http://localhost:6824/tags/climate-change/ Recent content in Climate Change on Ivan Carnevali Source Themes academia (https://sourcethemes.com/academic/) en-us Copyright &copy; {year} Sat, 12 Jul 2025 00:00:00 +0000 http://localhost:6824/project/climate-change/ Sat, 12 Jul 2025 00:00:00 +0000 http://localhost:6824/project/climate-change/ <p>This project explores whether <strong>climate change exacerbates economic and social disparities between northern and southern Italy</strong>, combining <strong>socioeconomic indicators</strong>, <strong>climate data</strong>, and <strong>advanced statistical modelling</strong>.<br> Data were collected from <strong>Eurostat</strong>, <strong>Copernicus</strong>, and <strong>ISTAT</strong> for the period 2000–2023, harmonized at the <strong>NUTS2 regional level</strong>.</p> <p>After data cleaning and integration, a <strong>hierarchical mixed-effects model</strong> was fitted to investigate the relationship between <em>GDP per capita</em> and multiple climatic and demographic predictors, including <em>temperature, precipitation extremes, wind speed, fertility rate, life expectancy,</em> and <em>employment rate</em>.<br> The model incorporated <strong>random intercepts and slopes</strong> for Italian regions, allowing for heterogeneity across the North, Centre, and South.</p> <h3 id="main-results">Main Results</h3> <ul> <li>Climate change acts as a <strong>multiplier of pre-existing regional inequalities</strong>.</li> <li>Southern regions exhibit <strong>negative random slopes</strong>, indicating slower or negative economic growth relative to the North.</li> <li><strong>Extreme precipitation</strong>, <strong>mean wind speed</strong>, and <strong>fire weather index</strong> significantly and negatively affect <em>GDP per capita</em>.</li> <li>A <strong>breakpoint analysis</strong> on national temperature series showed a structural change in 2013—before the Paris Agreement—suggesting <strong>no measurable effect yet from international climate policy</strong>.</li> </ul> <h3 id="methodology">Methodology</h3> <ul> <li><strong>Random-effects linear models</strong> with zone- and region-specific effects (<code>nlme</code> package)</li> <li><strong>Multicollinearity analysis</strong> and variable selection (<code>corrplot</code>)</li> <li><strong>Geospatial visualization</strong> of GDP and temperature trends (<code>ggplot2</code>, <code>sf</code>, <code>giscoR</code>)</li> <li><strong>Breakpoint detection</strong> for temperature time series (<code>strucchange</code>)</li> <li><strong>Data imputation</strong> via the LOCF (Last Observation Carried Forward) method</li> </ul> <h3 id="data-sources">Data Sources</h3> <ul> <li><strong>Eurostat</strong>: agriculture, economy, education, and tourism data</li> <li><strong>Copernicus</strong>: climate and environmental variables</li> <li><strong>ISTAT</strong>: demographic and migration statistics</li> </ul> <h3 id="downloads">Downloads</h3> <ul> <li><a href="http://localhost:6824/files/Population_and_climate_change.pdf">📄 Full Report (PDF)</a></li> <li><a href="http://localhost:6824/files/DATA.csv">📊 Dataset (CSV)</a></li> <li><a href="http://localhost:6824/files/Model.R">💻 R Code and Analysis (Markdown)</a></li> </ul> <hr> <p>This project demonstrates how <strong>integrating climate and socioeconomic data through advanced modelling</strong> can reveal subtle but significant disparities in resilience and adaptability across regions — providing a statistical foundation for <strong>evidence-based climate policy</strong> in Italy.</p>