However interesting and generally appealing scenario thinking, as discussed in my previous article, may be, it only provides a broad, qualitative picture of the context within which one is considering a significant investment decision. But indeed, a very important picture, even with all its caveats. Yet, we need numbers when it comes to sinking capital. No decision executive will sign off on an investment proposal that only consists of a fascinating narrative. She or he will want to see some underpinning of the potential profitability and capital efficiency. And some what ifs. In other words: we need quantification, we need a cash flow outlook of some sort.
In fact, it is one of the more difficult, and therefore avoided, challenges: to combine qualitative with quantitative thinking. Many restrict themselves to one of the two worlds. Yet I believe one has to try to cross the border. Quantification alone can often not tell the story by itself, but only qualitative narratives have the danger of becoming arm-waving.
But this quantification, if done well, does not come easy. How can we put numbers on the future? One way is to assume that whatever was going on in the past will replicate itself going forward. For some variables that is quite doable. If you are investing in wind or solar energy, the wind speed patterns and annual numbers of sun hours at a particular location can reasonably be inferred from historic records, with some averaging over multiple years. However, for many variables such an approach would not be credible.
Nevertheless, this is exactly what was (and is) common across many sectors. In fact, it is well known that this practice was one of the main causes of the financial crisis in 2008. The notion of ‘risk’ was strongly related to the degree of stock volatility in prior periods. Risk models were used that were only driven by historic behaviour; there was no forward looking element (see NYT, 2009). In essence, the quantitative models made people stop thinking. For the calculation of the minimum coverage for Dutch pension funds (i.e. stretching out multiple decades) the use of the current interest rate is mandatory. Similarly for the oil and gas reserves calculations for the SEC (US Securities and Exchange Commission) it is required to use, for the purpose of asset valuation, the currentoil price for several decades of remaining production. Of course these last two examples have auditing and legal dimensions, but they do illustrate the dilemma when considering future cash flows: we know that the value that some important variable assumes today may not be representative for the future, but we (think we) have nothing else (that is auditable).
For investment decisions and strategy development we will often need both a scenario approach and quantitative analysis. It will depend on the investment project where the emphasis lies, but I would argue that for major capital decisions or strategies both should have equal weight. What we see in practice is that (if done at all) scenario work and quantitative investment evaluations are poorly linked, perhaps because these different approaches are serviced by different departments, they require different styles of working, different areas of expertise.
And of course, to come back to the earlier point, the quantification developed should use historic data as a basis, but needs also to include a forward looking, judgemental dimension. Scenario narratives and (probabilistic and judgementally forward looking) quantification should go hand in hand, in some way, for maximum understanding and clarity.
For quantitative analysis we require a choice of models and methods, fit for purpose, not too complicated, but yielding consistent results. And we need mathematics, to do things smartly, quickly and consistently. To deal with uncertainty in the numbers, we need probability theory and probability distributions. There is no way we can arrive at exact estimates of all future variables (costs, prices, schedules, sales quantities, tax rates, etc.). But what we can do is to try to estimate a range (under certain scenario assumptions).
One of the most important distributions for this purpose is the lognormal distribution. It is representative of multiplicative processes (as is the normal distribution for additive processes). This means that the product of variables with ranges of uncertainty will tend to be lognormally distributed. Examples are volumes (L*W*H) and revenues (number of units solds * price). The lognormal distribution is also used quite a bit to model share price behaviour. It has an elegant mathematical formulation and allows for modelling upsides and downsides (with a trick). A recent article I came across (Surovtsev, D. and Sungurov, A. “Vaguely Right or Precisely Wrong?”: Making Probabilistic Cost, Time and Performance Estimates for Bluefield Appraisal. SPE 181904. SPE Economics & Management Journal, July 2017.) confirms that most cost, schedule and production variables are best represented by such a distribution.
Rather than (only) relying on integrated systems and black-box like simulation software, for practitioners and analysts it may be useful to get into the guts of the lognormal distribution, understand its mathematical articulation and have some practical formulas to calculate things by hand. It gets a bit nerdy, but do check out a useful article for this purpose in our free knowledge base (in which more articles are to come).
A next blogpost will again be about global scenarios in a qualitative discussion: what can we learn from our predecessors?
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In 2021, the makeup of renewables has also changed drastically. Technologies such as solar and wind are no longer novel, as is the idea of blending vegetable oils into road fuels or switching to electric-based vehicles. Such ideas are now entrenched and are not considered enough to shift the world into a carbon neutral future. The new wave of renewables focus on converting by-products from other carbon-intensive industries into usable fuels. Research into such technologies has been pioneered in universities and start-ups over the past two decades, but the impetus of global climate goals is now seeing an incredible amount of money being poured into them as oil & gas giants seek to rebalance their portfolios away from pure hydrocarbons with a goal of balancing their total carbon emissions in aggregate to zero.
