is a dynamic model that integrates forest inventory, growth, and harvesting behavior with economic responses to wood prices and landowner decision-making. The model is calibrated to the US South using observed behavior of private timberland owners, and reflects regional forest management practices, land ownership, and market conditions. Simulations were initialized with USDA Forest Service Forest Inventory and Analysis (FIA) and Timber Products Output (TPO) data, capturing forest conditions by species, age class, and ownership group. The model tracks changes in aboveground biomass and projects annual removals and growing stock by product class (pine sawtimber, pine pulpwood, hardwood sawtimber, and hardwood pulpwood). Soil organic carbon is not included, consistent with prior work showing limited sensitivity to management scenarios. We use a static baseline for pulpwood and sawtimber demand due to its alignment with recent market trends, but alternative baseline scenarios are detailed in the SI Appendix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLife cycle analysis of biomass products\u003c/h2\u003e \u003cp\u003eWe use published values to quantify cradle-to-grave carbon impacts across four key categories: harvest and transport emissions, fossil process emissions, carbon removal volumes, and displaced emissions due to substitution of carbon-intensive products. Our unit of analysis is one tonne of harvested carbon. We rely on data from life-cycle assessment studies that employ feedstocks and system boundaries closely matching those of the facilities modeled here. When available, data is sourced from studies employing the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model, and harvest and transport emissions are normalized across all product pathways using GREET-derived values. We assume grid carbon intensity of 336 g CO\u003csub\u003e2\u003c/sub\u003e\u003cem\u003ee\u003c/em\u003e/kWh across all products. Assumptions and facility-specific parameters are detailed in the SI Appendix. To project net CO\u003csub\u003e2\u003c/sub\u003e outcomes in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we take the mean of total life cycle carbon efficiency values for all products with and without CCS. We then apply those mean values to all facilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interest\u003c/h2\u003e \u003cp\u003eThe views expressed in this paper are those of the authors and do not necessarily reflect the views or positions of any organization with which they are affiliated. Multiple authors are employed by Carbon Direct Inc. The authors do not stand to gain financially from any specific outcome of this paper.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful for the study design and interpretation contributions of Rafael Broze and TJ Considine, the data visualization contributions of Jasper Croome and Peter Tittmann, and modeling parameterization contributions of Raju Pokharel.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIntergovernmental Panel on Climate Change (IPCC) (2022) \u003cem\u003eGlobal Warming of 1.5\u0026deg;C: IPCC Special Report on Impacts of Global Warming of 1.5\u0026deg;C above Pre-Industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/9781009157940\u003c/span\u003e\u003cspan address=\"10.1017/9781009157940\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoe S et al (2019) Contribution of the land sector to a 1.5\u0026deg;C world. 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Policy Econ 87:35\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWade CM, Baker JS, Latta G, Ohrel SB (2019) Evaluating Potential Sources of Aggregation Bias with a Structural Optimization Model of the U.S. Forest Sector. J Econ 34:337\u0026ndash;366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Latta G, Lee U, Lewandrowski J, Wang M (2021) Regionalized Life Cycle Greenhouse Gas Emissions of Forest Biomass Use for Electricity Generation in the United States. Environ Sci Technol 55:14806\u0026ndash;14816\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi DJ, Baker JS, Abt RC (2023) Quantifying additionality thresholds for forest carbon offsets in Mississippi pine pulpwood markets. Policy Econ 156:103059\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson JD, Abt RC, Abt KL, Baker J, Sheffield R (2022) Impacts of hurricanes on forest markets and economic welfare: The case of hurricane Michael. Policy Econ 140:102735\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng F et al (2025) Assessing Carbon Emission Impacts of Forest-Based Bioenergy in the Southern U.S. Environ Sci Technol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.est.4c06272\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.4c06272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bioenergy, climate change mitigation, forestry, forest economics, lca","lastPublishedDoi":"10.21203/rs.3.rs-6839932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6839932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiomass-based climate solutions, such as bioenergy, are pivotal for meeting climate goals, but they are contingent on sustainable feedstock supply. Early bioenergy development hotspots may offer lessons for broader bioenergy buildout. Here, we examine such a hotspot in the Southeastern US, where planned facilities would increase existing biomass demand by 53%. Using two biophysical forest economics models, we find that market responses, such as shifting wood sourcing, diffuse but do not eliminate land-use change and carbon impacts. New wood demand drives the conversion of 11% of existing natural upland forests into pine plantations. Total forest carbon is persistently lower than without bioenergy buildout, challenging key biogenic carbon accounting assumptions. We find that existing policy frameworks can greatly misrepresent baseline forest carbon accumulation and propose alternative approaches. Combining life cycle emissions and landscape carbon changes, bioenergy deployment achieves net CO\u003csub\u003e2\u003c/sub\u003e removal by 2036, driven by technologies with carbon capture.\u003c/p\u003e","manuscriptTitle":"Impacts of unprecedented wood demand for bioenergy in the Southeastern US","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 07:30:50","doi":"10.21203/rs.3.rs-6839932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5c5e2fc-616f-41ca-b3be-5501aca226f9","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-11T07:30:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 07:30:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6839932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6839932","identity":"rs-6839932","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}