AI x Climate: Our note to the future - Part I
A call for financial, community and solidarity-rooted investment in our climate futures
Dear Reader,
Welcome to part one of our two-part newsletter wrap-up!
Since August last year, we’ve connected 21 leading experts across technology, AI policy, social science and climate-related sectors to establish a cross-cutting dialogue at the intersection of AI and climate action in Asia. We have pored over countless research articles, policy documents and news items, all in a bid to make sense of this rapidly advancing space.
This process has yielded many learnings along the way. In contextualising existing concerns like AI adoption barriers to climate issues, we’ve found some contradictions. For instance, we’ve tracked emerging R&D on ways AI can be used to reduce emissions growing alongside initiatives on ways to reduce AI’s own emissions.
Our work is reflective of the impacts of global shifts in climate governance and funding on the Asian region, such as the geopolitical tensions shaping critical mineral supply chains and impacts to the climate financing landscape. A key takeaway from our research process was how entangled global systems and supply chains are, making coordinated, equitable climate action a precarious and complex endeavour.
To take on this complexity in the climate tech space, our experts emphasised the need for a whole-of-system lens to understand both the externalities and the suitability of AI approaches for climate action. This requires going beyond traditional data and funding frameworks to think of equitable technological interventions.
In part one, we give you our key takeaways from the past year on what’s needed for responsible climate AI. We propose a Call for Action for those driving the next generation of climate solutions. In part two, we will delve further into promising areas of AI intervention in climate adaptation, mitigation and resilience.
Throughout the series, we’ve seen that while funding ‘solutions’ is essential, it is not the only area that requires investment. We encourage investors to adopt a critical and expansive view of the kinds of financial, community, and solidarity-rooted investments needed to enable systemic change in this space.
Adoption Barriers
The integration of AI in climate-related sectors is complicated by a range of issues - infrastructure limitations, data gaps, lack of data sharing and reuse standards, and inadequate inter-disciplinary expertise.
While Asian countries like China are investing in better infrastructure, intra-regional inequity is creating ‘haves and have-nots’ when it comes to power and resources for compute. Further, diverging from resource scarcity narratives framing AI discourse for the Global South, Cindy Lin suggests that ‘collective restraint’ is already happening by government scientists in Indonesia designing solutions within and in response to local contexts and community needs.
Multi-stakeholder initiatives like the Lacuna Fund have sought to address data gaps by supporting local AI solutions and climate data curation in LMICs; but recent financial constraints threaten the sustainability of some projects. Synthetic data has been proposed as an alternative where data availability is low, but more research is needed to ensure reliability of models trained on such data.
Data stewardship and governance mechanisms are essential for distributing data-driven benefits with communities who provide the data. Regional frameworks centring FAIR and CARE Principles, such as the Asian Data Sovereignty Framework exist, but further efforts are needed to translate these standards into practice across countries.
Moreover, organisations like Climate Change AI are creating communities of practice, but given the need for granular domain expertise across geographies, there is scope for more regional and global collaboration.
Call for Action: Looking beyond policy and tech infrastructure
The focus on the tangible enablers for good climate tech can, however, obfuscate some of the pressing concerns around social and ecological factors that are deep-rooted in prevalent AI-climate trajectories.
AI development and adoption, as Tom Özden-Schilling and Tamara Kneese told us, have material consequences and invisible costs across the supply chain. While conversations on AI’s environmental impacts risk setting a zero-sum discourse on AI’s role in climate action, it is crucial to examine Asia’s unique challenges and develop framings for responsible tech development.
Below is a snapshot of larger systemic issues that need to be resolved to make AI adoption inclusive, effective and intentional. As Asia battles increasing climate impacts, the need for urgent action will spur various decisions at the policy and investment level. In order to avoid reactive approaches to looming climate shocks, we propose the below forward-looking agenda. We call on philanthropies, venture capitalists, family offices, and international organisations to look beyond the hype, and recognise the power you hold in shaping our collective climate futures. These concrete pathways are a starting point in aligning investments in climate tech with resilience, adaptive and justice-focused outcomes.
