Making climate action intelligent with AI

INDIA stands at a crossroads in its climate journey, where the scale of environmental stress collides with the speed of technological change. From choking urban air and disappearing groundwater to climate-vulnerable coastlines and heat-stressed agriculture, the country's challenges are vast, complex and deeply interconnected. Yet, India also has an unprecedented opportunity: to deploy artificial intelligence not as a futuristic add-on, but as a core instrument of climate action. When fused with policy intent, local data and community participation, AI can shift India's response from reactive crisis management to anticipatory and precision-driven solutions.Historically, climate action in India has relied heavily on retrospective data. While disclosure frameworks and action plans have expanded, they often remain disconnected from day-to-day operational decisions. This gap is evident in sectors such as power, manufacturing, buildings and urban services, where emissions and resource use are still managed after the fact rather than optimised in real time.AI enables a decisive shift. By analysing continuous streams of data from sensors, satellites, smart meters and operational systems, AI can detect inefficiencies, forecast risks and recommend interventions before environmental damage or resource loss occurs. This transition is essential for India to align its development trajectory with its climate goals.Talking about shifts, AI-driven Digital Twins mark a decisive turning point. By creating living, virtual replicas of physical assets, supply chains and buildings, continuously fed by sensor data and powered by AI, India can move from observing emissions to controlling them. AI enables these systems to detect hidden inefficiencies, predict failures before they waste energy and simulate what-if pathways that optimise cost, carbon and resilience. In this model, sustainability is no longer a cost-of-doing business, but a driver of efficiency, competitiveness and long-term climate leadership for India.In thermal power plants, AI-enabled digital twins are being used to monitor heat rates and combustion efficiency in real time. By identifying subtle thermodynamic losses that are invisible to manual monitoring, these systems allow operators to fine-tune boiler operations instantly, reducing coal consumption per unit of electricity generated and cutting emissions without compromising grid reliability.India's cities are on the frontline of heat stress and air pollution. Buildings alone account for a rapidly growing share of electricity demand. Traditional building management systems respond only after conditions change, switching on cooling once temperatures rise or occupancy increases.AI transforms this paradigm. In Hyderabad, Bengaluru and Pune, advanced building management platforms now integrate weather forecasts, occupancy patterns and user comfort preferences to proactively manage HVAC and lighting systems. Cooling loads are adjusted before peak demand hits, reducing emissions and strain on urban power grids.In the agricultural belt of Telangana, the Saagu Baagu (agricultural advancement) project illustrates the benevolent face of AI. Partnering with global forums, the state deployed AI tools to support chili farmers. By analysing soil health and weather data, the system provided precise, timely advice on pesticide and fertiliser usage. This led not only to environmental protection through reduced chemical load but also a 21% increase in yields. Here, AI was a bridge between climate science and traditional farming wisdom.Similarly, in Bihar, the Google Flood Hub initiative has used AI to process satellite imagery and hydrological data to forecast floods up to seven days in advance. But technology didn't stop at the server. It relied on trans-intermediaries — village volunteers — to interpret the alerts and warn their communities. This human-in-the-loop model saved livestock, assets and lives.The next leap is to build a structured pipeline where such solutions can be discovered, tested, validated and scaled across geographies. This is the intent of the IndiaAI Application Development Initiative (IADI), under which the IndiaAI Innovation Challenge has taken up climate as a core theme, identifying deployable, field-relevant solutions that can move beyond pilots into public-impact pathways.Under the Climate theme, multiple solutions have been selected that demonstrate how AI can strengthen environmental surveillance, forecast climate-linked health risks and generate real-time intelligence for disaster response and resilience planning. Four such solutions illustrate the breadth of climate-AI app, spanning health security, flood response, heat resilience and multi-hazard risk management.Climate change is also a health security issue. Rising temperatures, shifting rainfall patterns and increased humidity change mosquito breeding cycles and transmission patterns, intensifying risks of dengue, malaria and other such diseases. The Moskeet solution, selected under the IADI Climate theme, targets this intersection directly. The solution reports strong outcomes: 60% reduction in mosquito populations and 40% decrease in disease burden in areas of implementation. It also claims surveillance cost reductions of up to 85%, and fumigation cost savings of Rs 20-22% through more strategic targeting, while reducing pesticide overuse and resistance risks. This represents the shift India needs: from reactive fogging and post-outbreak response to anticipatory, evidence-guided prevention.Flood response depends on speed, coverage and confidence. Conventional optical satellite approaches are often constrained by cloud cover and limited daylight, precisely when floods are most intense. The DeepFlood solution addresses these gaps by using Synthetic Aperture Radar with deep learning models.The Heatwave Resilience concept proposes an AI-driven platform that integrates advanced climate forecasting with health data to create a heat-health risk classification system, providing predictions up to 10 days ahead at sub-district levels. It also includes risk communication through existing channels and Indic-language AI tools.Climate stress increasingly manifests as compound risk floods triggering landslides, extreme rain destabilising terrain and rapid urban development increasing exposure. The Multi-Hazard Susceptibility Mapping solution takes a systems approach.AI will not solve the climate challenge on its own. But when embedded into policy, infrastructure and markets, it can increase the effectiveness of every rupee spent and every tonne of emissions reduced. The transition needed is not just green, but intelligent — one where climate action is guided by real-time insight, predictive capability and systemic optimisation. States that embrace this shift early will position themselves as leaders in a low-carbon, tech-enabled future.The IndiaAI Innovation Challenge pipeline demonstrates that climate-AI future will be built through deployable solutions. AI works best when it becomes part of governance workflows, designed for scale, tuned to local realities and accountable to public outcomes.

Making climate action intelligent with AI

AI-driven Digital Twins mark a decisive turning point

Kavita Bhatia

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