Impacts of home shopping on vehicle operations and greenhouse gas emissions: multi-year regional study

Laghaei, Jamshid; Faghri, Ardeshir; Li, Mingxin

doi:10.1080/13504509.2015.1124471

Cited by 32 publications

(20 citation statements)

References 27 publications

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“…For example, lower prices might support increased consumption (Hertwich ), and additional leisure time can be spent at home or in public (Laghaei et al. ). Time at home likely creates additional energy demand for heating, cooling, lighting, or electrical devices; time spent in public places (e.g., shopping malls, cinemas) may reduce residential energy consumption, but place additional burdens on commercial or transportation sectors.…”

Section: Energy Consumption Effects Of Increasing E‐commercementioning

confidence: 99%

“…), or local transportation systems’ reactions to e‐commerce (Laghaei et al. ). Aggregate country‐level studies indicate a slight decrease in transportation due to e‐commerce (Weltevreden and Rotem‐Mindali ), potentially moderated by population density and transportation modes (Matthews et al.…”

Section: Energy Consumption Effects Of Increasing E‐commercementioning

confidence: 99%

“…Specifically, the present study provides an empirical, aggregate, ecosystem‐level investigation of online retail's environmental effects, in response to calls for an e‐commerce–specific, multiyear investigation (Laghaei et al. ). Few macroeconomic statistical models investigate the net environmental impacts of ICT more broadly, and those that do produce inconsistent results, some showing a beneficial influence (Romm et al.…”

Section: Introductionmentioning

confidence: 99%

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E‐Commerce Effects on Energy Consumption: A Multi‐Year Ecosystem‐Level Assessment

Dost

Maier

2017

J of Industrial Ecology

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SummaryThis research investigates the impact of e-commerce on energy consumption in all four sectors of the U.S. economy (commercial, industrial, residential, and transportation), using macroeconomic data from 1992 to 2015. These data capture all the development phases of e-commerce, as well as direct and rebound effects in and across sectors. Empirical dynamic models (EDMs), a novel methodology in industrial ecology, are applied to the e-commerce/energy relationship to accommodate for complex system behavior and statedependent effects. The results of these data-driven methods suggest that e-commerce increases energy consumption mainly through increases in the residential and commercial sectors. These findings contrast with extant research that focuses on transportation effects, which appear less prominent in this investigation. E-commerce effects also demonstrate state dependence, varying over the magnitude of e-commerce as a percentage of the total retail sector, particularly in commercial and transportation realms. Assuming these effects will continue in the future, the findings imply that policy makers should focus on mitigating the environmentally deteriorating effects of e-commerce in the residential sector. However, this investigation cannot provide root causes for the uncovered e-commerce effects. Robustness of the empirical findings, limitations of the novel EDM methodology, and respective avenues for future methodological and substantial research are discussed. Keywords:complexity dynamic systems e-commerce ecosystem modeling energy consumption rebound effect Supporting information is linked to this article on the JIE website

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Section: Energy Consumption Effects Of Increasing E‐commercementioning

confidence: 99%

Section: Energy Consumption Effects Of Increasing E‐commercementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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E‐Commerce Effects on Energy Consumption: A Multi‐Year Ecosystem‐Level Assessment

Dost

Maier

2017

J of Industrial Ecology

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“…With the increase in population across the United States, there has been an in-Journal of Transportation Technologies creased demand on the transportation network [1]- [7]. The additional vehicles on the roads have created heavy congestion and delays for drivers [4] [8] [9] [10] [11] [12]. This congestion has brought a rise in alternative modes of transportation [13]- [18].…”

Section: Introductionmentioning

confidence: 99%

Modeling Bicycle Conflict on Non-Motorized Paths on Suburban College Campuses

Nerwinski¹,

Faghri²,

Li³

2018

JTTs

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Bicycling is an important way for college students and employees to get around campuses. With a rise in bicycling on campuses there comes a rise in bicycle collisions with pedestrians walking to and from classes and work. The literature review showed many papers involving bicycle conflict modeling but on roads with motor vehicles. While some aspects of this research can be applied to non-motorized paths, there is a lack of research strictly focusing on only bicycles and pedestrians. This study aims to fill this knowledge gap by developing a model to identify locations on roads and paths (hotspots) on college campuses that are likely to have a bicycle collision and predict the likelihood of a serious bicycle crash on a non-motorized path based on the characteristics of the path. This study identifies those interactions between bicyclists and pedestrians on non-motorized paths on a suburban college campus in Newark, USA. Findings suggest that pedestrian density of a path is a major factor in the maximum speed bicyclists can achieve. The wider the path is, the higher the maximum speed is that a bicyclist can obtain. This is because a wider path width decreases the pedestrian density. The grade of the path has little effect on bicycle speeds. The results of the models were displayed on a GIS map that is visually appealing to a viewer. The paths were color coded based on their level of safety, so it is easy to observe problematic areas of the network. This technique can be applied to the entire campus network of non-motorized paths to study the whole system. This can then be used by planners and designers to identify areas that need upgrading and improve the overall safety of the non-motorized path system.

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“…Further, there is heightened investigation of and investment in technologies that minimize carbon dioxide emissions and the release gases with known and unknown genotoxic compounds to the atmosphere [1] [2] [3].…”

Section: Introductionmentioning

confidence: 99%

An Interactive Expert System Based Decision Making Model for the Management of Transit System Alternate Fuel Vehicle Assets

Vaughan¹,

Faghri²,

Li³

2017

IIM

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Traditionally, the process used by public transportation entities to determine the acquisition strategy for new vehicle asset is based upon a broad range of criteria. Vehicle cost has been cited as one of the more critical factors which decision makers consider. It is currently a common practice to consider other factors (life-cycle cost, fuel efficiency, vehicle reliability, environmental effects, etc.) that contribute to a more comprehensive approach. This study investigates the next generation of advancements in decision making tools in the area of the application of methods to quantify and manage uncertainty. In particular, the uncertainty comes from the public policy arena where future policy and regulations are not always based upon logical and predictable processes. The fleet decision making process in most governmental agencies is a very complex and interdependent activity. There are always competing forces and agendas within the view of the decision maker. Rarely is the decision maker a single person although, within the transit environment, there is often one person charged with the responsibility of fleet management. The focus of this research examines the decision making of the general transit agency community via the development of an expert systems prototype tool. A computer-based prototype system is developed which provide an expert knowledge-based recommendation, based upon variable user inputs. The results shown in this study show that a decision making tool for the management of transit system alternate fuel vehicle assets can be modeled and tested. The direct users of this research are the transit agency administrations. The results can be used by the management teams as a reliable input to inform their urban transit buses expansion decision making process. Keywords

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Impacts of home shopping on vehicle operations and greenhouse gas emissions: multi-year regional study

Cited by 32 publications

References 27 publications

E‐Commerce Effects on Energy Consumption: A Multi‐Year Ecosystem‐Level Assessment

E‐Commerce Effects on Energy Consumption: A Multi‐Year Ecosystem‐Level Assessment

Modeling Bicycle Conflict on Non-Motorized Paths on Suburban College Campuses

An Interactive Expert System Based Decision Making Model for the Management of Transit System Alternate Fuel Vehicle Assets

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