IDEAS home Printed from https://ideas.repec.org/p/cdl/itsdav/qt46x4f1dr.html
   My bibliography  Save this paper

National Impacts of E-commerce Growth: Development of a Spatial Demand Based Tool

Author

Listed:
  • Jaller, Miguel
  • Xiao, Runhua
  • Dennis, Sarah
  • Rivera-Royero, Daniel
  • Pahwa, Anmol

Abstract

This project aims to study the impacts of e-commerce on shopping behaviors and related externalities. The objectives are divided into five major tasks in this project. Methods used include Weighted Multinomial Logit (WMNL) models, time series forecasting, and Monte Carlo (MC) simulations. The American Time Use Survey (ATUS) and the National Household Travel Survey (NHTS) databases are used for identifying the independent and dependent variables for behavioral modeling. At the same time, the researchers collected all MSA population data from the U.S. Census Bureau and combined the shares of each variable from ATUS to generate a synthesized population, which serves as input into the MC simulation framework together with the behavioral model. This simulation framework includes the generation of shopping travel parameters and the calculation of negative externalities. The authors do this to estimate e-commerce demand and impacts every decade until 2050. The results and analyses provide information that supports the generation of shopping travel and the estimations of a series of negative externalities using MC simulation, which includes shopping travel parameters, last-mile delivery parameters, and emission rate per person. For different parameters, a unique probability distribution or a regression relation is obtained for different MSAs, and this distribution is fed into the subsequent MC simulation. Finally, the researchers simulated shopping behaviors for synthesized populations (until 2050) and to estimate the expected negative externalities. The MC simulation generates aggregate average vehicle miles traveled (VMT) and emissions (negative externalities) for different shopping activities in the planning years and different MSAs. View the NCST Project Webpage

Suggested Citation

  • Jaller, Miguel & Xiao, Runhua & Dennis, Sarah & Rivera-Royero, Daniel & Pahwa, Anmol, 2022. "National Impacts of E-commerce Growth: Development of a Spatial Demand Based Tool," Institute of Transportation Studies, Working Paper Series qt46x4f1dr, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt46x4f1dr
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/46x4f1dr.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patricia Mokhtarian, 2004. "A conceptual analysis of the transportation impacts of B2C e-commerce," Transportation, Springer, vol. 31(3), pages 257-284, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mommens, Koen & Buldeo Rai, Heleen & van Lier, Tom & Macharis, Cathy, 2021. "Delivery to homes or collection points? A sustainability analysis for urban, urbanised and rural areas in Belgium," Journal of Transport Geography, Elsevier, vol. 94(C).
    2. Zhouying Song, 2022. "The geography of online shopping in China and its key drivers," Environment and Planning B, , vol. 49(1), pages 259-274, January.
    3. Kunbo Shi & Long Cheng & Jonas De Vos & Yongchun Yang & Wanpeng Cao & Frank Witlox, 2021. "How does purchasing intangible services online influence the travel to consume these services? A focus on a Chinese context," Transportation, Springer, vol. 48(5), pages 2605-2625, October.
    4. Tang, Wei & Mokhtarian, Patricia L, 2009. "Accounting for Taste Heterogeneity in Purchase Channel Intention Modeling: An Example from Northern California for Book Purchases," Institute of Transportation Studies, Working Paper Series qt9mg5s5g8, Institute of Transportation Studies, UC Davis.
    5. Shao, Xiao-Feng, 2017. "Free or calculated shipping: Impact of delivery cost on supply chains moving to online retailing," International Journal of Production Economics, Elsevier, vol. 191(C), pages 267-277.
    6. Matthew Clark & Kate Gifford & Jillian Anable & Scott Le Vine, 2015. "Business-to-business carsharing: evidence from Britain of factors associated with employer-based carsharing membership and its impacts," Transportation, Springer, vol. 42(3), pages 471-495, May.
    7. Ben-Elia, Eran & Alexander, Bayarma & Hubers, Christa & Ettema, Dick, 2014. "Activity fragmentation, ICT and travel: An exploratory Path Analysis of spatiotemporal interrelationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 68(C), pages 56-74.
    8. Shulin Wang & Shanhua Wu, 2023. "Optimizing the Location of Virtual-Shopping-Experience Stores Based on the Minimum Impact on Urban Traffic," Sustainability, MDPI, vol. 15(13), pages 1-25, June.
    9. Zhou, Yiwei & Wang, Xiaokun (Cara), 2014. "Explore the relationship between online shopping and shopping trips: An analysis with the 2009 NHTS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 1-9.
    10. Shi, Kunbo & De Vos, Jonas & Cheng, Long & Yang, Yongchun & Witlox, Frank, 2021. "The influence of the built environment on online purchases of intangible services: Examining the mediating role of online purchase attitudes," Transport Policy, Elsevier, vol. 114(C), pages 116-126.
    11. Weltevreden, Jesse W.J. & Rotem-Mindali, Orit, 2009. "Mobility effects of b2c and c2c e-commerce in the Netherlands: a quantitative assessment," Journal of Transport Geography, Elsevier, vol. 17(2), pages 83-92.
    12. Kim, Woojung & Wang, Xiaokun Cara, 2022. "The adoption of alternative delivery locations in New York City: Who and how far?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 127-140.
    13. Jaller, Miguel & Pahwa, Anmol, 2023. "Coping with the Rise of E-commerce Generated Home Deliveries through Innovative Last-mile Technologies and Strategies," Institute of Transportation Studies, Working Paper Series qt5t76x0kh, Institute of Transportation Studies, UC Davis.
    14. Choo, Sangho, 2003. "Aggregate Relationships between Telecommunications and Travel: Structural Equation Modeling of Time Series Data," University of California Transportation Center, Working Papers qt4p78h623, University of California Transportation Center.
    15. Pernot, Delphine, 2021. "Internet shopping for Everyday Consumer Goods: An examination of the purchasing and travel practices of click and pickup outlet customers," Research in Transportation Economics, Elsevier, vol. 87(C).
    16. Cao, XinYu & Mokhtarian, Patricia L, 2005. "The Intended and Actual Adoption of Online Purchasing: A Brief Review of Recent Literature," Institute of Transportation Studies, Working Paper Series qt095934s0, Institute of Transportation Studies, UC Davis.
    17. John Gunnar Carlsson & Mehdi Behroozi & Raghuveer Devulapalli & Xiangfei Meng, 2016. "Household-Level Economies of Scale in Transportation," Operations Research, INFORMS, vol. 64(6), pages 1372-1387, December.
    18. Wu, Guoqiang & Hong, Jinhyun, 2022. "An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time," Journal of Transport Geography, Elsevier, vol. 102(C).
    19. Han Dong & Cinzia Cirillo & Marco Diana, 2018. "Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset," Transportation, Springer, vol. 45(2), pages 429-449, March.
    20. Ralph Hippe & Damien Demailly & Claude Diebolt, 2022. "The Digital Transition for a Sustainable Mobility Regime? A Long-Run Perspective," Working Papers of BETA 2022-19, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

    More about this item

    Keywords

    Engineering; Social and Behavioral Sciences; Behavior; Electronic commerce; Forecasting; Monte Carlo method; Pollutants; Shopping; Spatial analysis; Vehicle miles of travel;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:itsdav:qt46x4f1dr. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucdus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.