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Oregon's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Oregon had 2,736,598 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Pennsylvania's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Pennsylvania had 8,239,858 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Rhode Island's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Rhode Island had 705,677 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

South Carolina's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, South Carolina had 3,288,642 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

South Dakota's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, South Dakota had 549,894 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Tennessee's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Tennessee had 4,433,899 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Texas's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Texas had 19,095,227 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Utah's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Utah had 2,069,226 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Vermont's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Vermont had 406,641 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Virginia's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Virginia had 5,629,718 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

West Virginia's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, West Virginia had 1,129,785 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Washington's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Washington had 5,017,738 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Wisconsin's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Wisconsin had 3,762,091 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Wyoming's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Wyoming had 367,435 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Washington DC's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Washington DC had 416,300 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗

Pacific Northwest National Laboratory Regional Populations – 2020 Census: Richland Campus and Sequim Campus

The U.S. Department of Energy conducts radiological operations in south-central (Richland) and northwestern (Sequim) Washington State. Collective dose estimates to regional populations must be performed to provide a measure of the impact from site radiological releases. Results of the U.S. 2020 Census and, for the Sequim location only, the Canada 2021 Census, were used to determine counts and distributions for the residential population located within 50 miles (80 kilometers) of the Pacific Northwest National Laboratory Richland Campus and the Sequim Campus. Since the 2010 Census, total regional populations increased 25–40 percent. This revision updates and expands the data presented in the prior report by the addition of PNNL-Richland Campus population distribution information.

2020 census↗

Refueling Infrastructure Deployment in Low-Income and Non-Urban Communities

The U.S. National Blueprint for Transportation Decarbonization identifies the need to invest in infrastructure supporting low- and zero-emission vehicles, especially in low-income and overburdened communities, to eliminate nearly all greenhouse gas emissions from the transportation sector by 2050. The alternative fuel vehicle refueling property tax credit (26 U.S. Code § 30C) includes eligibility criteria intended to encourage investment in underserved communities based on the economic characteristics or urban character of the census tract in which the fueling infrastructure is installed. Eligible census tracts are those that qualify for the New Markets Tax Credit or that are not located within urban areas as defined by the U.S. Census Bureau. This study quantifies how many fueling-related amenities are currently located in census tracts that qualify and do not qualify for the 30C tax credit based on IRS Notice 2024-20. For existing electric vehicle charging stations, 51% of Level 2 and 60% of Direct Current Fast Charging public stations are located in eligible census tracts. 73% of natural gas, propane, and hydrogen fueling stations are in qualifying census tracts and 75% of biodiesel and renewable fuel stations are in qualifying census tracts. This compares with 73% of existing gas stations in eligible census tracts. For deploying the refueling infrastructure to satisfy future demand, this study shows that truck stops (94%), commercial truck stops (92%), and Federal Highway alternative fuel corridors (89%) are predominantly located in eligible locations. Additionally, significant percentages of the population (62%), light-duty vehicle registrations (64%), and medium- and heavy-duty vehicle registrations (68%) fall within eligible areas.

33 ADVANCED PROPULSION SYSTEMS↗

Analyzing Trip Chaining Behavior in New York State Using 2009 and 2017 National Household Travel Survey

Trip chaining, defined as the sequential linking of trips by individuals throughout a given day, provides critical insights into daily mobility patterns and activity sequencing. Understanding these patterns has significant implications for transportation demand forecasting, congestion management, and local economic activity. This analysis examines trip chaining behaviors in New York State (NYS) for the years 2009 and 2017 and compares the Middle Atlantic Census Division with other U.S. regions in 2022, utilizing data from the National Household Travel Survey (NHTS). Through demographic, geographic, and temporal analysis, this study characterizes how populations organize travel for work, personal errands, and social activities, providing empirical evidence of evolving trip chaining behaviors to inform transportation planning strategies.

99 GENERAL AND MISCELLANEOUS↗