eHighways: trucking by wire?

eHighways electrify heavy trucks via overhead catenary wires. They have been de-risked by half-a-dozen real-world pilots. High-utilization routes can support 10% IRRs on both road infrastructure and hybrid trucks. This 15-page report finds benefits in logistics networks, especially around ports, and hidden opportunities around integrating renewables?


Heavy trucks comprise 2% of global useful energy, via the c30-40% efficient combustion of 10Mbpd of oil products, emitting 1.4GTpa of CO2, which in turn is 2.5% of total global CO2 emissions. We have struggled in the past to get excited by decarbonized truck technologies while there has been most momentum behind LNG trucks in China (key numbers behind these themes are re-capped on pages 2-4).

eHighways are an alternative, providing electricity to the drive train of hybrid-electric trucks, through overhead catenary lines, via a pantograph. How eHighways work, what they cost, and results from past pilot projects, are described on pages 5-7.

eHighway economics seem favorable for truck operators, based on our capex build-up, and IRR calculations, discussed on pages 8-9.

This should garner attention across companies in ports, logistics, freight, mining and materials. There is also a fascinating read-across for integrating renewables, load-shifting and even providing synthetic inertia to grids, per pages 9-10.

eHighway economics for infrastructure investors, who actually build the eHighways, are more challenging however, and the key variable is utilization of the route. Scaling challenges, and other challenges with eHighways, are on pages 11-13.

Conclusions about eHighways, who benefits, exposed companies, links with rising global electricity demand, and changes to our oil demand forecasts are discussed on pages 14-15.

Finally, if eHighways did gain traction, it would compound the ongoing boom in grids, from transformers, to power generation, to T&D themes, and grid construction.

Global energy: supply-demand model?

This global energy supply-demand model combines our supply outlooks for coal, oil, gas, LNG, wind and solar, nuclear and hydro, into a build-up of useful global energy balances in 2023-30. Energy markets can be well-supplied from 2025-30, barring and disruptions, but only because emerging industrial superpowers will continuing using high-carbon coal.


Useful global energy demand grew at a CAGR of +2.1% per year since 1990, and +3.0% in 2015-19. Demand ‘wants’ to grow by +2% per year through 2030, due to higher populations and rising living standards (model here), but the dawn of the AI age increases the CAGR to 2.5% pa.

Renewables are exploding, especially solar additions, per our model here. Our latest numbers, updated again in early 2025, see solar module installations (on a DC basis) rising from 450GW in 2023, to 600 GW-DC in 2024, 700GW in 2025 and a full 1TW pa by 2028. This takes wind and solar from 5% of total useful global energy in 2024 to 12% by 2030.

Demand for hydrocarbons nevertheless increases too, in order to satisfy rising energy demand. Global coal use hit a new all time high of 8.8GTpa in 2024 and is seen rising mildly through 2027 (model here).

Oil plateaus at 104Mbpd in 2026-30 (model here) as OPEC and US shale (model here) offset decline rates elsewhere.

LNG supplies rise from 400MTpa in 2023 to 660MTpa by 2030 (risked) but the increases are mainly 2027+ (model here) while the call on US shale gas now looks like the stuff of dreams.

Other variables in the model include rising energy efficiency (note here), the need for a nuclear renaissance (note here), ideally scaling back the use of deforestation wood (model here) and others that can be flexed.

What is important about this balance is that it must balance. The first law of thermodynamics dictates that energy demand cannot exceed supplies. In the short-medium term, recent evidence suggests that emerging world coal is the balancing line, as China and India have consistently prioritized self-sufficient energy over decarbonization.

All of the other lines in the model can be stress-tested. Our own preferences would see more solar and more natural gas to meet more energy demand, which can genuinely improve human outcomes, both in energy transition and beyond. However our predictions for what will happen now make 1.5-2C climate targets feel challenging, and we are trying to do a better job of objectively forecasting what will happen.

Kardashev scale: a futuristic future of energy?

Possible uses of basically free solar energy.

A Kardashev scale civilization uses all the energy it has available. Hence this 16-page report explores ten futuristic uses for global energy, which could absorb an additional 50,000 TWH pa by 2050 (60% upside), mainly from solar. And does this leap in human progress also allay climate concerns better than pre-existing roadmaps to net zero?

Cool concept: absorption chillers, data-centers, fuel cells?!

