The impact of depot location,fleet composition and routing on emissions in city logistics

This paper investigates the combined impact of depot location, fleet composition and routing decisions on vehicle emissions in city logistics. We consider a city in which goods need to be delivered from a depot to customers located in nested zones characterized by different speed limits. The objective is to minimize the total depot, vehicle and routing cost, where the latter can be defined with respect to the cost of fuel consumption and CO₂ emissions. A new powerful adaptive large neighborhood search metaheuristic is developed and successfully applied to a large pool of new benchmark instances. Extensive analyses are performed to empirically assess the effect of various problem parameters, such as depot cost and location, customer distribution and heterogeneous vehicles on key performance indicators, including fuel consumption, emissions and operational costs. Several managerial insights are presented.     

Can regional transportation and land-use planning achieve deep reductions in GHG emissions from vehicles?

The Intergovernmental Panel on Climate Change estimates that greenhouse gas emissions (GHG) must be cut 40–70% by 2050 to prevent a greater than 2 °Celsius increase in the global mean temperature; a threshold that may avoid the most severe climate change impacts. Transportation accounts for about one third of GHG emissions in the United States; reducing these emissions should therefore be an important part of any strategy aimed at meeting the IPCC targets. Prior studies find that improvements in vehicle energy efficiency or decarbonization of the transportation fuel supply would be required for the transportation sector to achieve the IPCC targets. Strategies that could be implemented by regional transportation planning organizations are generally found to have only a modest GHG reduction potential. In this study we challenge these findings. We evaluate what it would take to achieve deep GHG emission reductions from transportation without advances in vehicle energy efficiency and fuel decarbonization beyond what is currently expected under existing regulations and market expectations. We find, based on modeling conducted in the Albuquerque, New Mexico metropolitan area that it is possible to achieve deep reductions that may be able to achieve the IPCC targets. Achieving deep reductions requires changes in transportation policy and land-use planning that go far beyond what is currently planned in Albuquerque and likely anywhere else in the United States.     

Reducing CO2 emissions in temperature-controlled road transportation using the LDVRP model

Temperature-controlled transport is needed to maintain the quality of products such as fresh and frozen foods and pharmaceuticals. Road transportation is responsible for a considerable part of global emissions. Temperature-controlled transportation exhausts even more emissions than ambient temperature transport because of the extra fuel requirements for cooling and because of leakage of refrigerant. The transportation sector is under pressure to improve both its environmental and economic performance. To explore opportunities to reach this goal, the Load-Dependent Vehicle Routing Problem (LDVRP) model has been developed to optimize routing decisions taking into account fuel consumption and emissions related to the load of the vehicle. However, this model does not take refrigeration related emissions into account. We therefore propose an extension of the LDVRP model to optimize routing decisions and to account for refrigeration emissions in temperature-controlled transportation systems. This extended LDVRP model is applied in a case study in the Dutch frozen food industry. We show that taking the emissions caused by refrigeration in road transportation can result in different optimal routes and speeds compared with the LDVRP model and the standard Vehicle Routing Problem model. Moreover, taking the emissions caused by refrigeration into account improves the estimation of emissions related to temperature-controlled transportation. This model can help to reduce emissions of temperature-controlled road transportation.     

Greenhouse gas emission benefits of vehicle lightweighting: Monte Carlo probabalistic analysis of the multi material lightweight vehicle glider

Vehicle lightweighting reduces fuel cycle greenhouse gas (GHG) emissions but may increase vehicle cycle (production) GHG emissions because of the GHG intensity of lightweight material production. Life cycle GHG emissions are estimated and sensitivity and Monte Carlo analyses conducted to systematically examine the variables that affect the impact of lightweighting on life cycle GHG emissions. The study uses two real world gliders (vehicles without powertrain or battery) to provide a realistic basis for the analysis. The conventional and lightweight gliders are based on the Ford Fusion and Multi Material Lightweight Vehicle, respectively. These gliders were modelled with internal combustion engine vehicle (ICEV), hybrid electric vehicle (HEV), and battery electric vehicle (BEV) powertrains. The probability that using the lightweight glider in place of the conventional (steel-intensive) glider reduces life cycle GHG emissions are: ICEV, 100%; HEV, 100%, and BEV, 74%.The extent to which life cycle GHG emissions are reduced depends on the powertrain, which affects fuel cycle GHG emissions. Lightweighting an ICEV results in greater base case GHG emissions mitigation (10 t CO₂eq.) than lightweighting a more efficient HEV (6 t CO₂eq.). BEV lightweighting can result in higher or lower GHG mitigation than gasoline vehicles, depending largely on the source of electricity.     

Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet

Municipal fleet vehicle purchase decisions provide a direct opportunity for cities to reduce emissions of greenhouse gases (GHG) and air pollutants. However, cities typically lack comprehensive data on total life cycle impacts of various conventional and alternative fueled vehicles (AFV) considered for fleet purchase. The City of Houston, Texas, has been a leader in incorporating hybrid electric (HEV), plug-in hybrid electric (PHEV), and battery electric (BEV) vehicles into its fleet, but has yet to adopt any natural gas-powered light-duty vehicles. The City is considering additional AFV purchases but lacks systematic analysis of emissions and costs. Using City of Houston data, we calculate total fuel cycle GHG and air pollutant emissions of additional conventional gasoline vehicles, HEVs, PHEVs, BEVs, and compressed natural gas (CNG) vehicles to the City's fleet. Analyses are conducted with the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model. Levelized cost per kilometer is calculated for each vehicle option, incorporating initial purchase price minus residual value, plus fuel and maintenance costs. Results show that HEVs can achieve 36% lower GHG emissions with a levelized cost nearly equal to a conventional sedan. BEVs and PHEVs provide further emissions reductions, but at levelized costs 32% and 50% higher than HEVs, respectively. CNG sedans and trucks provide 11% emissions reductions, but at 25% and 63% higher levelized costs, respectively. While the results presented here are specific to conditions and vehicle options currently faced by one city, the methods deployed here are broadly applicable to informing fleet purchase decisions.     

Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia

ABSTRACT

This paper investigates strategies that could achieve an 80% reduction in transportation emissions from current levels by 2050 in the City of Philadelphia. The baseline daily lifecycle emissions generated by road transportation in the Greater Philadelphia Region in 2012 were quantified using trip information from the 2012 Household Travel Survey (HTS). Emissions were projected to the year 2050 accounting for population growth and trends in vehicle technology for both the Greater Philadelphia Region and the City of Philadelphia. The impacts of vehicle technology and shifts in travel modes on greenhouse gas (GHG) emissions in 2050 were quantified using a scenario approach. The analysis of 12 different scenarios suggests that 80% reduction in emissions is technically feasible through a combination of active transportation, cleaner fuels for public transit vehicles, and a significant market penetration of battery-electric vehicles. The additional electricity demand associated with greater use of electric vehicles could amount to 10.8 TWh/year. The use of plug-in hybrid electric vehicles (PHEV) shows promising results due to high reductions in GHG emissions at a potentially manageable cost.     

VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.     

Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions

This paper examines the life-cycle inventory impacts on energy use and greenhouse gas (GHG) emissions as a result of candidate travelers adopting carsharing in US settings. Here, households residing in relatively dense urban neighborhoods with good access to transit and traveling relatively few miles in private vehicles (roughly 10% of the U.S. population) are considered candidates for carsharing. This analysis recognizes cradle-to-grave impacts of carsharing on vehicle ownership levels, travel distances, fleet fuel economy (partly due to faster turnover), parking demand (and associated infrastructure), and alternative modes. Results suggest that current carsharing members reduce their average individual transportation energy use and GHG emissions by approximately 51% upon joining a carsharing organization. Collectively, these individual-level effects translate to roughly 5% savings in all household transport-related energy use and GHG emissions in the U.S. These energy and emissions savings can be primarily attributed to mode shifts and avoided travel, followed by savings in parking infrastructure demands and fuel consumption. When indirect rebound effects are accounted for (assuming travel-cost savings is then spent on other goods and services), net savings are expected to be 3% across all U.S. households.     

