Energy Transition Data

Energy economics: an overview?

This data-file provides an overview of energy economics, across 175 different economic models constructed by Thunder Said Energy, in order to put numbers in context. This helps to compare marginal costs, capex costs, energy intensity, interest rate sensitivity, and other key parameters that matter in the energy transition. Our top five facts follow below.

This data-file model provides summary economic ratios from our different economic models across conventional fuels, conventional power, renewables, lower-carbon fuels, manufacturing processes, infrastructure, transportation and nature-based solutions.

For example, EBIT margins range from 3-70%, cash margins range from 4-80% and net margins range from 2-50%, hence you can use the data-file to ballpark what constitutes a “good” margin, sub-sector by sub-sector; and to screen different industries, according to the capital intensity, opex costs and resultant profitability (chart below).

Capital intensity ranges from $300-9,000kWe, $5-7,500/Tpa and $4-125M/kboed. So if you are trying to ballpark a cost estimate you can compare it with the estimated costs of other processes. The median average industry has a capex cost of $750/Tpa (chart below).

Capital intensity of different energy sources also varies by an order of magnitude (chart below). Each $1 dollar that is disinvested from new hydrocarbon capex ideally needs to be replaced by $25 invested in wind and solar, in order to add the same amount of primary energy to the global energy system (chart below, note here).

Economies of scale are visible in the data-file, across our models of Air Separation, Cables, Comminution, Compressors, Electric Motors, Electrowinning, Fans, Flotation, Gas Dehydration, Harmonic Filters, Heat Exchangers, Inverters, Motor Drivers, Pumps, Rankine Engines, Tanks and Turbines. Generally, making these units 10x larger reduces their unit costs by around 45%.

Cost reduction from scale for different energy technologies.

Interest rate sensitivity is visible in our overview of energy economics. Each 1% increase in capital costs re-inflates new energies 10-20%, infrastructure 2-20%, materials 2-6%, and conventional energy 2-5% (chart below, note here).

Marginal cost inflation per 1% WACC increase for different energy technologies, materials, and infrastructure projects.

The energy intensity of materials is visible across our models of Acetylene, Aluminium, Ammonia, Carbon Fiber, Cement, Copper, Cyanides, Desalination, Glass, H2O2, Hydrogen, Industrial Gases, Lithium Batteries, Methanol, NaOH/Cl2, Nitric Acid, Paper, Plastics, Silicon, Silver, Steel, Wood Products. As a rule of thumb, energy is 50% of the cash cost of typical materials.

Renewables stand out. Despite high capital intensity (35% of revenues, 2x the average), once constructed, they also have the highest cash margins (75%, also 2x the average). The rise of wind, solar and electrification make capex costs and capital costs increasingly important.

The full data are available in the data-file below. However, please be aware that this is simply a compilation of headline figures across our library of 175 economic models. Access to all of the underlying models is covered by a Thunder Said Energy subscription.

Hydrogen: overview and conclusions?

The best opportunities for hydrogen in the energy transition will be to decarbonize gas at source via blue and turquoise hydrogen, displacing ‘black hydrogen’ that currently comes from coal, and to produce small-scale feedstock on site via electrolysis for select industries. Some see green hydrogen becoming widespread in the future energy system. We think there may be options elsewhere, to drive more decarbonization, with lower costs, lower losses and higher practicality.

(1) Green hydrogen economy? Our main question mark is over “economy”. Costs are modeled at $7/kg, equivalent to $70/mcf natural gas, after generating renewable electricity, electrolysing water into hydrogen and storing the hydrogen. Levelized costs of electricity then reach 60-80c/kWh, for generating clean electricity in a fuel cell power plant, yielding a CO2 abatement cost of $600-1,200/ton (note here). We think costs matter in the energy transition and the entire world can be decarbonized via other means, for an average cost of $40/ton in the TSE roadmap to net zero.

(2) Fuels derived from green hydrogen are by definition going to be more expensive than the hydrogen itself. We have evaluated electro-fuels, green methanol, sustainable aviation fuels, hydrogen trucks, again finding CO2 abatement costs above $1,000/ton. Again, we think transportation can be decarbonized cost-effectively via other means.