Working principle of absorption chillers

Absorption chillers perform the thermodynamic alchemy of converting waste heat into coolness. Interestingly, their use with solid oxide fuel cells may have some of the lowest costs and CO2 for powering and cooling AI data-centers. This 14-page report explores the opportunity, costs and challenges.

Decarbonize shipping: alternative fuel costs?

Cost of shipping as a percent of final product costs for different ship fuels and their CO2 intensities. Green fuels can be low-carbon, but expensive

This data-file screens the costs of alternative shipping fuels, such as LNG, blue methanol, blue ammonia, renewable diesel, green methanol, green ammonia, hydrogen and e-fuels versus marine diesel. Shipping costs rise between 10% to 3x, inflating the ultimate costs of products by 0.1-30%, for CO2 abatement costs of $130-1,000/ton. We still prefer CO2 removals.


Shipping consumes 5Mbpd of global oil demand, emits 1.5% of the world’s CO2, and adds c1% to the final cost of a typical shipped product, using $1.8/gallon marine diesel at 9.0 kg/gal CO2 intensity.

This data-file appraises the costs of alternative shipping fuels, drawing on models from our prior work into methanol, lower-carbon ammonia, renewable-diesel, green hydrogen, and electrofuels.

In each case, we have estimated the increased fuel costs of alternative shipping fuels versus marine diesel; plus the increased capex costs of ships that can handle these different fuels, increased maintenance costs and other increased operational costs. This draws on our models of container ships, bulk shipping, LNG tankers and other vessel types.

The title chart above shows a base case where shipping with marine diesel fuel adds 1% to the final price of a product that is transported between continents, and emits about 100kg of CO2 per ton of product that is shipped. Alternative shipping fuels add 0.1 – 3.3% to this baseline cost.

LNG is most competitive, adding just c10% to total shipping costs in LNG-fueled ships – possibly much less, or even deflating costs, when oil prices are higher, or LNG prices are lower. But LNG only lowers CO2 emissions by c10%. And even this is debatable, if a gas-fired marine engine suffers from methane slip.

Blue methanol, blue ammonia and renewable diesel are next most economical, but add 0.4 – 0.7% to the final costs of shipped products, while achieving 60-70% reductions in the CO2 intensity of shipping. This equates to a decarbonization cost of $135-260/ton.

Most costly are green methanol, green ammonia, green hydrogen and e-fuels, which add 2.3-3.3% to the final costs of shipped products, while achieving 80-90% reductions in the CO2 intensity of shipping. Thus, the decarbonization costs are an eye-watering $700-1,000/ton.

The numbers do vary markedly, however, based on the products being shipped, especially their mass, their costs and the shipping distance, which can all be stress-tested in the data-file.

For bulk products such as sugar, iron ore or grains, shipping using marine diesel can comprise as much as 5-10% of product prices, hence switching to the green fuels above can inflate end product costs by 20-30%.

Cost of shipping as a percent of final product costs for different ship fuels. Green fuels can be prohibitively expensive

Conversely, for light but high-value products, such as iPhones, shipping costs are basically irrelevant. You can use any fuel you like, and it will not even sway final product prices by 0.00%. Most other products are in between. Numbers can be stress-tested in the model.

The most economic options to decarbonize shipping are through larger and more efficient ships, using high-quality hydrocarbon fuels and coupling these ships with nature-based CO2 removals. Decarbonization must increasingly prove it can be competitive. We have also looked at carbon capture on ships.

Global energy demand: false ceiling?

Can GDP decouple from energy demand? Wealthier countriesโ€™ energy use has historically plateaued after reaching $40k of GDP per capita. Hence could future global energy demand disappoint? This 15-page report argues it is unlikely. Adjust for the energy intensity of manufacturing and imports, and energy use continues rising with incomes.

Japan oil demand: breakdown over time?

Japan's oil demand from 1990 to 2023. Japan's oil demand peaked in 1996 at 5.8Mbpd and has since declined to 3.4Mbpd by 2023.

Japanโ€™s oil demand peaked at 5.8Mbpd in 1996, and has since declined at -2.0% per year to 3.4Mbpd in 2023. To some, this trajectory may be a harbinger of events to come in broader global oil markets? While to others, Japan has unique features that will not generalize?

The 7-page report, linked via the first button below, contains our own observations into Japan’s oil demand, which does not generalize globally.

