Data centers are large electricity consumers due to the high consumption needs of servers and their cooling systems. With the rapid growth of crypto-currency and artificial intelligence, their electricity consumption is expected to increase substantially. With the electricity sector being responsible for a large share of global greenhouse gas (GHG) emissions, it is important to lower the carbon footprint of data centers to meet GHG emissions targets set by international agreements. Moreover, uncontrolled data center integration into power distribution grids increases the stochasticity of electricity demand, thus increasing the need for reserve capacity and leading to operational inefficiencies and higher emissions. This work provides a method to size a PhotoVoltaic (PV) system and an Energy Storage System (ESS) for an existing data center looking to reduce both its carbon footprint and demand stochasticity through day-ahead dispatching. A scenario-based optimization framework is developed to jointly size the PV and ESS, minimizing the expected operational and capital expenditures and the carbon footprint of the data center complex. The model considers the life cycle assessments (LCA) of the systems and the dynamic carbon intensity of the upstream electricity supply. Case studies in different Swiss cantons and regions of Germany emphasize the need for location-aware sizing processes since the obtained optimal solutions strongly depend on the local electricity carbon footprint and on the irradiance conditions. The maximum carbon footprint reduction reaches approximately 50 % in Germany and 4 % in Switzerland. Installed power generation and energy storage capacities vary by up to 36 times across regions.