(3) How much can capex costs come down? There is an aspiration for electrolyser costs (presently around $1,000/kW on a full, installed basis) to deflate by over 75%. However, we have reviewed electrolyser costs line by line and wonder whether 15-25% deflation is more realistic (note here). Alkaline electrolysers vs PEMs are contrasted here. We have recently screened NEL’s patents to explore future cost deflation in electrolysers.

(4) Efficiency: the second law of thermodynamics. The absolute magic of renewables and electrification is their thermodynamics. These technologies can be 85-95% efficient end-to-end, precisely controlled, and ultra-powerful. A world-changing improvement on heat engines and an energy mega-trend for the 21st century. However, the thermodynamics of hydrogen depart from the trend, converting high-quality electricity back into a fuel. The maximum theoretical efficiency of water electrolysis is 83% (entropy increases). Real world electrolysers will be c65% efficient. End-to-end hydrogen value chains will be c30-50% efficient. We want to decarbonize the global energy system. It therefore seems strange to take 100MWH of usable, high-grade, low-carbon electricity, and convert it into 40MWH of hydrogen energy, when you could have displaced 100MWH of high-carbon electricity directly (e.g., from coal). And all the more so, amidst painful energy shortages.

(5) Backing up renewables? It is often argued that renewables will eventually become so abundant, especially during windy/sunny moments, that the inputs to hydrogen electrolysers will become free. We think this is a fantasy. Instead, industrial facilities and consumers will demand shift. Conversely, we are not even sure an electrolyser can run off of a volatile renewables input feed without incurring 5-10% pa degradation, or worse (if you read one TSE note on green hydrogen, we recommend this one).

(6) Operations, transport, logistics all feel strangely challenging. Our studies of patents suggest that electrolysers and fuel cells can be the Goldilocks of energy equipment. Past installations have declined at over 5% per year. Due to its small molecular size, 35-75% of hydrogen produced in today’s reformers can be lost. Some vehicles seek to store hydrogen fuel at 10,000 psi, which is 1.5x the pressure of hydraulic fracturing. Even in the space industry, rocket makers have been de-prioritizing hydrogen in favor of LNG (!) because of logistical issues. The costs of hydrogen transport will be 2-10x higher than comparable gas value chains, while up to 50% of the embedded energy may be lost in transportation: our overview into hydrogen transport is here, covering cryogenic trucks, hydrogen pipelines, pipeline blending, ammonia and toluene. Is a hydrogen truck really comparable with a diesel truck? (note here, models here). Finally, the gas industry is bending over backwards to stem methane leaks, due to methane’s GWP of 25x CO2, but hydrogen itself may have a GWP as high as 13x CO2.

(7) Will policy help? We are not sure. We are tempted to draw analogies to the Synthetic Fuels Corporation, bequeathed $88bn of US government money in 1980 amidst the oil shocks, which in today’s money is similar to the $325bn Inflation Reduction Act. It completely missed its targets of unleashing 2Mbpd of synfuels by 1992, due to challenging economics, thermodynamics, technical issues, logistical issues. What evidence can we find that green hydrogen will prove different to this historical case study?

(8) Niche applications can however be very interesting, where clean hydrogen is used as an industrial feedstock. An overview of today’s 110MTpa hydrogen market is here and underlying data are here. At large scale, we are currently most excited by using clean hydrogen in ammonia value chains and steel value chains, as the technology is fully mature and looking highly economical. It is also booming in the US. Elsewhere, an excellent large-scale application is to displace black hydrogen (made from coal), which is 20% of today’s hydrogen market and has a staggering CO2 intensity of 25 tons/ton. At smaller scale, there is also a weird and wonderful industrial landscape, using hydrogen to make products such as margarine or automotive glass. Putting an electrolyser on site beats shipping in hydrogen via cryogenic trucks. But these are also quite niche applications.

(9) Blue hydrogen is the most economical, low-carbon hydrogen concept we have found. Effectively this is decarbonizing natural gas at source, by reforming the methane molecule into H2 and CO2, the latter of which is sent directly for CCS. Our best overview of the topic is linked here. There are still c15% energy penalties. Costs are $1-1.5/kg in our models, to eliminate c90% of natural gas CO2.

