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In recent years, severe and persistent winter haze has appeared more frequently due to coal-combustion heating for residential buildings in the winter in northern China. To solve the problem, the Chinese government proposes to replace coal-combustion heating with electricity-driven air-source heat-pump heating. In this article, the performance of an air-source heat pump was studied for suburban residential building heating in the winter in Beijing, China. The heating load of a typical suburban residential building was evaluated using a building energy simulation program according to real weather data. The performance of the heat pump was then evaluated, and the electrical load for such building heating was analyzed based on the hourly ambient temperature and heating load of the studied building. Results showed that the highest electrical load occurred between 3:00 and 7:00 and the lowest electrical load appeared between 13:00 and 15:00. These results are helpful in guiding electrical load transfer in the power grid. Compared with coal-combustion heating, heat-pump heating could save 59% of annual operating cost and reduce the emission of CO2, SO2, and NOx by 54%, 78%, and 74%, respectively. This study is significant for wide applications of air-source heat-pump heating for suburban residential buildings in cities with weather like Beijing, China. [ABSTRACT FROM AUTHOR]

Carbon emissions play a pivotal role in driving global warming, with commercial and residential sectors ranking as the third-largest source after industrial and transportation sectors. Consequently, establishing a framework for spatial characterizing and predicting carbon emissions from these sectors is vital for effective carbon reduction planning. This study proposes an optimized Backpropagation Neural Network, incorporating a Spatial Weight Matrix, to model the relationship between land use and carbon emissions, taking spatial effects into account. Additionally, an optimized Random Forest model with variable search step lengths is developed for large-scale and multi-class land use prediction. Beijing, China serves as the study site, yielding the following findings: (1) The optimized Backpropagation Neural Network outperforms traditional models, achieving goodness of fit of 0.999 and 0.997 in training and testing datasets, respectively. (2) The optimized Random Forest model, while reducing prediction precision for ecological areas (Root Mean Square Error increases by 4.12 %), enhances overall model performance by 73.95 % in a single iteration. (3) By 2025, commercial and residential carbon emissions in Beijing are projected to reach 8.91 million tons. (4) The study characterizes the spatial patterns of commercial and residential carbon emissions at a 1 km resolution for the next 15 years, revealing statistical features. (5) Expansion of mixed commercial and residential land relies on service-oriented development, positively impacting regional carbon emission intensity control. • A novel carbon emission prediction model based on land-use subdivision is proposed. • Backpropagation Neural Network is optimized by the Spatial Weight Matrix. • Commercial and residential emissions in Beijing over the next 15 years are predicted. • Future commercial & residential emissions are characterized at resolution of 250 m. [ABSTRACT FROM AUTHOR]