This research project created a spreadsheet-based calculator to allow local land use planners to estimate the greenhouse gas emissions expected to result by 2035 from residential energy use under different local land use scenarios. The calculator allows users to input simple land use data available in general planning processes, as opposed to the parcel-level data required by advanced tools like CalEEMod, and is based upon the relationship between land use types and GHG emissions. Establishing that relationship involved analysis of the relationship between land use type and the median size of dwelling units within each land use type in a major metropolitan region in California (Fresno); the development of two statistical models explaining electricity and natural gas use observed in the 2005 Residential Energy Consumption Survey (RECS) dataset as a function of dwelling unit size and heating/cooling degree-days in a given location; and identification of the anticipated GHG intensity of the electricity and natural gas provided by every utility serving residential customers in California from 2012 to 2035. The calculator allows users to choose between using average or marginal GHG intensities of electricity for a given utility, to choose between using the CREC statistical model of recent usage or a blended model that incorporates CalEEMod projections of future usage into the model, and to choose whether a given utility will meet renewable generation targets as required in the Renewable Portfolio Standard. The calculator was evaluated through four separate methods: preliminary validation against the 2009 Residential Appliance Saturation Survey (RASS) dataset, field-testing with municipal planners, validation against actual electricity use data obtained from the Sacramento Municipal Utility District (SMUD) for the period between 2001 and 2011, and comparison with CalEEMod assumptions and results. Results of the evaluation collectively show that the calculator does a good job of producing estimates that closely match recent historical data, and is regarded as useful and accessible by the target audience of professional planners.