(10) Turquoise hydrogen is also among the more interesting concepts, pyrolysing the methane molecule at 600-1,200◦C into H2 and carbon black. Our base case cost is $2/kg, with a $500/kg price for carbon black. But if you can realize $1,000/kg for the carbon black, you could give the hydrogen away for free. We have screened patents from Monolith and expect others to come to market with technologies and projects.

Around 40 reports and data-files into hydrogen have led us to these conclusions above; listed in chronological order on our hydrogen category page. The best way to access our PDF reports and data-files is through a subscription to TSE research.

LNG: top conclusions in the energy transition?

Thunder Said Energy is a research firm focused on economic opportunities that drive the energy transition. Our top ten conclusions into LNG are summarized below, looking across all of our research.

(1) LNG markets treble in our energy transition roadmap, rising from 400MTpa today to 1,100MTpa by 2050, for a c4% CAGR. The main reason is to displace coal, which is 2x more CO2 intensive. This LNG growth rate is 1.5x faster than total global natural gas supply growth, which “merely doubles” from 400bcfd to 800bcfd, for a 2.5% CAGR. The world needs $20bn of new liquefaction capex per year. Our LNG outlook through 2050 is modeled here.

(2) Marginal cost is $10/mcf as a rule-of-thumb for the 2020s. This is summing up the economics across the entire value chain for gas production, gas processing, pipeline transportation, LNG liquefaction, LNG shipping and LNG regasification. The best projects work at $7/mcf. But prices will run well above marginal cost amidst under-supply.

(3) Under-supply in 2023-28 in our supply model augurs for $15-40/mcf spot global LNG prices. After adding +20MTpa of new LNG supplies each year from 2015 to 2022, we think the world will be lucky to add +10MTpa in 2023 and 2024. There is always a further risk of supply disruptions. Meanwhile, Europe’s 15bcfd of Russian gas imports, volumetrically equivalent to 110MTpa of LNG, are shifting. The best note covering our gas outlook is linked here and our European gas models are linked here.

(4) The key challenge is CO2. Liquefying natural gas at -160C requires 300-400kWh/ton of energy, depending on the LNG plant design. This results in 3-4 kg/mcf of Scope 1+2 CO2. Across the value chain, LNG will have 7-10kg/mcf of Scope 1+2 CO2. Adding the Scope 3 from combustion, we reach total CO2 intensity of 60-65kg/mcf. Coal is 130kg/mcfe. Yet it feels like we could die of energy shortages before gas critics listen to “relative CO2” reasoning and countenance long-term LNG contracts.

(5) Rising to the challenge. The LNG industry can satisfy its skeptics. This is earnestly happening. It includes measuring CO2 in LNG supply chains. Then offsetting it via nature-based CO2 removals. Or capturing CO2 from combustion, then sharing regas terminal infrastructure to liquefy it, and ship it away for disposal. We have written a full note on back-carrying CO2 here. CO2 abatement costs range from $50-125/ton, or $3.0-7.5/mcfe. This scores well on our cost curves.

(6) 2020s supply growth will be dominated by the US, which is particularly well placed to assuage gas shortages in Europe. US LNG can treble from 70MTpa in 2021 to 200MTpa by 2030. It requires an extra 17bcfd of gas (c18% total US gas supply growth), which in turn pulls on E&P activity in the Haynesville, Permian and Marcellus.

(7) Longer term supply growth will be dominated by the Middle East, which is particularly well placed to phase out China’s coal. These numbers are mind-blowing. As an idea, if China directly substituted all 4GTpa of its coal (10GTpa of CO2 emissions!), this would require 1,600 MTpa of LNG, i.e., 4x more than today’s entire global LNG market. If you read one note, to understand this topic, we would recommend this one.

(8) Smaller-scale LNG and transport upside? We have reviewed opportunities in LNG in transport, smaller-scale LNG, LNG-fueled trucks, LNG-fueled ships, eliminating methane slip, LNG fuelling stations, small fixed LNG plants, floating LNG plants. There are some interesting concepts, especially for specific applications. But we have not materially de-risked smaller-scale LNG upside in our numbers yet.

