Cornell Energy Institute, Cornell University, Ithaca, New York 14853, USA.
Environ Sci Technol. 2013 Jan 15;47(2):687-94. doi: 10.1021/es3029236. Epub 2012 Dec 31.
As a result of algae's promise as a renewable energy feedstock, numerous studies have used Life Cycle Assessment (LCA) to quantify the environmental performance of algal biofuels, yet there is no consensus of results among them. Our work, motivated by the lack of comprehensive uncertainty analysis in previous studies, uses a Monte Carlo approach to estimate ranges of expected values of LCA metrics by incorporating parameter variability with empirically specified distribution functions. Results show that large uncertainties exist at virtually all steps of the biofuel production process. Although our findings agree with a number of earlier studies on matters such as the need for wet lipid extraction, nutrients recovered from waste streams, and high energy coproducts, the ranges of reported LCA metrics show that uncertainty analysis is crucial for developing technologies, such as algal biofuels. In addition, the ranges of energy return on (energy) invested (EROI) values resulting from our analysis help explain the high variability in EROI values from earlier studies. Reporting results from LCA models as ranges, and not single values, will more reliably inform industry and policy makers on expected energetic and environmental performance of biofuels produced from microalgae.
由于藻类作为可再生能源原料具有广阔的前景，因此许多研究都采用生命周期评估（LCA）方法来量化藻类生物燃料的环境性能，但目前这些研究的结果并没有达成共识。我们的研究旨在解决之前研究中缺乏全面不确定性分析的问题，采用蒙特卡罗方法，通过将参数变异性与经验指定的分布函数结合起来，估算 LCA 指标预期值的范围。研究结果表明，在生物燃料生产过程的几乎所有步骤中都存在很大的不确定性。尽管我们的研究结果与一些早期研究的结论一致，例如需要湿脂质提取、从废物流中回收营养物质以及高能联产等，但报告的 LCA 指标范围表明，不确定性分析对于开发技术（如藻类生物燃料）至关重要。此外，我们的分析得出的能源投资回报率（EROI）值范围有助于解释早期研究中 EROI 值的高度变异性。通过将 LCA 模型的结果报告为范围而不是单个值，可以更可靠地为行业和决策者提供有关微藻生产的生物燃料的预期能量和环境性能的信息。
