What we did (results)
Europe United States
Carbon Tracker (2018). Nowhere to Hide. Carbon Tracker (2018). Nowhere to Hide.
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Why we did it (global coverage of economics at asset-level)
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Global coverage at
I need global
I don’t have
the data to
In 2016, I decided to model the operating economics of every
coal plant in the world to:
1. Provide an “investment-grade” dataset for mainstream investors.
2. Help campaigners with their project and thematic interventions.
3. Accelerate energy transition in Asia.
Why we did it (global coverage of project economics)
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Source: Powering Down Coal, Carbon Tracker (2018)
Today 2020 2021 2023 2024 2025 2027 2028 2030
China Turkey Ukraine Japan Korea Indonesia
Outcompeted Coal GW
Source: Powering Down Coal, Carbon Tracker (2018)
Why we did it (global coverage of relative economics)
Why we did it (modelling example)
Asset-level data is vital for influencing investors and asset-level
data is impossible without the utilisation rate
Schematic of variables to calculate viability of coal plant
Carbon Tracker (2019)
Calculating key variables require
utilisation rate data
Why we did it (Asian examples on why utilisation data is
Southeast Asia: Indonesia consumers lose from take-or-pay contracts for new coal; Philippines
investors at risk from carve out clause for new coal; and Vietnam overcapacity as a reason to
scale back newbuild.
China: renewed focus on overcapacity; pushing for accelerated retirements of coal plants in the
India: economic waste from low utilization and excess capacity
South Africa: revealing reasons for blackouts and massive economic waste to push for closure
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Our perspective (understanding information grades)
Client Earth collaboration How information grades interact
Carbon Tracker (2018)
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CE approaches CTI
about proposed coal
plant in Poland
CTI publishes burning
more money than coal
CE buys shares in Enea,
sues Enea and wins
Our perspective (disruption theory for maximising impact)
Two strategies with limited resources:
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1. Superior sensors and less data
2. Inferior sensors and more data
Disruption theory suggests this strategy will win
Our perspective (optimise for outcomes)
Sensors are becoming ubiquitous and are changing information flows in ways previously
unimaginable. This trend will change boundaries of governance and disclosure.
To maximise impact (and avoid over-engineering) we need to optimise for outcomes.
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Impact outcome: Theory of change for carbon emissions is a profound challenge (i.e. data
transparency ≠ emission reductions).
Technical outcome: To avoid over-engineering, we need a meta machine learning model to
identify relevant coefficients for the desired outcome.
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