As members of international climate teams gathered in Europe during London Climate Action Week, the continent was gripped by severe and punishing heatwaves. The timing served as a stark, global reminder of how urgently effective climate resilience solutions are needed. Extreme heat has rapidly emerged as one of the most dangerous climate risks worldwide, linked directly to nearly half a million deaths each year. Yet, despite its devastating toll, heat remains largely invisible in the ways governments traditionally manage disasters. Unlike a fast-moving flood or a violent storm, extreme heat arrives without sudden visual drama, and its mounting victims frequently go uncounted and unacknowledged in official disaster tallies.
In India, where heat-related deaths are projected to rise sharply by the end of the century under various climate scenarios, this policy blind spot is beginning to face a much-needed reckoning. Recently, a national finance commission called formally for heat and lightning to be classified as extreme weather events. This proposed policy shift carries profound implications, potentially unlocking vital investments and strategic coordination for an environmental hazard that has historically suffered from a severe lack of both resources and focused administrative attention.
However, recognizing a crisis and allocating funds are only effective if robust systems exist to ensure those resources actually reach the populations most vulnerable to rising temperatures. Authorities need precise, actionable intelligence regarding where extreme heat hits hardest, which demographic groups are most heavily exposed, and how public funds are ultimately deployed. Closing this critical operational gap is the core mission of the Intelligent Data Solution for Disaster Risk Reduction (IDS-DRR). Developed collaboratively by the Open Contracting Partnership and CivicDataLab, alongside subject-matter experts and local government officials, IDS-DRR was originally built to manage spending aimed at reducing flood risks. Now, the platform has been significantly expanded to cover extreme heat, bringing scattered, fragmented data together so that governments can transition away from reactive, ad-hoc disaster responses toward proactive, data-driven climate preparedness.
A Silent Killer Shrouded in Regional Blind Spots
Part of the persistent challenge in managing extreme heat stems from a historical, one-size-fits-all approach to administrative response protocols. In India, traditional national mandates have typically triggered heatwave protocols only when ambient temperatures cross the threshold of 40 degrees Celsius. This blunt, nationwide benchmark effectively flattens enormous regional differences and microclimates across a vast and geographically diverse country.
In the northeastern state of Assam, for instance, the mercury rarely climbs to that high national threshold, meaning that official heatwave days are almost never triggered through standard administrative channels. Yet, the region has warmed significantly over recent years, and at least eight heat-related deaths were officially reported between 2024 and 2025. Without a formal, government-recognized declaration of a heatwave, local officials frequently lack the legal authority or the budgetary mechanisms required to deploy emergency resources to protect residents.
Meanwhile, in the eastern coastal state of Odisha, temperatures regularly exceed 42 degrees Celsius, but it is the punishing humidity that inflicts the heaviest damage, inducing severe physiological strain that a standard thermometer alone cannot capture.
The public health toll of extreme heat is notoriously difficult to pin down precisely. When an individual with an underlying heart condition passes away during an extended hot spell, authorities can seldom draw a clean, indisputable line back to the ambient temperature. These fatalities accumulate quietly and invisibly, which explains why extreme heat has historically struggled to secure a dedicated budget within broader disaster risk reduction planning.
Mapping the Risk and Following the Public Money
To combat this informational vacuum, IDS-DRR integrates large volumes of previously fragmented data to map geographical areas where heat and vulnerability risks are highest, while simultaneously tracking how public money is being spent to combat them. The platform has already proven its utility in managing flood risks across three states: Assam, Himachal Pradesh, and Odisha. Its recent expansion now covers extreme heat across every district in Assam and Odisha. The platform relies on unlocking diverse streams of information, including meteorological hazard metrics, socioeconomic vulnerability indicators, and public procurement records.
The aggregated data allows officials to calculate objective indicators of heat risk at the district level, utilizing the established Intergovernmental Panel on Climate Change framework. Among the core datasets utilized are two decades of land surface temperature data, transformed from raw satellite imagery into an accessible, analytical format. Future iterations aim to offer even more granular analyses at the hyper-local ward level, where the urban heat island effect can be acutely observed. In densely built environments, concrete surfaces, dense urban infrastructure, and limited green cover trap heat, driving local temperatures well above surrounding rural areas. Static maps of this phenomenon have already been produced for the major cities of Guwahati and Bhubaneswar.
Mapping physical heat hazards, however, represents only half the battle. The platform also systematically scrapes public procurement portals to monitor where government funds are being invested. Unlike flooding, extreme heat rarely commands a dedicated line-item budget, meaning administrative responses are typically scattered and buried across various government departments. Much of this spending never appears in traditional tendering data because heat interventions are frequently implemented on an ad-hoc basis without requiring major construction. For instance, if a municipal government converts a local school into a makeshift cooling center during summer school holidays, that decision represents an administrative adjustment rather than a formal procurement process.
To uncover these hidden expenditures, IDS-DRR deploys customized algorithms to cast a wide data net. In Odisha alone, researchers identified and tagged 1,313 heat-related tenders valued at $218 million as of May 2026. Many of these projects served another primary function while indirectly mitigating heat; for example, a state forestry department plantation initiative helps improve urban green cover, which subsequently lowers both air and surface temperatures.
A closer look at the procurement data reveals the reactive nature of historical spending. Only seven tenders in Odisha, awarded between May and August 2025, explicitly referenced heatwaves and extreme heat, with all of them dedicated to constructing temporary shaded structures beside busy traffic junctions. Another 14 tenders, spanning May 2025 to June 2026, focused on building drinking water kiosks in high-traffic public spaces. The timing of these awards during peak summer months strongly suggests they served as emergency measures to cope with active heatwaves. The ten largest tenders by monetary value, all originating in 2021, were dedicated to broader upgrades for municipal drinking water supply systems.