The fleet size and mix pollution-routing problem

This paper introduces the fleet size and mix pollution-routing problem which extends the pollution-routing problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and routing cost, where the latter can be defined with respect to the cost of fuel and CO₂ emissions, and driver cost. Solving this problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one.     

Well-to-wheel costs,primary energy demand,and greenhouse gas emissions for the production and operation of conventional and alternative vehicles

This study provides a comprehensive comparison of well-to-wheel (WTW) energy demand, WTW GHG emissions, and costs for conventional ICE and alternative passenger car powertrains, including full electric, hybrid, and fuel cell powertrains. Vehicle production, operation, maintenance, and disposal are considered, along with a range of hydrogen production processes, electricity mixes, ICE fuels, and battery types. Results are determined based on a reference vehicle, powertrain efficiencies, life cycle inventory data, and cost estimations. Powertrain performance is measured against a gasoline ICE vehicle. Energy carrier and battery production are found to be the largest contributors to WTW energy demand, GHG emissions, and costs; however, electric powertrain performance is highly sensitive to battery specific energy. ICE and full hybrid vehicles using alternative fuels to gasoline, and fuel cell vehicles using natural gas hydrogen production pathways, are the only powertrains which demonstrate reductions in all three evaluation categories simultaneously (i.e., WTW energy demand, emissions, and costs). Overall, however, WTW emission reductions depend more on the energy carrier production pathway than on the powertrain; hence, alternative energy carriers to gasoline for an ICE-based fleet (including hybrids) should be emphasized from a policy perspective in the short-term. This will ease the transition towards a low-emission fleet in Switzerland.     

Planning for economic and environmental resilience

Climate protection will require major reductions in GHG emissions from all sectors of the economy, including the transportation sector. Slowing growth in vehicle miles traveled (VMT) will be necessary for reducing transportation GHG emissions, even with major breakthroughs in vehicle technologies and low-carbon fuels (Winkelman et al., 2009). The Center for Clean Air Policy (CCAP) supports market-based policy approaches that minimize costs and maximize benefits. Our research indicates that significant GHG reductions can be achieved through smart growth and travel efficiency measures that increase accessibility, improve travel choices and make optimum use of existing infrastructure. Moreover, we find such measures can deliver compelling economic benefits, including avoided infrastructure costs, leveraged private investment, increased local tax revenues and consumer vehicle ownership and operating cost savings (Winkelman et al., 2009).As a society, what we build – where and how – has a tremendous impact on our carbon footprint, from building design to transportation infrastructure and land-use patterns. The empirical and modeling evidence is clear – people drive less in locations with efficient land use patterns, high quality travel choices and reinforcing policies and incentives (Ewing et al., 2008). It is also clear that there is growing and unmet market demand for walkable communities, reinforced by demographic shifts and higher fuel prices (Leinberger, 2006, Nelson, 2007). Transportation policy in the United States must rise to meet this demand for more travel choices and more livable communities.The academic, ideological and political debates about the level of GHG reductions and penetration rates that can or should be achieved via smart growth and pricing on the one hand, or measures such as ‘eco-driving’ and signal optimization on the other, have served their purpose: we know which policies are ‘directionally correct’ – policies that reduce GHG emissions even though we may not know the scope of those reductions. Now is the time to implement directionally correct policies, assess what works best where, and refine policy based on the results. It is a framework that CCAP calls “Do. Measure. Learn.”The Federal government is poised to spend $500 billion on transportation (Committee on Transportation and Infrastructure, 2009). CCAP encourages Congress to “Ask the Climate Question” – will our transportation investments help reduce GHG emissions or exacerbate the problem? Will they help increase our resilience to climate change impacts or increase our vulnerability? And, while we’re at it, will our investment foster energy security, livable communities and a vibrant economy? Federal transportation and climate policies should empower communities to implement locally-determined travel efficiency solutions by providing appropriate funding, tools and technical support.     