(9) Cyclical industries reward counter-cyclical behaviours, and LNG is deeply cyclical. The title chart above shows this nicely, with spurts of growth, punctuated by plateaus, once per decade. It always feels uncomfortable to sanction projects when others are not. But our view is that bravery gets rewarded. “If you build it, the demand will come”.

(10) Companies. Incumbents benefit most from under-supply in the 2020s. Upcoming projects and their sponsors are summarized in our LNG supply model. We have also screened LNG shipping companies. But the question that fascinates us most is whether upcoming project sponsors can avoid the cost inflation that marred the past cycle, with some interesting evidence from patents in our note here.

Around 45 reports and data-files into LNG have led us to these conclusions above; listed in chronological order on our LNG category page. The best way to access our PDF reports and data-files is through a subscription to TSE research.

TSE Patent Assessments: a summary?

New technologies for the energy transition range across renewables, next-gen nuclear (fission and fusion), next-gen materials, EV charging, battery designs, CCS technologies, electronics, recycling, vehicles, hydrogen technologies and advanced bio-fuels. But which companies and technologies can we de-risk?

One way to appraise new technologies for the energy transition is to lock yourself in a room with a stack of patents from publicly available patent databases, read the patents, and then score them all on an apples-to-apples framework.

Our technology assessment framework is derived from 15-years experience evaluating energy technologies, from the best of the best world-changing technologies, to companies that ultimately turned out to have over-promised. The framework includes five areas:

(1) Specific problems. We find it easier to de-risk patents that pinpoint specific problems that have hampered others, and set about to solve these problems.

(2) Specific solutions. We find it easier to de-risk patents that pose specific solutions, whereas it is harder to de-risk technologies that are more vague.

(3) Intelligibility. We find it easier to de-risk patents that explain why their inventions work, often including empirical data and underlying scientific theory.

(4) Focused. We find it easier to de-risk patents that all point towards commercializing a common invention, and different aspects of that invention. Conversely, patenting 10 totally different solutions might suggest that a company has not yet honed in upon a final product.

(5) Manufacturing details. We find it easier to de-risk patents that explain how they plan to manufacture the inventions in question. Sometimes, very specific details can be given here. Otherwise, it may suggest the invention is still at the ‘science stage’.

The purpose of this data-file is to aggregate all of our patent assessments in a single reference file, so different companies’ scores can be compared and contrasted. The average score in our patent assessment framework is 3.5 out of 5.0, although there is wide variability in each category.

In each case, we have tabulated the scores we ascribed each company on our five different screening criteria, metrics on the companies’ size and technical readiness and a short descripton of our conclusion. You can also view all of our individual patent assessments chronologically.

CO2 intensity of materials: an overview?

This data-file tabulates the energy intensity and CO2 intensity of materials, in tons/ton of CO2, kWh/ton of electricity and kWh/ton of total energy use. The build-ups are based on 160 economic models that we have constructed, and the data-file is intended as a helpful summary reference. Our key conclusions on the CO2 intensity of materials are below.

Human civilization produces over 60 bn tons per year of ‘stuff’ across 40 different material categories, accounting for 40% of all global energy use and 35% of all global emissions.

Rules of thumb. Producing the average material in our data-file consumes 5,000 kWh/ton of primary energy and emits 2 tons/ton of CO2.

Energy breakdowns. As another rule of thumb, 30% of the energy inputs needed to make a typical material are electricity, 25% are heat and 45% are other input materials.

Ranges. All of these numbers can vary enormously (chart below). Energy intensity of producing materials ranges from 300 kWh/ton (bottom decile) to 150,000 kWh/ton (upper decile).

The average thermodynamic efficiency of producing these industrial materials is quantified at c20%, with an interquartile range from 5% to 50%. This is shown in the chart below and discussed in more detail here.

CO2 intensity of producing different materials also ranges from 0.5 tons/ton (bottom decile) to 140 tons/ton (upper decile).

Strictly, many of the largest contributors to global CO2 emissions, such as steel and cement, are not ‘carbon intensive’ (i.e., emissions are <2 tons/ton), they are simply produced in very large volumes.

