Renewable energy continues to grow fast in the US despite Covid 19. In a remarkable move, the market capitalization of NextEra Energy, the world’s largest producer of wind and solar energy, passed in December 2020 (at more than $145 billion) the one of Exxon Mobil, the oil giant which used to be the ‘Standard Oil’ created by John Rockefeller in 1870 and became ‘Exxon’ in 1972 before merging its operations with Mobil in 1999. Together with the negative prices reached by the WTI crude oil in April 2020, this is the evidence that a page of history is turned for the oil giants and a New Era is indeed taking place.

British Petroleum (BP), the UK-based company which greatly operates in the US and has turned involved in greener activities after the ecological disaster of Deepwater Horizon, the oil platform in the US part of the Gulf of Mexico and the deepest drilled oil field is making its first move into offshore wind via a partnership with Norway-based Equinor (the former Statoil). BP is paying Equinor $1.1 billion for a 50% stake in wind farms Equinor is developing off the coasts of New York and Massachusetts. The projects represent a combined generating capacity of 4.4 Gigawatts, enough to power more than 2 million homes. BP says it hopes to develop 50 Gigawatts of renewable energy capacity by 2030, which is 20 times its 2019 level.

Driving the news in the financial world is the arrival at BlackRock, the world’s largest asset management fund, of Paul Bodnar - a climate finance veteran and founder of the clean energy think tank ‘Center for Climate-Aligned Finance”- as Head of Sustainable Investing. According to Blackrock, Bodnar will lead "sustainable research, analytics, product development, and integration of Environmental, Social and Governance (ESG) considerations into the investment process," and help clients achieve ‘net zero emissions’.

Lastly, it is worth mentioning the book by Bill Gates ‘How to avoid a climate disaster’. The author proposes to analyze in a systematic manner the difference in cost between a fossil fuel – based activity and the same clean one: the premium to pay for being green in in electricity production, manufacturing, and heating. He observes that for cement and steel production as well as the airline industry, clean options at a low price are not presently available while clean versions of bunker fuel for cargoes would be three times more expensive. This analysis would give countries and industries some guidance on the way forward.

As macro-level modelling has become a key tool in guiding climate policies, the manner of representing the green energy sector is ultimately of importance for the way we support it. In particular, the development of green energies happens in a context that can be uneasy for economists where multiple market failures exist. If the financing of the green energy sector is not properly accounted for, with the different obstacles that exist to it, there is a risk that policies adopted are suboptimal.

A strand of studies assume that the green energy sector can develop optimally with regard to essentially one variable, which is the carbon price, or the social cost of carbon (SCC). The exercise consists then in estimating the mapping from the carbon price set to the corresponding energy mix. Moreover, existing scenarios vary in assumptions regarding the technology evolution of the green energy producers, as well as that of the carbon dioxide removal sector.

However, the primary use of the SCC shouldn’t mask the fact that many other conditions need to be met. Some of these challenges are addressed in recent iterations of the EIRIN model, whereby the green energy sector is represented on its own, independently from its brown counterpart. This allows for considering the more specific financing needs and potential constraints that the sector faces, as well as its competitive interaction with traditional energy providers.

Uncertainty and risk are central aspects to address in the design of transition policies, as they can shift the optimal path to take for the economy, and they are especially pervasive in the financial market dynamics. Thus, a better integration of the green energy sector along this dimension can provide a more exhaustive views of conditions that have to be met to achieve a cleaner energy mix.

Finally, in practice, the ability of the green energy sector to scale up optimally depends on its ability to take advantage of policies in place and available funding mechanisms for green projects. There again the figure of the sustainable energy expert is key, to leverage on the understanding of financing constraints and green financial products, so as to minimize operational risk and devise the most efficient growth paths for green energy

In computational finance, state-of-the-art numerical methods and computer methods are adopted to put a financial model into work. Methods like Monte-Carlo simulation, regression and machine learning are universal tools to simulate scenarios and access risks - one may use them for mathematical finance and climate models alike. Learning the numerical methods in finance gives one the tools needed to tackle climate models too.

But do we need financial modelling at all? Maybe you remember the fun question: "How much wealth would you have if you would have put $ 1 in a savings account 2000 years ago?" Investing at 1.5%, we would - theoretically - arrive at 8.6 trillion dollars! This behaviour reflects the time value of money - rational investors prefer to receive money today rather than the same amount of money in the future. It appears as if a small value today corresponds to a large value in the future, or, equivalently, a large value in the future corresponds to a small value today.
Such exponential growth is inherent to many simple financial models, used to value liabilities or future projects' costs and benefits.

However, what if we want to assess the impact of possible future damages? Think, for example, of the destruction caused by climatic calamities like hurricanes or floods in a given area. The probability of such extreme events has increased and will further increase due to climate change, and it is important to estimate the damage they would cause. Having in mind the idea of the time value of money described above, one could be tempted to discount the damage accordingly.
This discounting has, of course, a huge impact if we think about events in a quite far future, like twenty to hundred years: huge costs in the future appear to become small costs, and hence of a lower priority, today.

A consequence of this can be that one hazardously underestimates the priority to prevent future damages.
This is a dangerous misconception: while the valuation of a liability in the previously discussed form is well-grounded, it cannot be applied to access the priorities to fix or prevent future damages.

In [1], we try to tackle this problem by introducing a non-linear discounting for future damages. The so-called "discount factors" are different here, such that preventing environmental damages has a much higher priority.

This is an example of how an accurate understanding of mathematical finance is essential to comparing and understanding future scenarios.

Welcome to computational and mathematical finance.

[1] Christian Fries Discounting Damage:Non-Linear Discounting and Default Compensation, Preprint, 2021