Industry Data for the Energy Transition

Commodity prices: metals, materials and chemicals?

Annual commodity prices are tabulated in this database for 70 material commodities, as a useful reference file; covering steel prices, other metal prices, chemicals prices, polymer prices, with data going back to 2012, all compared in $/ton. 2022 was a record year for commodities. We have updated the data-file for 2023 data in March-2024.

Material commodity prices flow into the costs of producing substantively everything consumed by human civilization, and increasingly consumed as part of the energy transition. Hence this database of annual commodity prices is intended as a useful reference file. Note it only covers metals, materials and chemicals. Energy commodities and agricultural commodities are covered in other TSE data-files.

Source and methodology. The underlying source for this commodity price database is the UN’s Comtrade. This useful resource covers trade between all UN member countries, across thousands of categories, in both value terms ($) and mass terms (kg). Dividing values (in $) by masses (in kg) yields an effective price (in $/kg or $/ton). We have then aggregated, cleaned and averaged the data for 70 materials commodities.

The median commodity in the data-file costs $2,500/ton on an unweighted basis. Although this ranges from $20/ton for aggregates to $75M per ton for palladium metal.

2022 was a record year for material commodity prices. The average material commodity priced 25% above its 10-year average and 40 of the 70 commodities in the database made 10-year highs.

Steel prices reached ten-year highs in 2022, averaging $2,000/ton across the different steel grades that are assessed in the data-file. This matters as 2GTpa of steel form one of the most important underpinnings in all global construction. Our steel research is aggregated here.

Base metal prices averaged 40% above their ten-year averages in 2022, as internationally traded prices rose sharply for nickel, rose modestly for aluminium and zinc, and remained high for copper (chart below).

Battery metals and materials prices rose most explosively in 2022, due to bottlenecks in lithium, cobalt, nickel and graphite. This is motivating a shift in battery chemistries, both for vehicles and for energy storage. It also means that the average battery material in our data-file was higher priced than the average Rare Earth metal in the data-file (which is unusual, but not the first time).

Commodity chemicals all rose in 2022 across every category tracked in our chart below. These chemicals matter as intermediates. On average, sodium hydroxide prices reached $665/ton in 2022, sulphuric acid prices reached $140/ton and nitric acid prices reached $440/ton.

500MTpa of global plastics and polymers demand is covered in our plastics demand database. Both finished polymer prices (first chart) and underlying olefins and aromatics (as produced by naphtha crackers, second chart) prices rose sharply in 2022. Our recent research has wondered whether terms of trade are likely to become particularly constructive for polyurethanes.

Silicon prices matter as they feed in to the costs of solar, and traded silicon prices also reached ten year highs in 2022, before correcting sharply in 2023. Silica prices surpassed $70/ton, silicon metal prices reached $4,000/ton and polysilicon prices surpassed $30/kg (charts below).

The full database captures 70 globally traded materials commodities and their annual prices over time in $/ton, year by year, from 2012-2022. These are: Acrylonitrile prices, Adhesives prices, Aggregates prices, Aluminium prices, Ammonia prices, Battery Graphite prices, Benzene prices, Butadiene prices, Carbon Fiber prices, Cement prices, Cobalt prices, Cobalt Oxide prices, Cold Rolled Steel prices, Concrete prices, Copper prices, Copper Wire prices, Cumene prices, Electric Motor and Generator prices, Electrical Transformer prices, Epoxide prices, Ethanol prices, Ethylene prices, Ethylene Oxide prices, EVA prices, Formaldehyde prices, Glass Fiber prices, Gold prices, Graphite Anode prices, Graphite paste prices, HCl prices, HDPE prices, HF prices, Hot Rolled Steel prices, Hydrogen Peroxide prices, Integrated Circuit prices, LDPE prices, LiPF6 prices, Lithium Carbonate prices, Lithium Metal prices, Manganese prices, Manganese Oxide prices, Methanol prices, NaCN prices, Nickel prices, Nitric Acid prices, Paint prices, Palladium prices, PET prices, Phosphoric Acid prices, Platinum prices, Polyethylene prices, Polysilicon prices, Polyurethane prices, Propylene prices, Propylene Oxide prices, PTFE prices, Rare Earth Magnet prices, Scandium & Yttrium prices, Silica prices, Silicon Metal prices, Silver prices, Sodium Hydroxide prices, Stainless Steel prices, Steel Alloy prices, Sulfuric Acid prices, Toluene prices, Tubular Steel prices, Urea prices, Vehicle prices, Xylene prices, Zinc prices.

