Showing papers in "Transportation Research Part D-transport and Environment in 2017"

State-of-the-art technologies, measures, and potential for reducing GHG emissions from shipping – A review

Abstract: © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Electricity carbon intensity in European Member States: Impacts on GHG emissions of electric vehicles.

Abstract: The Well-To-Wheels (WTW) methodology is widely used for policy making in the transportation sector. In this paper updated WTW calculations are provided, relying on 2013 statistic data, for the carbon intensity (CI) of the European electricity mix; detail is provided for electricity consumed in each EU Member State (MS). An interesting aspect presented is the calculation of the GHG content of electricity traded between Countries, affecting the carbon intensity of the electricity consumed at national level. The amount and CI of imported electricity is a key aspect: a Country importing electricity from another Country with a lower CI of electricity will lower, after the trade, its electricity CI, while importing electricity from a Country with a higher CI will raise the CI of the importing Country. In average, the CI of electricity used in EU at low voltage in 2013 was 447 gCO2eq/kWh, which is the 17% less compared to 2009. Then, some examples of calculation of GHG emissions from the use of electric vehicles (EVs) compared to internal combustion engine vehicles are provided. The use of EVs instead of gasoline vehicles can save (about 60% of) GHG in all or in most of the EU MSs, depending on the estimated consumption of EVs. Compared with diesel, EVs show average GHG savings of around 50% and not savings at all in some EU MS.

The impact of flooding on road transport: A depth-disruption function

Abstract: Transport networks underpin economic activity by enabling the movement of goods and people. During extreme weather events transport infrastructure can be directly or indirectly damaged, posing a threat to human safety, and causing significant disruption and associated economic and social impacts. Flooding, especially as a result of intense precipitation, is the predominant cause of weather-related disruption to the transport sector. Existing approaches to assess the disruptive impact of flooding on road transport fail to capture the interactions between floodwater and the transport system, typically assuming a road is fully operational or fully blocked, which is not supported by observations. In this paper we develop a relationship between depth of standing water and vehicle speed. The function that describes this relationship has been constructed by fitting a curve to video analysis supplemented by a range of quantitative data that has be extracted from existing studies and other safety literature. The proposed relationship is a good fit to the observed data, with an R-squared of 0.95. The significance of this work is that it is simple to incorporate our function into existing transport models to produce better estimates of flood induced delays and we demonstrate this with an example from the 28th June 2012 flood in Newcastle upon Tyne, UK.

Satisfaction with the commute: The role of travel mode choice, built environment and attitudes

Abstract: Most of previous research that investigates the connections between the travel and satisfaction with travel has focused on the effect of the travel characteristics (e.g. travel mode choice, travel time, level of service, etc.) on satisfaction with travel. Little research has explored the role of the built environment or travel attitudes, two important factors for transport policies. Using data from a recent survey conducted in Xi’an, China, this study aims to quantitatively explore the relative effects of the built environment, travel attitudes, and travel characteristics on commute satisfaction. The data was analyzed using structural equation modeling. The model results suggest that commuting characteristics, including mode choice, congestion, and level of services of transit, all directly influence commute satisfaction. Attitudes have both direct and indirect effects on commute satisfaction, while the built environment only has indirect effects through influencing commuting characteristics.

Understanding the Impact of E-commerce on Last-Mile Light Goods Vehicle Activity in Urban Areas: The Case of London

Abstract: Growth in e-commerce has led to increasing use of light goods vehicles for parcel deliveries in urban areas. This paper provides an insight into the reasons behind this growth and the resulting effort required to meet the exacting delivery services offered by e-retailers which often lead to poor vehicle utilisation in the last-mile operation, as well as the duplication of delivery services in urban centres as competitors vie for business. A case study investigating current parcel delivery operations in central London identified the scale of the challenge facing the last-mile parcel delivery driver, highlighting the importance of walking which can account for 62% of the total vehicle round time and 40% of the total round distance in the operations studied. The characteristics of these operations are in direct conflict with the urban infrastructure which is being increasingly redesigned in favour of walking, cycling and public transport, reducing the kerbside accessibility for last-mile operations. The paper highlights other pressures on last-mile operators associated with managing seasonal peaks in demand; reduced lead times between customers placing orders and deliveries being made; meeting delivery time windows; first-time delivery failure rates and the need to manage high levels of product returns. It concludes by describing a range of initiatives that retailers and parcel carriers, sometimes in conjunction with city authorities, can implement to reduce the costs associated with last-mile delivery, without negatively impacting on customer service levels.

