As the energy crisis and environmental problems are exacerbated, rural carbon emissions have gradually attracted increasing attention. Meanwhile, rural areas have the important function of ecological carbon sequestration, so the research field of carbon emissions has gradually expanded from urban to rural areas in recent years. To systematically sort out the research progress regarding low-carbon rural areas, a total of 583 papers published during 2013–2023 were acquired through the bibliometric analysis method from the “professional field of science of building technology” in the core database of Web of Science. Then, the research stage, author cooperation, institutional cooperation, national distribution, and keyword co-occurrence of “low-carbon rural areas” were analyzed via data visualization analysis software VOSviewer, and the current hot issues, such as carbon emission calculation list and elements, carbon emission measurement method/model, and energy saving and emission reduction paths, were further summarized and reviewed. The statistical analysis results show that: (1) in most of the current research on rural carbon emissions, geographical characteristics and rural carbon emissions have been rarely analyzed; (2) there is a lack of interdisciplinary research, e.g., less cooperation with geographic information or other disciplines; (3) the differences and characteristics of rural carbon emission factors in different economies and regions have rarely been analyzed. Based on the analysis, this research pointed out that future research can be comprehensively carried out in formulating rural carbon emission inventory, establishing a multi-scale and multi-regional statistical method and so on. This research also provides a comprehensive analysis and summary of the existing research on the structure and model of carbon emissions in low-carbon rural areas and also presents the problems that need to be paid attention to in the future research, pointing out the research direction for relevant scholars.

Energy efficiency in high-density urban areas is increasingly gaining more attention as the energy crisis and environmental issues worsen. Urban morphology is an essential factor affecting the energy consumption and solar energy development potential of buildings. In response to the research gap of previous studies that only analyzed building energy consumption or solar energy potential from a single objective, this paper aims to combine the two objectives of block-scale building energy consumption and solar development potential to explore the joint influence of urban residential morphological elements on correlations between the two. By investigating and summarizing 100 sample cases of Wuhan city blocks, 30 urban residential block prototypes were constructed. The correlations between the leading morphological indicators of the blocks with the building energy consumption and solar energy potential of the residential prototypes were quantified, respectively. The study results show that at certain floor area ratios, the highest solar power generation can be achieved with a mixture of high-rise slabs and high-rise towers, but the building energy intensity level is relatively high; combining building energy consumption and solar power generation, the residential block form of high-rise towers and low-rise villas has incredible energy-saving potential. In addition, the regression analysis results show that three block form indicators, namely the roof-to-envelope area ratio, compacity, and site coverage, have the most prominent influence on building energy intensity and solar power generation, and they all show positive correlations. This study can provide suggestions for urban residential planners and managers to promote urban energy conservation at the design stage.

This study provides an in-depth analysis of the current status of the promotion and application of BIM software in China, highlighting its differences from the international market. Chinese BIM software shows advantages in data security, cost-effectiveness, user habit adaptability, and localization standards, but faces challenges in terms of technological maturity and ecosystem development. By employing bibliometric methods and utilizing literature resources from CNKI and the Web of Science, the study offers a comprehensive analysis of the market and policy environment and proposes targeted recommendations. The study indicates that Chinese BIM software has significant advantages in enhancing data security and cost-effectiveness but lacks technological maturity and requires improvements in ecosystem development. User habits are crucial for the promotion of BIM software, and the localization and adaptability of the software need further enhancement. To promote the widespread application of BIM in the architecture, engineering, and construction (AEC) industries, a robust market mechanism needs to be established. The government should strengthen policy support and improve regulatory frameworks; software developers should enhance product functionality and work closely with users; and application enterprises should actively provide feedback to promote software improvements. Moreover, the introduction of reasonable capital and the collaboration between educational institutions and enterprises to cultivate professional talent are critical for the promotion and application of BIM technology. The research emphasizes that forming a cross-departmental joint mechanism to promote a virtuous cycle in the AEC market is key to achieving the widespread application of BIM technology. These measures can provide strong market impetus for the development of BIM software, accelerate the maturity and ecosystem construction of Chinese BIM software, and ultimately realize the comprehensive upgrading of informatization and digitization in the construction industry.

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Rural buildings have high optimization potential as a major source of carbon emissions. However, the current research on carbon reduction in rural buildings is rough and lacks categorization and geographic studies. Coastal villages are more economically developed than other types of villages and have greater potential for energy saving. Therefore, this study takes the carbon emission data of buildings and life in 409 villages in typical coastal provinces of China as the basis and proposes optimization strategies for carbon reduction in coastal village buildings via cluster analysis and correlation analysis. The results show that the carbon emission characteristics of coastal villages can be categorized into three scenarios: for scenario 1, villages, their population, and village cultivated area are the core influencing factors of carbon emission, while for scenarios 2 and 3, the most central influencing factors are coal and electricity consumption. Therefore, different types of villages should be guided differently when studying carbon sinks and carbon emission projections in coastal villages. This study aims to establish a low-carbon performance quality assessment and optimization pathway for coastal villages, and the analysis of carbon emission influencing factors and the assessment and optimization provide theoretical support and quantitative methods for the optimization of carbon reduction in villages.