Oscar Wilde noted that the cynic is the man who knows the price of everything, but the value of nothing. To avoid falling into this trap, we also have economic models for most of the commodities in this commodity price database.

We will continue adding to this commodity price database amidst our ongoing research. You may find our template useful for running Comtrade queries of your own. Or alternatively, if you are a TSE subscription client and we can help you to use this useful resource, then please do email us any time.

Overview of lasers: by type, market size and efficiency?

The global laser market is worth $20bn pa, of which half comprises diode lasers (mainly used for fiber-optic communications) and around 30% is fiber lasers (mainly used for cutting, welding and heat-treating materials). These two laser types dominate because of their high, 30-80% efficiencies. This data-file is a brief overview of lasers.

Ruby lasers were the first laser systems to be demonstrated, going back to Theodore Maiman at Hughes Lab in 1960. Ruby material is irradiated by a short pulse of light from a xenon-filled flashtube, forming a laser system with 3 energy levels. Cr3+ electrons are promoted to E3, which is unstable, falling quickly back to E2, which is meta-stable. Stimulated emission occurs for the energy jump from E2 to E1 at 694.3 nm wavelength.

Other laser types that followed included Ar-Ne, YAG (using yttrium aluminium oxide garnet, doped with neodymium Nd3+) and CO2 (actually an outlier in that this is a molecular laser, whose meta-stable state comes from vibration patterns of the CO2 molecule).

Diode lasers now make up around half of today’s market, however, as they are 30-80% efficient, easy to manufacture and can be integrated with other semiconductors. They are “Fabry-Perot resonators”, comprising PIN junctions (similar to the PN junctions in LEDs and other semiconductors), bounded by a fully reflective mirror on one side and a partially reflective mirror on the other side. Electrons are injected into the N-semiconductor and holes are injected into the P-semiconductor. The intrinsic semiconductor between them minorly delays the recombination so that stimulated emission can occur, and also acts as a channel through which the laser light can oscillate, between the two reflective mirrors, whose interval distance must be a precise multiple of the laser light half-wavelength. Laser pulses are emitted from the non-reflective mirror side, and focused via a lens. Limits of diode lasers are that these devices tend not to exceed 1kW, they need a heat sink due to heat generation, and their collimation is not perfect.

Fiber lasers make up around 30% of today’s laser market, but are somewhat confusingly named. The laser pulses for fiber optic cables tend to come from diode lasers (see previous paragraph). Whereas fiber lasers use an optical fiber as their gain medium, usually by doping the fiber with a Rare Earth element (e.g., erbium, ytterbium, neodymium, dysprosium, praseodymium, thulium, holmium), then applying a fully reflecting Bragg grating to one side and a partially reflective Bragg grating to the other side. The active gain regions can meters-kilometers long, to support 1-15kW typical continuous power outputs (and over 30kW in Yb-doped lasers). Efficiency is 30-50%. Fiber lasers comprise over half of the industrial laser market, of which the largest category, at 40% is for cutting metals. In recent years, China has been driving deflation through fiber lasers costs.

Laser costs are also tabulated in the data-file, particularly for diode lasers (chart below), which in turn feeds into our estimates for the costs of fiber optic cables.

LNG plant compression: gas drives vs electric motors?

