能源对于工业发展和经济增长有无可替代的支持作用。而我国的石油对外依存度高，能源安全问题凸显。此外，化石能源消耗过程产生大量碳排放，不利于我国实现应对气候变化目标。发展生物质能源是应对上述问题的有效途径。其中生物燃料乙醇受到国家政策大力支持。木薯是生物燃料乙醇的优选生产原料。但在木薯燃料乙醇生产过程中存在难以避免的化石能源消耗和碳排放，为木薯燃料乙醇节能减碳的优势增加不确定性。此外，发展木薯乙醇或对水环境产生压力。因此，本研究将同时分析木薯燃料乙醇生命周期的水足迹、能耗足迹和碳足迹，识别木薯燃料乙醇生产系统中足迹产生的关键节点，为木薯燃料乙醇的发展寻求兼顾水环境健康、能源安全和缓解气候变化的调整措施和发展方向。 足迹分析主要基于生命周期评价方法开展研究。生命周期混合模型整合了传统生命周期模型和投入产出生命周期模型，系统边界覆盖广泛且数据准确性较高。但其含混的模型混合规则和低解析度的核算结果不利于提出具体改进措施或切实调整发展方向。本研究借鉴碳足迹分析中3-Scope分解方式改进生命周期混合模型，建立了3-Scope分解递进混合生命周期模型，并将碳足迹的分解方法推广到水足迹和能耗足迹的分析过程中，对木薯燃料乙醇生产系统进行水-能-碳足迹分析，解析足迹产生的引发原因和行为方。 将生产系统内部活动纳入Scope 1，应用过程性生命周期模型核算现场水-能-碳足迹：水足迹的主要来自原料种植收集阶段，其中，灰水足迹来自施肥环节的氮肥施用对木薯种植区域的引发的氨氮污染。绿水足迹由于木薯生长过程造成的雨水蒸散发。灌溉引水在生产现场贡献了极高蓝水足迹。能耗足迹主要来自木薯乙醇生产阶段精馏环节的蒸汽生产所耗原煤。碳足迹主要来自原料种植收集阶段氮肥施用引发的N2O逸散。Scope 2应用过程性生命周期模型核算外部能量生产引起的远程水-能-碳足迹：Scope 2中各阶段活动的足迹产生量由其各种形式消耗外部电力总量直接决定，足迹分布与外部电力消耗量分布相同。木薯乙醇生产阶段为消耗外部电力的主要阶段，其中粉碎、液化发酵、精馏和污水处理环节都有较多耗电。Scope 3应用投入产出生命周期评价模型核算外部物料生产引起的远程水-能-碳足迹。其中蓝水足迹由木薯乙醇生产阶段液化发酵环节和精馏环节的蒸汽使用活动引发，由支持蒸汽生产的其他制造业主要贡献。灰水足迹主要由原料种植收集阶段肥料和除草剂使用活动引发，由支持肥料和除草剂生产的农林牧渔业主要贡献。能耗足迹主要由木薯乙醇生产阶段精馏环节的蒸汽使用引发。由支持蒸汽生产的金属冶炼及压延加工业和煤炭开采洗选业主要贡献。碳足迹主要由木薯乙醇生产阶段水类辅助品的生产使用引发，由支持一次水生产的燃气及水的生产供应业在极大程度上贡献。 整合3个Scope对木薯燃料乙醇生产系统的足迹贡献发现，各类足迹主要由Scope 1贡献，最高为绿水足迹的100%，最低为蓝水足迹的82.80%。降低足迹应重点从系统内部寻求解决路径。Scope 2仅在碳足迹中有相对明显贡献，占全部碳足迹的2.56%。降低碳足迹可从电力生产过程寻求减排路径。Scope 3 对于蓝水足迹贡献了17.02%，对灰水足迹贡献8.74%，对能耗足迹贡献6.63%，对碳足迹的贡献可忽略不计。部门提高环境效益有利于系统水足迹降低。 基于上述结果提出三项降低水-能-碳足迹的优化方向并开展情景分析。以污泥作为有机肥替代58%的化肥，可使木薯乙醇生命周期灰水足迹降至原生产方式的48.43%，碳足迹降至73.10%。降低鲜木薯运量，可使木薯乙醇生命周期碳足迹降至原生产方式的89.29%，能耗足迹降至95.32%。将木薯乙醇厂家外购电比例增至100%，可使乙醇生命周期能耗足迹降至原方式的77.95%，碳足迹降至89.68%。每种优化方向都可同时得到各足迹的不同程度响应。从部门角度可提出以下利于木薯乙醇生产系统提升环境友好性的建议：农林牧渔业应提高用水效率，减少化肥使用；化学工业部门需降低能耗并加强废水高效处理技术；电力、热力的生产和供应业在生产时应同时注重降低水耗和能耗。

Energy has irreplaceable support for industrial development and economic growth. While China’s oil is highly dependent on import, the issue of energy security is highlighted. Besides, the fossil energy consumption process generates a large amount of carbon emissions, which is not conducive to China's goal of mitigating climate change. The development of biomass energy is an effective way to deal with the above problems. Among them, biofuel ethanol is strongly supported by national policies. Cassava is the preferred raw material for the production of biofuel ethanol. However, there are unavoidable fossil energy consumption and carbon emissions during the production of cassava fuel ethanol, which adds uncertainty to the energy-saving and carbon reduction for cassava fuel ethanol. In addition, the development of cassava ethanol exert pressure on the water environment. Therefore, this study will simultaneously analyze the water footprint, energy footprint, and carbon footprint of the life cycle of cassava-based ethanol, identify the key nodes in the cassava-based ethanol production system, and seek better ways for the development of cassava-based ethanol. Footprint analysis is mainly based on life cycle assessment. The Hybrid life cycle model integrates the traditional life cycle model and input-output life cycle model. The system boundary covers a wide range and the data accuracy is higher. However, the mixing rules of hybrid model and the resolution of results are not conducive to put forward specific improvement measures or effectively adjustment of development. This study uses the 3-Scope categories to improve the Hybrid life cycle model in carbon footprint analysis, establishes a 3-Scope hybrid life cycle model, and extends the carbon footprint categories to the analysis of water footprint and energy footprint. The water-energy-carbon footprint analysis of the cassava fuel ethanol production system was performed to analyze the causes and behaviors of the footprint. Incorporate the internal activities of the production system into Scope 1. Apply the process-based life cycle model to account for the onsite water-energy-carbon footprint: The water footprint mainly comes from the raw material planting collection phase, in which the gray water footprint is derived from the fertilization application of nitrogen fertilizer to the cassava cultivation area. The green water footprint