Issue 00
AI Innovation & Climate Action in Asia
Dear Reader,
Hello and welcome to the first issue of Code Green, a monthly newsletter published by Digital Futures Lab in partnership with Earth Venture Foundation.
Code Green showcases the latest interdisciplinary research and initiatives at the intersection of AI and climate action in Asia, a region at the frontline of climate impacts and digital transformation.
“Not another newsletter on climate tech!” you say. Here’s what makes us different. Our work traces the interconnectedness with which society and technology operate and develop. Here, we separate the heat from the hype around climate tech and focus on how AI innovation trajectories can align with just and equitable climate transitions in Asia.
Each newsletter will explore a specific theme, such as the opportunities and risks of using AI for precision agriculture or the role of AI in advancing energy justice in Asia. We’re also excited to announce a ten-part podcast series to complement the newsletter featuring dialogues with researchers, industry experts, investors, technologists and community leaders.
By tuning into Code Green, you aren’t only staying informed. You’re joining a community dedicated to driving meaningful climate action through human-centred approaches, responsible innovation and cutting-edge technology.
So, let’s get started! In this opening edition, we look at the landscape of innovation and action, highlighting key issues and areas of research.
Research Spotlight
The State of Play in Machine Learning for Climate Action
Accelerating climate change is one of humanity's greatest challenges, and current responses have been falling short. There is great excitement and anticipation around the potential for machine learning (ML) to provide powerful tools to tackle it, but what exactly is the state of play in this area? How advanced are these technologies, and in what fields can they help?
This article by experts at Climate Change AI, a global non-profit that catalyses impactful work at the intersection of climate change and machine learning, is a great way to get acquainted with the high-impact problems where ML can fill existing gaps. It highlights potential uses of AI in a wide range of sectors, including energy systems, agriculture, transportation disaster reduction, and climate financing. In energy systems, for example, AI can forecast demand and supply and improve system monitoring.
Using AI for climate action is not without its challenges and risks. This article by scholars from the Oxford Internet Institute highlights how AI can amplify social and ethical challenges already associated with AI more generally, such as unfair bias, discrimination, or opacity in decision-making. For example, data collected to build AI systems for managing energy demands may contain sensitive personal information and risk individual and group privacy.
Building a Supportive Ecosystem for AI in Asia
These global narratives on AI and Climate Action need to be contextualised to specific local contexts. Here, we highlight a landscape report from our recent project, AI + Climate Futures in Asia, where we mapped the maturity of the AI innovation ecosystem in the region. We found that while East Asia shows higher AI readiness compared to South Asia, both regions face common challenges like a lack of quality data and limited investments in R&D. For example, a lack of ground-level data in agriculture and concerns around cybersecurity in the power sector complicate high impact AI adoption in key sectors.
A key recommendation from our project was the need to develop equitable data-sharing mechanisms rooted in principles of collective responsibility, reciprocity, and accountability. Failure to do so can erode local knowledge systems and community agency, as this article by a leading expert on indigenous data sovereignty highlights.
Environmental Cost of AI
The environmental cost of developing and using AI is a significant complicating factor in the narrative around AI and climate change. Researchers at the AI startup Hugging Face and Carnegie Mellon University were among the first to calculate the carbon emissions of AI models for different tasks. They found that the most energy and carbon-intensive tasks involved the creation of new content, such as text and image generation and summarisation tasks.
Apart from AI use, the environmental costs of data centres – the physical infrastructure powering AI – must be acknowledged as countries like Singapore, China, and India rapidly expand their digital infrastructure to support AI capabilities. These data centres are both “thirsty & power hungry,” a report from China Water Risk tells us. In China alone, the water consumption of data centres could more than double by 2030, stressing already scarce water resources in the region.
Explore our reading list and resource library on our website.
Innovation Spotlight
Top-down approaches to building AI tools for climate action are likely to exclude the needs of those most vulnerable to climate change. Instead, we need a consultative approach that amplifies the agency and capacity of vulnerable communities.
CoRE Stack, a new initiative by the teams at Gram Vaani, IIT Delhi and Palakkad, BITS Pilani, and Well Labs, attempts to do just that! They have developed a technology stack to support communities in India in understanding the socio-ecological situation of their landscape, planning for the creation and repair of various natural resource management assets, and sharing these demands with relevant government programmes and donors for action.
Around the web
Visualise how your city may look in the future amidst changing weather patterns with Climate Zones by Derek Taylor.
Get tools and resources to become familiar with the Climate Finance space with this wiki by Climate Change AI.
Curious about what Responsible AI looks like in practice? Check out the new Global Index for Responsible AI. Or see the top 10 takeaways here.
Next up on Code Green
Responsible AI in Asia’s Energy Transitions: Our next newsletter covers the use of AI to manage energy transitions in Asia. It highlights emerging innovation opportunities in the region, from supporting the transition to renewables to optimising existing systems. But we also need to address concerns about energy equity, data privacy and security.
Precision Agriculture in Asia: Cutting-edge technologies like satellite imagery, AI-driven analytics, and IoT devices are poised to revolutionise the agricultural landscape globally. However, they could also exacerbate the gap between large agribusinesses and smaller farms. Our third newsletter explores this and other issues around precision agriculture.
Our first podcast episode is also out! Listen to leading experts at the intersection of AI and Climate Action in Asia from a policy, technical and financial lens.
Elina Noor, Senior Fellow, Carnegie Endowment for International Peace
Alpan Raval, Chief Scientist AI/ML, Wadhwani AI
Varad Pande, Partner and Director, Boston Consulting Group
Thank you for reading Code Green! If you found this newsletter useful, please subscribe and share it with your networks.
We would also love to hear from you—please do share your feedback and suggestions at codegreen@digitalfutureslab.in
All our newsletters and podcasts can be found at codegreen.asia
Earth Venture Foundation is a not-for-profit initiative providing grants and sponsorship to academic and scientific initiatives that contribute to the fight against climate change.
Digital Futures Lab is an interdisciplinary research and advisory firm studying the complex interactions between technology and society. Through evidence-based research, foresight, and public engagement, we work to develop pathways toward equitable, safe, and sustainable futures.