Changing climate reshapes age structure in China

1. Overview This project consists of 11 files covering an end-to-end analytical pipeline from climate exposure data processing and health effect modeling to population projection. The study is conducted at the prefecture-city level in China, combining historical observations and future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) to systematically assess both the short-term effects of temperature on fertility and long-term demographic trends. The files are categorized into four types: Jupyter Notebooks (spatial data processing and exposure calculation), R scripts (statistical modeling and population projection), Excel files (future scenario temperature exposure), and a ZIP archive (baseline population data). 2. File Description by Category 2.1 Jupyter Notebook Files (Spatial Data Processing & Exposure Calculation) (1) IDW_Combain_shp_and_nc.ipynb Implements Inverse Distance Weighting to spatially align ERA5 climate grid data (NetCDF format) with Chinese prefecture-level administrative boundaries (Shapefile). Computes daily temperature exposure metrics for each city, providing foundational exposure data for subsequent health effect analyses. (2) SSP_daily_exposure_calculate.ipynb Calculates future daily temperature exposure variables based on climate model outputs under different SSP scenarios. Supports multi-scenario, long-term exposure assessment and serves as input for risk projection. 2.2 R Script Files (Statistical Modeling & Population Projection) (1) discrete_time_cox.R Implements a discrete-time Cox model to analyze the association between preconception temperature exposure and conception risk. The model supports 3-month and 6-month exposure windows and outputs city-specific hazard ratios. It further integrates projected temperature changes to estimate conception risk and the probability of conception within 12 months under different SSP pathways. (2) fertility_rate.R Builds a multi-regional cohort-component population projection model. Based on baseline population structure and age-specific fertility, mortality, and migration rates, it simulates the dynamics of population size and age-sex structure for Chinese cities from 2011 to 2100, supporting population trend analysis under multiple SSP scenarios. 2.3 Microsoft Excel Files (Future Scenario Temperature Data Summary) All Excel files summarize quarterly and annual mean temperature exposure values calculated under different SSP scenarios from 2015 to 2100, covering three representative climate pathways: SSP1-2.6 (low warming scenario), SSP2-4.5 (medium warming scenario), SSP5-8.5 (high warming scenario) 2.4 ZIP Archive (Baseline Data) (1) baseline_population_data.zip Contains foundational population data required for the project, such as age structure, baseline fertility rates, and migration rates. It serves as the starting point for the population projection model.