Cost build-up of LNG refrigeration via gas drive, CCGT electric drive, and renewable electric drive.

This data-file compares the costs of refrigerant compression systems at LNG plants, across gas turbines, electric motors powered by CCGTs, or electric motors powered by renewable electricity. eLNG costs are higher, in capex terms, but this is balanced by lower opex. Payback times are short. Numbers in $/mcf and $/MTpa can be stress-tested in the data-file.

LNG liquefaction plants chill methane to -160C as refrigerant gases (nitrogen, propane) expand. Subsequently, around 300kWh/ton of useful energy is needed to recompress the refrigerant gases, so they can be recirculated and provide further cooling. Energy economics are built up from first principles in our overview of LNG liquefaction technologies.

Refrigerant compression is traditionally achieved via large centrifugal compressors, whose drive shafts are energized by burning gas in a directly coupled gas turbine. However, eLNG is also accelerating as an alternative, using electric motors instead of gas turbines.

This data-file compares the costs of gas-driven compression versus electric motor compression, either energized via on-site CCGTs, or via transmitting renewable electricity to the LNG plant. The costs depend on gas prices, electricity prices, incremental production, lower maintenance costs and CO2 prices (if applicable).

Capex costs of eLNG systems (i.e., electric motors for LNG refrigerant compression) will generally be up to c20% higher than for gas driven systems. Numbers are built-up from first principles in this data-file, and hinge on our other economic models, e.g., for transmission lines, compressors, electric motors and inverters.

Electric motors energized by CCGTs seem to be the most economical solution in our build-up, and can result in $0.15/mcf lower total costs of delivered LNG, while saving up to 80kTpa of CO2 per MTpa of LNG, due to higher efficiency. Higher up-time for a CCGT versus an aeroderivative gas turbine would also contribute to more LNG revenues.

Fully electric LNG trains, energized by renewable electricity, especially hydro, can eliminate the vast majority of the compression emissions associated with LNG liquefaction. This can also be cost-effective, if low-cost renewable electricity can be brought to the site, and if a c$50/ton CO2 price is applicable or can be passed on to LNG buyers.

Relative advantages of eLNG concepts, i.e., energizing electric motors for the refrigerant compression at LNG plants, were discussed in our recent research note, linked below. Please also see our LNG plant economic model and broader LNG research.

LNG plant compressors: chilling goes electric?

Material and manufacturing costs by region: China vs US vs Europe?

Material and manufacturing costs by region are compared in this data-file for China vs the US vs Europe. Generally, compared with the US, materials costs are 10% lower in China, and 40% higher in Germany, although it depends upon the specific value chain. A dozen different examples are contrasted in this data-file, especially for solar value chains.

To build up the numbers in this data-file, we have stress-tested a dozen of our economic models, using generalized assumptions to represent the US, China and Germany. This includes their energy costs, labor costs and tax regimes, including the EU’s carbon tax.

To make the analysis apples-to-apples, all costs are quoted on a full-cycle basis, with income covering a 10% cost of capital, and excluding subsidies. However, in some industries, China has been accused of selling product below full-cycle cost, helped via state support, to crowd out competition. This is not captured in our calculations.

Underlying materials costs are lowest in China, averaging -10% below the US, but ranging from -40% cheaper (e.g., for glass, silicon) to +30% dearer (contrasting China’s naphtha-cracker ethylene versus US ethane crackers). Lower costs almost all attribute to cheaper labor in China, where all-in employee costs are $15-20k pa versus $80-100k in the West. And manufacturing costs are likely 50-60% lower as well.

Cost comparison for different raw materials produced in China vs the US vs Germany

Materials costs are highest in Germany, averaging 40% above the US and 60% above China, as Europe’s industrial policy chickens have come home to roost. Every single line is higher in Europe. Costs are c10% higher due to industrial electricity prices (20c/kWh vs 6c/kWh), 10% is due to CO2 prices under the EU ETS (which have recently ranged from $60-100/ton), and 10% is due to higher supply-chain and logistics costs.