Moving From Ad-Hoc Relief to Targeted Resilience
Overlaying this financial procurement data with other critical socioeconomic indicators—such as local population density, healthcare infrastructure access, and the presence of outdoor laborers—enables decision-makers to evaluate risks at a hyper-local level.
This analytical approach has already demonstrated its practical value. When IDS-DRR was initially deployed to address severe flooding in Assam, it assisted state disaster management authorities in identifying ten high-risk districts. Armed with a robust, data-driven justification for resource allocation, officials successfully prioritized seven of those ten districts with targeted budgets and project approvals during the 2025 fiscal cycle.
For extreme heat, the platform’s interactive dashboards provide an equivalent capability, demonstrating to disaster management officials precisely how risk has evolved within specific districts and where local coping capacities fall short. This insight directly guides targeted interventions and strategic resource allocation decisions.
Insights From the Heat Models in Assam and Odisha
Analyzing heat risks across the districts of Assam and Odisha during the 2025 and 2026 summer seasons illustrates the shifting nature of climate hazards. In Assam, Sonitpur stood out as the sole very-high-risk district during the summer of 2025, accompanied by 11 other districts classified as high risk. By the summer of 2026, Dibrugarh emerged as the only very-high-risk district, driven by recording the highest number of extreme heat days in the state. Sonitpur saw its risk rating drop in 2026 primarily due to a decline in extreme heat days, which decreased from 26.63 days in 2025 to 11.73 days in 2026, lowering the climatic hazard while exposure and vulnerability metrics remained stable.
In Odisha, eight districts were classified as high risk during the summer of 2025, with no districts reaching the very-high-risk threshold. By the summer of 2026, Jajapur transitioned into the sole very-high-risk district, propelled by a dramatic surge totaling 68 extreme heat days. This intensified hazard, combined with persistently elevated exposure and vulnerability risks, pushed the district’s composite score upward, while Dhenkanal, Kendujhar, and Nayagarh remained classified as high risk.
Across both states, the primary driver of a district’s overall heat risk proved to be the sheer frequency of extreme heat days, accounting for roughly half to two-thirds of the composite risk score in higher-risk areas. However, districts experiencing similar climatic hazards can carry vastly different overall risk profiles due to underlying variations in local vulnerability. For instance, in districts like Jajapur and Kalahandi in Odisha, and Hojai, Tinsukia, and Udalguri in Assam, underlying vulnerability indicator scores contributed significantly to overall risk levels, demonstrating that local sensitivities substantially amplify environmental dangers. Conversely, districts with relatively high exposure but lower vulnerability—such as Mayurbhanj and Cachar—ranked significantly lower in overall risk, proving that population exposure alone does not dictate outcomes.
These findings underscore a central lesson: while climatic hazards like heatwaves serve as the primary catalyst, the dynamic interaction between hazard and vulnerability ultimately determines whether a district is classified as high risk. Understanding this complex interplay is what makes the analytical model exceptionally useful for prioritizing localized heat adaptation interventions and resource distribution.
Complementing, Consulting, and Collaborating on the Ground
For organizations and governments attempting to replicate this data-driven approach, local experts advise mapping the existing stakeholder ecosystem first to identify opportunities where new tools can add tangible value to ongoing initiatives. In Assam, the demand for extreme heat monitoring originated directly from state officials who were actively formulating regional heat policies amid a growing institutional recognition of rising temperatures.
The Assam State Disaster Management Authority had previously collaborated on developing the initial flood risk analytics module for IDS-DRR, utilizing those insights to refine regional flood management strategies. This established partnership provided a unique foundation for adapting the platform to inform the state’s heat action plan, ensuring it remained responsive to hyper-local needs while aligning with broader national frameworks. Backed by disaster management leadership overseeing statewide resource allocation, planning, and reporting, the project team was able to integrate new government datasets, develop advanced risk analytics, and expand procurement intelligence.
Furthermore, the team helped establish the Assam Heat Action Alliance, a multi-stakeholder forum bringing together disaster management authorities, UNICEF, various government departments, civil society organizations, and academic institutions to coordinate regional heat strategies. In a state lacking a dedicated heat taskforce, this alliance breaks down administrative silos, enabling diverse entities to develop localized action plans, vulnerability assessments, early warning communication systems, and targeted protective interventions for vulnerable populations.
In contrast, Odisha presented a different operational landscape. Facing multiple annual natural disasters, the state already possessed a functioning heat action plan and advanced technological infrastructure. What it required was a more effective mechanism to reach grassroots communities—a gap that the expanded platform was tailored to address.
A Scalable Blueprint for Global Climate Resilience
The implications of this ongoing data initiative extend well beyond the borders of India. Developers are actively working to have IDS-DRR recognized globally as a Digital Public Good for managing flood risks and public expenditures, a status that would make its underlying software architecture significantly easier for other nations to adopt and adapt.
The successful integration of heat-risk analytics demonstrates that the platform possesses the requisite flexibility to incorporate new hazards and diverse datasets over time—including droughts, landslides, and cloudbursts—thereby providing disaster management authorities with a unified decision-support system featuring integrated risk insights across multiple climate threats.
As global temperatures continue to climb, India’s data-driven experiment offers a vital lesson for the international community: fighting the escalating impacts of climate change relies as much on improving baseline knowledge, administrative transparency, and cross-departmental coordination as it does on reducing greenhouse gas emissions.
The development and deployment of this initiative were made possible through the generous support of the Patrick J. McGovern Foundation, a philanthropic organization dedicated to advancing artificial intelligence and data science solutions to foster a thriving, equitable, and sustainable future for all.
