This dataset contains data used in the Urban publication Who Has Access to Good Clean Energy Jobs? The dataset contains Standard Occupational Classification codes and titles for clean energy occupations, the number of jobs in each occupation, relevant clean energy sectors, individual measures related to job quality as well as an overall job quality score, and the typical level of education required at entry.

The research team used the work of the National Center for O*NET Development to identify occupations that will require enhanced skills, experience increased demand, or emerge in the three select clean energy sectors: energy efficiency, renewable-energy generation, and green construction. Job codes and job titles within those sectors come from the O*NET-SOC Taxonomy.

Job quality measures come from a previous Urban Institute publication titled Job Quality and Race and Gender Equity, which measures 11 elements of job quality, including wages and benefits, health and safety, union coverage, and stability. Overall job quality categories were calculated by summing the number of elements for which a particular job scored as “better than average”. High-quality jobs are those that received a score of 8 or higher. Medium-quality ratings correspond to scores of 5-7, and scores lower than that indicate low-quality jobs. Education requirements come from the U.S. Bureau of Labor Statistics’ Employment Projections program.

These data are combined with national and local demographic data from the 2022 American Community Survey Public Use Microdata Sample (ACS PUMS) in order to generate place-based statistics, accessed through IPUMS. The research team uses Geocorr from the Missouri Census Data Center to map each Public Use Microdata Area (PUMA) to in the census to the eight cities of interest based on the proportion of the population in a PUMA that lives in areas that overlap with the city. These proportions are combined with replicate weights provided in the American Community Survey Public Use Microdata Sample (PUMS) to determine how much weight to assign each observation in the sample.