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Building automation: energy savings, KNX case studies and companies?

High-quality building automation typically saves 30-40% of the energy needed for lighting, heating and cooling. This matters amidst energy shortages, and reduces payback times on $100-500k up-front capex. This data-file aggregates case studies of KNX energy savings, and screens 70 companies, from Capital Goods giants to private pure-plays.

KNX is an open standard for building automation. Thus a smart building can be given a central nervous system, called a bus, which is a green cable running throughout the electricals of the building. 500 hardware and software vendors from 45 countries make sensors, controllers, actuators and appliances that can be connected to the bus, and communicate with each other via KNX.

This data-file has reviewed 20 case studies of KNX building automation projects. Their average energy saving is 40%. In other words, well-automated ‘smart buildings’ use 40% less energy than they did prior to the building automation project.

In lighting, the average energy saving is 40%. For example, the KNX system might use a presence detector to switch off the lights if no one is in a room. Or it might use a lighting sensor to turn off lights if enough sunlight is already streaming in through the windows. Particularly sophisticated systems will detect ambient light across an entire room, and maintain a constant, pre-determined brightness level, which might mean dimming/amplifying lights nearer to/further from the windows.

In heating, the average energy saving is 30%. The KNX system might use temperature sensors to avoid over-heating a room, especially if no one is present, or scheduled to be present, or at night. Some systems link to window-sensors, and will not heat a room if a window is open. Or air quality sensors in ventilation ducts avoid over-ventilating a room and thereby cooling it.

In air conditioning, the average energy saving is 40%. Typical functionality is similar to heating described above. In addition, the KNX system might close blinds during the day to prevent heat gain in a room where no one is present, or automatically open windows at night to release heat when outside temperatures are cool.

Energy savings are clearly helpful amidst a protracted energy shortage. It is notable from the data-file that KNX projects have historically been expensive, costing $100-500k at a large commercial building where 3,000 KNX devices might be installed over >5,000m2. Payback times are around 5-10 years in normal times. But they are also a direct function of energy prices.

Thus we have screened 70 leading providers of KNX products. It is a large landscape of companies. But we think 30-40% of projects will typically feature at least some components from large European Capital Goods giants, such as ABB, Siemens and Schneider. 7-30% of projects feature components from a dozen, private, specialist companies (chart below).

The data behind the building automation energy savings, KNX case studies, and the full screen of leading companies can be downloaded via the button below.

Solar: energy payback and embedded energy?

What is the energy payback and embedded energy of solar? We have aggregated the consumption of 10 different materials (in kg/kW) and around 10 other line-items across manufacturing and transportation (in kWh/kW). Our base case estimate is 2.5 MWH/kWe of solar. The average energy payback of solar is 1.5-years. Numbers and sensitivities can be stress-tested in the data-file.

Our base case estimate covers a standard 560W solar panel, as is being manufactured in 2022-23, weighing 30kg, and having an efficiency of 22%.

By mass, this solar panel is about 65% glass, 15% aluminium, c10% polymers (mainly EVA encapsulants and PVF back-sheet), c3% copper. Photovoltaic silicon is only 5% of the panel by mass, but about 40% by embedded energy.

Another 10kg of material is contained in the balance of project, across inverters, wiring, structural supports, other electronics. Thus the energy embedded in manufacturing the panel is likely only around 60% of the total energy embedded in a finished solar project.

Our base case is that it will take around 2.5 MWH of up-front energy and release almost 3 tons of CO2 per kW of installed solar capacity. In turn, this suggests an energy payback of around 1.5-years and a CO2 payback of around 1.8-years.

The complexity of the solar value chain is enormous. Often it is also opaque. Thus the numbers can vary widely. We think there will be solar projects installed with an energy payback around 1-year at best and around 4-years at worst.

Our numbers do not include energy costs of power grid infrastructure or battery back-ups. This is simply a build-up for a vanilla project, trying to be as granular and objective as possible.

Inputs for the embedded energy and CO2 of different materials are drawn from our other CO2 screening work and economic models.

The other great benefit of constructing a detailed bill of materials for a solar installation is that we can use it to inform our solar cost estimates. Our best guess is that materials will comprise around half of the total installed cost of a solar installation in 2021-22 (chart below). There is going to be a truly remarkable pull on some of these materials from scaling up solar capacity additions.

We absolutely want to scale solar in the energy transition. This will be easiest from a position of energy surplus.

Please download the data-file to stress test our numbers around the embedded energy needed to construct a solar project, and the energy payback of solar.

Reaching criticality: nuclear re-accelerates?

Outlook for nuclear in the energy transition

400 GW of nuclear reactors produce 2,800TWH of zero carbon electricity globally each year. But the numbers have been stagnant for two decades. This is now changing. This 14-page note explains why. We expect a >3% CAGR through 2030, and hope for a 2.5x ramp through 2050. A ‘nuclear renaissance’ helps the energy transition.

