Making real options analysis more accessible for climate change adaptation. An application to afforestation as a flood management measure in the Scottish Borders
Making real options analysis more accessible for climate change adaptation. An application to afforestation as a flood management measure in the Scottish Borders
复制标题
使实物期权分析更容易用于适应气候变化。
DOI：
10.1016/j.jenvman.2019.05.077
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发表时间：
2019
影响因子：
8.7
通讯作者：
Dittrich R
中科院分区：
文献类型：
--
作者：
Dittrich R
气候变化的不确定性使适应投资的决策具有挑战性，特别是在涉及长期和不可逆转的前期成本的情况下。成本往往是直接和明确的，但收益可能是不确定的，只有在遥远的未来才会发生。稳健的决策方法，如真实的选项分析（罗阿）处理不确定性更好，因此是有用的指导气候变化适应的决策。罗阿允许通过制定可随时间调整的灵活战略来了解气候变化。适应气候变化的罗阿实际例子仍然相对有限，而且往往很复杂。我们提出了一个应用程序，使罗阿更容易获得的政策制定者通过使用用户友好的和免费提供的英国气候数据的UKCP 09天气发生器，它提供了预测未来的降雨量，推导转换概率的罗阿在一个简单的方式，并演示了如何分析可以实现在电子表格格式使用逆向归纳法。该应用程序是造林作为一种自然的洪水管理措施（NFM）在苏格兰的农村流域。罗阿阔叶造林的适用性作为NFM以前没有调查。根据不同的洪水损失成本、固定成本和折扣率，提出了不同的罗阿策略。结果表明，学习如何能够降低气候变化适应的总体投资成本，但造林的成本结构并不适合罗阿。
Climate change uncertainty makes decisions for adaptation investments challenging, in particular when long time horizons and large irreversible upfront costs are involved. Often the costs will be immediate and clear, but the benefits may be uncertain and only occur in the distant future. Robust decision-making methods such as real options analysis (ROA) handle uncertainty better and are therefore useful to guide decision-making for climate change adaptation. ROA allows for learning about climate change by developing flexible strategies that can be adjusted over time. Practical examples of ROA to climate change adaptation are still relatively limited and tend to be complex. We propose an application that makes ROA more accessible to policy-makers by using the user-friendly and freely available UK climate data of the UKCP09 weather generator, which provides projections of future rainfall, deriving transition probabilities for the ROA in a straightforward way and demonstrating how the analysis can be implemented in spreadsheet format using backward induction. The application is to afforestation as a natural flood management measure (NFM) in a rural catchment in Scotland. The applicability of ROA to broadleaf afforestation as a NFM has not been previously investigated. Different ROA strategies are presented based on varying the damage cost from flooding, fixed cost and the discount rate. The results illustrate how learning can lower the overall investment cost of climate change adaptation but also that the cost structure of afforestation does not lend itself very well to ROA.