Measuring transport related CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in transport related carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their transport CO₂ footprints by 84% when buying standard consumer electronics products.     

Explore the relationship between online shopping and shopping trips: An analysis with the 2009 NHTS data

The rapid growth of ecommerce brings great changes to the transportation system. However, most existing studies focus on the impact of ecommerce on freight system. Its impact on personal trips is relatively less studied. It is reasonable to argue that online shopping reduces the need of shopping trips by making goods accessible via door-to-door deliveries. On the other hand, online shopping may also create more shopping trips as online shoppers travel to stores to experience, compare or pick up the goods. Understanding the connections between online shopping and shopping trips is critical for transportation planners to prepare for changes that information technology will continue to bring to this nation in the future. Using the 2009 National Household Travel Survey (NHTS) data and a structural equation model (SEM), this paper disentangles the bidirectional connections between online shopping and shopping trips. Results show that online shopping encourages shopping trips while shopping trips tend to suppress the online shopping propensity. Besides, both online shopping and shopping trips are influenced by exogenous factors such as shoppers’ demographic features, regional specific factors and household attributes. A closer examination at the state level further confirms model validity while disclosing spatial variation in their relationship.     

A hazard-based approach to modelling the effects of online shopping on intershopping duration

Despite growing prevalence of online shopping, its impacts on mobility are poorly understood. This partially results from the lack of sufficiently detailed data. In this paper we address this gap using consumer panel data, a new dataset for this context. We analyse one year long longitudinal grocery shopping purchase data from London shoppers to investigate the effects of online shopping on overall shopping activity patterns and personal trips. We characterise the temporal structure of shopping demand by means of the duration between shopping episodes using hazard-based duration models. These models have been used to study inter-shopping spells for traditional shopping in the literature, however effects of online shopping were not considered. Here, we differentiate between shopping events and shopping trips. The former refers to all types of shopping activity including both online and in-store, while the latter is restricted to physical shopping trips. Separate models were estimated for each and results suggest potential substitution effects between online and in-store in the context of grocery shopping. We find that having shopped online since the last shopping trip significantly reduces the likelihood of a physical shopping trip. We do not observe the same effect for inter-event durations. Hence, shopping online does not have a significant effect on overall shopping activity frequency, yet affects shopping trip rates. This is a key finding and suggests potential substitution between online shopping and physical trips to the store. Additional insights on which factors, including basket size and demographics, affect inter-shopping durations are also drawn.     

Evaluating the environmental impacts of online shopping: A behavioral and transportation approach

Various fields and commercial sectors have witnessed a transformation with the advent of the internet. In the last decade, the retail sector in particular has witnessed the massive growth of e-commerce. This has also significantly altered our shopping experiences, influencing a range of decisions, from where, how, and how much to shop. With the consistent growth of e-commerce transactions, more trucks than ever before are entering cities today, bringing with them the negative externalities of increased congestion and pollution. This study first unravels underlying shopping behaviors–both in-store and online–using the 2016 American Time Use Survey (ATUS) data. The authors also develop an econometric behavioral model to understand the factors that affect shopping decisions. At a macro level, the disaggregate individual shopping behaviors are studied by implementing the model to synthetic populations to estimate potential vehicle miles traveled and environmental emissions in two metropolitan areas, Dallas and San Francisco (SF). Finally, the study estimates the impacts of rush deliveries, basket size, and consolidation levels by developing a breakeven analysis between in-store and online shopping. These results confirm the importance of managing the urban freight system, including delivery services and operations, to foster a more sustainable urban environment.     

Structural factors affecting land-transport CO2 emissions: A European comparison

The increase of CO₂ emissions generated by land-transport is a major policy concern of the European Union but the upward trend in transport use makes it difficult for member states to comply with Kyoto Protocol targets. This paper develops an input–output methodology to analyse the structure of CO₂ emissions from land-transport and applies this to several European Union countries. It shows how production linkages between sectors and the structure of final demand affect land-transport emissions in these countries. The paper confirms the relevance of the emissions-intensity factor to explain differences in the emissions of the transport sector across countries, but also shows the importance of technology-production linkages between sectors in an economic system that has usually been neglected in the past.     

The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary

Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.     

Incorporating online shopping into travel demand modelling: challenges,progress, and opportunities

There is a large body of literature, spanning multiple disciplines, concerned with the relationship between traditional (physical) shopping and associated travel behaviour. However, despite the recent rapid growth of digital retailing and online shopping, the impact on travel behaviour remain poorly understood. Although the issue of the substitution and complementarity between conventional and virtual retail channels has been extensively explored, few attempts have been made to extend this work so as to incorporate virtual retail channels into modelling frameworks that can link shopping and mobility decisions. Here, we review the existing literature base with a focus on most relevant dimensions for personal mobility. How online activity can be incorporated into operational transport demand models and benefits of such effort are discussed. Existing frameworks of shopping demand are flexible and can, in principle, be extended to incorporate virtual shopping and the associated additional complexities. However, there are significant challenges associated with lack of standard ontologies for crucial concepts and insufficiencies in traditional data collection methods. Also, supply-side questions facing businesses and policy-makers are changing as retailing goes through a digital transformation. Opportunities and priorities need to be defined for future research directions for an assessment of existing tools and frameworks.     

Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping

Searching product information and buying goods online are becoming increasingly popular activities, which would seem likely to affect shopping trips. However, little empirical evidence about the relationships between e-shopping and in-store shopping is available. The aim of this study is to describe how the frequencies of online searching, online buying, and non-daily shopping trips relate to each other, and how they are influenced by such factors as attitudes, behaviour, and land use features. Questionnaire data were collected from 826 respondents residing in four municipalities (one urban, three suburban) in the centre of the Netherlands. Structural equation modelling was used to examine the variables’ multiple and complex relationships. The results show that searching online positively affects the frequency of shopping trips, which in its turn positively influences buying online. An indirect positive effect of time-pressure on online buying was found and an indirect negative effect of online searching on shopping duration. These findings suggest that, for some people, e-shopping could be task-oriented (a time-saving strategy), and leisure-oriented for others. Urban residents shop online more often than suburban residents, because they tend to have a faster Internet connection. The more shopping opportunities one can reach within 10 min by bicycle, the less often one searches online.     

A cost and CO2 comparison of using trains and higher capacity trucks when UK FMCG companies collaborate

Companies working in a collaboration are able to achieve higher vehicle capacity utilisation and reduced empty running, resulting in lower costs and improved sustainability through reduced emissions and congestion. Collaboration produces higher volumes of goods to be moved than individual companies which means that further efficiencies may be possible by relaxing the freight mode constraints and considering rail and higher capacity vehicles. This paper explains how real world data has been used in a model to quantify the economic and environmental benefits in the FMCG sector delivered through collaboration utilising road and rail freight modes. Data for one month was provided by 10 FMCG companies and included freight transport flows between depots and customers, inter depot movements, and supplier collections. Detailed road and rail costs and operating characteristics were obtained and, with the transport flows, applied to a network design model which was used to validate the company data sets. A strategy examining the potential use of alternative higher capacity vehicles and rail for the flows between nine regional consolidation centres showed cost and CO₂ savings. Just under half the inter-regional flows benefited from double deck trailers, longer heavier vehicles for 30% of the flows and rail with different wagon configurations for the rest. In summary there was a 23% reduction in cost with 58% fewer road kilometres and a 46% reduction in CO₂ emissions. The ability to backhaul the same mode of transport between most of the regional centres was one of the strengths of this strategy.     

A self-adaptive method to equalize headways: Numerical analysis and comparison

In uncontrolled bus systems, buses tend to bunch due to the stochastic nature of traffic flows and passenger demand at bus stops. Although schedules and priori target methods introduce slack time to delay buses at control points to maintain constant headways between successive buses, too much slack required delay passengers on-board. In addition, these methods focus on regular headways and do not consider the rates of convergence of headways after disturbances. We propose a self-adaptive control scheme to equalize the headways of buses with little slack in a single line automatically. The proposed method only requires the information from the current bus at the control point and both its leading and following buses. This elegant method is shown to regulate headways faster than existing methods. In addition, compared to previous self-equalizing methods, the proposed method can improve the travel time of buses by about 12%, while keeping the waiting time of passengers almost the same.     

Evaluating the effects of speed reduce for shipping costs and CO2 emission

Global greenhouse gas emissions have driven up carbon dioxide levels beyond 400 parts per million, thereby increasing the rate of global warming. This paper conducted a thorough assessment of available operating strategies to identify the approach to speed reduction that is best able to minimize costs and reduce the impact of shipping on the environment. Our results indicate that optimum speed reduction is a dynamic process depending largely on charter rates and fuel prices. The significant cost advantages afforded by this approach could improve the competitiveness of ship operators.     

A model system for the assessment of the effects of car and fuel green taxes on CO2 emission

This study aims at developing a model system to examine the changes in the car market configuration, the life cycle CO₂ emission from automobile transport and the tax revenues due to different taxation policies. The model system specifically determines the effect of varying the weights of the tax components in the stages of a) car purchasing, b) car owning, and c) car using to the changes in the car class and age mix and the car users' driving pattern and behavior towards car class choice and decommissioning. Five sub-models comprise the model system, formulated using car ownership related data in Japan from 1980 to 1994. Performance tests conducted against the sub-models generally yielded encouraging results. The sensitivity analysis identified car usage tax as the most significant parameters in reducing CO₂. An increase in, ownership tax, on the other hand, significantly results to a shift to smaller cars, while the propensity to decommission and repurchase can be reduced by increasing the purchase tax and can be decreased by increasing the ownership tax. The model system was utilized to determine the impact of the 1989 tax reform and to forecast future scenarios using different taxation schemes. The model system is being further developed for possible future application in other countries.     