We can use our material cost indicators in this data-file to ask how much solar costs would re-inflate, if today’s China-dominated supply chain for modules and inverters were reshored to the West, or if trade with China were disrupted due to tariffs or geopolitical reasons.

Cost build-up and comparison for solar power projects in China vs the US vs Germany

Costs of recent solar projects are coming in at $600-700/kW in Europe and Asia, which reflects today’s China-dominated supply chain. Utility-scale solar costs in the US are higher, around $1,200/kW, as the US already has c50% tariffs on Chinese-made solar. So if the economic costs of re-shoring US solar supply chains is c$1,000/kW, tariffs persist, and the IRA offers a direct incentive of $40-70/kW+, then should we be seeing exciting momentum in the US domestic solar supply chain?

It turns out that ramping US solar supply chains is not without challenges. Already in 2025, REC Silicon has said it will shut its PV silicon plant in Moses Lake, Washington, as output was missing purity targets under its supply agreement with QCells. REC’s share price has now fallen 94% since its 2022 peak after the Inflation Reduction Act was signed. Meyer Burger (Swiss small-cap) also canceled plans for a 2GW module plant in Colorado in August-2024. And OCI/CubicPV canceled plans for a 10GW silicon wafer factory in February-2024.

The US solar supply chain includes First Solar (public thin-film solar company), Canadian Solar (building a 5 GW solar cell facility in Indiana) and QCells (developing a $2.5bn solar module plant in Georgia). Japan’s TOYO Solar also announced plans for a 2GW US facility in 2024. More details are in our screen of module makers.

Conversely, if Europe wished to re-shore solar supply chains, or could not for some reason import Chinese modules, this would approximately double European utility-scale solar costs, to $1,200/kW.

Similar cost differences were also found in our recent deep dive into battery value chains, especially for Chinese LFP versus Western LFP and NMC, or for wind turbine blades.

LFP batteries: cathode glow?

Deteriorating trade relations with China would be highly inflationary for Western renewables and electrification initiatives, and thus strangely bullish for other energy sources (eg LNG).

China coal production costs?

China coal production costs are estimated on a full-cycle basis in this data-file, averaging $75/ton across large, listed miners, with assets in Shanxi, Inner Mongolia and Shaanxi. The costs are increasing at $1.3/ton/year, as mines move deeper and into smaller seams. Smaller regional miners have 1.5-2x higher costs again, and will hit LNG price parity around 2030?

This data-file estimates the full-cycle costs of Chinese coal production, by aggregating the public market disclosures of China Shenhua Energy, China Coal Energy and Yanzhou Coal, all three of which are listed in both Hong Kong and Shanghai.

Specifically, to calculate full-cycle costs, we have aggregated unit costs reported by each company. On top of this, we require a 10% ROA on coal mining segment assets, as our way of reflecting capital costs. A 25% income tax rate on this economic return is also added. And we have added in relevant transport and G&A costs. These numbers can be compared and contrasted with our coal mining economic model.

China coal production costs comprise c$20/ton of capital costs, c$10/ton for labor, $10/ton for transportation to Eastern markets, $5-10/ton for each of past capex depreciation, washing/processing at the surface, income taxes, energy/materials; with smaller charges for maintenance, mining rights, G&A and compensation for subsidence, remediation works or other environmental line items. Data are aggregated in the file, with a separate tab for each company.

The average full-cycle cost for these large producers is $75/ton in 2021-23. Costs have generally been rising, at an average implied rate of $1.3/ton/year, as the industry moves into deeper resources and thinner seams. However, we think that the costs of smaller regional miners can be 50-100% higher again, for the marginal cost of Chinese coal.

These numbers are crucial to calibrating our cost curves and inform our 2025-30 global energy supply-demand model, 1700-2050 supply-demand model, China energy model, global electricity supply-demand models, coal supply-demand models, solar supply-demand models and gas supply-demand models.

