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.

Rooftop photovoltaic (PV) power generation uses building roofs to generate electricity by laying PV panels. Rural rooftops are less shaded and have a regular shape, which is favorable for laying PV panels. However, because of the relative lack of information on buildings in rural areas, there are fewer methods to assess the utilization potential of PV on rural buildings, and most studies focus on urban buildings. In addition, in rural areas, concentrated ground-mounted PV plants can be built on wastelands, hillsides, and farmlands. To facilitate the overall planning and synergistic layout of rural PV utilization, we propose a new workflow to identify different types of surfaces (including building roofs, wastelands, water surfaces, etc.) by applying a deep learning approach to count the PV potential of different surfaces in rural areas. This method can be used to estimate the spatial distribution of rural PV development potential from publicly available satellite images. In this paper, 10 km² of land in Wuhan is used as an example. The results show that the total PV potential in the study area could reach 198.02 GWh/year, including 4.69 GWh/year for BIPV, 159.91 GWh/year for FSPV, and 33.43 GWh/year for LSPV. Considering the development cost of different land types, several timespans (such as short-, medium-, and long-term) of PV development plans for rural areas can be considered. The method and results provide tools and data for the assessment of PV potential in rural areas and can be used as a reference for the development of village master plans and PV development plans.

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident’s time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision.

The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors.

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.