Behavioral Issues in Financing Low Carbon Power Plants

We consider the limit s to traditional finance in evaluating power projects and investigate the role biases and heuristics used by individuals and institutions play in investment decisions, particularly those affecting less familiar, lower-carbon electricity generation. Traditional finance relies on the principles and results of modern portfolio theory, such as the efficient market hypothesis, which tends to describe investment results in terms of mean percentage return, statistical risk (e.g. standard deviation), and reward-torisk ratios. For power projects, firms consider various financial criteria for comparing projects opportunities with unequal lifetimes. To forecast financial criteria for project opportunities, firms will n ormally project the cash flow profile, often using Monte Carlo simulations given the volatility of some point estimations. In addition, real options analysis can be integrated if the relevant option (abandonment, expansion, flexibility) value may be significant. Many potential risks already incorporat ed in the traditional finance prospective include: business and commercial risk, country (or political risk), exchange rate and interest rate risk, inflation and liquidity risk. Rather than assuming all investors are rational and all relevant facts will be interpreted correctly, institutional behavioral finance assumes firms make decisions according to their own objectives and constraints. Project decisions are affected by both the institutional framework and individual behavior. Behavioral characteristi cs can affect decision-making by contributing to biased forecasts, especially in those institutions or projects for which decision-making power is highly concentrated. Specific biases affecting power projects include: representativeness, overconfidence, anchoring-and-adjustment, aversion to ambiguity, endorsement effect, and loss aversion. Project decision -makers may not fully incorporate financial projections, as rather than researching or trusting that information they are forming own rules developed through experiments, making investment decision that is most prominent, and relying on heuristics. We analyze the risks and opportunities for shareholders, creditors, and equipment vendors in moving from the traditional to the behavioral model, with a speci al emphasis on ‘newer’ investment decisions found in lower-carbon generation such as renewable or advanced coal and CCS technologies.