Ironically, while we want to achieve an energy transition, it does require ramping up production of materials value chains that truly are CO2 intensive (i.e., emissions are above 20 tons/ton or even 100 tons/ton). This includes PV silicon and silver for solar panels; carbon fiber and rare earths for wind turbines; and lithium and SiC MOSFETs for electric vehicles. Ultimately these value chains also need to decarbonize in some non-inflationary way, which is a focus in our research.

Scope 4 CO2. Another complexity is that everything has a counterfactual. SiC MOFSETs might be energy intensive to produce but they earn their keep in long-term efficiency savings. Hence we recommend that the best way to evaluate total CO2 intensity is on a Scope 1-4 basis (note here).

Simplifications. Please note that in order to make this file remotely useful, we are guilty of simplifying and averaging quite complex and broad-ranging industries. More detail is available on different oil value chains (including oil sands and Permian shale in detail), gas value chains, coal grades, industrial boilers and burners by industry, construction materials and different plastics.

CO2 screening. In some industries, we have been able to aggregate CO2 curves, plotting the different CO2 intensities or energy intensities of different companies. The best example is looking at acreage position by position in the US oil and gas industry, refiners, gas pipelines, gas gathering, gas distribution, ethanol plants.

Other data-files on our website have aimed to tabulate the CO2 intensity of other value chains, but due to quirks of those value chains, we cannot plot the data in kWh/ton or CO2/ton. This includes the CO2 of different forms of transportation, digital processes, or hydrogen.

Agricultural commodities are also not captured in the data-file. We have estimated separately the CO2 intensity of different wood fuels, crop production, how it varies with fertilizer application, palm oil. All of our biofuels research is here.

Energy transition companies?

This database contains a record of every company that has ever been mentioned across Thunder Said Energy’s energy transition research, as a useful reference for TSE’s clients. The database summarizes over 3,000 mentions of 1,400 energy transition companies, their size, focus and a summary of our key conclusions, plus links to further research.

Our research library has become quite large, with over 1,300 research notes, data-files and models in the TSE research portal, since we started Thunder Said Energy in 2019. Hence the purpose of this data-file, which is only available to TSE’s full subscription clients, is to summarize all of the mentions of all of the companies, across all of our work.

For example, if a decision-maker is looking for information about ABC-Industries, and its linkage with energy transition, then a summary of key observations about ABC-Industries will be noted on the LongList tab, and all of the underlying mentions of ABC-Industries across different research notes can be filtered on the ‘Mentions’ tab, including links. Our methodology is described in the recent research note here.

Having a long list of energy transition companies, in a single database, also enables some interesting analytics, into the Very Hungry Caterpillar of companies in the world’s fast-evolving global energy and industrial landscape, amidst the transition to net zero.

The geographies that are most represented in our database of energy transition companies include the US (over 500 companies, 38% of the companies, 36% of the mentions), Europe (420 companies, 30%, 36%), China (115, 8%, 8%), Canada (95, 7%, 7%), Japan (70, 5%, 5%), Australia (36, 3%, 2%), Korea (35, 3%, 2%). And counting.

Zooming in a little further, there are 250 companies that have come up repeatedly in TSE research, or where we have conducted more in-depth work, across 8 sectors and 50 sub-sectors. 50 were CleanTech companies, of which 75% tended to private, and the remaining 25% were small-cap or mid-cap companies (chart below).

Other segments. 70 are capital goods companies, 30 are materials companies, and other heavily discussed industries in our research are energy, mining and semiconductors, ranging from small-privates to mega-cap giants.

Zooming in even further, there are 50 companies that have come up at least 5 times in TSE’s thematic research, which is focused on opportunities, themes and bottlenecks in the world’s transition towards net zero. These warrant a closer look.

For example, In 2021-22, we became obsessed with the idea that power electronic switchgear would increasingly be needed to help electricity scale up from 40% to 60% of the world’s energy system by 2050, save energy – from variable frequency drives to power factor management – and to accommodate more volatility in renewable-heavy grids. Thus the company we wrote most about in 2021-22 was Eaton. Which subsequently doubled.

