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\title{Spatiotemporal Pattern Evolution of Electrical Carbon Emissions from Demand-Side Perspective in Urban Regions during the Summer period}

\author[1]{Ying Tian}
\author[1,*]{Hui Cao}
\author[1]{Dapeng Yan}
\author[2]{Jinmei Chen}
\author[3]{Yayan Hua}

\affil[1]{Shaanxi Key Laboratory of Smart Grid and the State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Beilin District, Xi'an, Shaanxi Province,710049, P.R. China}
\affil[2]{Guangzhou Baiyun Power Supply Bureau, Guangdong Power Grid Co., Ltd, China Southern Power Grid, Baiyun District, Guangzhou, Guangdong Province, 510410, P.R. China}
\affil[3]{Wuxi Lion King Technology Co., Ltd, Jingkai District, Wuxi, Jiangsu Province, 214124, P.R. China}

\affil[*]{huicao@mail.xjtu.edu.cn}

\begin{abstract}

This study portrays the pattern of differential spatial and temporal evolution of electrical carbon emissions from the demand side, based on the data of approximately 3,000 distribution network transformer units in the studied cities under increasingly challenging environmental conditions. The approach integrates both temporal and spatial analysis to uncover dynamic patterns from a demand-side perspective of distributed network. Temporal analysis examines variations in electrical carbon emissions at monthly, daily, and hourly intervals, providing a detailed view of emission trends over time. Spatial analysis explores whether the electrical carbon emission patterns are spatio-temporally correlated by using correlation techniques. On this basis the spatial evolution is then explored by employing the standard deviation ellipse method. Additionally, the logarithmic mean divisia index analysis is applied to quantify the impact of various factors on electrical carbon emissions. Findings reveal significant temporal and spatial correlations in urban electrical carbon emissions covering the period from May to September 2018. The spatial analysis indicates a southwest-to-northeast trajectory in the movement of the ellipse's center of gravity over time, with higher emissions concentrated in the central district and lower levels in peripheral areas. Key driving factors influencing electrical carbon emissions include average temperature, finance and taxation, values of industrial production and commercial consumption. These insights contribute to a deeper understanding of the spatiotemporal dynamics of electrical carbon emissions.

\end{abstract}

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% * <john.hammersley@gmail.com> 2015-02-09T12:07:31.197Z:
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\section*{Supplementary materials}

\subsection*{China Regional Power Grid Baseline Emission Factor}

Table \ref{2018 China Regional Power Grid Baseline Emission Factor} is the China regional power grid baseline of carbon emission factors in 2018\cite{2018emissionfactor}.

\begin{table}[ht]
    \centering

    \begin{tabular}{|c|c|} 
    \hline
            Region & ${EF}_{grid}$($t{CO}_{2}/MWh$) \\
            \hline
       Northern China Regional Power Grid  & 0.9455  \\ 
       \hline
        Northeast China Regional Power Grid  & 1.0925  \\ 
        \hline
        Eastern China Regional Power Grid & 0.7937   \\
        \hline
        Central China Regional Power Grid & 0.8770  \\
        \hline
       Northwest China Regional Power Grid  & 0.8984  \\ 
       \hline
        Southern China Regional Power Grid & 0.8094  \\ 
        \hline
    \end{tabular}
\caption{2018 China Regional Power Grid Baseline Emission Factor}
\label{2018 China Regional Power Grid Baseline Emission Factor}
Note: This result is calculated based on the public summary data of grid-connected power plants.

\end{table}

\subsection*{Cluster diagram of electrical carbon emission}

The clustering outcomes are classified into five categories: high-high clusters, high-low clusters, low-high clusters, low-low clusters, and insignificant clusters. During the period from May to August 2018, a notable proportion of the high-high clusters were centered in the research district situated in close proximity to the central region.  In contrast, the high-low and low-low clusters were predominantly found in the southern section of the district. Conversely, the insignificant clusters and low-high clusters exhibited a scattered distribution across other regions as illustrated in Figure \ref{cluster}.

\begin{figure}[ht]
\centering
\includegraphics[width=\linewidth]{cluster.jpg}
\caption{Clustering results diagram by month}
\label{cluster}
\end{figure}

\subsection*{Concentration of electrical carbon emissions}

The data presented in Figure \ref{sd} illustrates the levels of electrical carbon emissions in the district across various transformers during the period spanning from May to September. The distribution indicates that a significant portion of transformers fall within the medium emission category, with a subset exhibiting notably low or high concentrations of emissions.

\begin{figure}[ht]
\centering
\includegraphics[width=\linewidth]{sd.eps}
\caption{Spatial distribution of electrical carbon emission concentration by month}
\label{sd}
\end{figure}

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