The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
The influence of urban form on GHG emissions in the U.S. household sector
Abstract
To better understand the role of sustainable urban development in greenhouse gas (GHG) mitigation, this study examines the paths by which urban form influences an individual household׳s carbon dioxide emissions in the 125 largest urbanized areas in the U.S. Our multilevel SEM analyses show that doubling population-weighted density is associated with a reduction in CO2 emissions from household travel and residential energy consumption by 48% and 35%, respectively. Centralized population and polycentric structures have only a moderate impact in our analyses. Given that household travel and residential energy use account for 42% of total U.S. carbon dioxide emissions, these findings highlight the importance of smart growth policies to build more compact and transit friendly cities as a crucial part of any strategic efforts to mitigate GHG emissions and to stabilize climate.
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Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology · doi:10.2307/2075823 · Cited by 18,724
Multilevel analysis techniques and applications
Hox, J. J., Moerbeek, M., & van de Schoot, R. (2002) · Cited by 6,003
Travel and the Built Environment: A Meta-Analysis
Ewing, R., & Cervero, R. (2010) · Journal of the American Planning Association · doi:10.1080/01944361003766766 · Cited by 5,360
Travel demand and the 3Ds: Density, diversity, and design
Cervero, R., & Kockelman, K. (1997) · Transportation Research Part D: Transport and Environment · doi:10.1016/s1361-9209(97)00009-6 · Cited by 4,562
A general multilevel SEM framework for assessing multilevel mediation
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010) · Psychological Methods · doi:10.1037/a0020141 · Cited by 3,796
Arnfield, A. J. (2003) · International Journal of Climatology · doi:10.1002/joc.859 · Cited by 3,543
The Dimensions of Residential Segregation
Massey, D. S., & Denton, N. A. (1988) · Social Forces · doi:10.1093/sf/67.2.281 · Cited by 3,483
Hox, J., Moerbeek, M., & van de Schoot, R. (2010) · doi:10.4324/9780203852279 · Cited by 3,469
Stabilization wedges: solving the climate problem for the next 50 years with current technologies
Pacala, S., & Socolow, R. (2004) · Science · doi:10.1126/science.1100103 · Cited by 3,114
Inventory of U.S. Greenhouse Gas Emissions and Sinks
Hockstad, L., & Hanel, L. (2018) · University of North Texas Digital Library (University of North Texas) · doi:10.15485/1464240 · Cited by 2,821
Cited by
Li, K., & Lin, B. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.07.185 · Cited by 833
Yang, Z., Gao, W., Han, Q., Qi, L., Cui, Y., & Chen, Y. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104201 · Cited by 303
How polycentric is urban China and why? A case study of 318 cities
Liu, X., & Wang, M. (2016) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2016.03.007 · Cited by 285
Wang, S., Fang, C., Wang, Y., Huang, Y., & Ma, H. (2015) · Ecological Indicators · doi:10.1016/j.ecolind.2014.10.004 · Cited by 262
Global environmental change and noncommunicable disease risks
Frumkin, H., & Haines, A. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040218-043706 · Cited by 194
Metadata
- Authors
- Lee, S., & Lee, B.
- Venue
- Energy Policy
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 68
- Pages
- 534-549
- ISSN
- 0301-4215 (ISSN-L)
- OpenAlex
- W1989372237
- Cited by
- 358
- References
- 73
- FWCI
- 40.83
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