Agilyx: plastic recycling breakthrough?

This data-file is a review of Agilyx’s plastic recycling technology, after assessing the company’s patents on our usual framework. We conclude that Agilyx has developed a novel and data-driven process, to remove challenging contaminants from feedstocks. Although it may involve higher complexity, higher reagent opex, and some challenges cannot entirely be de-risked from the patents.

Agilyx is a public company listed on the Oslo Euronext Growth, headquartered in Oslo, Norway, with presence in New Hampshire, Oregon, Switzerland and Denmark. The company has 90 employees at YE21.

Its commercial model is to license IP for categorizing thousands of different types of post-consumer plastics (on a royalty basis per kg), license its conversion technology for subsequent pre-processing and pyrolysis of those waste plastics, and also to sell patented equipment.

Agilyx’s long-term mission is to help increase the world’s share of 300MTpa of recycled plastic waste from 10% to 90%. Along the way, its goal is to be the “fastest growing and most profitable plastic recycling technology company”. The synthetic crude oil thereby created is envisaged to be lower carbon than conventional crude oil or virgin feedstock.

As its moat, Agilyx states “we are the only company in the market to offer an integrated solution for chemical recycling and feedstock management… [including] a proprietary technology for identifying, managing and pre-processing [plastic] waste” then breaking it down into synthetic crude oil using a pyrolysis process.

This data-file is a review of Agilyx’s plastic recycling technology. Based on its patents, we infer that Agilyx has developed an intriguing and novel approach to plastic recycling. We think it uses a large database to identify likely hetero-atom contaminants in mixed plastic waste, tailors alkali amendments to those contaminants prior to the pyrolysis stage, pyrolyses the mixture, quenches cracked gaseous hydrocarbons from the reactor, then polishes the depolymerized hydrocarbons by passing them through caustic process solutions that remove impurities. This is explained in the data-file.

However, we cannot entirely de-risk the technology based on the patents, while there are also some challenges from the patent library, including around operating conditions, complexity and operating costs. We have been following the next-gen plastic recycling theme since 2019. There is extremely exciting potential for plastic pyrolysis, but mixed progress to-date, including in 2022. Further and broader details are found in our plastic research.

Energy transition: top commodities?

Commodities needed for energy transition

This data-file summarizes our latest thesis on the top thirty commodities needed for the energy transition. We estimate that the average commodity will see demand rise by 3x and price/cost appreciate or re-inflate by 60%. The scatter is broad. Upside ranges from 2x to 30x for different metals, materials, plastics and capital goods markets.

The data-file contains a 6-20 line summaries of our view on each commodity, and ballparks numbers on the market size, future marginal cost, CO2 intensity and pricing.

As a useful summary, summarizing all of our research into energy technologies and energy transition to-date, we have also ‘ranked’ these 30 top materials and commodities, according to our long-run outlook in this data-file in the ‘Materials’ tab of the data-file.

The median average commodity sees its demand treble in the energy transition. The mean average commodity sees its demand rise 1.5x. Top quartile commodities see growth of 5-30x, although this is most often because they are smaller markets to begin with.

Although many commodities require sharp growth curves, if the world is going to reach net zero by 2050, this is not unprecedented. Some of the largest commodities are plotted below, from 1950-2050, with a weighted average growth CAGR of 2.5% per annum.

Long Run Structural Growth of Commodities Needed in Energy Transition

An apparent paradox in our energy transition roadmap, however, is that after rising at a 2.5% CAGR for the past 70-years, our aggregate models require the total tonnage off these commodities, as consumed by human civilization to move sideways from here. The main reason is phasing out higher carbon coal. This is only really realistic amidst a vast step up in solar, wind, power grids and natural gas as alternatives.

Commodities needed for the energy transition and covered in this data-file include Aluminium, Ammonia, Carbon Fiber, Coal, Cobalt, Copper, Ethylene Vinyl Acetate, Fluorinated Polymers, Fluorspar, Glass Fiber, Graphite, Hydrogen, Indium, Lithium, LNG, Mass Timber, Methanol, NdFeB Rare Earths, Nickel, Oil, Polyurethanes, PV Silicon, Silicon Carbide, Silver, STATCOMs, Steel, Sulphuric Acid, Tin, Uranium, Vanadium.

Further details on each commodity can be found by browsing our supply-demand models.

Another observation is that many of the commodities that excite us most, are strictly, becoming less commoditized, as we increasingly see evidence that the ramp-up of new energies – solar, wind, lithium ion batteries, electric vehicles – calls for advanced materials that confer higher performance and longevity. This is our new age of materials thesis. Leading examples are tabulated in the data-file.