Duck curves: US power price duckiness over time?

In solar-heavy grids, power prices trough around mid-day, then ramp up rapidly as the sun sets. This price distribution over time is known as the duck curve. US power prices are getting 25-30% more ducky each year, based on some forms of measurement. Power prices are clearly linked to the instantaneous share of wind/solar in grids.

The famous duck curve shows how intra-day power prices are impacted by the rise of solar, rising gently in the morning, troughing in the middle of the day, then rising rapidly in the evenings after the sun has set. Apparently this looks like a duck. But is the duck curve getting more ducky over time, as solar gets built out?

This data-file aims to measure the duckiness of duck curves, over time, across the big five US grid regions: CAISO, ERCOT, MISO, PJM and SPP. On average, over the past 3-years, pricing ramps from c$40/MWH at mid-day to $65/MWH at 6-8pm, partly due to solar generation profiles, and partly due to other demand patterns.

3-year average wholesale marginal price for the Big-Five US grid regions.

The duckiness of the duck curve has risen over time, across these grid regions, as solar scaled up from 3% of US electricity in 2020 to 6% in 2023. In 2021, power pricing at 6-8pm was 30% higher than at 11am-1pm, in 2022 it was 45% higher, in 4Q23 it was +56%, and in 3Q24 it was +110% higher (chart below).

Duckiness of US power prices from 2021 to 3Q 2024. Measured as the increase from noon to evening power prices.

However, there is a vast amount of volatility in the data. Other cuts show a less clear increase in duckiness, as shown below, averaging across our big-five regions.

Wholesale average marginal power prices by quarter for the Big-Five US grid regions.

California makes for the most direct case study of duck curves, as utility-scale solar comprises 25% of its electricity mix in 3Q24, up from 15% in 3Q21. In the past, we have looked at individual nodes in California from CAISO, as compared apples-to-apples in individual months, which does appear to show rising duckiness.

California electricity price change between August 2021 and August 2023 — California IntraDay Wholesale Power Prices in 2023 and in 2021

But again, other cuts show a more volatile pattern for CAISO, with strong seasonal effects. Perhaps duckiness has also been muted by a large battery build-out, doubling every year, with batteries supplying an average of 3GW from 8-9pm in 3Q23 and an average of 6GW from 8-9pm in 3Q24 (chart below).

CAISO TTM grid share by generation source from 3Q 2021 to 3Q 2024

The most significant driver of power prices that we can find in the file is the call on non-wind and non-solar generation. Prices spike when renewables are not generating and markets must be balanced by ramping up gas peakers or disincentivizing demand.

Power prices depending on renewables grid share for ERCOT and CAISO.

As a simplified rule of thumb, average power prices rise (fall) $2-3/MWH for every 5% decrease (increase) in the share of renewables in the grid. In CAISO, when marginal prices fall below $10/MWH it is almost always associated with wind and solar supplying >80% of the grid, and when prices rise above $100/MWH, wind and solar are usually supplying <10%.

We do think power grids are growing more volatile over time. This is yet another tracker, breaking down the hour-by-hour patterns and duckiness.

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.

US power generation under development over time?

An all-time record of 180GW of new power generation is currently under development in the US in 4Q24, enough to expand the US’s 1.3TW power grid by almost 15%. This data-file tracks US power generation under development, as a leading indicator for gas turbine, wind, solar and battery demand. Gas turbines and battery co-deployments are accelerating in 2024, while wind and solar initiations are slowing on grid bottlenecks?

This data-file captures the development pipeline of new US power capacity, based on 860M reports from the EIA, which cover all existing and proposed generating units of >1MW of greater. As a leading indicator for wind, solar, gas turbine and battery demand, we have aggregated the data in these c110 monthly reports, from 2015 to 2024, to track the pipeline over time, and how expectations have progressed.

