This data-file compiles all of our insights into publicly listed companies and their edge in the energy transition: commercialising economic technologies that advance the world towards ‘net zero’ CO2 by 2050.
Each insight is a differentiated conclusion, derived from a specific piece of research, data-analysis or modelling on the TSE web portal; summarized alongside links to our work. Next, the data-file ranks each insight according to its economic implications, technical readiness, its ability to accelerate the energy transition and the edge it confers on the company in question.
Each company can then be assessed by adding up the number of differentiated insights that feature in our work, and the average ‘score’ of each insight. The file is intended as a summary of our differentiated views on each company.
The screen is updated monthly. At the latest update, in June-2022, it contains 260 differentiated views on 140 public companies.
This data-file captures the economics for a typical fuel-retailing “petrol station” to earn a 10% unlevered IRR, based on data from companies in the space, into capex, opex, margins and costs.
A typical EBIT margin is around 17c/gallon. This is derived from a c6% margin on direct fuel sales; but in addition, around 10-20% of revenues are from selling convenience retail products at a higher, c25-30% margin.
Economics are more attractive at larger service stations with higher throughput volumes, which in turn, allows for lower fuel retail margins. Please download the data-file to stress test the economics.
The data-file captures the economics of hydroprocessing at an oil refinery, such as hydrotreating or hydrocracking, to remove impurities such as sulphur, and upgrade heavier product into lighter product.
Our base case modelrequires a $7.5/bbl upgrade spread to earn a 10% IRR across a new unit. CO2 emissions are quantified from hydrogen production. Input assumptions are based on past projects and technical papers, including capex costs (in $M/kbpd) and hydrogen utilization (in scf/bbl).
It is possible to decarbonizehydroprocessing by using green hydrogen instead of grey hydrogen, but the result is a 3x increase in the upgrading spread required for economical running of the unit.
This Excel model calculates long-run oil demand to 2050, end-use by end-use, year-by-year, region-by-region; across the US, the OECD and the non-OECD. Underlying workings are shown in seven subsequent tabs. The model has been updated in Mar-2021 to reflect COVID and autonomous vehicles.
The model runs off 25 input variables, such as GDP growth, electric vehicle penetration and oil-to-gas switching. You can flex these input assumptions, in order to run your own scenarios.
Our scenarioforesees a plateau at c103Mbpd in the 2020s, followed by a gradual decline to below 90Mbpd in 2050. This reflects 7 major technology themes, assessed in depth, in our recent deep-dive report and COVID considerations, assessed in depth in a further deep-dive report.
Without delivering these technology themes, demand would most likely keep growing to 130Mbpd by 2050, due to global population growth and greater economic development in the emerging world. Our pre-COVID model is also included as a separate file for reference.
This model captures the economics and CO2 intensity of methanol production in different chemical pathways.
Different tabs of the modelcover grey methanol production from gas reforming, blue methanol from blue hydrogen and industrially captured CO2, green methanol from green hydrogen and direct air capture CO2, and finally bio-methanol.
Inputs are takenfrom a wide survey of technical papers, cost breakdowns and energy intensity data. These are also broken down in the data-file.
Based on the analysis, we see interesting potential for bio-methanol and blue methanol as liquid fuels with lower carbon intensity than conventional oil products. You can stress-test input assumptions in the underlying model tabs.
Which refiners are least CO2 intensive, and which refiners are most CO2 intensive? This spreadsheet answers the question, by aggregating data from 130 US refineries, based on EPA regulatory disclosures.
The full databasecontains a granular breakdown, facility-by-facility, showing each refinery, its owner, its capacity, throughput, utilisation rate and CO2 emissions across six categories: combustion, refining, hydrogen, CoGen, methane emissions and NOx (chart below).
This data-file tabulates the details of companiesin the methanol value chain. For incumbents, we have quantified market shares. For technology providers, we have simply tabulated the numbers of patents filed into methanol production since the year 2000. For new, lower-carbon methanol producers, we have compiled a screen, noting each company’s size, patent library and a short description (chart above).
Almost 1% of global CO2 comes from distillation to separate crude oil fractions at refineries. An alternative is to separate these fractions using precisely engineered polymer membranes, eliminating 50-80% of the costs and 97% of the CO2. We reviewed 1,000 patents, including a major breakthrough in 2020, which takes the technology to TRL5. Refinery membranes also comprise the bottom of the hydrogen cost curve. This 14-page note presents the opportunity and leading companies.
This data-file reviews over 1,000 patents to identify the technology leaders aiming to use membranes instead of other separation processes (e.g., distillation) within refineries.
Covered companies in the screen include Air Liquide, Air Products, Aramco, BASF, BP, Chevron, Dow, ExxonMobil, GE, Honeywell, IFP, MTR, Praxair, Shell, WR Grace and Zeon. A brief overview is prented for each company, along with a summary of their recent patent filings, and all the underlying details.
Operational data are also presented for two interesting cases: Exxon’s recent refinery membrane breakthrough (chart below) and Air Products’s PRISM membranes for hydrogen separation.
This model captures the energy economics of a pipeline carrying oil or water. Specifically, we have modelled energy requirements using simple fluid mechanics, and modelled costs using past projects and technical papers, which are tabulated in the data-file.
Our conclusionsshow the requisite costs, energy and CO2 intensities of different pipelines (below).
You can stress test the economicsdirectly in the model, by varying pipeline tariffs, capex costs, energy costs, CO2 prices, maintenance costs, pipeline diameter, pipeline distance, pipeline elevation, pipeline materials, fluid viscosity and compressor efficiencies.
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