## 4 Discussion
### Uncertainty Analysis  
In the bottom-up approach, the estimation of SIUS was derived from multiple data resources, making it essential to conduct an analysis of uncertainty. This study employed a semi-quantitative evaluation method for uncertainty analysis [55; 56]. Data sourced from statistical and survey reports are considered more reliable than estimates or data from other literature sources. Additionally, in calculations that incorporate multiple estimation factors, this study assumes that the more estimation factors used, the higher the degree of uncertainty in the resulting data. Based on this assumption, various data sources were categorized into distinct levels of uncertainty (Table 3). \"Low\" denotes an uncertainty of \(\pm\)10%, \"Medium\" denotes an uncertainty of \(\pm\)20%, and \"High\" denotes an uncertainty of \(\pm\)30%.  
Due to the complexity of equipment in the machinery sector, the uncertainty of the result is classified as medium, and the sectors account for 19.5% of the total SIUS. Compared to previous studies, the machinery sector's share of SIUS in Sichuan Province was 21% in 2018 [1] and 19.5% in Henan Province. As the GDP of these two provinces was similar in 2018, it can be seen that the ratio of the machinery sector's SIUS in this study aligns closely with previous research. Furthermore, due to limited data on per capita non-residential floor area in urban and rural areas, the SIUS of non-residential buildings was calculated using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **Method** & \
\begin{tabular}{c} **Time** \\ **Estimated** \\ \end{tabular} &
\begin{tabular}{c} **Per Capita** \\ **SIUS (vcap)** \\ \end{tabular} & \multicolumn{1}{c}{**References**} \\ \hline USA & Top-down & 2002 & 14.3 & USGS (2005) [51] \\ USA & Top-down & 2004 & 11-12 & [PERSON] et al. (2006) [8] \\ USA & Top-down & 2004 & 10.92 & [PERSON] et al(2009) [42] \\ New Haven (USA) & Bottom-up & 2000 & 9.2 & [PERSON] et al. (2007) [52] \\ Australia & Top-down & 2005 & 10\(\pm\)2 & [PERSON] et al. (2011) [3] \\ Canada & Top-down & 2005 & 12\(\pm\)2 & [PERSON] et al. (2011) [3] \\ Wakayama (Japan) & Bottom-up & 2004 & 2.08 & [PERSON] and [PERSON] (2009) [53] \\ CIS and the Middle & Bottom-up & 2019 & 8.2 & [PERSON] (2023) [13] \\ East and Others & Bottom-up & 2003 & 10 & [PERSON] et al. (2006) [54] \\ Steiermark (Austria) & Bottom-up & 2003 & 10 & [PERSON] et al. (2006) [54] \\ Global & Average use life method & 2013 & 2.8 & [PERSON] et al. (2010) [1] \\ China & Bottom-up & 2018 & 5.9 & [PERSON] et al. (2020) [14] \\ Handan (China) & Bottom-up & 2005 & 1.33 & [PERSON] et al. (2008) [9] \\ Zhejiang (China) & Bottom-up & 2016 & 5.48 & [PERSON] et al. (2020) [10] \\ Fujian (China) & Bottom-up & 2016 & 7.6 & [PERSON] et al. (2020) [11] \\ Nanjing (China) & Bottom-up & 2016 & 6.2 & [PERSON] et al. (2019) [15] \\ Shenzhen (China) & Bottom-up & 2016 & 2.5 & [PERSON] et al. (2019) [15] \\ Henan (China) & Bottom-up & 2023 & 5.09 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparative analysis of per capita SIUS in different regions.  
the method provided by [PERSON] et al. (2015) [57], resulting in medium uncertainty. These two sectors account for 32.8% of the total SIUS. The data for the electricity infrastructures sector comes from the statistical yearbook, but detailed data are not disclosed in 2020-2023, leading to medium uncertainty. The uncertainty in the domestic appliances sector arises from the incomplete accounting of all communication appliances, as well as video and automotive appliances, resulting in medium uncertainty. Overall, the uncertainty in the estimated results of this study is considered acceptable.