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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.

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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.

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.

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).

The volatility of solar generation is evaluated in this case study, by tracking the output from a 275MW solar project, at 5-minute intervals, throughout an entire calendar year. Output is -65% lower in winter than summer, varies +/-10% each day, and +/- 5% every 5-minutes, including steep power drops that in turn require back-ups.

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Darlington Point is a 333MW-dc and 275MW-ac PV solar facility, in New South Wales, Australia, equidistant between Sydney and Adelaide, 500km inland, at -35ºS latitude. As a case study for large-scale solar generation, we have evaluated its output, every 5-minutes, over the course of 2023 (105,000 data-points!).

Darlington Point ran at a 23% average load factor in 2023, generating 545 GWH of electricity. However, the data-file illustrates four types of solar volatility.

We see the volatility of solar generation most fairly by looking at the load profile in the median day of each month across the year. In other words, output was higher than shown in the median day, across 50% of the days in the month, and lower across another 50% of the days.

Seasonal volatility is extreme. Darlington Point achieved a very high load factor of 35% during the peak of summer, from December to February, but just 12% load factor in June, which means winter output was 65% lower than summer output. Backstopping seasonal volatility is challenging for batteries.

Daily volatility averaged +/- 10%. In other words, output on any typical day of solar generation was likely to be +/- 10% higher or lower than the previous day, due to changes in weather.

Intra-day volatility sees output ramping in the morning, plateauing in the afternoon, then declining in the evening. The intra-day pattern varies month-by-month. February was the ‘best month’ as most days were sunny. Often generation declined in the afternoon, which we think is due to convective cloud formation.

Minute-by-minute volatility averages +/- 5% every 5-minutes. However, there is a sharp skew in the data, as output is consistently zero at night, many days contained stable generation for long periods, and then have sharp power drops due to cloud cover. On some days, output varies +/- 10% every 5-minutes. This is another reason solar requires back-ups.

Data in the file are from Australia’s Energy Market Operator (AEMO). The statistical analysis and collation into Excel are our own. Flipping through the tabs of the data-file is a nice way to visualize volatility.

Conclusions are similar to other data-files we have compiled into solar volatility. We see increasing value in backstopping volatility across global energy systems.

Solar insolation varies from 600-2,500 kWh/m2/year at different locations on Earth, depending on their latitude, altitude, cloudiness, panel tilt and azimuth. This means the economics of solar can also vary by a factor of 4x. Seasonality is a key challenge at higher latitudes. Active strategies are emerging for orienting solar modules.

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1,353 W/m2 of solar energy arrives at the top of the Earth’s atmosphere, based on the Planck Equation, equivalent to almost 12,000 kWh/m2/year. Amazingly, solar is changing the world, even though only c2-3% of this energy is ultimately getting harnessed today.

((The location of the losses in the chart above is also a reason for exploring solar in space, then beaming the power back to Earth)).

50% of all solar energy is inaccessible due to night time (chart below). Another 20-40% is inaccessible as it is absorbed by the atmosphere and clouds (depending on location). And of the insolation that does reach a solar module, only c20-25% is currently converted into useful electricity, in todays best HJT modules.

Calculating the insolation ultimately available for solar modules depends on the mass of atmosphere that is traversed by incoming sunshine, which varies hour-by-hour, with the elevation of the sun in the sky (i.e., vertical height) and its azimuth (i.e., compass point bearing).

Calculating these numbers is quite complex, because the Earth is 23º declinated on its axis. Hence the sun’s elevation and azimuth vary hour-by-hour, day-by-day and by location. Nevertheless, the charts below plot elevation and azimuth at a 45-degree latitude, based on 8,760 calculations throughout the year (24 hours per day x 365 days). The latitude can be varied in the data-file, which also contains hour-by-hour granularity.

Insolation at ground level can thus be calculated, based on the mass of air that has been traversed (chart below left). However, fixed solar modules are not always pointed directly at the sun. This can sacrifice 30-60% of the maximum available insolation, simply due to misalignment (chart below right), which is also calculated hour by hour in the data-file.

For fixed modules, losses can be minimized by matching the tilt of the panels to the latitude at which they are situated (chart below). The losses can be reduced even further with solar trackers, which rotate the panels to follow the sun, although this does also add cost.

It is usually best to orient solar modules directly South (in the North Hemisphere). But efficiency may be sacrificed for economics! West-facing panels generate one-third less energy than South-facing ones. But the generation profile is 2-4 hours later, to smooth out the duck curve.

Insolation available to solar modules can realistically vary from 700 – 2,400 kWh/m2/year, depending on latitude and cloudiness. These numbers can be stress-tested in the data-file.

Depending on latitude, generation will also be 0-80% lower in the winter versus the summer. This is visible in the charts above, as high latitudes have short days in the winter, while even when the sun is up, it is only sitting at a low angle in the sky. This seasonality is extremely challenging to back up economically using batteries.

The full data-file allows you to calculate solar insolation, and resultant solar generation, hour-by-hour and then on a fully annualized basis; by stress-testing latitude, elevation, module tilt, module azimuth, cloud cover, tracking efficiency and module efficiency. This is helpful for informing our solar economic models. The numbers match our findings from assessing real-world solar volatility. A fantastic resource that helped us with the equations is pveducation.org.