{"id":23366,"date":"2022-05-02T06:21:31","date_gmt":"2022-05-02T09:21:31","guid":{"rendered":"https:\/\/drika.com.br\/blog\/?p=23366"},"modified":"2026-08-12T17:38:25","modified_gmt":"2026-08-12T20:38:25","slug":"reducing-carbon-emissions-in-construction","status":"publish","type":"post","link":"https:\/\/drika.com.br\/blog\/reducing-carbon-emissions-in-construction\/","title":{"rendered":"Reducing Carbon Emissions in Construction"},"content":{"rendered":"

\"carbon<\/p>\n

These fixed effects help ensure that the estimated relationships reflect within-region, over-time variations rather than cross-regional or temporal shocks. To determine whether future construction industry can provide housing and infrastructure for population, we used OLS models to estimate relationships between historical population and construction carbon footprints for each individual country (Data S3). Tests such as KPSS (Kwiatkowski-Phillips-Schmidt-Shin), PP tests, Augmented Dickey-Fuller (ADF), Pearson Correlation Test, Ljung-Box Test, Durbin-Watson Test, ARCH and GARCH test are run. To ensure robustness, a series https:\/\/www.cs-coding.com\/category\/real-estate\/<\/a> of statistical tests is run prior to regression to avoid multicollinearity50, autocorrelation51, heteroscedasticity52, etc. We then calculate the full intensity matrix of the global economy, reflecting the supply chain interdependencies of construction-related carbon footprints (Eqs. 1\u20134).<\/p>\n