<aside> 📡 Scope
Artificial intelligence (AI) and climate action are specialised and complex topics, especially when combined. To understand their intersection in Asia, the Digital Futures Lab, supported by the Rockefeller Foundation, gathered a network of experts across nine countries to create forward-looking insights. The goal was to foster a dialogue that is anticipatory, collaborative, and propositional by employing the foresight methodology.
The interaction between climate change and technological advancements is uncertain and unpredictable. Traditional research methods often fall short in this context because they typically focus on analysing past and current trends rather than anticipating future developments. Both climate change and AI technologies are areas in which developments are rapid and can lead to unforeseen consequences, and this makes it essential to explore multiple potential futures.
The foresight methodology is particularly well-suited for understanding the effects and future impacts of climate change, as it offers an intuitive approach that allows us to consider a wide range of possibilities and prepare for different outcomes. By using these methods, we can help decision-makers navigate the complexities of AI and climate change, build resilience against potential risks, and identify opportunities for proactive interventions. This approach not only anticipates future challenges but also empowers stakeholders to take strategic actions today, fostering a proactive rather than reactive stance towards climate change and technological advancements.
The foresight methodology involves three key steps: horizon scanning, trends rating, and scenario-building.
1) Horizon Scanning: Collecting information on early signs of significant changes (signals).
How it works: We create a database of signals and assess them based on criteria like credibility, novelty, likelihood, impact, and relevance. For example, a new AI technology predicting crop failures due to climate changes is a signal.
A snapshot of the Signals Database curated for the AI + Climate Futures in Asia Project
2) Trends Rating: Identifying key trends from the signals and evaluating their potential scale and impact.
How it works: Experts rate these trends to determine which ones are most important. For instance, the trend of using AI for identifying energy demand and supply was rated against its likelihood of scaling and its perceived impact on climate change mitigation.
Trends rating done by experts on the impact and scalability likelihood of utility management
Trends rating done by experts on the impact and scalability likelihood of utility management
3) Scenario-Building: Creating different possible futures to understand how current decisions might affect the future.
How it works: Experts focus on key themes and develop different scenarios for the future in the areas covered by those themes. For example, we focused on two main themes—agriculture and AI, and climate data. In a workshop in August 2023, experts developed four scenarios for the year 2035. These scenarios explored different ways agriculture and climate data governance could evolve.
Method used: The 2x2 scenarios method considered two critical uncertainties—participation and institutional capacity. This helps us think about different future outcomes and their implications.
The 2x2 Scenario Quadrants