Hi. Please check

1. Growth curve methods

2. Scenario analysis

3. Precursor events

I have experimented these methods for computational technologies like cloud computing.

Hydrogen powered vehicles are not very promising. I think this is the main issue with the forecasts of new technologies. 80% of the promising new technologies sink before have a significant impact. CDs, blue-rays, walk-man are great examples of short term technological spread, cars are in the other end with their more than 100 years of development. Who could forecast the number of cars and technologies in 1900, in 1940 or 1970? Who could forecast PCs in 1980? These are the easy ones, but predict walk-man or blueray was not possible at all.

Instead of forecasting a specific technology, it is better to focus on the direction of change since 80% of the new technologies will disappear very quickly. For example:

- the future transportation will use renewable energy sources (what percentage by 2030-40-50 so on),

- autonomous machinery replaces drivers,

- robots take jobs what will cause a transformation of the labour market and capitalism.

If you have a serious of forecasts like that you can identify groups of technologies will develop and others will deteriorate.

To me, the term "forecasting" implies making point estimates for specific times in the future. I generally consider this to be a fool's errand, due to:

- Uncertainty in the rate of future technological progress

- Uncertainty in consumer preferences

- Feedback effects that can lead to non-linear growth patterns or stymie seemingly promising technologies.

These compound one another to make any point forecasts unreliable. You can certainly build a model using the frameworks suggested above, but that doesn't mean it will tell you anything useful.

Dear Don,

Thanks for your answer.

I'm taking into consideration the idea of obtaining some figures regarding the stock of vehicles (hydrogen, electric...) at national level, in order to assist other modelling frameworks in establishing targets. I'm open to any suggestion or idea on how to get some preliminary figures of the stock of vehicles. At this initial point it is very important to know what to expect regarding specific technologies, i.e. checking some raw numbers (1 million electric vehicles is attainable as goal by 2030? 3 millions? etc.). I'm exploring the way that those figures (such type, stock) are assumed in other models (energy systems optimisation models).

Regards,

Diego

Dear Diego,

The short answer is that I don't know of any ideal solution here, and my advice is to be skeptical of those who claim to have the answer. I realize that is not the most helpful advice. Some things you can do instead:

- In your other (e.g. energy system) models, seek robustness to a range of outcomes for vehicle sales, rather than optimizing for a single growth path.

- Focus on ranges instead of point estimates, by varying key assumptions and parameters.

- Look at historical data on rates of growth. Is there a precedent for a new technology growing at the pace that is being projected? If not, ask yourself what is so different about this technology than every other technology before it. Here's one source on rates of automotive technology growth (also published as SAE paper 2012-01-1057): http://web.mit.edu/sloan-auto-lab/research/beforeh2/files/Zoepf_MS_Thesis.pdf

- For the 5-8 year timeframe, you can hunt down auto manufacturers' product plans and product cycles. If new EV models are entering the lineup, you can generate bottom-up estimates of ranges of EV sales, informed by historic market shares of new EV models. Then you can make some assumptions about growth over the remaining years.

The above can be helpful in judging the reasonableness of your estimates.

Thanks a lot, Don. Totally agree. I'm gonna check the MS thesis. Looks interesting.

https://ec.europa.eu/research/energy/pdf/hydrogen-report_en.pdf