Navigation

Exploring the Digital Nation and Training the Clean Energy Workforce

Enhancing State Clean Energy Workforce Training to Meet Demand: This National Governors Association paper describes state efforts that have emerged thanks to funding from the American Recovery and Reinvestment Act (ARRA). The study confirms that state policy is a key factor in driving the demand for investment and the creation of green jobs. It also points out that states with aggressive green energy programs are under an urgent need for workforce development. To this end, it proposes five initiatives: (1) developing statewide curricular and certification programs with community colleges; (2) coordinating state, local and private sector workforce training efforts; (3) improving access to available training; (4) using data to assist clean energy workforce development; and (5) leveraging private sector funding to build larger, long-term training programs.

Exploring the Digital Nation: Home Broadband Internet Adoption in the United States: This report by the Economics and Statistics Administration and the Department of Commerce’s National Telecommunications and Information Administration expands upon the findings of the NTIA February 2010 study. It confirms that people of color, rural residents, disabled persons, and low-income households are least likely to adopt to broadband. The report recognizes that although broadband adoption has grown exponentially over the past nine years, the digital divide persists among the groups listed above. The surveyed non-adopters explained that they do not subscribe to broadband because services are too costly, they lack interest, they lack an adequate computer, and there are no available broadband services. The significance of these factors, however, varied across non-users, with affordability and demand generally dominating. Importantly, the study finds that certain groups in the population have lower adoption rates even after taking account of differences that typically affect broadband usage. This is case between low-income and high-income households after controlling variables in education, age, race and ethnicity, and geographic location. The same goes for whites versus African American and Latinos, after controlling for household characteristics, and for urban versus rural residents.