Due to large energy consumption and carbon emissions (ECCE) in the building sector, there is huge potential for carbon emission reduction, and this will strongly influence peak carbon emissions and carbon neutrality in the future. To get a better sense of the current research situation and future trends and to provide a valuable reference and guidance for subsequent research, this study presents a summary of carbon peak and carbon neutrality (CPCN) in buildings using a bibliometric approach. Three areas are addressed in the review through the analysis of 364 articles published from 1990–2021: (1) Which countries, institutions, and individuals have conducted extensive and in-depth research on CPCN in buildings, and what is the status quo of their collaboration and contributions? (2) What subjects and topics have aroused wide interest and enthusiasm among scholars, and what are their time trajectories? (3) What journals and authors have grabbed the attention of many scholars, and what are the research directions related to them? Moreover, we propose future research directions. Filling these gaps will enrich the research body of CPCN and overcome current limitations by developing more methods and exploring other practical applications.

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.

With increasingly high housing prices, the urban housing problem has changed from an economic issue to a livelihood issue in China. Taking 32 major cities in China as an example, this paper employed data from 2007 to 2016 to build a panel data model to empirically study the impact of population migration on urban housing prices. From the two perspectives of the national level and regional level (eastern region, central region and western region), the results of this study showed that (1) on the national level, population inflow had a significant positive correlation with urban housing prices, where a population inflow rate increase of 1% increased urban housing prices by 0.31%; and (2) on the regional level, a population inflow rate increase of 1% increased urban housing prices in the eastern region by 1.34%, but population inflow had no obvious impact on the urban housing prices in the central and western regions. Based on the results, this study suggested addressing housing supply imbalances through housing product diversification and affordable housing system improvement, and addressing construction land supply imbalances by building a perfect system linking land-use planning to population; at the same time, it also suggested building more nationally central cities following the urbanization trend, and taking this as the key to developing urban agglomerations, reasonably decentralizing the population flow, promoting the healthy and stable development of the real-estate market and advancing sustainable urbanization. The above conclusions have practical significance for China and other developing countries to coordinate population and urban development in the process of rapid urbanization.

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.

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.