The ascent of drones?

In 2019, we argued drones would be the single most disruptive technology to gain share in the 2020s, with potential to save over 500MTpa of CO2 emissions, while re-shaping urban consumption, retail and manufacturing (note here).

This data-file aims to tabulate key news flow and data-points around the ascent of drone technologies, across dozens of news stories, running back to 2016. We find an acceleration of activity due to COVID. Full details are overleaf.

Weatherization of homes: what methods and costs?

The “weatherization” of 2M American homes is part of Presidental candidate, Joe Biden’s proposed energy policy. The aim is to reduce energy costs, consumption and emissions.

Hence, this data-file estimates the costs and payoffs of five different weatherization methodologies: attic insulation, air sealing, window upgrades, window coverings and smart thermostats.

We find the most cost-effective options are smart themostats and air sealing. Ballpark numbers, further details and implications are explored in the data-file.  This adds to our recent work on the decarbonization of heat.

Tree database: forests to offset CO2?

Nature-based solutions are among the most effective ways to abate CO2. Forest offsets will cost $2-50/ton, decarboning liquid fuels for <$0.5/gallon and natural gas for <$1/mcf (chart below).

The data-file tabulates hundreds of data-points from technical papers and industry reports on different tree and grass types. It covers their growing conditions, survival rates, lifespans, rates of CO2 absorption (per tree and per acre) and their water requirements (examples below).


Carbon offsets: costs and leading companies?

This data-file tabulates the costs of carbon offsets being offered to consumers and commercial customers by c30 companies. Prices are surprisingly low, ranging from $4-40/ton of CO2.

Which projects are most economical? Costs are lowest at forestry projects, particularly at companies where you pay “per tree” rather than “per ton” of CO2. They are also lower at non-profits (which also means contributions are tax-deductible). Finally, they are lowest at companies undertaking projects directly, rather than as “middlemen” (charts below).

Are they CO2 offsets real? The also file contains detailed notes on each company, to assess their credentials. Moreover, it tabulates 1,600 carbon offset projects which are assured by agencies such as the ‘Verified Carbon Standard’, Gold Standard and Green-E, for a broader perspective.

Offset your own CO2? We have used the data-file to select and allocate carbon offsetting dollars to Eden Reforestation, One Tree Planted, The Gold Standard and Sea Trees. We are happy to discuss CO2 offsetting with TSE clients and those using the data-file.

Vehicle costs: cars, SUVs, hybrids, EVs and hydrogen?

This data-file quantifies the cost per mile of vehicle ownership for different categories of vehicles. Our methodology looks across the prices of 1,200 second hand vehicles, to correlate how the re-sale value of each make and model degrades per mile that has accumulated on its odometer (chart above).

Hybrids and basic passenger cars are most economical. Trucks and SUVs are 2x more costly. EVs are another 25% more costly again, and will have lost c60% of their value after 100,000 miles. Hydrogen cars have the highest costs and will have lost over 90% of their value after 100,000 miles (chart below).

Underlying data are shown in the input tab across ten makes and models, to see how the re-sale value of each vehicle degrades with mileage. This may help you appraise what a particular second hand purchase “should” cost (example below) if you are among the many non-drivers considering a vehicle purchase as a result of the COVID crisis.


A database of historical commodity prices and disruptions

This data-file aggregates long-term historical prices of commodities going back to 1800, and running up until 1970, predominantly in the US, using academic records and census data.

Covered commodities include bricks, coal, copper, cotton, nails, rock oil, steel rails, sugar, turpentine, whale oil, wheat and wood. A combined profile of all of these commodities is aggregated in the index above.

In particular, we focus in upon the disruption of whale oil as a lighting fuel and the disruption of wood as a heating fuel, using granular data into pricing and demand. Our conclusions are presented in a research note linked here.

Fiscal incentives for US decarbonization?

This data-file tabulates fiscal incentives that exist to abate CO2, across individual categories in the energy transition, based on current legislation in the United States.

In each case, we have calculated the implied cost to abate CO2,  under the various policies, as measured in dollars per ton ($/ton). Included in the file are Production Tax Credits, Blenders Tax Credits, Investment Tax Credits, Equipment Tax Credits and Section 45Q.

Our view is that the current system is overly-complex and arbitrary. It provides large incentives for specific technologies that happen to have policy support, and no incentives (or small incentives) for other technologies that could make a larger decarbonization impact at a much lower cost.

Fuel cells: decline rates?

This data-file captures the generation profiles of c100 fuel cell power plants, installed to-date in the US. Total fuel cell generation has been rising at a rate of 0.15TWH per year since 2010, almost entirely powered by natural gas.

An acceleration of fuel cellsis seen by many commentators in the future green hydrogen economy. We are more excited by behind the meter systems to compensate for the overbuilding of renewables. But what operating assumptions are reasonable, based on past data?

Advantages and disadvantages of fuel cells are summarized based on past projects’ operating parameters. We find fuel cell sysems are 2-3x more efficient than gas turbines at small scale. However, fuel cells’ flexibility and longevity is lower. Surprisingly, we also find that the electrical conversion efficiency of fuel cells deteriorates markedly over time.

A subset of the data is split out for Bloom Energy, the leading manufacturer of Solid Oxide Fuel Cells, which operates 30% of all fuel cells in the US. Leading companies in fuel cells are screened here, based on evaluating their patent filings.

Gas power: decline rates?

This data-file tabulates the power generation profiles of 3,000 US natural gas-fired power plants, which have reported data to the US EIA, aggregated using in-house web-scraping software.

Unlike wind and solar assets, which exhibit clear decline rates of 1.5% and 2.5% per year, natural gas assets run at c44% of their peak utilization rates on average, which does not change materially over time, flexing within an interquartile range that spans from 14% to 74%.

In other words, gas power plants provide flexibility and long-term reliability in a grid, as they are dialled up and dialled down over time to meet demand. This is also illustrated by looking at the underlying data of individual power plants in the file (chart below).

The data-file also presents a cautionary tale from California. To accomodate 40TWH of new utility-scale renewables generation, we show that 35TWH of gas generation has now been permanently shuttered and another 11TWH has been idled. These closures are equivalent to 30% of California’s baseload and 17% of its peakload power capacity, providing one explanation for the State’s recent rolling black-outs. Full details are split out in the data-file.

Wind power: decline rates?

This data-file tabulates the ‘decline rates’ of 1,215 US wind power plants, which have reported data to the US EIA, using in-house web-scraping and aggregation software.

Across the entire data-set, we find wind farms take two years to ramp up. Generation peaks in year 3. It declines at 1.0% per year up to year ten (when production tax credits tend to roll off), then decline at 3.5% from year 10 to 18.

However, the data are highly variable. Hence this data-file gives full granularity on the underlying data-points, so you can stress test assumptions. We also discuss variables that may lower future decline rates.

The ‘Conclusions’ tab explores the consequences. US wind generation profiles are not dissimilar from well-managed oil and gas fields; some projects may suffer 2% lower IRRs versus forecasts if they have not factored in declines; and declines will also become more material over time, slowing the ascent of wind’s share in the power mix (chart below).

The full data-file can be downloaded below. Alternatively, a PDF of the conclusions is available here for clients with our ‘written insights‘ subscription.