Algorithms as guidelines for climate governance

One of the primary issues with involving algorithms in climate governance is overcoming the “risk of meaningless machines” (Latzer & Festic, 2019). An algorithm can surely produce a great number of solutions to how humankind could reduce carbon emissions and decrease overall pollution, but how can we be sure that such solutions would be socially sustainable, and executable in realistic socio-technological contexts? For instance, an essay was recently published in The Economist on tackling climate change that was written by an algorithm (The Economist, 2019). It was appreciated for its clarity and reliability, but criticized for the vagueness of its solutions, such as “alternative economy”. The solutions produced by the algorithm did not differ much from solutions made by climate experts, and thus this experiment hints that AI may not be capable of solving climate change on its own or serving as a guiding, governing authority on a global scale.

However, if we comprehend climate change as a permanent condition and its governance as a number of ever-expanding activities (Bulkeley, 2016), AI might have its benefits. As there is proof that AI can successfully help in reducing pollution or mitigating its adverse effects locally (Rolnick et al., 2019, 2), it is not far-fetched to suggest that a number of AI solutions could be helpful in the construction of global climate governance. For instance, AI could be used to create a roadmap on how different human actions actually affect the climate and what kind of effects the changing climate has on current human activities. This group of solutions would ultimately form a guideline representing climate realities that societies could refer to in deliberative democracies. Therefore, these AI implementations should not only exist on a global scale, but also vertically at different levels of regulation.

The tentative suggestion above, however, is yet superficial and flawed. Some considerations that should be made are whether the mentioned set of AI solutions would actually be mutually compatible and comprehensible; if the suggestions

produced on different levels are contradictory, then AI does not bring much value into climate governance. In addition, the biases and power structures related to the creation and implementation of AI should be taken into serious consideration.

5.4 Conclusion

In conclusion, there are many benefits of implementing algorithmic decision-making into global climate governance. Global climate governance would be most effective as a system that considers climate change as an all-encompassing condition that needs constant assessment in all domains and practices of societies. However, many practical issues remain around implementing algorithms, such as accountability, accessibility, and property rights issues.

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Conclusion

In this research paper we considered the challenges and possibilities of governing climate change from various viewpoints. Climate change has become a social phenomenon, which demands all humans and countries to take a stance. The big challenge with governing climate change is the vast pool of complex information circulating and the amount of different actors involved. There is a need for a shared understanding of the social reality of climate change: the economic discourse needs to move to an idea about a common world that we have to protect.

Game theory points out that finding a common path with climate change is difficult, because there is a great temptation to “free ride”. Because individual

countries look for their own self-interest, we have to create a climate agreement that is attractive, and the best option for all countries. Countries will not join any agreement

“just to save the world”. Governments have a big responsibility in governing, but citizens can affect governance by means of deliberative democracy. When citizens take part in public discussions and cooperate with governments, there is hope for a better understanding of the issues at hand and action to be taken.

Publics gets much of their knowledge of climate change from different media, and journalism is a central actor in translating and conveying complex scientific information to the public. By expanding climate change coverage outside the science desk and using new technologies such as interactive storytelling, journalism could enhance the discussion around climate change. One future hope is the use of algorithmic governance to help humans cope with the masses of information and actions. With the help of algorithms, we could create a more comprehensive plan for climate change — one that encompasses all human activities and creates a more sustainable way of life across nations.

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