Beware volatility! Soft bottlenecks can be defined as markets that will be tightened by the desire to accelerate the energy transition ever faster. Thus their prices and margins will generally rise. Supply will be available, prices will simply have to rise. Hard bottlenecks, however, may not be surmountable at any price, and we especially think this is the case for power grids. But inverse bottlenecks are most frightening. These materials are needed for the ascent of energy transition technologies, but whose demand and pricing unexpectedly collapse, because for a few months-years, these commodities are in a position of relative over-supply, due to another material being the hard bottleneck. For example, in a research report published in January-2023, we wrote “We are wondering whether PV silicon could see this kind of pricing action in 2023, as it said that China’s fabs will ramp from 300 GW at YE22 to 540 GW at YE23, while global gas shortages are going to disrupt production of silver and FPs“. In our view, timing volatile commodity bottlenecks is one way that active managers can add value as the energy transition impacts practically every supply chain on the planet.

We will continue adding to this data-file over time, as part of our ongoing energy transition research. Please contact us any time if you are a TSE client, and you think there is a particular commodity we should be adding while tracking commodities needed for the energy transition.

Nuclear capacity: forecasts, construction times, operating lives?

How much nuclear capacity would need to be constructed in our roadmap to net zero? This breakdown of global nuclear capacity forecasts that 30 GW of new reactors must be brought online each year through 2050, if the nuclear industry was to ramp up to 7,000 TWH of generation by 2050, which would be 6% of total global energy.

Our outlook for nuclear energy is evolving. Adding 30GW pa of new nuclear capacity per year would be a massive escalation from, as the world has only added around 6 GW per year of new capacity in the past decade.

However, there is precedent, as the world installed 25-30 GW pa of new nuclear reactors at peak, during the mid-1980s, and after a wave of project-sanctioning that followed major energy crises in 1973-74 and 1979-80.

The total base of active, installed nuclear capacity is around 400 GW today, for perspective. Leading countries include the US, with c100 GW of capacity, France with 60GW, China with 50GW and Russia with 30GW. Japan’s nuclear capacity is presently around 45GW, but a large portion of the installed base remains offline post-Fukushima.

Moreover, the average nuclear plant in the world today has been running for 36-years, which means that 10GW of reactor capacity could shut down each year through 2050.

Underlying the analysis is a database of 700 nuclear reactors, including c440 in operation, c200 that have been shut down or decommissioned and around 60 that are in construction. A helpful source in compiling our forecasts is publicly available data from the IAEA, which we have aggregated and cleaned.

The data also show operating lives of nuclear plants, and construction times of nuclear plants, which average 7.5 years from breaking ground through to first power; across different reactor designs and across different countries.

Finally, this breakdown of global nuclear capacity data-file allows you to filter upon individual countries, such as the US, Germany, France of China.

Weird recessions: can commodities de-couple?

In a ‘weird recession’, GDP growth turns negative, yet commodity prices continue surprising to the upside. This 10-page note explores three reasons that 2022-24 may bring a ‘weird recession’. There is historical precedent, prices must remain high to attract new investment and buyers may stockpile bottlenecked materials. How will this affect different industries?

How do commodities perform during recessions?

How do commodities perform in recessions? Industrial metals are usually hit hardest, falling 35% peak-to-trough. Energy price spikes partly cause two-thirds of recessions, then typically trade back to pre-recession levels. Precious metals, mainly gold, tend to appreciate in financial crises. Data are compiled in this file, across recessions back to 1970.

Industrial metals are typically hit hardest, declining 35% peak-to-trough and still trading -20% lower in the year after the recession ended compared to the year before it began.

Energy is more mixed, typically declining -23% peak-to-trough, but in two-thirds of the recessions, energy prices continued spiking for an average of 6-months after the recession started, suggesting that energy shortages were a cause.

Gold is an outlier. In the median average recession, real gold prices have been +5% higher in the 12-months following the end of the recession, compared to the 12-months preceding its start. Other precious metals tended to be 10-15% lower, industrial metals tended to be 20-30% lower, and energy commodities tended to be “flat”.

Each recession is unique, hence while the averages are useful, we think it may be even more useful to delve into the underlying tabs of this data-file, to review individual commodities in individual recession contexts.

The Great Recession of 2008-09 has become an archetype for asset price performance during recessions, for those of us who lived through it. However, in commodity terms, it was unusually severe. Six of the twelve commodities in this data-file experienced their worst peak-to-trough decline of any recession, while another three of the twelve experienced their second worst declines.