Modelling inter-urban transport flows in Italy: A comparison between neural network analysis and logit analysis

In the present paper a modal split problem is analysed by means of two competing statistical models, the traditional logit model and the new technique for information processing, viz. the feedforward neural network model. This study aims to explore the modal split between rail and road transport modes in Italy in relation to the introduction of a new technological innovation, the new High-Speed Train. The paper is sub-divided into two major parts. The first part offers some general considerations on the use of neural networks in the light of the increasing number of empirical applications in the specific area of transport economics. The second part describes the Italian case study by using the two above mentioned statistical models. The results highlights the fact that the two adopted models, although methodologically different, are both able to provide a reasonable spatial forecasting of the phenomenon studied. In particular, the neural network model turns out to have a slightly better performance, even though there are still critical problems inherent in its application.     

A joint trip timing store-type choice model for grocery shopping,including inventory effects and nonparametric control for omitted variables

In principle, stochastic modelling methods are ideally suited to the analysis and forecasting of discretionary travel; they formalise both the capriciousness and continuity which are empirically typical of recurrent choice. In practice, the development of theoretically justifiable but tractable stochastic models has appeared to be an illusive goal in transportation research and stochastic models have found little favour. Recent statistical results on the nonparametric characterisation of mixing distributions now enable stochastic models to simultaneously represent a much greater variety of behaviour while, at the same time, actually reducing problems over tractability. The consequent case for reappraisal is illustrated by the development and calibration of a new joint timing/choice model for shopping travel. This model has sound theoretical underpinnings, permits complex variation in the frequency and regularity of shopping due to both observed and unobserved characteristics and constraints, and yet is readily calibrated from diary data.     

A valuation of the environmental performance of vehicles: an analysis and comparison of two methodologies

The European Clean Vehicle Directive was introduced in 2009 to create an obligation on public authorities to take into account the impact of energy consumption, carbon dioxide (CO₂) emissions and pollutant emissions into their purchasing decisions for road transport vehicles. This should stimulate the market for clean and energy-efficient vehicles and improve transport's impact on environment, climate change and energy use. Therefore the so-called ‘Operational Lifetime Cost’ of a vehicle is calculated, divided into the cost for energy consumption, CO₂ and pollutant (nitrous oxide, particulate matter, non-methane hydrocarbons) emissions. In Belgium, a different methodology has been developed to calculate the environmental impact of a vehicle, called ‘Ecoscore’, based on a well-to-wheel approach. More pollutants are included compared to the Clean Vehicle methodology, but also indirect emissions are taken into account. In this paper, both methodologies are compared and used to analyze the environmental performance of passenger cars with different fuel types and from different vehicle segments. Similar rankings between both methodologies are obtained; however, the large impact of energy use (and CO₂ emissions) in the Clean Vehicle methodology disadvantages compressed natural gas cars, as well as diesel cars equipped with particulate filters, compared to the Ecoscore methodology.     

Regulating CO2 emissions of transportation in Europe: A CGE-analysis using market-based instruments

This paper analyzes the use of market-based emission regulation instruments to address the carbon dioxide emissions of transportation. Simulations with a static multi-region computable general equilibrium model show that including transportation into the European emission trading system is superior to a closed emission trading system for transportation or a tax-based approach. Furthermore, we show that exempting transportation from emission regulation is the most favorable approach in terms of welfare. This counterintuitive result is due to a large tax-interaction effect caused by high pre-existing fuel taxes in the transport sector.     

Trade-offs in CO2-oriented vehicle tax reforms: A case study of Greece

We estimate demand for automobiles in Greece using a model of product differentiation and use the model to evaluate carbon-based tax schemes that could shift consumer purchases towards low CO₂ emission cars. We find that careful policy design, supported by appropriate modeling, can bring about substantial environmental benefits without losing control of economic parameters such as public finances or firm profits. This contrasts to the results of recent vehicle tax reforms in European countries, which turned out to be more costly than initially expected.     

Home-based trip end models — A comparison between category analysis and regression analysis procedures

The trip end models which have been used in past transportation studies are briefly summarised. Problems associated with the use of zone-based models are outlined and reasons are given to support the development of models at the household rather than zonal level.It is suggested that recent developments which have taken place in household-based models have not been entirely logical. In particular, arguments between regression models and category analysis models have been confused with the use of aggregate (zonal) as against disaggregate (household) data — regression models being associated with the use of zonal data and category analysis models with household data. Misunderstood arguments and false notions regarding sample sizes have directed attention from the regression analysis approach.A detailed comparison of the category analysis and regression analysis methods for developing household-based trip end models is given. Both methods have been applied using data from the Monmouthshire Land Use Transportation Study. The regression results reported are from a very preliminary analysis and contain a number of anomalies, although it is thought that sufficient work has been done to provide an objective evaluation of the two methods.It is recommended that the household regression approach should be further investigated since it has advantages as a modelbuilding procedure and makes better use of sample data. A certain amount of categorisation of household types is necessary and the investigations would attempt to determine the best balance between categorisation and regression fitting. Further development will be restricted if the trend towards minimum sample sizes of about 1000 households is continued. Larger samples should be taken in certain circumstances to pursue development work.     

Noise effects and real estate prices: A simultaneous analysis of different noise sources

We simultaneously analyze the effects of alternative noise sources to isolate their relative harms. This research adds to the literature, which has only analyzed one noise source or has aggregated the noise levels of different sources. Flight noise had the most negative effect on housing prices, and road and train noises had similar but smaller effects.