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.
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 July-2020, it contains 167 differentiated views on 87 public companies.
What are the top technologies to transform the global energy industry and the world? This data-file summarises where we have conducted differentiated analysis, across c80 technologies (and counting).
For each technology, we summarise the opportunity in two-lines. Then we score its economic impact, its technical maturity (TRL), and the depth of our work to-date. The output is a ranking of the top technologies, by category; and a “cost curve” for the total costs to decarbonise global energy.
Download this data-fileand you will also receive updates for a year, as we add more technologies; and we will also be happy to dig into any technologies you would like to see added to the list.
Industrial heat comprises around 20% of global CO2 emissions, but around half of all the heat generated may ultimately be wasted.
Hence, this model simplifies the economics of using a heat exchanger to recover waste heat from an industrial facility, based on the engineering equations of heat exchange and recent technical papers.
Our base case IRR is 6%, in the US, due to low, $3/mcf gas prices. This is uplifted to above 20%, either if we assume European gas prices (around $6/mcf) or a $50/ton CO2 price. IRRs can reach 40% if we assume both.
High IRRs may be necessary to unlock waste heat recovery. First, each project is complex, with large amounts of engineering, and implementation disrupts operations at a plant. Second, although IRRs are high, NPVs are low, as many projects will be small-scale. For example, the NPV10 may be less than $1M on a single, small heat exchanger project, even if it achieves a 40% IRR.
This data-file models the economics of a new renewable diesel plant, converting waste oils into green diesel. It is based on technical papers and cost estimates from past projects.
A strong, c25% IRR isattainableif renewable diesel maintains a $1.0/gallon pricing premium to conventional diesel, as has been historically supported by the blenders tax credit.
The IRR is obliterated and falls to zero if this premium is lost, for example, due to emerging competition from carbon offsets. Please download the model to flex our input assumptions and stress test the economics.
Fuel retailers have a game-changing opportunity seeding new forests. They could offset c15bn tons of CO2 per annum, enough to accommodate 85Mbpd of oil and 400TCF of annual gas use in a fully decarbonized energy system. The cost is competitive, well below c$50/ton. It is natural to sell carbon credits alongside fuels and earn a margin on both. Hence, we calculate 15-25% uplifts in the value of fuel retail stations, allaying fears over CO2, and benefitting as road fuel demand surges after COVID.
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 May-2020 to reflect COVID.
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 c104Mbpd 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 calculates the uplift in FCF and NPV for a fuel-retail station that offers CO2-offsets at the point of sale, alongside selling fuel. The rationale, and the different models that could be employed are outlined in our recent deep-dive research note.
In both models shown above, annual FCF can be uplifted by 15-30%, while fuel retail stations’ NPV can be uplifted by 15-25%, depending on the portion of consumer that purchase the carbon credits.
Gross profits from selling $50/ton carbon credits may be around 3x the typical EBIT margins of retail stations, hence we explore a particular sales model that can at least double fuel retail NPVs.