## 2 Materials and Methods
### Calculation of LCE  
In this study, the LCE consist of DLCE and ILCE [14]. Specifically, the DLCE represent direct carbon emissions from grassland, forestland, cropland, water, and unused land, while ILCE represent carbon emissions from construction land and cropland through energy consumption. In this study, the carbon emission coefficient method was used to simulate the DLCE of different land types in the GZA [32]. According to the previous studies and IPCC [16], we determined the carbon emission coefficients for different land use types in the study area, as shown in Table 2. Meanwhile, the DLCE can be calculated according to Equation (1). In addition, we derived the monthly emission data regarding the production of fossil-fuel CO2 emissions at a spatial resolution of 1 km from the ODIAC platform. Then, the annual ILCE can be calculated from the monthly data based on Equation (2) (Figure A2).  
\[DLCE=\sum\!C_{i}=\sum\!S_{i}\times V_{i} \tag{1}\]  
\[ILCE=\sum\!M_{i} \tag{2}\]  
where C\({}_{i}\) represents the DLCE for the land-use types i (t); Si donates the area of the land-use type i (hm\({}^{2}\)); and V\({}_{i}\) means the carbon emission coefficient of the land-use type i (t-hm\({}^{2}\)-a\({}^{-1}\)). M\({}_{i}\) donates the monthly carbon emissions data obtained from the ODIAC (t) [9].  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Type** & **Spatial Resolution/Formula** & **Source** \\ \hline \multicolumn{3}{c}{European Space Agency} \\ Land Use/Cover & 300 m & ([[http://maps.elei.ucl.ac.be/CCI/viewer/](http://maps.elei.ucl.ac.be/CCI/viewer/)]([http://maps.elei.ucl.ac.be/CCI/viewer/](http://maps.elei.ucl.ac.be/CCI/viewer/)), accessed on 1 June 2022) \\ \multicolumn{3}{c}{Odiac—Fossil fuel CO2 emission data product} \\ Carbon Emission & 1 km & ([[http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html](http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html)]([http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html](http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html)), accessed on 1 May 2022) \\ \multicolumn{3}{c}{NOAA website NGDC Data Center} \\ Nighttime Light & 1 km & ([[https://www.ngdc.noaa.gov/eog/download.html](https://www.ngdc.noaa.gov/eog/download.html)]([https://www.ngdc.noaa.gov/eog/download.html](https://www.ngdc.noaa.gov/eog/download.html)), accessed on 4 August 2022) \\ \multicolumn{3}{c}{Open Spatial Demographic Data and Research—WorldPop} \\ Population Grid & 1 km & ([[https://www.worldpop.org/project/list](https://www.worldpop.org/project/list)]([https://www.worldpop.org/project/list](https://www.worldpop.org/project/list)), accessed on 13 August 2022) \\ \multicolumn{3}{c}{National Tibetan Plateau/Third Pole Environment Data Center} \\ Gross Primary Productivity & 1 km & ([[https://data.tpdc.ac.cn/zh-hans/data/5826635-3](https://data.tpdc.ac.cn/zh-hans/data/5826635-3) be7-4f26-bcd5-b56a3c6b7]([https://data.tpdc.ac.cn/zh-hans/data/5826635-3](https://data.tpdc.ac.cn/zh-hans/data/5826635-3) be7-4f26-bcd5-b56a3c6b7), accessed on 13 September 2022) \\ \multicolumn{3}{c}{China Geospatial Data Cloud} \\ Digital Elevation Model (DEM) & 90 m & ([[https://www.gscloud.cn/sources/accessdata/305?pid=302](https://www.gscloud.cn/sources/accessdata/305?pid=302)]([https://www.gscloud.cn/sources/accessdata/305?pid=302](https://www.gscloud.cn/sources/accessdata/305?pid=302)), accessed on 10 August 2022) \\ \hline \end{tabular}
\end{table}
Table 1: Description of data sources used in this study.  
\begin{table}
\begin{tabular}{c c} \hline
**Land Use Type** & **Carbon Emission Factor** \\ \hline Cropland & 0.422 \\ Grassland & \(-\)0.021 \\ Forestland & \(-\)0.644 \\ Wetland & \(-\)0.0006132 \\ Unused land & \(-\)0.005 \\ Water & \(-\)0.253 \\ \hline \end{tabular}
\end{table}
Table 2: Carbon emission coefficient of land use type (t-hm\({}^{-2}\).a\({}^{-1}\)).