Methodology. We downloaded monthly commodity prices from the World Bank pink sheets. We then translated these nominal prices into real terms using the US CPI. Next, we downloaded a list of recession dates from the NBER. We indexed commodity prices at 100 at the start of each recession. Then we plotted the pricing performance 12-months prior to the start of the recession through to 12-months after the end of each recession. We computed three metrics for each commodity in each recession: peak-to-trough price decline, TTM average-to-trough price decline, and average pricing in the year after the recession had ended versus average pricing in the year before the recession began. Finally, we aggregated the data for each recession and took an average.

How do commodities perform in recessions? Commodities assessed in the data-file include oil, natural gas, coal, corn, iron ore (precursor to steel), aluminium, copper, zinc, nickel, platinum, silver and gold.

Recessions assessed in the data-file include the Global Financial Crisis of 2007-09, the collapse of the Dot Com bubble in the early 2000s, after the First Gulf War in the early 1990s, after the 1980+ oil shock, after the 1973-74 oil price shock, and the monetary-induced recession of 1969-70. We have also published detailed reviews into energy crisis and bursting bubbles.

Nitric acid: production costs?

Global production of nitric acid is 60MTpa, in a $25bn pa market, spanning c500 production facilities. c80% of the world’s nitric acid is used to make ammonium nitrate, for fertilizers and explosives in the mining sector. This data-file is a breakdown of nitric acid production costs, based on evaluating the energy economics, capex and other operating costs.

A nitric acid price of $350/ton is needed to generate a 10% IRR in our base case model, assuming a plant costing $500/Tpa in capex. Economics can be stress-tested in the data-file.

The largest input costs is ammonia, which is progressively oxidized using the Ostwald process, a high-temperature catalytic oxidation reaction, using a platinum-rhodium catalyst, at low-medium pressures (further details in the notes tab). Unfortunately, this means nitric acid prices will spike to $600/ton in a gas crisis or times of severe gas shortages.

CO2 intensity is estimated at 1.8 tons/ton, but can realistically vary from 1 to 4 tons/ton. The process itself is not energy intensive. We estimate that the electricity consumption per ton of nitric acid is below 25 kWh/ton (you can compare all of our economic models).

However, one-third of the CO2 intensity is inherited from ammonia inputs. And most significantly, the production process can emit anywhere from 0.1 kg/ton to 10 kg/ton of N2O, a powerful greenhouse gas, which a 298x higher global warming potential (GWP) than CO2.

Companies in the nitric acid value chain are mentioned in the ‘notes’ tab. It may be interesting to explore companies such as Clariant, Johnson Matthey and BASF for their catalyst technologies (Clariant is marketing a post-processing catalyst that can break down 95% of the N2O).

There are also specialist manufacturers of blasting explosives for the mining industry, such as Dyno Nobel and Orica, further down in the value chain; adding to our recent work into specialist mining equipment companies.

Please download our nitric acid production costs model, in order stress test capex, opex, and other cost lines.

CO2 removals: teak plantations, Nicaragua?

The “Nicaragua High Impact Reforestation Program” should remove over 100,000 tons of CO2 from the atmosphere, by row-planting teak trees across >500 hectares of former pasture land in Nicaragua. It is our fourth detailed case study of nature based CO2 removals in 2022, with a price of $45/ton, and a passable score of 70/100 on our framework. But this Nicaragua reforestation case study also illustrates some challenges and debates around nature-based solutions.

Great virtues of this project are that it is real, incremental and measurable. The CO2 credits are certified by Gold Standard, and we were able to review 100 pages of documentation from independent auditors, verifying the CO2 removals; which seem to be measured conservatively, including a 20% ‘buffer’ for reversals.

This region of Nicaragua has been 80% deforested for cattle-grazing. There is a clear CO2 benefit to reforesting former pasture-land. GDP per capita is below $2,000 in the country. Hence it is also helpful for well-meaning investors to provide capital to convert degraded land into carbon-absorbing forests. CO2 credits contribute to the return on that investment.

Furthermore, around 25-30% of the total area in the project is residual forest, especially around water-courses, which will be preserved. This is nature-positive. Although CO2 credits are not being issued against this forest conservation.

However, this particular reforestation project is 100% row-planted teak, which will be harvested and re-planted on a 20-year cycle. Teak is not even native to Central America, but originates from South-East Asia. Some critics might argue that a short-life, row-planted mono-culture is not really ‘a forest’. And if you are not creating a forest, is it really re-forestation?

This is the logic behind the project achieving a score of 70/100 on our framework. Further details and debates are laid out in the data-file, exploring whether this Nicaragua reforestation case study can be considered ‘permanent’ (yes and no!), ‘biodiverse‘ or ‘nature positive’. So are the key numbers from our review.

We have made a $1,000 allocation to this project, in order to offset 22 tons of CO2. Our goal is to support one nature-based CO2 project each month, and to size the allocation according to the ‘score’ each project achieves on our framework.

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