## 3 Results and Discussion
### Forecasting the GHG Mitigation Resulting from Wider Solar-Powered Wi-Fi Deployment  
Using the results of this case study, we assessed the impact of wider deployment. We developed a simple forecasting methodology for the potential GHG mitigation if solar-powered Wi-Fi is deployed at the state level. As an example, we will estimate the potential  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Signal Level Ranges (dBm)**} \\ \cline{3-10}  & & [\(-\)100: \(-\)95] & [\(-\)95: \(-\)90] & [\(-\)90: \(-\)85] & [\(-\)85: \(-\)80] & [\(-\)80: \(-\)75] & [\(-\)75: \(-\)70] & [\(-\)70: \(-\)65] \\ \hline \multirow{2}{*}{Seena 1–3 AP} & \% & 0 & 0 & 2 & 7 & 21 & 26 & 17 & 27 \\ \cline{2-10}  & E & & & & & 360 Wh & & & & \\ \hline \multirow{2}{*}{Seena 2–4 AP} & \% & 0 & 0 & 1 & 6 & 14 & 23 & 20 & 36 \\ \cline{2-10}  & E & & & & & 480 Wh & & & & \\ \hline \multirow{2}{*}{Seena 3–5 AP} & \% & 0 & 0 & 0 & 1 & 11 & 19 & 22 & 47 \\ \cline{2-10}  & E & & & & & 600 wh & & & & \\ \hline \multirow{2}{*}{Seena 4–6 AP} & \% & 0 & 0 & 0 & 1 & 11 & 16 & 20 & 52 \\ \cline{2-10}  & E & & & & & 720 Wh & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results of the four scenarios in terms of coverage (%) and energy demand (E) are provided for different scenarios and signal levels.  
of GHG emission if solar-powered Wi-Fi is deployed throughout all university campuses in the state. For this, we need to quantify three key parameters:  
* Amount of GHG reduction of new technology vs. incumbent technology. In this case, solar energy is used instead of fossil energy to generate the required electricity to power the Wi-Fi network. This estimation should be performed per access point.
* Estimation of the overall market size. This could be represented by the number of access points to cover all university campuses.
* The potential market capture of the new technology vs. incumbent technology. Deployment cost is the main factor. Other factors (e.g., complexity of the technology deployment) may also affect this parameter.  
The overall methodology to assess potential GHG mitigation when introducing a new technology is provided in Figure 9.  
The first parameter is the focus of this manuscript and has been estimated in the previous paragraph. The second parameter could be obtained using the following equation:  
\[\text{Market Size }=\text{ A}\times\left(\frac{\text{S}_{\text{Country}}}{ \text{S}_{\text{ENST}}}\right) \tag{10}\]  
where A is the number of access points in the current case study, SCountry is the total number of students in the country, and SENST is the number of students in the current case study.  
Finally, market capture is probably the most difficult parameter to estimate. In the absence of a detailed quantification, we will assume an arbitrary number for this parameter. Assuming a market capture of 50%, the overall GHG mitigation through solar-powered Wi-Fi in academic campuses is about 1089 kg per day and 397,485 kg per year. Even if it is relatively small when compared to the total GHG emission in the ICT sector 720 Mt [24] or in Algeria (170 Mt in 2019) [25], wider deployment of Wi-Fi in other communities could provide additional opportunities for further GHG reductions. With increasing digitalization in multiple sectors and the wider use of video streaming, we are expecting the potential GHG reduction due to solar Wi-Fi deployment to significantly increase.  
Several factors could affect this forecast. We did not account for hourly and seasonal variations of solar irradiation and load. Another critical factor is related to the potential benefit of using excess solar electricity production to address demand within and outside the campus. Indeed, in countries such as Algeria, the grid is under stress, with significant demand for electricity during hot summer days.