Dynamic global warming impact assessment integrating temporal variables: Application to a residential building in China

Climate warming is a global concern, and buildings have been recognized as a major contributor because the carbon emissions during their entire life cycles constitute a large share of the total value (almost 40% globally). Many life cycle assessments of buildings have been conducted to quantify the associated global warming impacts. However, few studies have considered the potential temporal variation over the long lifetimes of buildings. In this study, various temporal variables were combined to evaluate the dynamic life cycle global warming impact of a residential building in China. First, annual material and energy consumption data throughout the entire life cycle were acquired from questionnaire survey, statistical reports and literatures. Five dynamic variables (household size, usage behavior, replacement and improvement of components, waste treatment, and energy mix) and their effects on consumption levels were considered. Second, a dynamic inventory analysis tool (DyPLCA) was used to transform the temporal consumption data into dynamic greenhouse gas quantities. The global warming effects of these emissions were quantified using a dynamic characterization tool (DynCO(2)). Finally, emission reduction targets for future decades were used to weight the severities of the impacts at different times. The dynamic instantaneous and cumulative global warming impacts of the building were calculated. Dynamic and static assessment results were compared. We also analyzed the contribution of each dynamic variable to the final results and found that the dynamic variables had very different effects (ranging from-42.00% to 45.34%). This study provided an operable dynamic assessment model and available dynamic data for the global warming impact assessment of buildings in China.

Automated summary

This study evaluated the dynamic life cycle global warming impact of a residential building in China by considering various temporal variables and using dynamic assessment tools. The findings showed significant differences in the effects of different dynamic variables on the global warming impacts of the building. This study provides a dynamic assessment model and dynamic data for the assessment of global warming impacts of buildings in China.