Scenario Analysis. Scenario analysis is a widely used technique by ESG analysts, which allows us to consider a range of different potential scenarios for how ESG risks could impact on company forecasts. This allows us to consider how these risks could then impact on the company's valuation, particularly when compared with the baseline valuation for the company. Many analysts already use scenario analysis when they're forecasting their core assumptions for growth and profitability by identifying an upside and a downside case for their forecasts. However, the difference with ESG scenarios is that they can be materially different to the base case and an extremist can help identify operations that may not be viable in certain scenarios. For example, if stringent new regulations reduce the profitability of certain activities. There are typically two main outputs from scenario analysis. Firstly, a best case/worst case valuation for the company, and the range of outcomes here would help us to identify the risk associated with the valuation. The second output is a probability weighted valuation, where each scenario is given a probability and the valuation of the company is based on the probability weighted average of all of these. For example, if the scenario analysis gave valuations for the company of 160 million and 120 million, and both of these scenarios have a 50% probability, this would give a probability weighted valuation of 140 million. Now, the main benefit of scenario analysis, especially when compared with benchmarking, is that the output is directly linked to the valuation of the company. So for investors wanting to fully integrate ESG risk into their investment decisions, scenario analysis is hugely important in deriving a target price, which incorporates ESG risk. Also, because the scenarios focus on the impact of ESG risks on forecast earnings in cash flows, this approach is inherently forward-looking, requiring analysts to consider not just the operations of the company as they stand today, but also how the scenarios would impact on the company's strategy and planned investments in the future. Linked to this, therefore, is the ability to consider both the upside and the downside risk. That is the extent to which different scenarios provide both opportunities and risks for the company. For example, if new regulation presents a threat to a company's existing operations, that's downside risk, but say the company is already investing heavily in new technology that could help it to navigate these threats better than its peers, then this may actually present an opportunity or upside risk in the long term. So that's the benefits, however, there's a number of challenges with scenario analysis, and first and foremost of these is the fact that there are inherent uncertainties in the scenarios and the assumptions that they rely on. Although this is the case for all forecasts, it's even more so when considering hypothetical scenarios. Linked to this is the fact that even apparently simple scenarios can be very complex to model. For example, if there's dependencies in a scenario, where a decision on certain future investments is dependent on forecast profitability of operations, this can be very difficult to model. A final challenge with scenario analysis is that there's generally very limited data provided by the companies that help us with building the scenarios. Now, the one exception here is for climate change, where there's various initiatives, such as the Task Force for Climate Change Disclosure, known as the TCFD, which is encouraging companies to provide disclosure on the impacts of various climate change scenarios.