2013
DOI: 10.1016/j.jfe.2013.05.003
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A multi-sector intertemporal optimization approach to assess the GHG implications of U.S. forest and agricultural biomass electricity expansion

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Cited by 48 publications
(49 citation statements)
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“…For instance, Ref analyzed the marginal cost of forest supplies for bioenergy and quantified the level of bioenergy demand, given that cheaper residue resources have become depleted and it becomes more profitable to use roundwood for energy purposes than to continue extracting additional harvest residues. The possible supply and cost of agriculture‐based bioenergy will here have an important indirect influence on forest C balances over time, by influencing the demand growth for forest‐based bioenergy …”
Section: Resultsmentioning
confidence: 99%
“…For instance, Ref analyzed the marginal cost of forest supplies for bioenergy and quantified the level of bioenergy demand, given that cheaper residue resources have become depleted and it becomes more profitable to use roundwood for energy purposes than to continue extracting additional harvest residues. The possible supply and cost of agriculture‐based bioenergy will here have an important indirect influence on forest C balances over time, by influencing the demand growth for forest‐based bioenergy …”
Section: Resultsmentioning
confidence: 99%
“…First, “the level of ease with which land can move between sectors and uses will have a large impact on the effectiveness of biopower policy” (Latta et al , p. 380). Such flexibility would lead to greater reliance on energy crops, agricultural residues, and, to a lesser extent, short‐rotation woody crops (hybrid poplar and willow).…”
Section: Biomass Electricitymentioning
confidence: 99%
“…Inter-temporal models can provide insights on both the short-and long-term effects of biofuel production. Based on the FASOM-GHG model, Latta et al [155] developed an inter-temporal PE model of the forest and agricultural sectors to assess the effect of biomass electricity expansion on LUC and GHG emissions in the USA. At different points in time, inter-temporal optimisation procedures yielded different biomass feedstock portfolios and GHG performances.…”
Section: Modelling Time Dynamicsmentioning
confidence: 99%