Our oil market supply demand balance is informed by a 45-line model, running month-by-month out to 2025. This download contains both the model, and a 4-page summary of our outlook, from mid-2021.
After ten years forecasting oil markets, our humble conclusion is that all oil models are wrong. Some are nevertheless useful. To be most useful, our model takes a Monte Carlo approach to the key uncertainties, to quantify the “risk” of positive and negative surprises (illustrative example below).
Please download the model to see, and to flex our input assumptions. Included with the download is a PDF summary of our oil price thesis in mid-2021, which is also available separately, linked here.
Important note. The past 2-3 years have been a nightmare for oil market supply demand balance forecasting. We think there is over 3Mbpd of oil demand pent up still to recover in 2022+ from post-COVID. Also, to state the obvious, if there is a major disruption to Russian oil supplies, then oil markets will be under-supplied. And so we do not think we can currently add value by ‘forecasting’ oil markets at all in 2022. We will re-visit this topic in depth in 2023.
This data-file screens for the technology leaders in fiber-optic cables, which are crucial for the digitization of industries and the world’s structural shift towards remote-working.
The file starts by tracking 37,000 patents filed into fiber optic cabling, where the pace of research has risen at a 14% CAGR since 2009, with 75% of 2019’s patents filed in China and 18% in the US.
The 2019 data are shown more granularly in the ‘2019’ tab, aggregating descriptions of 4,000 patents and the companies behind them.
From these patents, we identify and evaluate the largest listed companies in fiber-optics, including a helpful profile of each company, their revenues, and the percent of their revenues from fiber-optic cables.
The impacts of COVID-19 on global oil demand are extremely uncertain. However, this model aims to help you bound the uncertainties, disaggregating 2020 oil demand in the developed and the developing world, as a function of some simplifying assumptions: GDP declines, flight cancellations, travel reductions and the pace of the crisis’s resolution.
Please note this model has been superceded by our more granular estimates, here. The model may still be useful to stress test influences on quarterly oil demand due to different COVID-related variables.
US gasoline is the largest component of global oil demand, at c9Mbpd, or c9% of the global market. Hence we have modelled how it could be impacted by COVID-19, looking line by line, across a granular, c100-line breakdown.
A -2Mbpd contraction is possible in 2Q20, if 34% of all US workplaces close temporarily and 50% of non-essential travel is cancelled. This is an extreme scenario, commensurate with a c5pp slowdown in US GDP, comparable to the “Great Recession” of 2008-09 in economic terms, but with 8x deeper demand destruction for gasoline.
Such steep declines are not inconceivable, from a modelling perspective. They could underpin a c10Mbpd YoY collapse in global oil demand.
How quickly could demand rebound? Very minimal long-term impacts persist from 2022 onwards, with demand destruction of just 60kbpd in 2023-24. We can even construct scenarios where US gasoline demand surprises to the upside, rising +0.5Mbpd, if COVID is brought under control. So when the oil market does turn, it may turn very quickly.
To run your own scenarios, please download the model.
Global oil demand is going through an unprecedented disruption. In the short-term, this is due to COVID-19. In the long-term, it is due to the rise of the internet and the energy transition. To contextualise how demand will change, we have aggregated granular data on travel-miles in the US and the UK.
This data-file breaks down all miles travelled by individuals in the US and UK, according to 20 different categorizations on 20 distinct tabs: by purpose, by vehicle type, by journey distance, by age, by income category, and by urban location; plus we assess remote working’s impact on commuter-miles, and internet retail’s impact on shopping-miles.
The data are derived from the US National Household Travel Survey, which was last conducted in 2017, collecting a day’s data across 1M journeys from 250,000 individuals in the United States; and the UK Department of Transportation’s National Travel Surveys, which interviews and tabulates travel-diaries from 14,000 – 20,000 individuals each year since 2002.
For TSE clients, we will be happy to run further, bespoke data and charting requests. Please contact us if this would be useful.
We have compiled a database of over 100 companies, which have already flown c40 aerial vehicles (aka “flying cars”) and the number should rise to c60 by 2021.
The data substantiates our conclusion that aerial vehicles will gain credibility in the 2020s, the way electric vehicles did in the 2010s. Our latest updated in early-2020 shows strong progress was made in 2019 (chart below).
The database categorizes the top vehicle concepts by type, company, year-founded, company-size, company-geography, backers, fuel-type, speed, range, take-off weight, payload, year of first prototype, target commercial delivery date, fuel economy and required battery weights.
Some vehicle concepts are extremely impressive and credible; but a few may find it more challenging to meet the ranges they have promised at current battery densities…
We estimate costs and carbon intensities per use for twenty low-utilisation household objects: the average is $13 per use and 1.3kg of CO2, respectively. Both are high numbers.
The biggest determinant is the number of uses per item. We fear that once purchased by a consumer, the average item on our list will be used just c20 times in its entire lifetime.
More extensive “sharing” will be enabled by drone delivery technologies, potentially saving $150bn of annual sales and 15MTpa of CO2 emissions across these 20 items items alone. Across the entire US economy the savings could reach $1trn and 100MT per year.
This is a simple model of long-term LNG demand, extrapolating out sensible estimates in the world’s leading LNG-consuming regions. On top of this, we overlay the upside from two nascent technology areas, which could add 200MTpa of potential upside to the market. Backup workings are included.
This data-file contains all our data on the energy economics of e-scooters, a transformational technology for urban mobility, where demand has exploded in 2018 and 2019. And for good reason. The data-file includes:
- Our projections of the oil demand destroyed by scooters
- Our projections of the electricity demand created by scooters
- Number of US travel-trips using shared bikes and scooters from 2010-18
- Scooter costs versus car and taxi costs per mile
- Average ranges and battery sizes of incumbent scooter models
- Relative energy economics of scooters versus gasoline cars and EVs
- Relative time taken to charge scooters versus EVs using solar panels
- The proportion of scooter trips that replace gasoline car trips in eight cities
- Profiles of the top 4 e-scooter companies
- A timeline of shared mobility from 1965 to 2018.
The download will also enable you to adjust the input assumptions, to test different scenarios.
This data-file tabulates consumer attitudes towards aerial vehicles, based on the best perception study we have seen in the technical literature.
It summarises attitudes towards aerial vehicles in four countries, covering overall attitudes, and how they are influenced by geography, income, age, gender, education levels and length of commute.
It also identifies the top six concerns, and how sensitive each one is to different input parameters.