Climate change is one of the
greatest challenges facing humanity today, with severe and far-reaching consequences for the planet and its inhabitants. The need for urgent and effective action to mitigate and adapt to the impacts of climate change has never been more pressing. We assume that only emerging technologies can help in providing solutions, but there is a blind spot in most of the climate debates, and that blind spot is native intelligence. Known as indigenous knowledge or traditional knowledge, it refers to the knowledge, practices, and wisdom that have been developed and passed down through generations by indigenous and local communities. It encompasses the ability of ecosystems to self-regulate, adapt to changing conditions, and maintain balance and harmony. Native intelligence is evident in the intricate web of relationships among species, the efficient use of resources, and the ability of ecosystems to recover from disturbances.
Indigenous communities often have a deep understanding and knowledge of their local ecosystems, including plants, animals, weather patterns, and natural resources.
When it comes to climate action, native intelligence can play a crucial role in several ways. Indigenous communities often have a deep understanding and knowledge of their local ecosystems, including plants, animals, weather patterns, and natural resources. This knowledge can provide valuable insights for climate action, such as identifying and protecting important biodiversity hotspots, understanding how ecosystems function and interact with climate processes, and developing sustainable resource management practices that promote ecosystem resilience. Indigenous communities have historically developed strategies to adapt to changing environmental conditions, including climate variability. These strategies, which have been refined over generations, can provide valuable lessons for climate adaptation efforts. For example, indigenous practices such as terracing, water harvesting, and agroforestry can help manage water resources and prevent soil erosion, which are important for climate-resilient agriculture.
Indigenous communities often have traditional practices that promote sustainable land management and reduce greenhouse gas emissions. For instance,
traditional fire management practices, such as controlled burning, can prevent uncontrolled wildfires and reduce carbon emissions. Similarly, traditional agroecological practices, such as mixed croppi ng and crop rotation, can promote soil health and sequester carbon in soils, contributing to climate change mitigation. Indigenous cultures often have spiritual, cultural, and ethical connections with the environment, which shape their values and behaviours towards nature. This deep-rooted wisdom can foster a sense of stewardship and respect for the environment, leading to sustainable practices and behaviours that contribute to climate action, such as conservation, waste reduction, and community-based governance.
Machines and humans
Climate change, driven by human activities such as deforestation, industrial emissions, and fossil fuel consumption, has severe environmental, social, and economic impacts. However, AI can be harnessed to accelerate the transition towards a sustainable and resilient future by aiding in various aspects of climate change mitigation, adaptation, and monitoring. With smart automation, large volumes of data can be processed, with speed and accuracy and without human boredom; machines can help in this climate action endeavour too.
Indigenous communities often have traditional practices that promote sustainable land management and reduce greenhouse gas emissions.
AI
can contribute to addressing climate change by analysing vast amounts of data and generating valuable insights. AI algorithms can process and analyse complex data sets, including climate models, satellite imagery, weather patterns, and historical data, to identify patterns and trends that can inform decision-making. For example, AI can help
optimise renewable energy systems by analysing data on weather patterns, energy demand, and grid operations to optimise the deployment and management of renewable energy sources like solar and wind. AI can also optimise the charging and discharging of energy storage systems, such as batteries, to ensure the efficient use of renewable energy and reduce greenhouse gas emissions.
AI can also play a critical role in improving climate change prediction and modelling. Climate models, which are used to simulate and predict climate patterns and impacts, are complex and require extensive computational power. AI can help enhance the accuracy and efficiency of climate models by leveraging machine learning algorithms to process and analyse large volumes of data. In addition, by incorporating indigenous ecological knowledge—such as traditional weather forecasting methods or phenological indicators used by local communities—into these models, AI can enhance their accuracy and relevance for local contexts. By analysing data on climate hazards, infrastructure vulnerabilities, and social factors, AI can identify areas at risk of flooding, drought, or other climate-related disasters. This information can be used to develop strategies for building resilient infrastructure, improving disaster response, and protecting vulnerable populations. This can help improve our understanding of climate dynamics, predict extreme weather events, and assess the impacts of climate change on ecosystems and human populations.
AI can also assist in optimising resource management and reducing environmental impacts. For example, AI can be used in
precision agriculture, where sensors and data analytics can optimise crop management practices, reduce water and chemical usage, and minimise waste. It can also be employed in smart transportation systems to optimise traffic flows, reduce emissions from vehicles, and improve logistics and supply chain operations, leading to more efficient and sustainable transportation.
AI can help enhance the accuracy and efficiency of climate models by leveraging machine learning algorithms to process and analyse large volumes of data.
AI can help monitor and manage natural resources such as forests, oceans, and wildlife habitats. For example, AI can analyse satellite imagery to detect
deforestation or illegal fishing activities, track wildlife migration patterns, and assess the health of ecosystems. This information can be used to inform conservation efforts, protect biodiversity, and mitigate the impacts of climate change. For example, the Indian Space Research Organisation (ISRO) has developed the National Carbon Project, which uses AI and remote sensing to map and monitor India's forest cover and estimate carbon emissions and removals, aiding in
evidence-based forest conservation and management decisions.
Furthermore,
AI can contribute to climate change adaptation by helping communities and governments better understand and respond to the impacts of climate change. For instance, AI can analyse data on
coastal erosion, sea level rise, and storm surge patterns to support the planning and implementation of adaptation measures such as building sea walls, relocating vulnerable communities, and protecting critical infrastructure.
AI can assist stakeholders in developing and implementing effective climate policies. It can analyse data on emissions, economic indicators, and social factors to model the potential impacts of different policy options. This can help policymakers make evidence-based decisions on climate mitigation and adaptation measures, considering the costs, benefits, and trade-offs of different policy scenarios. AI can also facilitate the monitoring and verification of climate commitments, such as those outlined in the
Paris Agreement, by analysing satellite data, remote sensing, and other sources to assess progress towards climate goals.
AI and Machine Learning (ML) can facilitate
climate finance and investment decisions by analysing and processing vast amounts of data related to climate-related projects, risks, and returns. This can help in identifying investment opportunities in sustainable projects and in supporting the allocation of resources to climate actions that have a higher impact on mitigating climate change.
The collaboration between native intelligence—which encompasses the traditional knowledge and practices of indigenous and local communities—and artificial intelligence can offer powerful solutions for climate action.
It can also facilitate citizen engagement and empowerment in climate action. Through AI-powered platforms, citizens can access information, participate in decision-making processes, and contribute to climate-related initiatives. AI can also enable real-time monitoring and reporting of environmental data, empowering individuals and communities to hold businesses and governments accountable for their environmental impacts. AI can analyse social media data, sentiment analysis, and other communication channels to understand public perceptions, attitudes, and behaviours related to climate change.
Conclusion
It is important to recognise that the integration of AI into climate actions raises ethical, social, and governance challenges that need to be carefully addressed. Issues such as bias in data, transparency, accountability, and privacy must be adequately addressed to ensure that AI is deployed in a fair, just, and responsible manner and that the benefits of AI in climate actions are shared equitably among different stakeholders. The collaboration between native intelligence—which encompasses the traditional knowledge and practices of indigenous and local communities—and artificial intelligence can offer powerful solutions for climate action. After all, native intelligence is the accumulated knowledge over centuries of human knowledge and something that can make the AI engines far more effective. By combining the power of AI with the wisdom of native intelligence, we can develop more effective, equitable, and sustainable climate actions that benefit people and the planet.
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