A timely analysis for carbon emission reduction in buildings is an effective global response to the crisis of climate change. The logarithmic mean Divisia index (LMDI) decomposition analysis approach has been extensively used to assess the carbon emission reduction potential of the buildings sector. In order to simplify the calculation process and to expand its application scope, a new open-source Python tool (PyLMDI) developed in this article is used to compute the results of LMDI decomposition analysis, including multiplicative and additive decomposition. Users can quickly obtain the decomposition result by initializing the input data through a simple class data structure. In addition, the carbon emissions from commercial buildings are used as a numerical example to demonstrate the function of PyLMDI. In summary, PyLMDI is a potential calculation tool for index decomposition analysis that can provide calculation guidance for carbon emission reduction in the buildings sector. The data and codes for the numerical example are also included.

The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter. The key findings show the following: (1) The major driving forces of carbon emissions from commercial buildings in China were found to be the population size and energy intensity of carbon emissions, and their elastic coefficients were 0.6346 and 0.2487, respectively. (2) The peak emissions of the commercial building sector were 1264.81 MtCO₂, and the peak year was estimated to be 2030. Overall, this study analyzed the historical emission reduction levels and prospective peaks of carbon emissions from China’s commercial buildings from a new perspective. The research results are helpful for governments and decision makers to formulate effective emission reduction policies and can also provide references for the low-carbon development of other countries and regions.

The rapidly growing carbon emissions of residential building operations have become an obstacle to China’s commitment to achieving its carbon-neutral goals by 2060, but they also demonstrate great carbon mitigation potential. To help buildings reach carbon neutrality targets, this study decomposes the drivers of carbon emissions and evaluates the changes in carbon mitigation of residential buildings across 30 Chinese provinces from 2000 to 2018. The results indicate that (1) the operational carbon intensity increased in most provinces and the average annual growth rate across the 30 provinces was 4.2%; (2) from 2001 to 2018, North China and Northeast China had the highest average annual carbon mitigation intensity, at 602.7 and 376.9 kg of carbon dioxide per household, respectively. However, Northwest China had the highest carbon mitigation efficiency, with a carbon mitigation rate of 23.5%; and (3) in most cases, the total carbon mitigations of the operational residential buildings assessed at the provincial scale higher than those assessed nationwide, with a difference of 14.4 million tons of carbon dioxide on average. In addition, this study reviewed the energy efficiency codes for residential buildings and summarized effective energy efficiency measures. Overall, this study fills a gap in our understanding of carbon mitigation tools and provides a reference for the evaluation of historical carbon mitigation effects in the operation of residential buildings.