Asia is facing a unique set of climate challenges. The region is the world's most disaster-prone, experiencing, on average, six natural disasters a year over the past three decades. The world's top five fastest sinking coastal cities are in Asia, and the region is home to 70% of the global population susceptible to sea-level rise. Additionally, around 412.9 million people in South Asia are severely food-insecure. High temperatures are causing more illnesses and deaths, with increasing cases of dengue and malaria in Thailand and Malaysia, and respiratory diseases in Indonesia due to air pollution.

Urgent climate solutions are needed, and machine learning (ML) technologies are expected to play a significant role. A host of big multilateral actors have already taken significant bets on the impact of AI on mitigating climate change. In 2023, the United Nations Framework Convention for Climate Change’s Technology Mechanism set up the Artificial Intelligence for Climate Action initiative to understand how AI can serve as a potential technological tool in enhancing and expanding transformative climate solutions. The World Economic Forum has also recognised the role of data driven and digital technologies to assess climate risks, identify intervention areas and support dynamic decision-making in the face of climate threats.

ML can potentially improve our understanding of complex systems, optimise existing systems, and advance scientific research. To understand how AI can support climate action in Asia, the Digital Futures Lab, supported by The Rockefeller Foundation, explored the opportunities and limitations of AI building climate resilience, focusing on the areas of agriculture, energy, and disaster response across nine countries: Bangladesh, China, India, Indonesia, Malaysia, Thailand, The Philippines, Singapore, and Vietnam. The research for the project was undertaken from February to November 2023.

Some key takeaways from the research:

Varying Readiness Levels: Building AI-driven climate action strategies for Asia begins with acknowledging that different countries are at varying stages of digital transformation. For example, while Singapore and China have advanced AI ecosystems, countries like Bangladesh are still setting the stage for AI adoption, highlighting the need for tailored strategies based on each country's unique circumstances.

Bridging the Data Divide: One of the major hurdles in leveraging AI for climate action is the unequal accessibility to climate data. While some countries have robust data infrastructures, others struggle with fragmented and inadequate data. Creating equitable data-sharing mechanisms and investing in local data collection are essential steps to ensure all countries can benefit from AI innovations.

Interdisciplinary Collaboration: Addressing Asia's climate challenges requires a collaborative approach that transcends traditional boundaries. Interdisciplinary expertise and partnerships among data scientists, climate scientists, local stakeholders, and policymakers are crucial for developing effective AI-driven climate solutions that are context-specific and inclusive.

From Top-Down to Bottom-Up: Current data collection approaches are often top-down and driven by market interests. Shifting to a bottom-up approach that prioritises local needs and incorporates traditional knowledge can lead to more effective and sustainable AI solutions. This shift requires financial and technical support for local stakeholders to build and maintain data infrastructures.

Localised Solutions for Unique Challenges: Asia's diverse climate challenges require localised solutions. AI applications in agriculture, for instance, need to account for varying agro-climatic zones and resource availability. Similarly, AI-driven disaster response tools must be designed to address the specific needs of different regions, considering factors like digital access and infrastructure.

Guarding Against Power Imbalances: Without proper safeguards, there's a risk of AI development leading to power imbalances, with private companies potentially monopolising access to AI tools. Establishing ethical guidelines and regulatory frameworks can help prevent the displacement or appropriation of traditional knowledge systems and ensure fair distribution of AI benefits.

Evaluating Impact: To ensure AI solutions for climate action are effective, it's crucial to develop reliable metrics for evaluation. This involves creating standards and frameworks for assessing the impact of AI interventions which can guide future investments and policy decisions.

Balancing Costs and Benefits: While AI holds great promise for addressing climate issues, it's important to consider the environmental and social costs involved with developing these technologies. The deployment of AI technologies should be aligned with sustainable practices to avoid exacerbating existing problems.

The Role of Philanthropy and Development Organisations: Development organisations and philanthropic investments play a critical role in supporting responsible AI for climate action. By funding foundational research, pilot projects, and community-driven initiatives, they can help unlock the potential of AI to address climate challenges in Asia. Their involvement ensures that AI interventions are equitable, inclusive, and sustainable.