Assessment of Embodied Water in Purchased Electricity for LinkedIn
LinkedIn aims to be water positive by 2030. Watersheds are under increasing stress globally necessitating the need for more responsible stewardship of this resource by industry, governments, and individuals. Water consumption to meet the energy demands of the tech sector, particularly from the growth of data centers, presents a challenge in this regard. LinkedIn aims to assess their embodied water in their purchased electricity (i.e., Scope 2 water use) to track their water use impacts and continue their journey towards becoming water positive. We developed a methodology utilizing the Berkley Laboratory Water Impact Tool to estimate LinkedIn’s domestic Scope 2 water withdrawal and consumption (water withdrawn but not returned to surface water). We also clarified water stress from LinkedIn’s purchased energy using two methods, AWARE 2.0 and the Aqueduct 4.0 tool. Using geospatial data and mapping software we mapped LinkedIn’s Scope 2 water footprint across the United States and identified which electrical grids are impact hotspots. We find that the ERCT (Texas) and SRVC (the Carolinas) electrical grids exhibit the highest water withdrawal, while the NWPP (Northwest) has the highest water consumption. The highest water stress region for both Aqueduct and AWARE 2.0 is CAMX (California). Our method lays the foundation for additional Scope 2 water assessments across LinkedIn’s global portfolio of facilities. To aid in this, we developed statistical models of plant-level water consumption as a surrogate to estimate water use in regions lacking analogs to the Water Impact Tool. In addition to considering their Scope 2 water use, we propose that LinkedIn estimate their Scope 3 water usage since Semiconductor chip manufacturing is extremely water intensive. This project is a stepping stone to reaching LinkedIn’s water positive goals for the future. By embracing this challenge, LinkedIn has the potential to be a sustainability leader in the tech sector.
Vivian Fong (SD)
Stella Li (SS)
Treye Meadows (SD)
Roxanne Wang (SD,SS)
Bonny Wysocki (SD)