Environmental sustainability in the service industry of transportation and logistics service providers: Systematic literature review and research directions

Abstract: This paper provides a systematic review on the subject of environmental sustainability in the service industry of logistics service providers (LSPs) to analyse the advances of the literature on the topic and pick out appropriate research questions to investigate. The literature review has been performed using two academic databases and spans the years 1960–2014. The paper shows that despite the increasing number of papers on the subject, several areas of research are still being neglected. In particular, the paper highlights six main literature gaps concerning the classification of green initiatives, the impact of green initiatives on LSP performance, the evaluation of sustainability performance, the factors influencing the adoption of environmental sustainability initiatives, the customer perspective in the sustainable supply chain, and the information and communication technologies supporting green initiatives. Starting from these six gaps, eight research questions have been identified. These research questions represent possible emerging areas of investigation on the topic.

Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry

Abstract: This research paper estimates carbon dioxide (CO2) emissions and vehicle-miles traveled (VMT) levels of two delivery models, one by trucks and the other by unmanned aerial vehicles (UAVs), or “drones.” Using several ArcGIS tools and emission standards within a framework of logistical and operational assumptions, it has been found that emission results vary greatly and are highly dependent on the energy requirements of the drone, as well as the distance it must travel and the number of recipients it serves. Still, general conditions are identified under which drones are likely to provide a CO2 benefit – when service zones are close to the depot, have small numbers of stops, or both. Additionally, measures of VMT for both modes were found to be relatively consistent with existing literature that compares traditional passenger travel with truck delivery.

Reverse and closed loop supply chain coordination by considering government role

Abstract: Due to the increasing number of end-of-life (EOL) products and their related environmental concerns, much attention has been paid to reverse logistics. In this paper, we consider a two-echelon reverse supply chain (RSC) with one manufacturer and one retailer who try to improve sustainable consumption by increasing customers’ willingness to return used products through offering a discount or a direct fee in exchange for bringing back EOL products. Afterward, the model is extended to consider a closed loop supply chain (CLSC). Quantity discounts and increasing fee contracts are proposed to coordinate supply chains. Then, government role in improving coordinated supply chains through donating different incentives (tax exemption and subsidy) to supply chain members are analyzed. Results show that total channel profit in the coordinated case is improved. Also, in the proposed models, each member has enough motives to participate in the plan. In addition, results demonstrate that government-sponsored incentives to the manufacturer are preferred to the retailer.

Consumer purchase intentions for electric vehicles: Is green more important than price and range?

Abstract: In view of global warming and climate change, a transition from combustion to electric vehicles (EVs) can help to reduce greenhouse gas emissions and improve air quality. However, high acquisition costs and short driving ranges are considered to be main factors which impede the diffusion of EVs. Since electricity needs to be produced from renewable energy sources for EVs to be a true green alternative, the environmental performance of EVs is also presumed to be an important factor. This paper investigates the role of environmental performance compared to price value and range confidence regarding consumer purchase intentions for EVs. To develop our hypothesis, we interview 40 end-user subjects about their beliefs toward EVs. Then, we perform 167 test drives with a plug-in battery EV and conduct a survey with the participants to test the hypothesis. Results of a structural equation modeling support the hypothesis that the environmental performance of EVs is a stronger predictor of attitude and thus purchase intention than price value and range confidence.

Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai

Abstract: The impacts of the built environment characteristics in residential neighborhoods on commuting behavior are explored in the literature. Scant evidence, however, is provided to scrutinize the role of the built environment characteristics at job locations. Studies also overlooked the potential error correlations between commuting mode and commuting distance due to the unobserved factors that influence both variables. We examined the impacts of the built environment characteristics at both residential and job locations on commuting mode and distance, by applying a discrete-continuous copula-based model on 857 workers in Shanghai. In contrast with studies of Western countries, we showed residential built environment characteristics are more influential on commute behavior than the built environment characteristics at job locations. This suggests the importance of local specificity in policymaking process. We also found the proportion of four-way intersections, road density, and population density in residential areas are negatively associated with driving probability, with elasticity amounts of −1.00, −0.23, and −0.08, respectively. Hence, dense and pedestrian- and cyclist-oriented development help to reduce travel distance and encourage walking, biking, and transit modes of travel.

