Challenges in the estimation of greenhouse gas emissions from biofuel-induced global land-use change

被引:37
|
作者
Warner, Ethan [1 ]
Zhang, Yimin [1 ]
Inman, Daniel [2 ]
Heath, Garvin [1 ]
机构
[1] Natl Renewable Energy Lab, Golden, CO 80401 USA
[2] Natl Renewable Energy Lab, Natl Bioenergy Ctr, Golden, CO 80401 USA
来源
关键词
land-use change; biofuel; greenhouse gas; sustainability; equilibrium model; literature review; meta-analysis; biomass; ETHANOL;
D O I
10.1002/bbb.1434
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The estimation of greenhouse gas (GHG) emissions from a change in land-use and management resulting from growing biofuel feedstocks has undergone extensive - and often contentious - scientific and policy debate. Emergent renewable fuel policies require life cycle GHG emission accounting that includes biofuel-induced global land-use change (LUC) GHG emissions. However, the science of LUC generally, and biofuels-induced LUC specifically, is nascent and underpinned with great uncertainty. We critically review modeling approaches employed to estimate biofuel-induced LUC and identify major challenges, important research gaps, and limitations of LUC studies for transportation fuels. We found LUC modeling philosophies and model structures and features (e.g. dynamic vs. static model) significantly differ among studies. Variations in estimated GHG emissions from biofuel-induced LUC are also driven by differences in scenarios assessed, varying assumptions, inconsistent definitions (e.g. LUC), subjective selection of reference scenarios against which (marginal) LUC is quantified, and disparities in data availability and quality. The lack of thorough sensitivity and uncertainty analysis hinders the evaluation of plausible ranges of estimates of GHG emissions from LUC. The relatively limited fuel coverage in the literature precludes a complete set of direct comparisons across alternative and conventional fuels sought by regulatory bodies and researchers. Improved modeling approaches, consistent definitions and classifications, availability of high-resolution data on LUC over time, development of standardized reference and future scenarios, incorporation of non-economic drivers of LUC, and more rigorous treatment of uncertainty can help improve LUC estimates in effectively achieving policy goals. (c) 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
引用
收藏
页码:114 / 125
页数:12
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