Impacts of green certification programs on energy consumption and GHG emissions in buildings: a spatial regression approach

• Main Authors: Zhang, Limao, Li, Rongyao
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Civil engineering; Green Certification Programs; Energy Star Labels
• Online Access: https://hdl.handle.net/10356/161973
• Institution: Nanyang Technological University

For better energy assessment and management in the buildings industry, a spatial regression method is presented to quantitively measure the impacts of green certification programs on energy consumption and Greenhouse Gas (GHG) emissions in buildings. Spatial autocorrelation analysis is developed to determine the spatial pattern of energy consumption and GHG emissions. A Gaussian kernel function method is employed to calculate the spatial weighting matrix, and an Akaike Information Criterion method is implemented to choose the function bandwidth in the spatial weighting matrix. Subsequently, a geographically weighted regression (GWR) model based on the spatial weighting matrix is constructed to estimate energy consumption and GHG emissions in buildings. Several error criteria are employed to verify the development of the GWR model compared with the ordinary least square (OLS) model. Regression coefficient, Pearson correlation coefficient, and Spearman correlation coefficient analyses are implemented to investigate the effects of green certification programs on building energy consumption and GHG emissions. A case study in Seattle city in 2015 is used to implement the GWR model and validate the improved goodness-of-fit of this regression model. The results imply that: (1) Energy stars programs as the driving force would save energy consumption by 13.2% on average and reduce GHG emissions by 3.24% on average; (2) The fitted GWR models can significantly improve the model's goodness-of-fit and accuracy with an adjusted R2 value of 0.572 in modeling energy consumption and that of 0.626 in modeling GHG emissions compared with the OLS models (with an R2 of 0.538 and 0.594, respectively); (3) Older buildings, taller buildings, and larger buildings with energy star labels have been demonstrated to have a big potential to significantly save more energy and reduce more GHG emissions. This spatial regression approach can be used as an assessment tool for professionals and authorities to detect the influence factors on energy consumption and GHG emissions in buildings and make environmentally sustainable plans for buildings in cities.