Impact of driving style and road grade on gaseous exhaust emissions of passenger vehicles measured by a Portable Emission Measurement System (PEMS)

Abstract: Motivated by the upcoming Euro-6c regulation including Real Driving Emissions (RDE), the present study addresses the impact of different driving styles and route characteristics on on-road exhaust emissions. Gaseous emissions of two Diesel test vehicles (Euro-5 and Euro-6) were measured using a Portable Emission Measurement System (PEMS) on an RDE compliant test route. The driving parameters relative positive acceleration (RPA), mean positive acceleration (MPA) and v ∗ a pos 95 allowed a favorable classification of different driving styles. The comparison of driving parameters for normal PEMS trips with reference data obtained from the World harmonized Light-duty Test Cycle (WLTC) and from Field Operational Tests (FOT) indicated a good representation of normal driving. Severe driving led to elevated CO 2 and NO x emissions as compared to normal trips while CO and HC did not allow a distinct classification of different driving styles. Route characteristics of four different routes were investigated applying the parameter cumulated altitude gain using Google Elevation data. The distance specific NO x emissions were in the same range for trips with comparable driving dynamics on routes with similar cumulated altitude gain. Based on repetitive measurements the road grade was calculated within 100 m segments. CO 2 and NO x emissions measured by a PEMS showed a linear increase with road grade. Larger emissions at higher road grades could be explained by more frequent high engine load points. In this study cumulated altitude gain and road grade were directly correlated to emissions measured by the PEMS and the step from 0 to 5% road grade led to a CO 2 increase of 65–81% and a NO x increase of 85–115%.

Does tourism degrade environmental quality? A comparative study of Eastern and Western European Union

Abstract: The purpose of this paper is to investigate the effect of tourism on economic growth and carbon dioxide emissions in Eastern and Western European Union (EU) countries by incorporating FDI and trade in the production and CO2 emission functions. We apply panel econometric techniques which account for cross-sectional dependence and heterogeneity. The results of Westerlund panel cointegration test confirm a long-run equilibrium relationship among the variables. Results from long-run elasticities suggest that tourism stimulates economic growth in Eastern and Western EU countries. However, tourism increases CO2 emissions in Eastern EU but decreases in Western EU. This indicates that tourism has an adverse effect on the environment in Eastern EU. Finally, short-run heterogeneous panel causality test results suggest that tourism causes CO2 emissions in Eastern EU while economic growth and CO2 emissions cause tourism in Western EU. Overall, our findings suggest that tourism plays an important role in accelerating economic growth; however, its role on CO2 emissions largely depends on the adaptation of sustainable tourism policies and efficient management.

Well-to-wheel analysis of greenhouse gas emissions for electric vehicles based on electricity generation mix: A global perspective

Abstract: In the transport sector, electric vehicles (EVs) are widely accepted as the next technology paradigm, capable of solving the environmental problems associated with internal combustion engine vehicles (ICEVs). However, EVs also have environmental impacts that are directly related to the country’s electricity generation mix. In countries without an environmentally friendly electricity generation mix, EVs may not be effective in lowering greenhouse gas (GHG) emissions. In this study, we analyzed the extent to which the GHG emissions associated with EVs differs among 70 countries in the world, in relation to their domestic electricity generation mix. Then, we compared the results with the GHG emissions from the ICEVs. Countries with a high percentage of fossil fuels in their electricity generation mix showed high GHG emissions for EVs, and for some of these countries, EVs were associated with more GHG emissions than ICEVs. For these countries, policies based on the positive environmental impact of EVs may have to be reconsidered. In addition, different policies may need to be considered for different vehicle types (compact car, SUV, etc.), because the ability of EVs to reduce GHG emissions compared to that of ICEVs varies by vehicle type.

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Abstract: Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.

Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices

Abstract: A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and ‘behaviorally-realistic’ model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. More generally, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.