Hence we have started a new quarterly series of research reports and updates to this database, simply noting the companies that have featured most prevalently in our research over the trailing several months, and since the inception of TSE, as a useful summary for decision-makers who have not necessarily been able to read 100% of our output, and may wish to dig deeper into these companies as part of their own processes. The latest instalment covers our energy transition conclusions in 1Q24.

Energy transition: key conclusions from 1Q24?

The data-file is exclusively available to TSE subscription clients. Any purchases of the data-file will be automatically converted into a TSE full subscription. And we will continue updating the database over time.

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. We fear chronic under-supply if the world decarbonizes, rising to 5% shortages in 2030. Another scenario is that emerging world countries bridge the gap by ramping coal. Numbers can be stress-tested in the model.

Useful global energy demand grew at a CAGR of +2.5% per year since 1990, and +3.0% per year since 2000. Demand would ‘want’ to grow by +2% per year through 2030, due to rising populations and rising living standards (model here). We have pencilled in +1.75% pa growth to this model to be conservative.

Combustion energy is seen flat-lining in our net zero scenario. This includes global coal use peaking at 8.4GTpa in 2024 then gently easing to 2010 levels by 2030 (model here). It includes oil demand, rising to 102Mbpd in 2024 (data here), then plateauing (model here) as OPEC and US shale (model here) offset the decline rate impacts of conventional under-investment. It includes risked LNG supplies rising +70% from 400MTpa in 2022 to almost 700MTpa by 2030 (model here). While our roadmap to net zero would need to see global gas growing at +2.5% per year through 2050 (model here), this data-file has pencilled in flat production in 2022->30, as we think that latter scenario currently looks more likely to transpire.

Renewable energy is exploding. Our model of wind and solar capacity additions is linked here and discussed here. In our roadmap to net zero, solar more than doubles from c220GW of new adds in 2022 to 500GW by 2030, while wind rises from c100GW of new adds in 2022 to 150GW by 2030.

Other variables in the model include rising energy efficiency (note here), the need for a nuclear renaissance (note here) and other variables that can be flexed.

What is wrong with this balance is that it does not balance. The assumptions pencilled into the model see an under-supply of global energy of about 3% in 2025, rising to 5% in 2030. I.e., by 2030, the world will be “half a Europe” short of energy. The first law of thermodynamics dictates that energy demand cannot exceed supplies. So what would it take to restore the balance? Well, pick your poison…

(1) Slower demand growth could re-balance the model. Very high energy prices might mute demand growth to only +1.25% per year, although this would be the slowest pace of demand growth since the Great Depression, lower even than during the oil shocks (useful data here). Unfortunately, our view is that pricing people out of the global energy system in this way is in itself an ESG catastrophe.

(2) Ramping renewables faster could re-balance the model, although it would require an average of 1 TW pa of wind and solar capacity additions each year in 2024-30, and over 2 TW pa of wind and solar additions by 2030 itself, which is 3x higher than in our roadmap to net zero (discussion here). For perspective, this +2TWpa solution requires primary energy investment to quadruple from $1trn pa to at least $4trn pa, which all needs to be financed in a world of rising rates. It means that global wind and solar projects will consume over 200MTpa of steel, which is 2x total US steel production, and yet steel would not even qualify as a ‘top ten’ bottleneck, in our wind bill of materials or solar bill of materials. This scenario also requires a 3x faster expansion of power grids and power electronics than our base case estimates (see the links). Is any of this remotely possible?

(3) Continue ramping coal? The main source of global energy demand growth is the emerging world. The emerging world is more likely to favor cheap, dirty coal. Or worse, deforestation for firewood. Thus another way to ameliorate under-supply in our global energy supply-demand balance is if global coal continues growing, reaching a new peak of 9GTpa in 2030. Unfortunately, this scenario also sees global CO2 hitting a new peak of 54GTpa in 2030.

(4) Pragmatic gas? Another means of re-balancing the global energy system is if global gas production rises at 2.5% per year, which is the number required, and that is possible, on the TSE roadmap to net zero (model here). This scenario does see global CO2 falling by 2030. The main problem here is that pragmatic natural gas investment has become stranded in no man’s land, within a Manichean duality of fantasies and crises.