Quantifying the environmental and economic benefits of cooperation: A case study in temperature-controlled food logistics

Inefficient road transportation causes unnecessary costs and polluting emissions. This problem is even more severe in refrigerated transportation, in which temperature control is used to guarantee the quality of the products. Organizing logistics cooperatively can help decrease both the environmental and the economic impacts. In Joint Route Planning (JRP) cooperation, suppliers and customers jointly optimize routing decisions so that cost and emissions are minimized. Vendor Managed Inventory (VMI) cooperation extends JRP cooperation by optimizing routing and inventory planning decisions simultaneously. However, in addition to their economic advantages, VMI and JRP may also yield environmental benefits. To test this assertion, we perform a case study on cooperation between a number of supermarket chains in the Netherlands. The data of this case study are analyzed to quantify both the economic and environmental benefits of implementing cooperation via JRP and VMI, using vehicle routing and an inventory routing models. We found that JRP cooperation can substantially reduce cost and emissions compared with uncooperative routing. In addition, VMI cooperation can further reduce cost and emissions, but minimizing cost and minimizing emissions no longer result in the same solution and there is a trade-off to be made.     

The Pollution-Routing Problem

The amount of pollution emitted by a vehicle depends on its load and speed, among other factors. This paper presents the Pollution-Routing Problem (PRP), an extension of the classical Vehicle Routing Problem (VRP) with a broader and more comprehensive objective function that accounts not just for the travel distance, but also for the amount of greenhouse emissions, fuel, travel times and their costs. Mathematical models are described for the PRP with or without time windows and computational experiments are performed on realistic instances. The paper sheds light on the tradeoffs between various parameters such as vehicle load, speed and total cost, and offers insight on economies of ‘environmental-friendly’ vehicle routing. The results suggest that, contrary to the VRP, the PRP is significantly more difficult to solve to optimality but has the potential of yielding savings in total cost.     

Cost-effectiveness of reductions in greenhouse gas emissions from High-Speed Rail and urban transportation projects in California

A rising trend in state and federal transportation finance is to invest capital dollars into projects which reduce greenhouse gas (GHG) emissions. However, a key metric for comparing projects, the cost-effectiveness of GHG emissions reductions, is highly dependent on the cost-benefit methodology employed in the analysis. Our analysis comparing California High-Speed Rail and three urban transportation projects shows how four different accounting framings bring wide variations in cost per metric tonne of GHG emissions reduced. In our analysis, life-cycle GHG emissions are joined with full cost accounting to better understand the benefits of cap-and-trade investments. Considering only public subsidy for capital, none of the projects appear to be a cost-effective means to reduce GHG emissions (i.e., relative to the current price of GHG emissions in California’s cap-and-trade program at $12.21 per tonne). However, after adjusting for the change in private costs users incur when switching from the counterfactual mode (automobile or aircraft) to the mode enabled by the project, all investments appear to reduce GHG emissions at a net savings to the public. Policy and decision-makers who consider only the capital cost of new transportation projects can be expected to incorrectly assess alternatives and indirect benefits (i.e., how travelers adapt to the new mass transit alternative) should be included in decision-making processes.     

Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km². The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.     

The net effects of the built environment on household vehicle emissions: A case study of Austin,TX

Growing concerns over climate change have led to an increasing interest in the role of the built environment to reduce transportation greenhouse gas (GHG) emissions. Many studies have reported that compact, mixed-use, and well-connected developments reduce vehicle miles traveled (VMT). Others, however, argue that densification and mixture of land uses can slow down vehicle movements, and consequently generate more driving emissions. Methodologically, VMT is only a proxy, not an exact measure of emissions. This study quantifies the net effects of the built environment on household vehicle emissions through a case study of Austin, TX. The study employed structural equation modeling (SEM) techniques and estimated path models to improve understanding of the relationship between the built environment and vehicle emissions. The results show a rather complex picture of the relationship. Densification can reduce regional vehicle emissions despite its secondary effect of reduced vehicle travel speed. A 1% increase in density was found to reduce household vehicle emissions by 0.1%. However, intensification of the design feature of the built environment in developed areas may work in the opposite direction; the modeling results showed a 1% increase in grid-like network being associated with 0.8% increase in household vehicle emissions. Based on the results, the study addressed the potential of and the challenges to reducing vehicle emissions through modifying the built environment in local areas.     

