24 August 2019
The Paris Score. A Transactional Data Approach
Following the New Shape Forum in May 2018, the Global Challenges Foundation Technology Working Group has developed The Paris Score: A Transactional Data Approach to Incentivizing Environmentally Beneficial Behavior.
Members of the Working Group: John Bowley, Carin Ism, Sebastian Romero, Kristian Rönn, Soushiant Zanganehpour, Ruben Zondervan, and H. Freitas.
The white paper can be requested from the Future of Governance Agency.
The 2015 Paris Agreement, with its establishment of quantifiable “Nationally Determined Contributions” was initially received as the most promising multilateral effort at addressing anthropogenic climate change ever attempted. In the years since, however, the global resolve embodied in the Agreement has become jeopardized by the increasingly accepted view that the Agreement lacks effective enforcement mechanisms. At the same time, spurred by erupting populist sentiment around the world, several key actors have announced their intention to withdraw from the Agreement, with others likely to follow. Lack of an effective enforcement mechanism and the spectre of widespread withdrawal have put the continued viability of the Agreement, and all of its enormous promise, into question.
This paper proposes that the emerging technologies which enable big data analysis and distributed ledger tokenization can be used to allow sub-national actors – corporate entities, municipalities and individuals – to effectively and more efficiently pursue the Paris Agreement’s goals, and to allow those entities and remaining committed nations to carry on its work, even in the face of governmental recalcitrance, due to lack of political will, or mass abandonment. In a context where centralized enforcement is not an option, this paper proposes a decentralized system of asymmetric engagement, so as to allow small actors to punish large, environmentally-damaging actors (or, incentivize them to adopt best practices). Technological precedent for the application of data sets in the manner proposed already exists and needs only to be applied in the context of climate change.