(5) Some combination? The world is complex. It is unlikely that a single lever will be pulled to resolve under-supply in our global energy supply-demand balance. In 2023, we think economic weakness will mask energy under-supply, mute energy prices, and lure many decision makers into looking at spot pricing and thinking “everything is fine”. Please download the model to stress-test the numbers, and different re-balancing solutions…

Market concentration by industry in the energy transition?

What is the market concentration by industry in energy, mining, materials, semiconductors, capital goods and other sectors that matter in the energy transition? The top five firms tend to control 45% of their respective markets, yielding a ‘Herfindahl Hirschman Index’ (HHI) of 700.

This data-file compiles market concentration data across 30 company screens that we have built to-date, across our energy transition research. Specifically, we know the market share of different companies based on these screens.

A useful rule of thumb across the data-set is that the top five firms tend to control 45% of their respective markets, ranging from 20% in the least concentrated industries to 100% in the most concentrated.

The average ‘Herfindahl Hirschman Index’ (HHI) of energy, materials and manufacturing sectors is 700, varying from 200 in the least concentrated industries to 4,000 in the most concentrated ones.

Does market concentration determine profitability? 50% correlations are found between concentration and operating margins over the cycle within these industries.

Energy sectors covered in the database of market concentration by industry include global LNG, US E&P, US refining, Western coal, LNG shipping. Mining sectors covered include aluminium, copper, cobalt, lithium, nickel, uranium, silica and silver.

Materials and manufacturing sectors covered in the data-file include ASUs, autos, battery binders, carbon fiber, gas turbines, glass fiber, hydrogen, methanol, mining equipment, polyurethanes, vacuum pumps, VFDs, wind turbines, and different grades of semiconductors.

Energy and mining are less concentrated than materials, capital goods and semi-conductors, in-line with the idea they are ‘commodities’.

The data-file also covers market size, which itself correlates both with market concentrations and profitability structures. Although we also maintain a larger database for market sizing in the energy transition.

Market concentration matters for decision makers in the energy transition. Hence we have written a research report spelling out seven useful rules of thumb that are based on this data-file. We will continue expanding the data-file over time for TSE clients.

Nature-based CO2 removals: a summary?

This data-file is an overview of nature-based CO2 removal projects that we have been supporting at Thunder Said Energy. Our research ‘scores’ different nature-based projects on a 100-point scale, using criteria to check whether they are real, incremental, measurable, permanent and bio-diverse. The average project supported so far scores 70/100 and sells CO2 offsets at $5-50/ton.

In 2022, we spent $8,000 to support five projects, which have most likely ‘credited’ 480 tons of CO2, for an average cost of $16/ton. Projects span across Costa Rica, Nicaragua, Kenya, Uganda, Indonesia and Madagascar.

The average nature-based reforestation initiative that we supported in 2022 scored 70/100 on our framework for assessing nature-based CO2 removal projects, and was priced at $17/ton of CO2.

Two of the projects scored over 80/100. Whereas three of the projects were given lower scores, due to question marks around whether they were fully incremental, fully measurable, or fully bio-diverse.

Overall we were least concerned about whether the projects were real, as most of them were issuing CO2 offsets that had been certified by Verra or Gold Standard, independently audited and with detailed documentation.

Overall we were most concerned about whether the projects were permanent, in turn a good reason to consider complementary solutions such as CCS and DAC projects?

Statistical distributions are also explored in this data-file, as there are clearly going to be ‘uncertainties’ in natural remediation projects: both implementing the projects over 40-year timeframes and quantifying the CO2 benefits.

The statistical distributions of nature-based CO2 removals are not normally distributed. We estimate our own probability distributions in the data-file. More on CO2 measurement in our allometry research.

A Monte Carlo approach can be used to quantify nature-based CO2 removals across a portfolio. Overall, we are 75% confident that the projects we supported in 2022 have offset over 400 tons of CO2, and 90% confident they have offset over 300 tons of CO2.

You can download this data-file for an overview of nature-based CO2 removal projects we have supported to-date. Or see our nature-based CO2 removals category for full details on the underlying projects.

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.

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