Given the increasing urgency to find solutions, policymakers are looking towards technology and particularly AI as a potential tool in the fight against climate change. With advancements in research, even traditional physics is being combined with AI to build more accurate weather forecasting tools. For example, Google recently developed NeuralGCM, the first machine learning application for accurate weather forecasting – predicting a range of scenarios, using traditional physics-based atmospheric factors with AI components.
In climate change affected sectors like agriculture where crop yield is decreasing and food security is threatened because of extreme weather events, initiatives like the AEGIS project are leveraging AI to study how ancient environmental DNA helped crop plants adapt to climate changes. At a global level, the United Nations Climate Technology Centre and Network (CTCN) mechanism recognises the role of AI to simplify our understanding of complex systems, make precise predictions, and build climate resilience.
However, with rising awareness about the impacts of AI on people and the planet, AI solutions for climate action must be grounded in the needs of communities affected by climate change and driven by inclusive, participatory processes. A top-down approach that prioritises the views of ‘experts’ over local knowledge risks displacing traditional wisdom that has adapted to climate effects for centuries. This displacement can be exacerbated by technology solutions that require specific skills and resources to operate, leaving local communities out of the equation.
Why meaningful participatory approaches are essential
The importance of participatory approaches in climate action cannot be overstated. Without proper consultation, initiatives risk displacing indigenous communities, harming livelihoods, and facing local resistance. Examples from Cambodia’s REDD+ project, Indonesia’s nickel mining and the Philippines’ electric transport policy illustrate these pitfalls.
Marginalised communities often bear the brunt of decisions aimed at achieving net-zero transitions despite contributing the least to greenhouse gas emissions. Inclusive consultation builds trust and ensures equitable outcomes. For instance, a recent study highlights that consultative processes that are slow and deliberate and mechanisms where risks and limitations are ‘actively discovered, effectively communicated, and fairly distributed’ can foster credibility and trust in new AI technologies in sectors like agriculture.Inclusive consultation not only builds trust and ensures equitable outcomes but also uncovers opportunities for community-driven solutions, as seen in India’s Commoning for Resilience (CORE) Stack initiative, a collaborative effort between research partners, grassroots organisations, and community volunteers to leverage data for natural resource management.When adopting nature-based solutions or leveraging technologies like satellite data and AI for climate action, it’s crucial to respect traditional knowledge systems and community autonomy. Applying CARE principles (collective benefit, authority to control, responsibility, and ethics) and obtaining free, prior, and informed consent (FPIC) from indigenous communities are vital steps in the consultative process. While participatory approaches can be resource-intensive and time-consuming, they are essential for aligning climate solutions with community needs. Technology developers, policymakers, and researchers must engage with local communities to understand their lived realities and ensure that climate initiatives do not exacerbate existing inequalities or create new ones.
The role of philanthropic organisations
Philanthropic organisations can play a critical role in supporting participatory processes. They can prioritise local knowledge systems and experiences when developing climate-tech solutions. They can also foster interdisciplinary, multi-stakeholder collaborations and innovative models for climate financing, including private sector support, to account for different perspectives and needs in decision-making.
Supporting civil society organisations by ensuring they have access to the appropriate mentoring, technical aid, and capital to build capacity for indigenous and local communities is crucial. Such efforts can help articulate community demands and aspirations before wider audiences and forums. Moreover, the availability of resources will go a long way in fostering localised climate interventions by communities, along with problem-driven data collection, communication, monitoring and evaluation of data insights.
Finally, it is vital to map community impacts and consult with intended beneficiaries of climate AI solutions from the program or project conception stage. Allocating funds for deliberations with community representatives throughout a project’s life cycle is essential for meaningful engagement.
The benefits of inclusive, participatory processes in developing AI-driven climate action strategies far outweigh the drawbacks. By building trust and securing multistakeholder buy-in, we can develop solutions that are not only technologically advanced but also socially equitable and effective in addressing the complex issue of climate change.
The writer are Deepali Khanna, Vice President, Asia Region Office & Urvashi Aneja, Founder & Director, Digital Futures Lab.