1. Avoiding technology dependencies and lock-in
Top-down solutions forged from available datasets and based on providers’ assumptions about the climate problem, may erode local knowledge and diminish autonomy of community stakeholders. For example, largescale agri-business apps offering crop advisory and disease detection, alongside fertilisers and pesticides, risk creating information asymmetries, and dependencies while constraining farmer autonomy through product lock-ins. Community-driven interventions that centre local needs and non-tech alternatives are key to avoiding power imbalances between users and technology providers.
2. Bridging misaligned interests
Enabling meaningful participation between myriad stakeholder groups in the climate tech space means addressing divergent, and at times contradictory interests. While companies and developers may be motivated by ROI, researchers want to collect more data, and users may want to see tangible gains. This makes building equitable and sustainable climate solutions a very complex task. Experts emphasised the need for building shared meaning and continuity in climate action work across stakeholder groups, instead of fostering one-time interventions that disrupt community livelihoods. Research across sectors highlighted the importance of ensuring suitable participatory formats. Strategies to account for cultural norms, technical usability, accessibility of language, explainability and transparency mechanisms all impact the shared trust and collaboration needed to implement AI responsibly.
3. Building slow to avoid fail-fast approaches
Investment in participatory AI needs to extend beyond community engagement as a check-list item. Typical Silicon Valley ‘fail-fast’ approaches prioritise rapid deployment, which can have negative impacts on livelihoods and user adoption in the contexts they are deployed in. In the case of agriculture, short-lived start-up prototypes deployed without consideration of local practices and community collaboration can cost farmers an entire season’s yield, and result in distrust and non-adoption by farmers. Intentional investment in dialogue, infrastructure maintenance and capacity-building is essential to ensure the long-term sustainability of projects.
4. Dispelling climate AI hype
In addition to stakeholder capacity-building to understand the technical nuances of AI adoption, it is equally necessary for government officials, community representatives and policymakers to be able to critically assess the limits and suitability of AI for climate action, and frameworks for responsible data sharing and (re)use. Investments in critical AI and data literacy can help bridge knowledge gaps and promote the responsible use of AI.
5. Investing in climate impact reduction, and not efficiency alone
Increased energy efficiency isn’t a silver bullet for sustainability, and in technology development we must ask, what becomes invisible under such a narrow lens? An overt focus on the energy efficiency of generative AI development, for example, prioritises increased productivity at the expense of monitoring CO2 levels and water consumption and broader environmental indicators. Accelerated hardware advancement can result in increased e-waste and inaccessible costs can encourage sustained use of energy-intensive hardware. A critical climate lens that challenges techno-solutionism, and asks - how does this solution improve adaptation, mitigation or resilience? - during project initiation and design is important to ensure AI’s benefits outweigh its costs in addressing climate change.
6. Shifting away from resource-intensive growth trajectories
From EVs to AI, we’ve seen the need to decouple economic growth from resource-intensive growth trajectories and better assess AI’s full human and ecological impacts. Research has shown the increasing parallels between the AI supply chain and “green-extractivism” in which mineral supply chains reinforce historical inequities tied to colonisation. Despite promises of economic diversification and growth, labour concerns across this supply chain range from environmental impacts of mining and refining on community livelihoods, exploitative working conditions, and precarious and limited data centre jobs. A whole-of-system approach to climate tech investments, including support for R&D, and collaboration in developing frameworks for assessing AI’s social, economic and environmental impacts across the supply chain are necessary.
Around the Web
Check out these Climate Change AI Interactive Summaries of core learnings from their paper “Tackling Climate Change with Machine Learning,” in which they highlight high impact applications for AI in climate.
The AI & Environment Resource Hub is a curated collection of knowledge, tools, events and insights at the intersection of AI and sustainability.
For visual guides and discussions on the planetary impacts of the AI supply chain, drop by the AI + Planetary Justice Alliance.
A huge, warm thank you to all Code Green readers and listeners who have tuned in this past year. Your engagement has been encouraging in a landscape where we urgently need more voices. We hope you’ve learned as much throughout this series as we have!
Access the entire series on codegreen.asia
Share this with your friends who you think may be interested, or if you see any dots connecting from your work for a potential collaboration, write to us at codegreen@digitalfutureslab.in
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Credits
Writing: Dona Mathew, Meredith Stinger | Illustrations: Nayantara Surendranath | Layout Design and Editorial Support: Shivranjana Rathore