Over the past decade, an average of 4 GW of new power generation projects have been added to the queue each month. 1 GW of previously proposed projects have been abandoned each month. And another 2.2 GW of projects have been completed each month. Hence, the overall project queue has grown 0.8 GW larger per month, rising from 90 GW in September-2015 to 183 GW in September-2024 (charts above).

New power generation capacity projects initiated in the US from Q3 2015 to Q3 2014.

Fears over power grid bottlenecks and rising interconnection times are strongly supported by the data. 75% of the increase in the overall pipeline size is in projects that have not yet commenced construction and are thus effectively sitting in a queue.

Development times have not changed materially, although they have always been quite variable. In the data-file, we have tracked 1,715 projects from the time they were first proposed, to the time when they were completed. Their average construction time was 0.8 years, with an average delay of 0.4 years versus initial estimates. These are shorter than the development times for other energy infrastructure.

Looking across the life-cycle of projects that entered the 860M reports during the planning stage (rather than later), the average development time was 2.1 years, including 0.9 years of planning, 0.3 years of permitting, 0.8 years of construction, and an average delay of 0.6 years versus initial estimates. Larger projects tend to take longer.

Development times vs planned capacity of gas, solar, and wind power projects in the US

Delays in constructing power generation facilities are also heavily skewed, as 10% of the projects comprise 50% of the delays.

Distribution of delays in start-up times for gas, solar, wind, battery, and other power projects versus their originally planned timelines

In 2024, renewables momentum has slowed, gas has re-accelerated, but grid-scale battery activity is accelerating fastest and now making an all-time peak, based on tracking new projects being added to the EIA’s 860M filings. Numbers are in the data-file for TSE clients.

Gas, solar, wind, and battery power projects initiated in the US from 2016 to 2024. Each data point is for the trailing twelve months

Again this supports the notion that bottlenecked power grids are hindering the ramp of wind and solar, while we specifically see battery co-deployments as a route to expedite bottlenecked projects. The re-acceleration of natural gas projects also supports our outlook on US natural gas and our outlook on gas turbines. We will continue updating this data-file over time.

Solar plus batteries: the case for co-deployment?

US natural gas: the stuff of dreams?

EV incentives: vehicle taxes by country?

Taxes on new ICE vehicle purchases in different countries

Vehicle taxes by country are tabulated in this data-file, based on vehicles’ pre-tax prices, tailpipe emissions, weight, engine size and power. They range from <10% of the cost of the underlying vehicle in the US, through to 150% in Norway, and can also be well above 100% in other Northern European countries such as Netherlands, Denmark and France.

Super-high taxes on ICEs have been successful in promoting EV adoption, especially in Northern Europe, but how palatable is this option more broadly, especially in countries with large domestic auto industries?

In one of the most entertaining energy-themed advertisements of all time, Will Ferrell laments Norway’s lead over the United States in electric vehicle ownership. 90% of Norway’s new vehicle purchases were BEVs/PHEVs in 2023, and electric vehicles make up 26% of the fleet. What is not in the advertisement is the tax policy that has propelled EVs to such high adoption in many Northern European economies.

This data-file quantifies vehicle taxes by country, which turns out to be a complex calculation, with sliding-scale formulae linked to vehicles’ tailpipe CO2 emissions (Norway, Netherlands, France, Denmark, UK, Germany), weight (Norway, France, Australia, Japan), value (all geographies, but especially Denmark) and engine power (Italy, France, Japan, Germany).

ICE vehicle taxes by country are plotted above, for a vehicle with a $25k pre-tax vehicle purchase price, 1.8 ton gross weight, 30mpg fuel economy, and 2.0L engine with 180hp of engine power. You can stress-test all of these variables in the data-file, and the tax consequences flow through the file. Typical vehicle parameters are available here.

