| Summary: | Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-36). Forest harvest for bioenergy is growing rapidly, spurred by the European Commission's declaration that bioenergy is carbon-neutral. Bioenergy advocates argue that the carbon released upon the combustion of harvested wood should eventually be reabsorbed from the atmosphere when the harvested land regrows. Recent studies, however, find that wood bioenergy can exacerbate climate change because it is less efficient than the fossil fuels it displaces, and because regrowth takes time and is uncertain. Other land use, land-use change, and forestry (LULUCF) practices can also cause significant carbon fluxes to and from the atmosphere that vary over time as the carbon sequestered in the biomass and soils on each land type changes. Understanding these complex interactions requires an explicit dynamic model that accounts for various land uses and regions, each with carbon content and flux characteristics specific to their respective vegetation, soil distributions, and climatic domains. This work extends the widely used C-ROADS climate model, originally developed with a single biosphere, to incorporate this level of detail. Built up from a diverse set of highly resolved geospatial databases for land cover, soils, climatic domains, and other relevant characteristics, the model aggregates the data into six land use types (natural forest, harvested forest, cropland, pasture, permafrost, and developed/other land) within six major regions (the US, EU, China, India, Other Developed Nations, and Other Developing Nations). It is used to analyze the impact of harvesting forests for bioenergy. Because wood bioenergy is less efficient than the fossil fuels it displaces, the first impact is an increase in atmospheric CO₂ . If the land regrows as forest, this carbon debt can eventually be repaid. ...
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