Assessment of the GHG Reduction Potential from Energy Crops Using a Combined LCA and Biogeochemical Process Models: A Review
Jiang D.; Fu J. Y.; Huang Y. H.; Hao M. M.
2014
关键词long-term experiments soil organic-carbon greenhouse-gas flux biome-bgc climate-change boreal forest daycent model marginal land mineral soils fuel ethanol
英文摘要The main purpose for developing biofuel is to reduce GHG (greenhouse gas) emissions, but the comprehensive environmental impact of such fuels is not clear. Life cycle analysis (LCA), as a complete comprehensive analysis method, has been widely used in bioenergy assessment studies. Great efforts have been directed toward establishing an efficient method for comprehensively estimating the greenhouse gas (GHG) emission reduction potential from the large-scale cultivation of energy plants by combining LCA with ecosystem/biogeochemical process models. LCA presents a general framework for evaluating the energy consumption and GHG emission from energy crop planting, yield acquisition, production, product use, and postprocessing. Meanwhile, ecosystem/biogeochemical process models are adopted to simulate the fluxes and storage of energy, water, carbon, and nitrogen in the soil-plant (energy crops) soil continuum. Although clear progress has been made in recent years, some problems still exist in current studies and should be addressed. This paper reviews the state-of-the-art method for estimating GHG emission reduction through developing energy crops and introduces in detail a new approach for assessing GHG emission reduction by combining LCA with biogeochemical process models. The main achievements of this study along with the problems in current studies are described and discussed.
出处Scientific World Journal
收录类别SCI
语种英语
ISSN号1537-744X
内容类型SCI/SSCI论文
源URL[http://ir.igsnrr.ac.cn/handle/311030/29804]  
专题资源利用与环境修复重点实验室_资源地理与国土资源研究室_SCI/SSCI期刊论文
推荐引用方式
GB/T 7714
Jiang D.,Fu J. Y.,Huang Y. H.,et al. Assessment of the GHG Reduction Potential from Energy Crops Using a Combined LCA and Biogeochemical Process Models: A Review. 2014.
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