## 1 Introduction
Relying simply on large-scale production units to reduce the carbon footprint of the ICT sector is not the right strategy. The integration of clean and decentralized energy solutions within wireless networks during the planning stage is a better option. However, the advantages of these solutions are difficult to quantify. Furthermore, optimizing the production and use of electricity requires integrated assessment tools for reducing net cost and GHG emissions. Numerous accounting tools have been developed for specific sectors with significant differences in terms of system boundaries and granularity. System boundary definition may lead to truncations that could impact economic viability when using different carbon credit markets. A top-down approach is often used, but it limits considerations related to the impact of local factors and understanding the link with specific technological characteristics. This leads to significant uncertainty in the long-term overall forecasting and identification of major technological gaps. This truncation often leads to significant errors [10] and increases uncertainty in the overall long-term forecasting and identification of key technology gaps. As such, there is a need to develop a GHG forecasting framework validated through case studies. The choice of case studies is critical when developing and validating any GHG accounting methodology. The case study should be well defined and specific but easily reproduced elsewhere for potential deployment. According to the CISCO global index report, Wi-Fi represents around half of data traffic. With increasing mobile data demand, Wi-Fi hotspot use is expected to grow fourfold from 2018 to 2023 [11]. Globally, there will be nearly 628 million public Wi-Fi hotspots by 2023, up from 169 million hotspots in 2018 [12]. Green energy sources powering Wi-Fi access points are thus critical. Recent publications demonstrated the potential of solar energy as the main energy source to power satellite internet access points [13] and base stations [14; 15]. To the best of our knowledge, there is no peer-reviewed publication on the optimization of community solar Wi-Fi systems, including detailed GHG estimation. Developing countries are seeing an explosion in the use of wireless networks [16], often powered by fossil sources. Like most African countries, Algeria has seen an increase in cell phone usage by 18% from 2012 to 2020 [17]. With more than 1.5 million students over 106 university campuses, students often rely on free Wi-Fi networks to access the internet. The majority of these students have limited internet access at home. The cost structure of a wireless network subscription is often prohibitive. Local authorities are trying to provide free access, albeit with limited strategies for deploying sustainable solutions. In particular, with the dominance of fossil electricity and the abundance of solar energy, photovoltaic-powered wireless networks could provide sustainable deployment of Wi-Fi in most university campuses and other communities. In this paper, we will describe a new bottom-up GHG calculation framework to account for the deployment of community-based Wi-Fi-powered solar energy. This framework also includes a methodology to estimate energy consumption as a function of the number of Wi-Fi access points using newly developed MATLAB code. We will use a specific case study related to a university campus to assess the GHG reduction mitigation impact if the proposed solar Wi-Fi solution is deployed at the country scale.