Building emission reduction feasibility assessment considering uncertainty

The building sector accounts for 37% of global carbon emissions, which is commonly recognized as a significant contributor to carbon emissions. The emission reduction related to the building sector has received widespread attention. Scientific calculation and assessment of building carbon emissions are fundamental to comprehensively understanding emission characteristics and supporting carbon reduction design with no doubt. However, most of the current mainstream building carbon emission calculation methods neglect the stochastic nature of emission parameters. This study introduced a building emission reduction feasibility assessment considering uncertainty, which defined the carbon compliance probability for buildings, established a probabilistic model for building emission reduction potential, derived probability assessment calculation methods for six distributions, and proposed a probabilistic characterization method for emission reduction feasibility. Validation with a practical case indicates that the proposed model requires minimal computational resources and offers rapid assessment speed; compared with Monte Carlo simulation with a sample size of 106, the proposed method uses only 44.2% of the computational time. Analysis of two real-world engineering cases yields carbon compliance probabilities of 0.8778 and 0.7489, respectively. At a 95% confidence level, the maximum carbon reduction relative to the baseline will not exceed 93.8 tCO2e and 3173 tCO2e for the two cases.

Geometry · Machine learning · Reduction (mathematics) · Sociology · Uncertainty quantification · Uncertainty reduction theory · Air Quality and Health Impacts · Building Energy and Comfort Optimization · Computer Science · Environmental Impact and Sustainability · Environmental Science · Mathematics