Scope 4 CO2 reflects the CO2 avoided by an activity. This 11-page note argues the metric warrants more attention. It yields an ‘all of the above’ approach to energy transition, shows where each investment dollar achieves most decarbonization and maximizes the impact of renewables.
Electric vehicles: chargers of the light brigade?
This 14-page note compares the economics of EV charging stations with conventional fuel retail stations. They are fundamentally different. Our main question is whether EV chargers will ultimately get over-built, as retailers look to improve their footfall and accelerate the energy transition. This means prospects may be best for charging equipment and component manufacturers.
Fuel retail: economics of a petrol station?
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.
Hydroprocessing: 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 model requires 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 decarbonize hydroprocessing 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.
Long-Run Oil Demand Model
This model forecasts 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. We see demand gently rising through the 2020s post-COVID, peaking at 104Mbpd in 2030, then gently falling to 85Mbpd by 2050 in the energy transition.
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 scenario foresees gently increases from 101Mbpd in 2022 to 104Mbpd in 2030. This is the peak for global oil demand. It is followed by a gradual decline to 85Mbpd 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. Our pre-COVID model is also included as a separate file for reference, for anyone wishing to audit how numbers have changed.
Oil demand by region. OECD oil demand has already peaked, at 50Mbpd in 2005, had softened to 48Mbpd in 2019 and is seen falling to 23Mbpd by 2050, effectively all for hard-to-decarbonize sectors such as planes and freight.
Non-OECD oil demand has risen from 35Mbpd in 2005 to 52Mbpd in 2019 and is seen rising to 62Mbpd by 2050. This is even after two-thirds of future EM passenger cars are assumed to be electric.
Without delivering the technology improvements in our research, total global oil demand would most likely keep growing to 130Mbpd by 2050, due to global population growth and greater economic development in the emerging world.
Please download the data-file to stress test your own long-run oil demand forecasts, and evaluate oil demand by category, oil demand by region and oil demand by end use.
Methanol production: the economics?
This model captures the economics and CO2 intensity of methanol production in different chemical pathways.
Different tabs of the model cover 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 taken from 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.
US Refiners: CO2 cost curve?
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 database contains 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).

Assessed companies include Aramco, BP, Chevron, Citgo, Delek, ExxonMobil, Koch, Hollyfrontier, Marathon, Phillips66, PBF, Shell and Valero.
Methanol: leading companies?
This data-file tabulates the details of companies in 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).
Low-carbon refining: insane in the membrane?
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.
Refinery membranes: where’s the IP?
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.