Abstract
Net present value (NPV) is a widely used technique in capital budgeting. In this paper, we develop a Bayesian NPV framework using a Gibbs sampler. This approach allows decision-makers to integrate their knowledge, past experience, and uncertain and volatile cash flows from carbon emissions credits into decisions dealing with energy efficient, sustainable manufacturing equipment. The results indicate NPV is highly dependent on the nature of volatility and uncertainty of the cash flows. Without inclusion of this information through the Bayesian framework results, NPV becomes overstated, and thus it may provide biased guidance for the investment. The results developed in this paper further show that the frequency of very high and low cash flows and to a lesser degree their variability adversely impacts NPV. The results may also explain reasons for the economic phenomenon known as the energy efficiency gap.
Notes
1. Diverse opinions exist on how this rate should be decided. We assume the rate is decided by the decision-makers based on a company policy regarding capital budgeting. The rate used would be the same as the one used for other capital acquisitions.
2. If the company has cash flow information from similar projects that additional information may be incorporated as additional observations (samples) in the model.
3. The covariance matrix is obtained by multiplying the correlation matrix by diagonal matrices consisting of standard deviations.
4. If a distribution has three or more parameters, then it may be possible to change kurtosis by specifying the additional parameters.
5. Let xi indicates the discounted cash flow for year i then the left-hand side of the equation that follows this sentence is the variance of the total discounted cash flow. . Since all covariances are assumed positive, we have:
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