In the average country globally, taxes add c35% onto the pre-tax purchase price of an ICE vehicle. However, the range is wide, varying from <10% in many US States, to >100% in France, Denmark, Netherlands and of course Norway, which reaches 150%. Yes, a vehicle with these parameters costs 1.5x more in taxes in Norway than the vehicle itself.

Tax exemptions for electric vehicles are offered in almost all of these countries. Norway, for example, has exempted new vehicles from both VAT and other purchase taxes. In Denmark and the Netherlands, EVs receive large deductions from vehicle purchase taxes. In many countries, EVs also receive direct fiscal incentives.

Decelerating EV sales growth has been a theme that has worried us in our 2024 research. One factor that could re-accelerate EV sales growth is the ratcheting up of taxes on ICE vehicles. But on the other hand, it is interesting to note that the countries that have implemented large vehicle taxes tend not to have a large domestic auto industry. Whereas for obvious reasons, there may be opposition to inflating the costs of new vehicle purchases by 2x from leading vehicle makers in their home markets.

Mapping vehicle value chains?

Electric vehicles: the road to cost parity?

Electric vehicles: saturation point?

Solar+battery co-deployments: output profiles?

Output of a solar+battery co-deployment power plant on a typical summer day.

Solar+battery co-deployments allow a large and volatile solar asset to produce a moderate-sized and non-volatile power output, during c40-50% of all the hours throughout a typical calendar year. This smooth output is easier to integrate with power grids, including with a smaller grid connection. The battery will realistically cycle 100-300 times per year, depending on its size.

The output from a standalone solar installation is notoriously volatile, varying +/- 5% every 5-minutes on average, plus sudden power spikes and drops, and achieving an annual utilization factor of just 20%.

But how does co-deploying solar+batteries lower the volatility? This data-file uses real-world data, from an Australian solar asset, measured at 5-minute intervals, and then applies simple rules about when to flow power into and out of the batteries, to maximize the delivery of 100MW, smooth, non-volatile power.

The solar+battery output also includes synthetic inertia and frequency regulation, which helps rather than hinders overall grid stability.

The title chart above shows how the output profile of our solar+battery system might behave on a summer’s day, with the net asset providing 100MW to the grid for 24-hours. Excess solar is shunted to the battery throughout the day. Then the battery is gradually discharged to zero after sunset. This model works well in the summer.

However solar generation is highly seasonal, and on a winter’s day, this exact same battery does help to keep output stable at 100MW, but it only achieves 20% of a full charge-discharge cycle, as there is simply not enough solar generation to fill the battery. The bigger the battery, the less likely it gets full in the summer, and the less utilized it is in the winter.

Output of a solar+battery co-deployment power plant on a typical winter day.

This can be stress-tested in the data-file. We can also calculate the number of charge-discharge cycles that different batteries achieve, if they are charged exclusively with solar generation. Some decision-makers assume daily charging-discharging when modeling the economics of batteries, but this is shown to be much too optimistic (below).

Number of charge-discharge cycles achieved by a battery per year depending on the ration of battery capacity to co-deployed solar capacity

Overall, remarkably, solar+battery co-deployment model means that a 275MW solar installation + a 275MW battery can dispatch 95% of its generated output through a mere 100MW grid connection. This is why co-deploying renewables+batteries can help to surmount power grid bottlenecks. And in turn, this is why we think battery co-deployment is accelerating.

How much solar power is dispatched (ie utilized) for a 275MW solar project depending on the size of its grid connection and capacity of co-deployed batteries.

If a battery is run purely for solar smoothing, with 1MW of battery capacity per MW of solar, then the battery will tend to achieve 180 charge-discharge cycles throughout the year, and it will allow a 275MW solar asset to output precisely 100MW to the grid in c50% of the time throughout an entire year (but still producing no power about 40% of the time).

The production profiles vary month by month. The results vary with battery sizing and charging-discharging rules. These sizings and rules can be stress-tested in the data-file, to assess how different-sized batteries result in different dispatch rates and charge-discharge cycle counts.

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