India’s cities are heating up, and so is its inequality. As concrete replaces trees and glass towers rise beside informal settlements, three crises now converge: widening social and gender divides, uneven urbanisation, and intensifying climate threats—especially deadly heat. At the same time, artificial intelligence (AI) is rapidly shaping how cities plan and deliver services. The key question is not whether AI will drive India’s climate response, but whether it can do so equitably—protecting women, informal workers, and the urban poor.
By 2025, half of humanity lives in cities, which bear the brunt of climate impacts. Indian cities are heating nearly twice as fast as rural areas, creating lethal “urban heat islands.” Around 16 per cent of Indians remain in multidimensional poverty—many living in informal settlements with tin roofs, scarce water, and no cooling or health services. Globally, gender gaps persist: only 68.8 per cent of the economic gender gap is closed, with parity still a century away. These inequalities play out daily—in who works outside, who walks furthest for water, and whose care burdens rise with every heatwave.
Climate projections show that by 2030, 10 million more people will face food insecurity, with South Asia among the hardest-hit regions. As urbanisation races toward 68 per cent of the world’s population by 2050, poor neighborhoods will face the highest risk. For women and informal workers—overrepresented in precarious and unpaid care roles—these overlapping failures are already visible.
India has begun linking urban climate and social policy through tools like Heat Action Plans (HAPs). Ahmedabad’s pioneering 2013 HAP, launched after a deadly heatwave, used IMD forecasts, geospatial maps, and public awareness drives to cut heat-related deaths. Its success inspired other cities and shaped NDMA guidelines. Delhi’s 2025 HAP represents a new generation—embedding AI and satellite data to create building-level vulnerability maps. Collaborations with IIT Mandi and partners like Resilience AI help identify the hottest clusters, enabling neighborhoods such as Vivekananda Camp to install low-cost measures—reflective paints, shade structures, and drinking water points. Technology strengthens, rather than replaces, community action. Other cities are experimenting too. Varanasi’s ward-level HAPs align with forecasts to 2050 and guide investments in cool roofs, green spaces, and public health. Together, these efforts signal a shift from emergency heatwave response to anticipatory, data-driven planning.
Behind these initiatives lies an expanding ecosystem of climate and heat data. Satellites such as Sentinel and Landsat, combined with IMD forecasts, reveal who lives in hottest zones, where tin roofs dominate, and where tree cover has vanished. Used well, these data power predictive models that help target relief—extra ambulances, clinic hours, or multilingual alerts. But data without local verification can mislead. Maps often contain errors about building types or occupancy, giving a false sense of precision.
Experts and practitioners emphasise verification and participation. Government data, satellite imagery, and AI models must be grounded in community knowledge—workers counting water taps or measuring indoor temperatures often provide insights that models miss. Decision-making, as one sustainable energy expert from Maharashtra noted, must rely on “verified, not automated” data.
Panelists discussing AI and climate resilience also highlighted governance of data. A foundation program leader pointed to India’s UPI experience: clear public purpose and interoperable platforms can democratise innovation. Climate data needs similar collaborative ecosystems that allow secure, standardised sharing across agencies, firms, and civil society—else each builds its own silo, leaving poorer communities invisible.
A philanthropy representative warned against the illusion of completeness in digital dashboards. Clean maps may hide unregistered slums or waste piles on unmapped streets. “AI cannot replace human judgment,” she argued; inclusive design requires community voices. From the tech side, a geospatial AI manager echoed the “garbage in, garbage out” warning and raised another concern: AI’s own energy footprint. The computing power behind massive AI models consumes energy comparable to parts of large cities—so resilience planning must also limit AI’s environmental costs.
Yet, AI’s promise remains strong. Cities under the Smart Cities Mission are using geospatial AI to map sprawl, guide infrastructure, and balance land use. Private firms like AlphaGeo simulate growth to predict where informal settlements might arise, helping planners allocate resources better.
AI tools can also directly reduce inequality. Equity analytics identify zones where high heat risk coincides with poor access to cooling or healthcare, guiding municipal investments in trees, water, and public cooling centers. For India’s 8-15 million gig workers, such tools can be life-saving: delivery platforms now test AI-based alerts that modify shifts during red-heat warnings and mark nearby rest points.
New financial tools such as parametric insurance—piloted by Plutas Analytics and Digit Insurance—trigger automatic payouts when heat thresholds are breached, giving informal workers immediate relief from lost income or health shocks. Apps like the updated Heat Stress Mobile App provide real-time alerts on temperature and humidity, advising on hydration or rest—now adapted for Indian users confronting record heatwaves.
These innovations show that AI can turn vast climate data into localised, usable insights—if embedded in real institutions. Under India’s National Action Plan on Climate Change, missions such as Jal Jeevan and Sustainable Agriculture increasingly use AI to detect groundwater arsenic, forecast rainfall, and advise farmers. Applied with a gender lens—recognising women’s role in water and food systems—these tools can build economic resilience.
Grassroots organisations have led this translation of high-tech to low-cost. The Mahila Housing Trust in Ahmedabad helps women in slum communities install affordable cooling—jute coolers, white roofs, better ventilation—while training them as climate leaders. These initiatives not only lower temperatures but empower women to shape urban adaptation policies.
Globally, frameworks like the UN’s Gender Snapshot and development bank financing are aligning around gender-responsive climate action, linking funds to inclusion metrics. Institutions such as ICRISAT use AI to deliver hyper-local weather and crop advisories to smallholder farmers, often led by women—bridging science with rural livelihoods.
Still, AI can deepen exclusion if the most vulnerable remain invisible in data. Informal workers, internal migrants, and slum residents often do not appear in official records, and women’s domestic or informal work rarely features in occupational data. Without correcting these gaps, AI systems will perpetuate inequality even as they model “solutions.”
The path forward rests on three principles:
1. Accurate, context-aware data—ground surveys must complement satellites and need capacity and community partnerships to interpret outputs.
2. Collaboration—across public, private, and civic actors—with open, safe data-sharing standards.
3. Community co-design and co-creation—those enduring the heat should define what problems AI solves and what interventions fit their realities.
India stands at a turning point. Its AI-powered urban future will be judged not by speed or scale, but by whether it reduces vulnerability. Used wisely, AI and climate data can guide fairer investments and protections; used poorly, they will cement bias in the next generation of “smart” cities. The promise lies not in algorithms alone but in relationships—between planners and residents, data and lived experience, technology and trust.
Yamini Atmavilas is a social impact leader working on unlocking resourcing, building strategy, and helping optimise impact across gender & equity, economic inclusion, health, and energy
Views expressed are the author’s own and don’t necessarily reflect those of Down To Earth