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Clean vs Oil Project Pricing - How to Correlate and Predict Pricing

Clean Energy and Emerging Markets

Summer - I can almost hear the Beach Boys in the summer breeze - but I digress.

As you know, Post Road Advisors has entered into a cooperation agreement with SEA-Enterprises and we are cooperating on several projects. One interesting one is to establish a Waste to Energy facility in Mali and Nigeria, Africa. These facilities would process 150 tons of trash per year to relieve pressure on landfills, incinerate the trash in a clean facility, create 'syngas' to power a combined-cycle turbine to generate electricity for the national grid. Ash and heavy metals are encapsulated in glass bricks using the abundant sand (silica) in these Sahel and sub-Sahel countries.

So this project and our financial calculations bring to the point of - how to calculate the sales price of clean 'syngas' or 'clean energy'?

In the graph above, Andreas Schreyer of The Green Investor compares pricing of clean energy using as a proxy the ETF (Exchange Traded Fund) Power Shares Global Clean Fund vs Platts West Texas Crude price. As you can see there is a very strong correlation - in fact - the correlation coefficient is 0.9 with a perfect correlation between prices being 1.

The point is that Emerging Markets are key players in this sector with biomass and solar clean-energy projects and are affecting the clean energy price grid. It is critical for financial lenders to be able to predict price fluctuations using regression analysis techniques using WTC historical prices as a potential predictor of clean energy project out-put pricing. This is necessary to justify the financial proposals. We will address Multilateral Lenders Agency (MLA) in a later article.

But what does this suggest? Clearly, as oil fluctuates in price, synfuels will also (note the slight lag of the synfuels vs oil prices in the graph) and begin a correlated relation to the price of oil. These elements should be incorporated in sensitivity analyses for project evaluation. In this way we can better predict the pricing required for clean energy projects to service debt and equity. Therefore, we now have a relatively crude- but justifiable-model to sensitize clean energy and Emerging Market renewable energy models for financial projections to evaluate the projects. From here, with this high correlation coefficient, we can derive the NPV and provide lenders with adequate information to make a reasoned decision.

Now, to the beach.

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