Roadside vegetation design characteristics that can improve local, near-road air quality

Abstract: As public health concerns have increased due to the rising number of studies linking adverse health effects with exposures to traffic-related pollution near large roadways, interest in methods to mitigate these exposures have also increased. Several studies have investigated the use of roadside features in reducing near-road air pollution concentrations since this method is often one of the few short-term options available to reduce near-road air pollution. Since roadside vegetation has other potential benefits, the impact of this feature has been of particular interest. The literature has been mixed on whether roadside vegetation reduces nearby pollutant concentrations or whether this feature has no effect or even potentially increases downwind pollutant concentrations. However, these differences in study results highlight key characteristics of the vegetative barrier that can result in pollutant reductions or increase local pollutant levels. This paper describes the characteristics of roadside vegetation that previous research shows can result in improved local air quality, as well as identify characteristics that should be avoided in order to protect from unintended increases in nearby concentrations. These design conditions include height, thickness, coverage, porosity/density, and species characteristics that promote improved air quality. These design considerations can inform highway departments, urban and transportation planners, and developers in understanding how best to preserve existing roadside vegetation or plant vegetative barriers in order to reduce air pollution impacts near transportation facilities.

Mobile taxi booking application service’s continuance usage intention by users

Abstract: The long-term development of a mobile booking taxi application service depends on the continued use of its passengers. The aim of this study is to investigate the determinants of the mobile taxi booking application service’s continuance intention, using the technology continuance theory by including the perceived risk and subjective norms. The data were collected by surveying 387 users of the mobile taxi application service. The data were analysed by applying the partial least squares technique. The analysis showed that the technology continuance theory has extensive power to explain the continuance intention to use the mobile booking taxi application. Subjective norms also have a significant effect on the attitude of mobile booking taxi application users which represents an important contribution to technology continuance theory extension. The theoretical and practical significances of the study have been discussed.

Impact of road gradient on energy consumption of electric vehicles

Abstract: We investigate the impact of road gradient on the electricity consumption of electric vehicles (EVs) by combining long-term GPS tracking data with digital elevation map (DEM) data for roads in Aichi prefecture, Japan Eight regression models are constructed and analysed to compare the differences between linear and logarithmic forms of trip energy consumption, differences between considering the road gradient or not, and differences between considering the fixed effects of EVs or not By categorizing gradients and assigning a percentage of the trip distance to each category, a significantly better model of electricity consumption can be achieved The results of this study are a novel contribution toward understanding the challenges and benefits associated with downgrade braking on energy regeneration

The built environment and travel behavior in urban China: A literature review

Abstract: Interests in studying of the built environment impacts on travel behavior have proliferated from North America to other parts of the world including China. Until very recently, there has been very little research into travel behavior in China. However, during the last decade, there has been a fast growing interest in studying the built environment and travel behavior in Chinese cities, perhaps motivated by China’s unprecedented urbanization and rapid urban transport development. Case studies from China provide new insights into the impacts of built environment on travel behavior that can help to enrich existing scholarship. However, currently there is a generally poor understanding of the role played by Chinese research and how it has enriched the international literature. This paper aims to fill this gap by reviewing studies in and outside China by both Chinese and non-Chinese scholars. The focus is on the contribution of these studies to the international literature. We identify four areas of contribution: how the built environment has been developed and its implications for travel behavior; the importance of housing sources in defining residential built environment and explaining travel behavior; the unique Danwei (or work unit) perspective on jobs-housing relationships and commuting behavior; and the importance of neighborhood types in explaining travel behavior in Chinese cities. The findings from this review should be relevant for researchers interested in developing future studies that will further advance geographic knowledge of the built environment and travel behavior, specifically in China and with broader global contexts.

A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows

Abstract: We develop a model to optimize the location of public fast charging stations for electric vehicles (EVs). A difficulty in planning the placement of charging stations is uncertainty in where EV charging demands appear. For this reason, we use a stochastic flow-capturing location model (SFCLM). A sample-average approximation method and an averaged two-replication procedure are used to solve the problem and estimate the solution quality. We demonstrate the use of the SFCLM using a Central-Ohio based case study. We find that most of the stations built are concentrated around the urban core of the region. As the number of stations built increases, some appear on the outskirts of the region to provide an extended charging network. We find that the sets of optimal charging station locations as a function of the number of stations built are approximately nested. We demonstrate the benefits of the charging-station network in terms of how many EVs are able to complete their daily trips by charging midday—six public charging stations allow at least 60% of EVs that would otherwise not be able to complete their daily tours without the stations to do so. We finally compare the SFCLM to a deterministic model, in which EV flows are set equal to their expected values. We show that if a limited number of charging stations are to be built, the SFCLM outperforms the deterministic model. As the number of stations to be built increases, the SFCLM and deterministic model select very similar station locations.

