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Farm systems assessment of bioenergy feedstock production: Integrating bio-economic models and life cycle analysis approaches

机译:农场系统对生物能源原料生产的评估:整合生物经济模型和生命周期分析方法

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摘要

Climate change and energy security concerns have driven the development of policies that encourage bioenergy production. Meeting EU targets for the consumption of transport fuels from bioenergy by 2020 will require a large increase in the production of bioenergy feedstock. Initially an increase in ‘first generation’ biofuels was observed, however ‘food competition’ concerns have generated interest in second generation biofuels (SGBs). These SGBs can be produced from co-products (e.g. cereal straw) or energy crops (e.g. miscanthus), with the former largely negating food competition concerns. In order to assess the sustainability of feedstock supply for SGBs, the financial, environmental and energy costs and benefits of the farm system must be quantified. Previous research has captured financial costs and benefits through linear programming (LP) approaches, whilst environmental and energy metrics have been largely been undertaken within life cycle analysis (LCA) frameworks. Assessing aspects of the financial, environmental and energy sustainability of supplying co-product second generation biofuel (CPSGB) feedstocks at the farm level requires a framework that permits the trade-offs between these objectives to be quantified and understood. The development of a modelling framework for Managing Energy and Emissions Trade-Offs in Agriculture (MEETA Model) that combines bio-economic process modelling and LCA is presented together with input data parameters obtained from literature and industry sources. The MEETA model quantifies arable farm inputs and outputs in terms of financial, energy and emissions results. The model explicitly captures fertiliser: crop-yield relationships, plus the incorporation of straw or removal for sale, with associated nutrient impacts of incorporation/removal on the following crop in the rotation. Key results of crop-mix, machinery use, greenhouse gas (GHG) emissions per kg of crop product and energy use per hectare are in line with previous research and industry survey findings. Results show that the gross margin – energy trade-off is £36 GJ−1, representing the gross margin forgone by maximising net farm energy cf. maximising farm gross margin. The gross margin–GHG emission trade-off is £0.15 kg−1 CO2 eq, representing the gross margin forgone per kg of CO2 eq reduced when GHG emissions are minimised cf. maximising farm gross margin. The energy–GHG emission trade-off is 0.03 GJ kg−1 CO2 eq quantifying the reduction in net energy from the farm system per kg of CO2 eq reduced when minimising GHG emissions cf. maximising net farm energy. When both farm gross margin and net farm energy are maximised all the cereal straw is baled for sale. Sensitivity analysis of the model in relation to different prices of cereal straw shows that it becomes financially optimal to incorporate wheat straw at price of £11 t−1 for this co-product. Local market conditions for straw and farmer attitudes towards incorporation or sale of straw will impact on the straw price at which farmers will supply this potential bioenergy feedstock and represent important areas for future research.
机译:气候变化和能源安全问题推动了鼓励生物能源生产的政策的制定。要在2020年之前达到欧盟关于消耗生物能源运输燃料的目标,就需要大量增加生物能源原料的生产。最初观察到“第一代”生物燃料的增加,但是对“食品竞争”的关注引起了对第二代生物燃料(SGB)的兴趣。这些SGB可以由副产品(例如谷物秸秆)或能源作物(例如miscanthus)生产,而前者在很大程度上消除了食品竞争的担忧。为了评估SGB原料供应的可持续性,必须量化农场系统的财务,环境和能源成本以及收益。先前的研究已经通过线性规划(LP)方法获得了财务成本和收益,而环境和能源指标则主要在生命周期分析(LCA)框架内进行。评估在农场一级供应副产品第二代生物燃料(CPSGB)原料的财务,环境和能源可持续性的各个方面,需要一个框架来允许量化和理解这些目标之间的取舍。提出了结合生物经济过程建模和LCA的用于管理农业能源和排放权衡的建模框架(MEETA模型)的开发,以及从文献和行业来源获得的输入数据参数。 MEETA模型根据财务,能源和排放结果量化了耕作农场的投入和产出。该模型明确捕获肥料:作物与产量的关系,加上秸秆并入出售或出售,并结合/去除对轮作中的下一个作物的营养影响。作物混合,机械使用,每千克作物产品的温室气体(GHG)排放量和每公顷能源消耗的关键结果与先前的研究和行业调查结果一致。结果显示,毛利–能源权衡是£36 GJ -1 ,代表通过最大化农场净能源cf放弃的毛利率。最大化农场的毛利率。毛利率-GHG排放权衡是0.15千克 -1 CO2当量,表示当将GHG排放量降至最低时每千克CO2当量所放弃的毛利率。最大化农场的毛利率。能源与GHG排放之间的权衡是0.03 GJ kg -1 CO2当量,量化了在最大程度减少GHG排放量的基础上,每千克CO2当量所产生的农场系统净能源减少量。最大化农场的净能源。当农场的毛利率和农场的净能源都最大化时,所有谷物秸秆都将打包出售。该模型对谷物秸秆不同价格的敏感性分析表明,对于这种副产品,以11英镑t -1 的价格掺入小麦秸秆在财务上是最优的。秸秆的当地市场状况以及农民对掺入或出售秸秆的态度将影响秸秆价格,农民将以这种价格供应这种潜在的生物能源原料,并代表了未来研究的重要领域。

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