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As China's urbanization process continues to accelerate, building energy consumption is becoming an increasingly serious issue, and more attention is being focused on the use of sustainable green buildings to control building energy consumption. The aim of this study is to establish a method to quantitatively predict the impact of greening on building energy consumption. First, we used NASA satellite data to establish the Beijing Land Characteristics Index, including land surface temperature (LST), normalized differential vegetation index (NDVI), building silhouette (BS), impervious surfaces index (ISI), and modified normalized difference water index (MNDWI). Then, we used the spatial autoregressive model to explore the relationship between building greening and outdoor temperature and finally used the degree-day method to establish the connection between building greening and building energy consumption. The regression results show that the outdoor temperature of the building has a high spatial autocorrelation. For every NDVI increase of 0.1, the outdoor temperature is reduced by 1.317 °C. Using the final model to calculate the Shuangjing commercial district data, for every NDVI increase of 0.1 in the Shuangjing area (116.4656241, 39.89360353), the building energy consumption decreased by 7.771%. The results of this study provide strong support for urban greening policy implementation and a more accurate quantitative forecasting method for greening investment and building energy consumption to meet the general cleaner production trend in building construction. • The impact of greening on reducing land surface temperature is studied. • Findings reveal that urban park construction reduces building energy consumption. • Regression model analyses are conducted based on the spatial extent of Beijing. • A method for quantitatively predicting building energy consumption is used based on greening. [ABSTRACT FROM AUTHOR]