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

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  • Latta, Gregory S.
  • Baker, Justin S.
  • Ohrel, Sara

Abstract

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.

Suggested Citation

  • Latta, Gregory S. & Baker, Justin S. & Ohrel, Sara, 2018. "A Land Use and Resource Allocation (LURA) modeling system for projecting localized forest CO2 effects of alternative macroeconomic futures," Forest Policy and Economics, Elsevier, vol. 87(C), pages 35-48.
    • Handle: RePEc:eee:forpol:v:87:y:2018:i:c:p:35-48
    DOI: 10.1016/j.forpol.2017.10.003
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    Citations

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    Cited by:

    1. Miguel Riviere & Sylvain Caurla & Philippe Delacote, 2020. "Evolving Integrated Models From Narrower Economic Tools : the Example of Forest Sector Models," Post-Print hal-02512330, HAL.
    2. Baker, Justin S. & Van Houtven, George & Phelan, Jennifer & Latta, Gregory & Clark, Christopher M. & Austin, Kemen G. & Sodiya, Olakunle E. & Ohrel, Sara B. & Buckley, John & Gentile, Lauren E. & Mart, 2023. "Projecting U.S. forest management, market, and carbon sequestration responses to a high-impact climate scenario," Forest Policy and Economics, Elsevier, vol. 147(C).
    3. Pokharel, Raju & Latta, Gregory S., 2020. "A network analysis to identify forest merchantability limitations across the United States," Forest Policy and Economics, Elsevier, vol. 116(C).
    4. Miguel Riviere & Sylvain Caurla, 2020. "Representations of the Forest Sector in Economic Models [Les représentations du secteur forestier dans les modèles économiques]," Post-Print hal-03088084, HAL.
    5. Adams, Darius M. & Latta, Gregory S. & Crandall, Mindy S. & Guerrero Ochoa, Isabel G., 2019. "The importance of incorporating intertemporal and spatial log market dynamics in projections of residue-based biomass supply for liquid biofuel production in western Oregon and Washington, USA," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    6. Daigneault, Adam & Johnston, Craig & Korosuo, Anu & Baker, Justin S. & Forsell, Nicklas & Prestemon, Jeffrey P. & Abt, Robert C., 2019. "Developing Detailed Shared Socioeconomic Pathway (SSP) Narratives for the Global Forest Sector," Journal of Forest Economics, now publishers, vol. 34(1-2), pages 7-45, August.

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