The data-file, linked via the second button below, contains all of the underlying data, to interrogate Japanese oil demand over time.


Our roadmap to net zero sees global oil demand rising to 105Mbpd in the mid-late 2020s, then declining at a rate of -1%pa to 85Mbpd by 2050. But does Japanโ€™s decline in oil demand, set a precedent for steeper declines ahead?

This 7-page note argues that there are key features of Japan’s energy mix, which mean its history cannot be generalized more broadly: including Japan’s reliance on imports motivating efficiency gains across the board (pages 2-3), declines in manufacturing activity (pages 4-5) and the underlying structure of Japan’s oil market, which has always been weighted to easy-to-substitute categories (pages 6-7).

The underlying data-file breaks down Japan’s oil demand over time, based on data from METI, across Passenger Vehicles, Commercial Vehicles, Motorcycles, Taxis, Buses, Trucking, Rail, Aviation, Shipping, Agriculture, Mining, Construction, Steel, Chemical Feedstock, Chemicals Heat, Materials, Food, Industrial Heat, Industrial Steam, Retail, Hotels, Restaurants, Hospitals, Schools, Waste Collection, Commercial, Power Generation, Residential Heat, Refineries, Lubricants, Asphalts, Petcoke, annually, from 1990 to 2023.

The underlying data-file also breaks down Japan’s oil demand across all of these categories, for different oil products: total oil products, gasoline, distillates, jet fuel and fuel oil.

Further data is available on the TSE site into Japan’s gas and power demand, energy security, population and GDP, and other commodities supply-demand.

Energy security: right to self-determine?

The average major economy produces 70% of its own energy and imports the other 30%. This 12-page note explores energy self-sufficiency by country. We draw three key conclusions: into US isolationism; Europeโ€™s survival; and the pace of EV adoption, both in China and in LNG-importing nations.

Mainspring Energy: linear generator breakthrough?

Linear generator technology can convert any gaseous fuel into electricity, with c45% electrical efficiency, and >80% efficiency in CHP mode. This data-file reviews Mainspring Energy’s patents. We conclude that the company has locked up the IP for piston-seal assemblies in a linear generator with air bearings, but longevity/maintenance could be a key challenge to explore.


EtaGen was founded in 2010 by three Stanford engineers, and rebranded as Mainspring Energy in 2020. Its headquarters are in Menlo Park, California; and the company has c400 employees, having closed a $290M Series E financing in 2022.

Mainspring is commercializing a linear generator, which is low-cost, reliable, flexible and can use any clean fuel (e.g., natural gas, biogas, hydrogen, ammonia), in sizes from 230kW to multiple-MW, >45% electrical efficiency and >80% total thermal efficiency in CHP mode.

In a linear generator, the compression of fuel and air causes a uniform and flameless combustion reaction to occur, releasing the energy from the fuel, but creating no NOx emissions. The energy from combustion pushes a piston through a cylinder (or in Mainspring’s case, two pistons, through two cylinders). Stator magnets in each piston move past coils in each cylinder, inducing a current. An air spring on the other side of the cylinder is thereby compressed, and re-expands to drive the piston back to its starting point.

Illustration of the working principles of a linear generator.

The main advantages are the simplicity, which could in principle translate into lower capex, compared to the blades and precision-engineered compression and turbine stages within a gas turbine.

Higher efficiency can also be unlocked by harnessing the expansion of combustion gases directly, rather than having to convert it into rotary motion, per the loss attributions for conventional thermal generation. On the other hand, maximum efficiency will always be lower for low-temperature combustion, due to the laws of thermodynamics.

From reviewing Mainspring’s patents, we think there are three main challenges for commercializing linear generators. The main challenge is linked to longevity and maintenance.

Mainspring’s patents focus upon piston-seal assemblies, and seem to have locked up the IP for its linear generator designs. This may also be relevant to other companies aiming to commercialize linear generators, such as Hyliion in the vehicle sector.

Energy intensity of AI: chomping at the bit?

Rising energy demands of AI are now the biggest uncertainty in all of global energy. To understand why, this 17-page note is an overview of AI computing from first principles, across transistors, DRAM, GPUs and deep learning. GPU efficiency will inevitably increase, but compute increases faster. AI most likely uses 300-2,500 TWH in 2030, with a base case of 1,000 TWH.

Copyright: Thunder Said Energy, 2019-2025.