Governments worldwide are adopting ambitious policies to reduce greenhouse gas (GHG) emissions. A New York State (NYS) legislative mandate requires net zero statewide GHG emissions by 2050 in part through decarbonizing electricity generation. However, increasing renewable energy capacity, including utility scale solar (USS), competes with land-uses such as agriculture and forestry. This case study evaluates USS historic land use to project future demand for land to meet NYS’s 2050 GHG goal. Data collected from open-source solar databases were combined with USS boundaries obtained through manual and automated digitization and Monte-Carlo and Maximum Entropy modeling were used to project the likely area and land use characteristics of future sites built to meet the projected 2050 demand for electricity. Demand for solar energy in NYS is projected to reach 116–125 terawatt hours per year by 2050, when electrification of current fossil-fueled heating and transportation sectors is taken into account. By analyzing the performance of over 300 existing USS sites across NYS, we project that approximately 100 GWDC of USS capacity can meet this demand. We found an average power density of 0.62 MWDC/ha of land for fixed axis sites and 0.59 MWDC/ha for single axis tracked sites. Stochastic modeling of power density trends over time indicates that the 2050 mandate will require between 71,072 and 128,784 hectares (ha) depending on siting variables. If trends continue, we project that between 21 386 and 27 233 ha of cropland and between 14,985 and 18,463 ha of forest could be converted to USS. For future scenarios in which conversion of annual row crop land and high-quality soils were limited, there was an increase in distance to transmission lines, number of parcels required, and complexity of site shapes, which would likely increase solar development costs. These results help bound the likely land use changes that will occur to meet electric sector GHG mitigation mandates. These results also provide information about the benefits and trade-offs of restricting the conversion of current agricultural land to solar energy production. Additionally, the approach we developed, combining analysis of fenced area, capacity factors, trends in power density over time, and projecting likely future locations for solar stochastically is applicable to many global regions with solar development on agricultural lands.

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