Greenhouse gas emissions from ships in ports – Case studies in four continents

Abstract: Emissions of GHG from the transport sector and how to reduce them are major challenges for policy makers The purpose of this paper is to analyse the level of greenhouse gas (GHG) emissions from ships while in port based on annual data from Port of Gothenburg, Port of Long Beach, Port of Osaka and Sydney Ports Port call statistics including IMO number, ship name, berth number and time spent at berth for each ship call, were provided by each participating port The IMO numbers were used to match each port call to ship specifications from the IHS database Sea-web All data were analysed with a model developed by the IVL Swedish Environmental Research Institute for the purpose of quantifying GHG emissions (as CO2-equivalent) from ships in the port area Emissions from five operational modes are summed in order to account for ship operations in the different traffic areas The model estimates total GHG emissions of 150,000, 240,000, 97,000, and 95,000 tonnes CO2 equivalents per year for Gothenburg, Long Beach, Osaka, and Sydney, respectively Four important emission-reduction measures are discussed: reduced speed in fairway channels, on-shore power supply, reduced turnaround time at berth and alternative fuels It is argued that the potential to reduce emissions in a port area depends on how often a ship revisits a port: there it in general is easier to implement measures for high-frequent liners Ships that call 10 times or less contribute significantly to emissions in all ports

The multi-port berth allocation problem with speed optimization and emission considerations

Abstract: The container shipping industry faces many interrelated challenges and opportunities, as its role in the global trading system has become increasingly important over the last decades. On the one side, collaboration between port terminals and shipping liners can lead to costs savings and help achieve a sustainable supply chain, and on the other side, the optimization of operations and sailing times leads to reductions in bunker consumption and, thus, to fuel cost and air emissions reductions. To that effect, there is an increasing need to address the integration opportunities and environmental issues related to container shipping through optimization. This paper focuses on the well known Berth Allocation Problem (BAP), an optimization problem assigning berthing times and positions to vessels in container terminals. We introduce a novel mathematical formulation that extends the classical BAP to cover multiple ports in a shipping network under the assumption of strong cooperation between shipping lines and terminals. Speed is optimized on all sailing legs between ports, demonstrating the effect of speed optimization in reducing the total time of the operation, as well as total fuel consumption and emissions. Furthermore, the model implementation shows that an accurate speed discretization can result in far better economic and environmental results.

Identifying key assumptions and differences in life cycle assessment studies of lithium-ion traction batteries with focus on greenhouse gas emissions

Abstract: The various studies that consider the life cycle environmental impacts of lithium-ion traction batteries report widely different results. This article evaluates the inventory data and results to identify the key assumptions and differences in the studies. To aid the identification, we compile the reported life cycle greenhouse gas emissions of batteries. The studies find production-related emissions in the range of 38–356 kg CO 2 -eq/kW h. One of the main sources of the large variations stems from differing assumptions regarding direct energy demand associated with cell manufacture and pack assembly. Further differences are due to assumptions regarding the amount of cell materials and other battery components. The indirect emissions associated with the use phase depend on the conversion losses in the battery, the energy required to transport the weight of the battery, and the carbon intensity of the electricity. Of the reviewed studies assessing the use phase, all estimate energy use associated with conversion losses while only one considers the mass-induced energy requirement. Although there are several industrial end-of-life treatment alternatives for lithium-ion batteries, very few studies consider this life cycle stage. Studies using the “recycled content” approach report emissions in the range of 3.6–27 kg CO 2 -eq/kW h battery, while studies using the “end-of-life“ approach report emission reductions in the range of 16–32 kg CO 2 -eq/kW h battery. The uncertainty associated with the end-of-life results is high as the data availability on industrial process is limited. Based on our findings, we discuss how the life emissions of lithium-ion traction batteries may be reduced.

