Energy justice;
Energy efficiency;
Energy policy;
Tax credits;
Quantile regression;
SPLIT INCENTIVES;
IMPACTS;
D O I:
10.1016/j.energy.2024.131449
中图分类号:
O414.1 [热力学];
学科分类号:
摘要:
The Inflation Reduction Act (IRA) promises to deliver $270 billion in tax incentives starting in 2023, expanding on the existing $18 billion in federal income tax credits for clean energy investments. Despite the continued investment in clean energy tax credits, not all communities have historically benefited equally from these programs. This work investigates the presence of disparities in the residential energy tax credit (RETC) program, which was recently expanded under the IRA. We use quantile regression models to explore disparities in the participation in and average value received from the RETCs across demographics. Because tax credit programs result in second-tier benefits such as job creation, we compare the relationship between RETC participation and the presence of clean jobs across demographics. We find that rural communities, renter-occupied communities, and communities of color are disproportionately participating less in the RETC. However, we observe that when renter-occupied or communities of color do participate, they see higher average value comparatively as well as more clean jobs associated per tax return with the RETC. While disparities across demographic groups persist in participation in the RETC, these findings suggest that renter-occupied or communities of color see more benefits when they do participate.
机构:
State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou,350007, China
School of Geographical Sciences, Fujian Normal University, Fuzhou,350007, ChinaState Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou,350007, China
Wang, Qiang
论文数: 引用数:
h-index:
机构:
Kwan, Mei-Po
Fan, Jie
论文数: 0引用数: 0
h-index: 0
机构:
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing,100191, China
Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing,100191, ChinaState Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou,350007, China
Fan, Jie
Lin, Jian
论文数: 0引用数: 0
h-index: 0
机构:
Graduate Program in Environmental Science, State University of New York College of Environmental Science and Forestry, Syracuse,NY,13210, United StatesState Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou,350007, China
机构:
Fujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350007, Peoples R China
Fujian Normal Univ, Sch Geog Sci, Fuzhou 350007, Peoples R ChinaFujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350007, Peoples R China
Wang, Qiang
Kwan, Mei-Po
论文数: 0引用数: 0
h-index: 0
机构:
Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China
Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China
Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3584 CB Utrecht, NetherlandsFujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350007, Peoples R China
Kwan, Mei-Po
Fan, Jie
论文数: 0引用数: 0
h-index: 0
机构:
Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100191, Peoples R China
Chinese Acad Sci, Key Lab Reg Sustainable Dev Modeling, Beijing 100191, Peoples R ChinaFujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350007, Peoples R China
Fan, Jie
Lin, Jian
论文数: 0引用数: 0
h-index: 0
机构:
SUNY Syracuse, Coll Environm Sci & Forestry, Grad Program Environm Sci, Syracuse, NY 13210 USAFujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350007, Peoples R China