Greenhouse gas reduction potential of advanced traffic control

Reducing greenhouse gas (GHG) emissions from transportation in the context of the climate change issue and the associated Kyoto Agreement of 1997 is a challenge. Since urban transportation is a major contributor to greenhouse gases, measures are required to reduce these emissions. Given that during peak periods, road vehicles propelled by petroleum fuel‐based internal combustion engines produce a high level of GHG emissions due to stop and go operations, measures to improve traffic flow can play an effective mitigation role. This paper describes a simulation‐based methodology and a case study for the quantification of GHG emission reduction owing to advanced traffic control systems.     

Lightweighting shipping containers: Life cycle impacts on multimodal freight transportation

Freight transportation by truck, train, and ship accounts for 5% of the United States’ annual energy consumption (U.S. Energy Information Administration, 2017a). Much of this freight is transported in shipping containers. Lightweighting containers is an unexplored strategy to decrease energy and GHG emissions. We evaluate life cycle fuel savings and environmental performance of lightweighting scenarios applied to a forty-foot (12.2 meters) container transported by ship, train, and truck. Use phase burdens for both conventional and lightweighted containers (steel reduction, substitution with aluminum, or substitution with high tensile steel) were compared to life cycle burdens. The study scope ranged from the transportation of one container 100 km to the lifetime movement of the global container fleet on ships. Case studies demonstrated the impact of lightweighting on typical multimodal freight deliveries to the United States. GREET 1 and 2 (Argonne National Laboratory, 2016a,b) were used to estimate the total fuel cycle burdens associated with use phase fuel consumption. Fuel consumption was determined using modal Fuel Reduction Values (FRV), which relate mass reduction to fuel reduction. A lifetime reduction of 21% in the fuel required to transport a container, and 1.4% in the total fuel required to move the vehicles, cargo, and containers can be achieved. It was determined that a 10% reduction in mass of the system will result in a fuel reduction ranging from 2% to 8.4%, depending on the mode. Globally, container lightweighting can reduce energy demand by 3.6 EJ and GHG emissions by 300 million tonnes CO₂e over a 15-year lifetime.     

Effects of battery chemistry and performance on the life cycle greenhouse gas intensity of electric mobility

Lithium traction batteries are a key enabling technology for plug-in electric vehicles (PEVs). Traction battery manufacture contributes to vehicle production emissions, and battery performance can have significant effects on life cycle greenhouse gas (GHG) emissions for PEVs. To assess emissions from PEVs, a life cycle perspective that accounts for vehicle production and operation is needed. However, the contribution of batteries to life cycle emissions hinge on a number of factors that are largely absent from previous analyses, notably the interaction of battery chemistry alternatives and the number of electric vehicle kilometers of travel (e-VKT) delivered by a battery. We compare life cycle GHG emissions from lithium-based traction batteries for vehicles using a probabilistic approach based on 24 hypothetical vehicles modeled on the current US market. We simulate life-cycle emissions for five commercial lithium chemistries. Examining these chemistries leads to estimates of emissions from battery production of 194–494 kg CO₂ equivalent (CO₂e) per kWh of battery capacity. Combined battery production and fuel cycle emissions intensity for plug-in hybrid electric vehicles is 226–386 g CO₂e/e-VKT, and for all-electric vehicles 148–254 g CO₂e/e-VKT. This compares to emissions for vehicle operation alone of 140–244 g CO₂e/e-VKT for grid-charged electric vehicles. Emissions estimates are highly dependent on the emissions intensity of the operating grid, but other upstream factors including material production emissions, and operating conditions including battery cycle life and climate, also affect life cycle GHG performance. Overall, we find battery production is 5–15% of vehicle operation GHG emissions on an e-VKT basis.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.