scholarly journals Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique

2020 ◽  
Vol 54 (5) ◽  
pp. 2606-2614 ◽  
Author(s):  
Israel Lopez-Coto ◽  
Xinrong Ren ◽  
Olivia E. Salmon ◽  
Anna Karion ◽  
Paul B. Shepson ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Inverse Modeling ◽  
Modeling Technique ◽  
Washington Dc ◽  
Baltimore Metropolitan Area

scholarly journals Top-down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley

2017 ◽  
Vol 122 (6) ◽  
pp. 3686-3699 ◽  
Author(s):  
Yu Yan Cui ◽  
Jerome Brioude ◽  
Wayne M. Angevine ◽  
Jeff Peischl ◽  
Stuart A. McKeen ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Methane Emissions ◽  
San Joaquin Valley ◽  
Modeling Technique ◽  
Top Down

Impacts of Rail Transit Accessibility on Firm Spatial Distribution: Case Study in the Metropolitan Area of Washington, DC

2019 ◽  
Vol 2673 (11) ◽  
pp. 220-232
Author(s):  
Hiroyuki Iseki ◽  
Hyunjoo Eom
Keyword(s):  
Metropolitan Area ◽  
Industrial Development ◽  
Public Transit ◽  
Agglomeration Economies ◽  
Rail Transit ◽  
Washington Dc ◽  
Transit Oriented Development ◽  
Industry Agglomeration ◽  
Positive Effects ◽  
Agglomeration Effects

Agglomeration economies can arise in areas with high concentrations of firms, which can be facilitated by improved transportation accessibility. Accessibility can be improved by public transit infrastructure, especially in combination with careful planning for transit-oriented development (TOD) that creates compact, high density, mixed-use, and pedestrian-friendly built environments in proximity to public transit infrastructure. Although the literature on TOD has increasingly shown positive effects on residential development and property values, its effects on commercial and industrial development, location of firms, and associated agglomeration economies are less clear and require more empirical study. This study analyzes firm location patterns by industry/sector in the metropolitan area of Washington, DC and examines whether significant spatial clusters have developed in relation to: 1) the presence of Metrorail stations; and 2) the presence of specific industry firms in the earlier year, using kernel density analysis and multinomial logit (MNL) regression. The analysis results indicated that firms in certain industries, such as finance and insurance/real estate and public administration, are more likely to benefit from proximity to Metrorail stations than other industries. Furthermore, firms in several industries show the effects of agglomeration within the same industry while several combinations of industries exhibit cross-industry agglomeration effects. The findings of this study contribute to the understanding of which industry sectors are more likely to be located in proximity to rail transit stations and TOD areas and to the understanding of agglomeration effects within the same industry and between different industries.

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