Super El Nino report: Co-author explains methodology behind '451,000 excess deaths' estimate
The University of Chicago's Energy Policy Institute released a report stating that the super El Nino could result in approximately 15,800 extra heat-related deaths in India within the next six months. Emily Grover-Kopec, a co-author of the study, explained that the researchers utilized two components for their estimation: seasonal temperature forecasts and temperature-mortality relationships.
The temperature-mortality relationships were assessed using one of the most widely recognized seasonal forecasts from the European Centre for Medium-Range Weather Forecasts. Grover-Kopec, who also serves as a director at the climate and energy practice at the Rhodium Group, mentioned that the methodology employed in this study differed from the previous one that predicted deaths over longer periods.
The 30-year period from 1996 to 2025 was chosen for this particular report to ensure the numbers remained relevant to decision-makers' current planning and actions, aiming to inform resource allocation and early interventions to save lives. Abhiyant Tiwari, who leads the climate resilience and health team at Natural Resources Defense Council in India, highlighted that the study serves as an early warning and emphasizes the need for action.
However, Tiwari cautioned that the mortality estimates should be treated cautiously as they are model-based projections yet to undergo peer review. He emphasized that India's Heat Action Plans and stronger institutional frameworks can provide a solid foundation for sustained preparedness and protection of vulnerable communities.
Harshal Ramesh Salve, a public health professional and faculty member at the All India Institute of Medical Sciences, stressed the urgency of implementing heat mitigation strategies, such as heat action plans, data-driven policymaking, and establishing data-sharing mechanisms at national and local levels.
Written by urgent.news from Hindustan Times's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.