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Productive building energy efficiency (BEE) work is an approved factor in the progress of sustainable urbanization in China, with the assessment of carbon emission reduction in China’s public buildings (CERCPB) being an essential element of this endeavor. Nevertheless, such evaluation has been hampered by inadequate and inefficient approaches; this is the first study to utilize the Logarithmic Mean Divisia Index Type I (LMDI-I) to decompose the equation of China’s public building carbon emissions (CPBCE) with the connected driving factors (population in China, floor areas of China’s existing public buildings, building service level index of China’s existing public buildings, and the comparable CPBCE intensity), and this equation was established by the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The LMDI and STIRPAT approaches subsequently assessed the CERCPB values from 2001 to 2015. The results indicated that: (1) Only the contribution of the comparable CPBCE intensity to CPBCE was negative during 2001–2015; this represents the CERCPB value for the period. (2) The assessment results indicated that CERCPB has accumulated considerably with the swift progress of BEE work in China in 2001–2015. The CERCPB values in 2001–2005, 2006–2010, and 2011–2015 were 69.29, 158.53, and 277.86 million tons of carbon dioxide, respectively. (3) This study demonstrated that the positive effect of implementing public BEE work in China had led to significant results in 2001–2015, which can be regarded as a prerequisite for producing the considerable accumulation of CERCPB over this period. Overall, this study illustrated the feasibility of employing the LMDI and STIRPAT approaches for assessing the CERCPB value. Accordingly, we believe the results of this study are a significant driving force in the next phase of the development of the carbon emission control strategy of public buildings and sustainable urbanization in China

How to Measure Carbon Emission Reduction in China’s Public Building Sector: Retrospective Decomposition Analysis Based on STIRPAT Model in 2000–2015