Addressing Uncertainty in Efficient Mitigation of Agricultural Greenhouse Gas Emissions
Addressing Uncertainty in Efficient Mitigation of Agricultural Greenhouse Gas Emissions
复制标题
解决有效减缓农业温室气体排放的不确定性
DOI：
10.1111/1477-9552.12269
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发表时间：
2018
影响因子：
3.4
通讯作者：
Eory V
中科院分区：
文献类型：
--
作者：
Eory V
农业部门作为温室气体排放的一个重要来源，面临着减少其对气候变化的影响的压力。关于农业温室气体减排的融资和监管决策通常是通过对该部门潜在温室气体减排的成本效益分析得出的。这种分析的常用工具是自下而上的边际减排成本曲线（MACC），它评估减排方案并计算其累积的成本效益减排潜力。MACC在很大程度上是确定性的，通常不反映基础输入变量的不确定性。我们在自下而上的农业MACC中分析了温室气体减排估计的不确定性，这些不确定性能够进行定量评估。我们的分析确定了成本效益分析中不确定性的来源和类型，并通过蒙特卡洛分析将不确定性传播到MACC来估计结果的统计不确定性。就苏格兰农业而言，农业用地的成本效益减排潜力的不确定性（以变异系数表示）在各种情景下介于9.6%和107.3%之间。这意味着实际减排量低于估计减排量一半的概率从<1%（不确定性最低的情景）到32%（不确定性最高的情景）不等。不确定性的主要贡献者是采用率和减排率。虽然大多数缓解方案在某些情景下似乎是“双赢”的，但许多方案很可能在成本效益低和成本效益高之间转换。
The agricultural sector, as an important source of greenhouse gas (GHG) emissions, is under pressure to reduce its contribution to climate change. Decisions on financing and regulating agricultural GHG mitigation are often informed by cost‐effectiveness analysis of the potential GHG reduction in the sector. A commonly used tool for such analysis is the bottom‐up marginal abatement cost curve (MACC) which assesses mitigation options and calculates their cumulative cost‐effective mitigation potential. MACCs are largely deterministic, typically not reflecting uncertainties in underlying input variables. We analyse the uncertainty of GHG mitigation estimates in a bottom‐up MACC for agriculture, for those uncertainties capable of quantitative assessment. Our analysis identifies the sources and types of uncertainties in the cost‐effectiveness analysis and estimates the statistical uncertainty of the results by propagating uncertainty through the MACC via Monte Carlo analysis. For the case of Scottish agriculture, the uncertainty of the cost‐effective abatement potential from agricultural land, as expressed by the coefficient of variation, was between 9.6% and 107.3% across scenarios. This means that the probability of the actual abatement being less than half of the estimated abatement ranged from <1% (in the scenario with lowest uncertainty) to 32% (in the scenario with highest uncertainty). The main contributors to uncertainty are the adoption rate and abatement rate. While most mitigation options appear to be ‘win–win’ under some scenarios, many have a high probability of switching between being cost‐ineffective and cost‐effective.