due to the evaporates from the growth of cassava. Irrigation contributes much blue water footprint to the phase. The energy footprint is mainly derived from raw coal consumed in steam production in the rectification section of the cassava ethanol production stage. The carbon footprint mainly comes from the N2O emission caused by the application of nitrogen fertilizer in the raw material collection stage. Scope 2 uses the process-based life cycle model to account for offsite water-energy-carbon footprint caused by external energy production: footprint at each stage of activity is directly determined by the amount of external power consumed by its various forms, footprint and external power consumption with the same distribution. The cassava ethanol production phase is the main phase of external power consumption, and each stage consumes similar power. Scope 3 uses input-output life cycle assessment models to account for offsite water-energy-carbon footprints caused by the production of external materials. Among them, the blue water footprint is triggered by the use of steam in the liquefaction and fermentation stages of the cassava ethanol production stage, and is mainly contributed by other manufacturing industries supporting steam production. The gray water footprint is mainly caused by the use of fertilizers and herbicides during the harvesting stage of raw materials, and it is mainly contributed by the agriculture, forestry, animal husbandry and fishery that support the production of fertilizers and herbicides. The energy footprint is mainly caused by the use of steam in the rectification section of the cassava ethanol production stage. The main contribution is from the metal smelting and rolling processing industry supporting coal production and the coal mining and washing industry. The carbon footprint is mainly caused by the production and use of water-assisted products in the production stage of cassava ethanol, and the production and supply of gas and water that support primary water production contributes to a large extent. Overview the three Scopes can we found that every footprint were mainly contributed by Scope 1. The highest was 100% of the green water footprint, and the lowest was 82.80% of the blue water footprint. Reducing the footprint should focus on seeking solutions from the inside of the system. Scope 2 only makes a relatively significant contribution to the carbon footprint, accounting for 2.56% of the total carbon footprint. Reducing the carbon footprint can seek solutions from the electricity production process. Scope 3 contributes 17.02% to the blue water footprint, 8.74% to the greywater footprint, 6.63% to the energy footprint, and contributes negligibly to the carbon footprint. Three optimization schemes for reducing water-energy-carbon footprint were proposed and scenario analysis was carried out. Using sludge as an organic fertilizer to replace 58% of the fertilizer, the life cycle grey water footprint was reduced to 48.43% of the original production way, and the carbon footprint was reduced to 73.10%. Reducing the transport capacity of fresh cassava can reduce the life cycle carbon footprint to 89.29% of the original, and the energy footprint can be reduced to 95.32%. Increasing the proportion of purchased electricity of cassava ethanol producers to 100% will reduce the energy footprint to 77.95% of the original, and reduce the carbon footprint to 89.68%. Each of the optimization schemes can obtain different response of each footprint simultaneously. From the perspective of the sector, the following suggestions for improving the environmental friendliness of the cassava ethanol production system can be proposed: agriculture, forestry, animal husbandry and fishery should improve the efficiency of water use and reduce the use of chemical fertilizers; the chemical industry sector needs to reduce energy consumption and strengthen the wastewater treatment technology; electricity, heat production and the supply industry should pay attention to reducing water consumption and energy consumption simultaneously.