# Lithium Metal: The Key to Green Transportation
## 2 Data and Methods  
This research was carried out in a methodical manner using quantitative research theories as a guide. It was necessary to construct a research design (Figure 4) that outlines the complete process of the study in order to make sure that the data collection and analysis go as smoothly as possible. The study follows the correlation research guidelines to establish a relationship between LRM, EVs, and CO2 emissions from road transportation. The influence of EVs on LRM production has also been investigated. Moreover, the other parameters involved in the green transportation cycle have also been observed. Archival data, one of the correlational research methods, has been used to collect data. This method helps track the statistical patterns of the above-said variables. The data collected spanned a decade (since 2010), which was chosen based on the time span of the EVs' significance increase. Sets of data have been collected from different sources, including public sources and specialized institutions, and published articles. The US Geological Survey (USGS) conducts research and publishes annual reports on global mineral commodities. Its reports have been used to sort data on lithium resources, production, and reserves [59]. Many studies of lithium minerals have previously been conducted [18; 26; 39; 40; 60; 61; 62] and their important data for this study were collected and reviewed. The carbon emissions and global temperature-related data have been collected from public institutions' websites [63; 64] and in various research articles like [65; 66; 27; 30; 67]. Canays [68], IEA, and other publications provide data that helps to obtain an understanding of LIB manufacturing, consumption, and electric vehicles [69; 70; 71; 72; 29]. Figure 4: Conceptual research design. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Main Mineral** & **Formula** & **Lithium Content (\%)** \\ \hline USA & spodumene & LiAlSi2O6 & 3.73 \\ pegmaties & Not specified & 0.49 \\  & Hectorite & Na0.3(Mg.Li)Si4O10(OH)2 & 0.53 \\  & pegmaties & Not specified & 0.49 \\  & spodumene & LiAlSi2O6 & 3.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Statistical analysis has been conducted to examine and verify the hypothesis. For instance, Pearson correlation analysis was applied to identify the interdependence between lithium and electric vehicles, which in turn determines the degree of green transport. The correlation coefficient Equation (1) that fits the entire value of a variable between \(-1\) and \(1\) has been used to measure the strength of the relationship between lithium production and EV sales and other related factors. \[r=\frac{\sum{(xi-\overline{x})(yi-\overline{y})}}{\sqrt{\sum{(xi-\overline{x})^ {2}\sum(yi-\overline{y})^{2}}}} \tag{1}\]  
where \(r\) stands for correlation coefficient, \(xi\) stands for the values of the x-variable in a sample, \(\overline{x}\) represents the mean of the x-variable, \(yi\) stands for the values of the y-variable in a sample, and \(\overline{y}\) stands for the mean of the y-variable. According to the characteristics of green transport cycle parameters, EV was identified as a control factor that may influence the behavior of other variables. To investigate its influence on lithium production, an algorithm (Algorithm 1) has been applied. It is seen that the timing and manufacturing of EVs are influenced externally by different factors, including EV market demand and the availability of EV stocks. That market demand seems to be the trigger for the entire process. The algorithm has been used to demonstrate how EV demand initiates and dictates the entire system of EV manufacturing and the raw material supply chain. Number of EV demand was taken as input of the algorithm to run.