Benchmarks for permanent carbon in low-carbon probabilistic design of concrete structures: A case study of China

Abstract

China currently accounts for over 25% of global anthropogenic carbon emissions. Cutting down the carbon emissions of the construction industry is a major task for achieving the carbon neutrality goal in China. However, research on benchmarks and probability distribution models for China’s embodied carbon emissions remains insufficient. To address this gap, this study defined “permanent carbon” by distinguishing the low time-variability part of embodied carbon emissions in buildings. Based on samples of 114 buildings in China, statistical and regression methods were utilized to construct a prior probability distribution model for permanent carbon of concrete structures, considering differences in building types. By applying Bayesian updating methods, posterior distributions were obtained based on latest region-specific information, addressing the issue of outdated data over time. Taking the East China region as an example, we found that the regional adjustment significantly affected carbon emission estimates, with varying adjustment factors for different building types. Transportation carbon emission benchmarks were influenced by transportation modes and distances, while distribution types remained stable. This study employs the Bayesian updating method in a novel way to help establish carbon emission benchmarks, support the formulation of carbon reduction targets, and aid the low carbon design during the early stage of building projects.