Traditionally, the European players have led this drive. Which is unsurprising, since the EU has been the most driven in this acceleration. But even the US giants are following suit. In the past year, Chevron has poured an incredible amount of cash and effort in pioneering renewables. Its motives might be less than altruistic, shareholders across America have been particularly vocal about driving this transformation but the net results will be positive for all.
Chevron’s recent efforts have focused on biomethane, through a partnership with global waste solutions company Brightmark. The joint venture Brightmark RNG Holdings operations focused on convert cow manure to renewable natural gas, which are then converted into fuel for long-haul trucks, the very kind that criss-cross the vast highways of the US delivering goods from coast to coast. Launched in October 2020, the joint venture was extended and expanded in August, now encompassing 38 biomethane plants in seven US states, with first production set to begin later in 2021. The targeting of livestock waste is particularly crucial: methane emissions from farms is the second-largest contributor to climate change emissions globally. The technology to capture methane from manure (as well as landfills and other waste sites) has existed for years, but has only recently been commercialised to convert methane emissions from decomposition to useful products.
This is an arena that another supermajor – BP – has also made a recent significant investment in. BP signed a 15-year agreement with CleanBay Renewables to purchase the latter’s renewable natural gas (RNG) to be mixed and sold into select US state markets. Beginning with California, which has one of the strictest fuel standards in the US and provides incentives under the Low Carbon Fuel Standard to reduce carbon intensity – CleanBay’s RNG is derived not from cows, but from poultry. Chicken manure, feathers and bedding are all converted into RNG using anaerobic digesters, providing a carbon intensity that is said to be 95% less than the lifecycle greenhouse gas emissions of pure fossil fuels and non-conversion of poultry waste matter. BP also has an agreement with Gevo Inc in Iowa to purchase RNG produced from cow manure, also for sale in California.
But road fuels aren’t the only avenue for large-scale embracing of renewables. It could take to the air, literally. After all, the global commercial airline fleet currently stands at over 25,000 aircraft and is expected to grow to over 35,000 by 2030. All those planes will burn a lot of fuel. With the airline industry embracing the idea of AAF (or Alternative Aviation Fuels), developments into renewable jet fuels have been striking, from traditional bio-sources such as palm or soybean oil to advanced organic matter conversion from agricultural waste and manure. Chevron, again, has signed a landmark deal to advance the commercialisation. Together with Delta Airlines and Google, Chevron will be producing a batch of sustainable aviation fuel at its El Segundo refinery in California. Delta will then use the fuel, with Google providing a cloud-based framework to analyse the data. That data will then allow for a transparent analysis into carbon emissions from the use of sustainable aviation fuel, as benchmark for others to follow. The analysis should be able to confirm whether or not the International Air Transport Association (IATA)’s estimates that renewable jet fuel can reduce lifecycle carbon intensity by up to 80%. And to strengthen the measure, Delta has pledged to replace 10% of its jet fuel with sustainable aviation fuel by 2030.
In a parallel, but no less pioneering lane, France’s TotalEnergies has announced that it is developing a 100% renewable fuel for use in motorsports, using bioethanol sourced from residues produced by the French wine industry (among others) at its Feyzin refinery in Lyon. This, it believes, will reduce the racing sports’ carbon emissions by an immediate 65%. The fuel, named Excellium Racing 100, is set to debut at the next season of the FIA World Endurance Championship, which includes the iconic 24 Hours of Le Mans 2022 race.
But Chevron isn’t done yet. It is also falling back on the long-standing use of vegetable oils blended into US transport fuels by signing a wide-ranging agreement with commodity giant Bunge. Called a ‘farmer-to-fuelling station’ solution, Bunge’s soybean processing facilities in Louisiana and Illinois will be the source of meal and oil that will be converted by Chevron into diesel and jet fuel. With an investment of US$600 million, Chevron will assist Bunge in doubling the combined capacity of both plants by 2024, in line with anticipated increases in the US biofuels blending mandates.
Even ExxonMobil, one of the most reticent of the supermajors to embrace renewables wholesale, is getting in on the action. Its Imperial Oil subsidiary in Canada has announced plans to commercialise renewable diesel at a new facility near Edmonton using plant-based feedstock and hydrogen. The venture does only target the Canadian market – where political will to drive renewable adoption is far higher than in the US – but similar moves have already been adopted by other refiners for the US market, including major investments by Phillips 66 and Valero.
Ultimately, these recent moves are driven out of necessity. This is the way the industry is moving and anyone stubborn enough to ignore it will be left behind. Combined with other major investments driven by European supermajors over the past five years, this wider and wider adoption of renewable can only be better for the planet and, eventually, individual bottom lines. The renewables ball is rolling fast and is only gaining momentum.
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