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Peer-reviewedCC BYJournal of the Royal Statistical Society Series A (Statistics in Society)

Mortality forecasting under climate risk: a stochastic approach with distributed lag nonlinear models

  • Jiacheng Min1
  • Han Li1
  • Thomas Nagler2
  • Shuanming Li1
  1. 1Department of Economics, The University of Melbourne Centre for Actuarial Studies, , Melbourne, VIC ,
  2. 2Department of Statistics, LMU Munich, Munich Center for Machine Learning , Munich ,
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Abstract

Assessing climate-driven mortality risk has become an emerging area of research in recent decades. In this article, we propose a novel approach to explicitly incorporate climate-driven effects into single- and multipopulation stochastic mortality models. The model consists of two components: a stochastic mortality model, and a distributed lag nonlinear model (DLNM). The stochastic component captures nonclimate long-term trend, volatility, and seasonal patterns in mortality rates. The DLNM component captures nonlinear and lagged effects of climate variables on mortality, and the impact of heat waves and cold waves across age groups. For model calibration, we propose a backfitting algorithm that disentangles climate-driven mortality risk from nonclimate-driven stochastic mortality risk. We demonstrate improved short-term (1–18 months) forecasting performance against four alternative models, using data from Athens, Lisbon, and Rome. As an application of the proposed models, we utilize future UTCI data to provide mortality forecasts under two representative concentration pathway (RCP) scenarios, accounting for both stochastic mortality improvement and climate risk. Results show declining winter mortality and rising summer mortality. Although we expect slightly lower overall mortality in the short term under RCP8.5 than RCP2.6, a long-term increase in mortality is anticipated under RCP8.5.

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Cite

BibTeXRIS
BibTeX
@article{min2026mortality,
  title = {{Mortality forecasting under climate risk: a stochastic approach with distributed lag nonlinear models}},
  author = {Jiacheng Min and Han Li and Thomas Nagler and Shuanming Li},
  journal = {Journal of the Royal Statistical Society Series A (Statistics in Society)},
  year = {2026},
  month = jun,
  doi = {10.1093/jrsssa/qnag082},
  url = {https://doi.org/10.1093/jrsssa/qnag082},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Mortality forecasting under climate risk: a stochastic approach with distributed lag nonlinear models
AU  - Jiacheng Min
AU  - Han Li
AU  - Thomas Nagler
AU  - Shuanming Li
PY  - 2026
DA  - 2026/06/18
JO  - Journal of the Royal Statistical Society Series A (Statistics in Society)
DO  - 10.1093/jrsssa/qnag082
UR  - https://doi.org/10.1093/jrsssa/qnag082
AB  - Assessing climate-driven mortality risk has become an emerging area of research in recent decades. In this article, we propose a novel approach to explicitly incorporate climate-driven effects into single- and multipopulation stochastic mortality models. The model consists of two components: a stochastic mortality model, and a distributed lag nonlinear model (DLNM). The stochastic component captures nonclimate long-term trend, volatility, and seasonal patterns in mortality rates. The DLNM component captures nonlinear and lagged effects of climate variables on mortality, and the impact of heat waves and cold waves across age groups. For model calibration, we propose a backfitting algorithm that disentangles climate-driven mortality risk from nonclimate-driven stochastic mortality risk. We demonstrate improved short-term (1–18 months) forecasting performance against four alternative models, using data from Athens, Lisbon, and Rome. As an application of the proposed models, we utilize future UTCI data to provide mortality forecasts under two representative concentration pathway (RCP) scenarios, accounting for both stochastic mortality improvement and climate risk. Results show declining winter mortality and rising summer mortality. Although we expect slightly lower overall mortality in the short term under RCP8.5 than RCP2.6, a long-term increase in mortality is anticipated under RCP8.5.
SN  - 0964-1998
SN  - 1467-985X
ER  -