## 5 Conclusions  
This study focused on the downscaling of BCEs using integrating multi-source remote sensing data and the influence factors of BCE growth. The BTH urban agglomeration and the NCRJ were selected as the study areas. The prefecture-level BCEs in 2000, 2005, 2010, 2015, and 2019 were calculated using statistical data. The MLR model was constructed between prefecture-level BCEs and remote sensing data including the nighttime light, population, temperature, enhanced vegetation index, and built-up land to downscale BCEs to grid scale on a 1 km resolution in the BTH and NCRJ. Compared with the downscaling method using nighttime light data alone, adding other factors effectively improved the downscaling results. The R\({}^{2}\) (coefficient of determination) of fitting results between prefecture-level BCEs and EDGAR in the BTH and NCRJ are both above 0.8. The R\({}^{2}\) of county-level fitting results in the BTH and municipality-level fitting results in the NCRJ are 0.67 and 0.92, respectively. In general, the multi-scale estimation results in this study presented relatively high accuracy. The BCEs of the BTH urban agglomeration increased from 76.07 million tons to 158.57 million tons from 2000 to 2019. The increases in BCEs at the prefecture, county, and grid levels are obvious. The emission scale of Beijing was the highest in the region and showed a downward trend between 2015 and 2019. Other cities in the BTH maintained an increasing trend during the study period. From the perspective of the spatial distribution, high-emission areas were concentrated in Beijing, Tianjin, and Tangshan. The total BCEs of the NCRJ increased slowly and dropped from a peak of 123.71 million tons in 2015 to 109.93 million tons in 2019. The BCEs at the prefecture level, municipality level, and grid level showed a significant downward trend between 2015 and 2019. The most high-emission areas were concentrated in Tokyo. An LMDI model was used to decompose the influencing factors of BCE growth. It was found that the decrease in energy intensity and population density are the main negative factors, and the growth of GDP per capita and urban expansion significantly promote the growth rate of BCEs. Due to the rapid development of the BTH urban agglomeration, the intensity of positive or negative effects of influencing factors is higher than that of the NCRJ. This study attempted to verify the feasibility and accuracy of downscaling BCEs using multi-source remote sensing data in different regions. Based on the multi-scale emission analysis and the decomposition of influencing factors in study areas, the regional emission characteristics in different development stages are highlighted to provide a reference for the rapidly developing BTH urban agglomeration from the perspective of urban growth. **Author Contributions:** Conceptualization, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); methodology, Y.Z. ([PERSON]); software, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]) and C.J. ; formal analysis, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]), Y.Z. ([PERSON]) and J.W. ; resources, Y.Z. ([PERSON]); data curation, Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]),Y.Z. ([PERSON]) and C.J. ; writing--review and editing, Y.Z. ([PERSON]) and J.W. ; visualization, Y.Z. ([PERSON]), Y.Z. ([PERSON]) and C.J. ; supervision, Y.Z. ([PERSON]); project administration, Y.Z. ([PERSON]); funding acquisition, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** The data used in this study are available on request from the corresponding author. **Acknowledgments:** This study was supported by Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. **Conflicts of Interest:** The authors declare no conflict of interest.