Carbon language

Current climate discourses ask how to limit ourselves (how to protect the climate within the limits of our legal, political, economic systems). Future discourses will shift, asking how to protect ourselves (our legal, political, economic systems) in the limits of the climate system.

This site aims to imagine such a narrative shift (for climate-relevant decisions in diverse geographies and sectors) and to test it in practice.

We focus on analysis (“Which discourses do we see, which can we even see?”). This includes ethics, to give people the tools to ask ethical questions more effectively. In an increasingly chaotic (post-)covid19 world over the coming years, the need for systems thinking and interdisciplinarity may be easier to imagine than even in 2019.

To handle the necessary contradictions and complexity, analysis starts from a focus on language.

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Carbon language is a mental model to learn how we make climate-relevant decisions. It aims to show how we use language to decarbonize (or not) our legal, political, and economic systems.

Starting from deep decarbonization (zero global CO2 emissions) and defining language broadly makes analysis of these complex systems more efficient. It distinguishes between the climate impacts from human and non-human processes. This helps us avoid using mental models of human cognition for processes that follow other logics, for example machine learning / artificial intelligence (AI) or pandemics.

A focus on language makes analysis more interdisciplinary, better able to handle complexity. This is necessary to cover climate-relevant scales (individual to planetary) and timescales (seconds to millennia). For systems thinkers, such a language of deep decarbonization is called for because climate discourse suffers from asystemic thinking in disciplinary silos and a lack of attention to process. Different disciplines often talk past each other.

The stunning gap between climate science¹ and climate economics² rose to public attention when William Nordhaus received a Nobel Prize for modeling catastrophic climate change as an optimal scenario. Other gaps are less visible but no less important, including the disconnect between climate science and computer science (AI research). We may need new mental models to mitigate fast-moving systemic problems like the climate impacts of AI. As individuals and societies, can we learn to use language more effectively?

In this sense, the failure of climate mitigation is a failure of translation.

Cartoon: Gerhard Mester (non-commercial use free after notifying, collage PM


  1. Garrett, T. J. 2012. “No Way out? The Double-Bind in Seeking Global Prosperity alongside Mitigated Climate Change.” Earth System Dynamics 3(1): 1–17. doi: 10.5194/esd-3-1-2012
  2. Hickel, Jason. 2018. “The Nobel Prize for Climate Catastrophe.” Foreign Policy.