Predicting ship fuel consumption based on LASSO regression

Abstract: During the voyage, predicting fuel consumption of ships under different sea-states and weather conditions has been a challenging and far-reaching topic, because there are a great number of feature variables affecting the fuel consumption, including main-engine status, cargo weight, ship draft, sea-states and weather conditions, etc. Data driven statistical models have been employed to model the relationship between fuel consumption rate and these voyage parameters. However, some of the feature variables are highly correlated, e.g. wind speed and wave height, air pressure and wind force, cargo weight and draft etc., thus a typical multiple collinearity problem arises so that the fuel consumption cannot be accurately calculated by using the traditional multiple linear regression. In this study, the LASSO (Least Absolute Shrinkage and Selection Operator) regression algorithm is employed to implement the variable selection for these feature variables, additionally, it guides the trained predictor towards a generalizable solution, thereby improving the interpretability and accuracy of the model. On the basis of the LASSO, a novel ship fuel consumption prediction model is proposed. Experimentally, the superiority of the proposed method was confirmed by comparing it with some existing methods on predicting the fuel consumption. The proposed method is a promising development that improves the calculation of the fuel consumption.

Can the development of electric vehicles reduce the emission of air pollutants and greenhouse gases in developing countries

Abstract: Developing the electric vehicle (EV) industry is generally considered to be an effective way of easing the imbalance between the supply and demand of oil, and, in addition, the pressure to reduce environmental pollution. Developed countries and most developing countries including Brazil, Russia, India, and China (so-called ‘BRIC’ countries) are actively promoting the development of EVs. By studying different types of widely-used gasoline internal combustion engine vehicles (ICEVs) and EVs, we compare the effect on the environment of utilizing EVs in both developed and developing countries. This is achieved by using a ‘well-to-wheel’ method. The results show that compared to gasoline ICEVs, EVs have a significant effect on CO2 emission reduction. However, the corresponding air pollution due to SO2, PM10, NOx, etc. for a given EV varies substantially in different countries because of the influence of several factors (electrical power structure, line loss rate, and so on). As developing countries use larger proportions of thermal power or present high line loss rates, pollutant emission produced by a certain EV is much higher than that in developed countries. Taking China as a typical developing country as an example, this research dynamically predicts the environmental effects expected in 2020 and 2025 due to a developing EV industry. Predictions are based on a method of Monte Carlo simulation and consider the government’s development plan for energy. Finally, according to the results obtained, policies and suggestions for the development of the EV industry in developing countries are proposed.

Lifecycle modeling and assessment of unmanned aerial vehicles (Drones) CO2e emissions

Abstract: There are no studies that model the potential effectiveness of Unmanned Aerial Vehicles (UAVs) or drones to reduce CO 2 e lifecycle (including both utilization and vehicle phase) emissions when compared to conventional diesel vans, electric trucks, electric vans, and tricycles. This study presents a novel analysis of lifecycle UAV and ground commercial vehicles CO 2 e emissions. Different route and customer configurations are modeled analytically. Utilizing real-word data, tradeoffs and comparative advantages of UAVs are discussed. Breakeven points for operational emissions are obtained and the results clearly indicate that UAVs are more CO 2 e efficient, for small payloads, than conventional diesel vans in a per-distance basis. Drastically different results are obtained when customers can be grouped in a delivery route. UAV deliveries are not more CO 2 e efficient than tricycle or electric van delivery services if a few customers can be grouped in a route. Vehicle phase CO 2 e emissions for UAVs are significant and must be taken into account. Ground vehicles are more efficient when comparing vehicles production and disposal emissions per delivery.

Environmental effect of CI engine using microalgae methyl ester with doped nano additives

Abstract: Algae are organisms that grow in marine environments and use carbon dioxide and light to create bio-mass. There are two groupings of algae: microalgae and macroalgae. Macroalgae are the large, multi-cellular algae often seen growing in ponds. Microalgae, on the other hand, are tiny, unicellular algae that normally grow in suspension within a body of water. Algae oil from microalgae has the possible to become a sustainable fuel source as biodiesel. Microalgae are produced through photosynthesis by utilizing sunlight, water, carbon dioxide and other nutrients. The Botryococcus braunii algal oil was extracted by mechanical extraction method. The transesterification reaction of Botryococcus braunii algal oil with methanol and base catalyst was used for the production of biodiesel. The samples B20 were prepared for each methyl ester obtained from Botryococcus braunii algal oil separately and then the doping of TiO 2 and SiO 2 nanoparticles were added to the each B20 blend samples at a dosage of 50 ppm and 100 ppm with an aid of ultrasonicator. Moreover, in the absence of any engine modifications, the performance and emission characteristics of those blend samples have been investigated from the experimentally measured values such as density, viscosity, calorific value, etc. while the engine performance was also analyzed through the parameters like BSFC, BTH, exhaust emission of CO, HC, NOx and CO 2 . The experimental results reveal that the use of biodiesel blend with nano additives in diesel engine has exhibited good improvement in performance characteristic and reduction in exhaust emissions.

