Energy storage: batteries versus supercapacitors?

Supercapacitors may eclipse lithium ion batteries in the hybridization of transport and industry. Their energy density is improving. Potential CO2 savings could surpass 1bn tons per year. IRRs of 10-50% can be achieved, even prior to CO2 prices. These are our conclusions after reviewing 2,000 Western patents. GE, Siemens, Skeleton and ZapGo are among the leading companies exposed to the theme.

The Top Technologies in Energy

What are the top technologies to transform the global energy industry and the world? This data-file summarises where we have conducted differentiated analysis, across c80 technologies (and counting).

For each technology, we summarise the opportunity in two-lines. Then we score its economic impact, its technical maturity (TRL), and the depth of our work to-date. The output is a ranking of the top technologies, by category; and a “cost curve” for the total costs to decarbonise global energy.

Download this data-file and you will also receive updates for a year, as we add more technologies; and we will also be happy to dig into any technologies you would like to see added to the list.

Autonomous vehicles: where’s the IP?

This data-file quantifies the number of patents filed into autonomous vehicles, by year, by geography and by patent family, looking across 37,000 patent filings since  the year 2000. Patent activity has risen at a 27% CAGR over the past decade, indicating a rapid pace of research activity.

The leading patent filers are ranked, including some of the world’s leading automotive companies, tech companies and retail companies. It is interesting to compare the relative activity levels among companies such as Denso, MobilEye, TuSimple, Uber, Waymo and Zoox (recently acquired by Amazon), versus Ford, GM, Honda, Toyota, Volvo et al.

Our notes and a data-pull of all the underlying 2019 patents follow. We find autonomous vehicles could entrench a 10% acceleration in road travel post-COVID, and displace c15% of all air-miles on sub-1,000 mile journeys.

Electric Rail Energy Economics?

This data-file models the energy economics of constructing new electric rail lines, to displace automobile traffic and accelerate the energy transition.

Under our base case forecasts, a mid-sized electric rail project would struggle economically, without tax-support, while saving around 1kT of CO2 per track-mile per year.

The economics depend heavily upon prices, costs and passenger numbers. Double-digit returns are achievable outside the United States, based on >75% lower apparent capex costs, especially for lines carrying c10,000 passengers per day.

CO2 prices do not materially change the picture, only adding around c1.5pp to our base case IRRs, even at a CO2 price of $500/ton, near the top of our cost-curve.

 

US Air Passenger Miles and Fuel Economy?

This data-file tabulates statistics on the US aviation sector, from the Bureau of Transport Statistics, to compute the fuel economy of US air travel, per plane-mile and per passenger-mile.

In 2019, 10M US flights carried 930M passengers 1.1 trn passenger-miles. The latest data in the file run to February-2020.

Fuel economy per passenger mile has risen at a 2.8% CAGR since 2003. Flight numbers have fallen by -0.4% pa and flights have become 0.8% longer.  But load factors have improved by 0.7pp each year, spreading 0.5 plane miles per gallon across more passengers.

Urban Traffic by Time and by Travel Speeds?

We have quantified the average speed of automobiles on a dozen highways and expressways flowing into New York City from Long Island, CT and New Jersey, to quantify how traffic ebbs and flows over time.

Traffic is most severe at 4-5pm, second worst at 8-9am, but least severe at 4-5am. The data in the file are from 2H19.

We can compute average vehicle fuel economy, as a function of these traffic speeds.  Moderate-severe traffic congestion curtails average vehicle fuel economy by 15-45% on highways leading in to a city.

Long-distance travel: by purpose and mode of transport

This database breaks down long-distance travel (defined by a distance greater than 100-miles) by purpose, by transportation type and by distance category, for the average person in the US, and in aggregate.

It is based on over 1.2GB of raw data, collected by the US FHWA and US DOT in 2007-11, which is still widely cited around the Academic literature and thus relevant to assessing the post-COVID landscape.

The data show the full breakdown of long-distance travel by plane, car, bus and train; for business, leisure activities and commuting; from 100-miles to 4,000-miles; how these different factors co-vary; and how they have changed between 2010 and 2017.

The chart below illustrates the headline data, aggregating all modes of transport by purpose and travel distance. Alternate versions of the chart are available for just planes, automobiles, buses and trains.

US Vehicle Sales by Fuel Economy: Cars, Trucks and SUVs

This data-file tabulates vehicle sales and fuel economy for different vehicle classes, to illustrate how vehicle purchasing decisions change under different oil price regimes.

As a general rule, real oil prices below $40/bbl have seen a -0.8% annual deterioration in the fuel economy of the aggregate US vehicle purchase, due to greater shares for SUVs and trucks.

Conversely, real oil prices above $80/bbl have seen a c2% annual improvement in fuel economy, as consumers preferred to purchase a greater share of more fuel-efficient sedans and other vehicles.

Remote possibilities: working from home?

The COVID-19 crisis will structurally accelerate remote working. The opportunity can save 30% of commuter journeys by 2030, avoiding 1bn tons of CO2 per year, for a net economic benefit of $5-16k per employee. This makes remote work materially more impactful than electric vehicles.

Working remotely: the economics, the opportunity?

We quantify the economic benefits of working remotely between $5-16k per employee per year, as a function of income levels, looking line-by-line across time savings, productivity gains, office costs and energy costs. The model allows you to flex these input assumptions and test your own scenarios.

Based on our research, we think the proportion of remote work could step up from 2009 and 2017 levels (quantified in the file) to displace 30% of all commutes by 2030. This conclusion is justified, by summarizing an excellent technical paper, and a granular breakdown of jobs around the US economy, looking profession-by-profession.