An analysis of institutional pressures, green supply chain management, and green performance in the container shipping context

Abstract: This paper presents a study which utilized a conceptual framework with institutional theory as its base to empirically evaluate the impact of institutional pressures, internal green practices, and external green collaborations on green performance. Factor analysis was employed to identify the key institutional pressures (i.e. coercive, normative and mimetic pressures), internal green practices (i.e. green shipping practices and green operations), external green collaborations (i.e. green collaboration with supplier, green collaboration with partner, and green collaboration with customer), and green performance (i.e. reduction of pollutants, and perceived green brand) dimensions. We collected data from surveyees employed by 129 container shipping companies and agencies in Taiwan, and applied a structural equation model (SEM) to test the research hypotheses. The findings revealed that institutional pressures have positive effects on internal green practices; internal green practices positively influence external green collaborations; internal green practices and external green collaborations positively influence green performance but institutional pressure is not positively associated with external green collaborations. Theoretical contributions and managerial implications are presented to help container shipping operators improve green performance.

A sustainability assessment of ports and port-city plans: Comparing ambitions with achievements

Abstract: The challenge for port developments is to minimize long-term uncertainties associated with port operations, risk of increased costs, and large environmental impacts. The aim of this study is to develop a comparative methodology to assess the sustainability performance of a mixed set of ports (different locations, sizes). This methodology involves ranking various long-term port plans and port vision documents against a set of social, economic, and environmental key performance indicators (KPIs) in order to evaluate and interpret future sustainable port-city development plans. The assessment aims to determine the efficiency and sustainability of each of the case study port plans, relative to other ports. Furthermore, the assessment ranks the considered ports based on comparison of pressures within the ecosystems and society, using publically available data in order to evaluate future changes resulting from these pressures. The classification and ranking of each port have been used to gauge the ability of each port to achieve its sustainability goals for port planning as set out in their port plans. The comprehensive results have been compared with the long-term port plan KPIs to evaluate an array of measures both quantitatively and qualitatively. Most of the highest ranking ports have developed a combination of integrated plans, measures, and regulations for sustainable port developments. This indicates that green-port policies need to be interlinked via social, economic, and environmental dimensions utilizing an integrated approach in order to realize maximum potential and strengthen port processes aimed at developing a sustainable port.

Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions

Abstract: Electric vehicles (EVs) have great potential to reduce transportation-related fossil fuel consumption as well as pollutant and greenhouse gas (GHG) emissions, due to their use of renewable electricity as the sole energy source. Therefore, the wide-spread deployment of EVs is regarded as an attractive means to mitigate the environmental problems (e.g., air pollution and climate change) resulting from transportation activities. Government agencies are trying to promote EV deployment by allocating considerable funding as well as promulgating supportive policies. However, the mass adoption of EVs is still impeded by the limited charging infrastructure and all-electric-range (AER). All these lead to a critical research topic: the EV energy consumption analysis and estimation under real-world traffic conditions, which is fundamental to various types of EV-centred applications that aim at improving the EV energy efficiency and extending the AER. For example, eco-routing systems for EVs rely on accurate link-level energy consumption estimation to calculate the EV energy consumption costs of the different route options. In this work, to obtain an accurate link-level energy consumption estimation model for EVs, the energy consumption under real-world traffic congestion is decomposed based on two proposed impact factors: positive kinetic energy (PKE) and negative kinetic energy (NKE). Upon this decomposition, a data-driven model is built to estimate EV energy consumption on each roadway link considering real-world traffic conditions. Finally, the model performance is evaluated by comparing with the performance of baseline model adapted from existing models. The results show that the proposed EV link-level energy consumption estimation model outperforms the existing models in terms of accuracy, implying that it is quite promising in various on-board EV applications.