贵州省道路客运交通运输碳排放清单研究

本文以贵州省道路客运交通运输中出租车和公交车作为研究对象,采用IPCC能耗统计法计算客运交通运输温室气体中CO2的排放、在NEDC工况下对温室气体CH4、N2O排放进行核算,建立了2017年贵州省交通道路运输温室气体碳排放清单。结果显示,贵州省道路客运交通中出租车万人均碳排放量为公交车的2.67倍。CH4排放的主要来源于天然气为燃料的公交车,N2O排放的主要来源于汽油为燃料的出租车。     

公路运输温室气体排放评价分析研究

本文通过公路运输温室气体排放评价的内容和目的进行分析,明确评价体系的基本功能。从排放量、运输服务、燃料消耗结构、基础设施服务水平、管理政策及信息化水平等方面构建公路运输温室气体排放的评价指标体系,并运用层次分析法和德尔菲法对指标权重进行计算。     

芜湖市近十年碳排放研究及结构对策分析

文章基于芜湖市历年的各类统计数据,利用《IPCC2006国家温室气体清单指南》推荐的基准方法,结合当地的实际情况分为能源消耗、工业生产过程、区域人口生理活动、以及机动车排放四大类估算出2001年至2010年十年间该地区的CO2排放总量以及结构的巨大变化,并将其与经济、人口等方面综合分析,得出该地区的排放规律趋势,并针对这些排放的特点提出了芜湖市CO2减排的建议措施。     

贵阳市道路公共交通CH4和N2O排放清单研究

本文采用平均速度来表征公共交通行驶对排放的影响,速度修正因子来计算欧洲NEDC下的CH_4和N_2O排放因子。对山地城市贵阳公共交通按不同燃料、车龄、车辆保有量、年平均行驶里程及测算的排放因子,建立2014年贵阳市公交车、出租车CH_4和N_2O排放清单,结果表明,天然气为燃料的公共交通是CH_4的主要来源,汽油车是N_2O排放的主要来源。因此,通过减少温室气体CH_4和N_2O的排放,是实现贵阳市发展绿色低碳健康城市有效途径。     

基于MOVES模型的石家庄市机动车排放清单研究

机动车尾气污染给石家庄市空气污染带来的影响不容忽视,为有效缓解和控制石家庄市的交通污染现状,需要对交通污染物进行精确量化。文章结合实地调研数据,对MOVES模型的主要输入参数进行了本地化,结合行驶里程和保有量数据计算出了石家庄市机动车排放清单,并分析了机动车的排放分担率。     

江门市机动车排放清单及控制策略研究

文章结合机动车实际活动水平和排放模型,建立了江门市机动车污染物排放清单。江门市机动车排放污染物共计8.1万吨,主要排放物为CO和NO_X,CO和HC的排放以摩托车为主,NO_X与PM2.5排放以柴油车为主,低排放标准车型的排放量较高。根据江门市机动车排放清单,建议加强摩托车的管控措施,淘汰高排放汽车,完善城市公共交通系统,大力推广新能源汽车。     

公路与水路运输节能减排量化分析和比较

通过研究汽车与船舶的油耗特点,结合燃油种类与CO_2排放量的关系,建立了计算公路运输与水路运输单位耗油量与单位CO_2排放量的数学模型。通过敏感性分析,研究车速或航速与载货率对车辆或船舶单位CO_2排放量的影响。以从厦门港到盐田港的干散货运输为例,做了节能减排计算分析,并将结果与用其他方法获得的结果对比。在此基础上,对公路运输与水路运输节能减排做了敏感性分析研究,可以计算出能够体现水路运输节能减排优势的临界货运量。结果表明本文中介绍的方法能够为核算、比较公路运输与水路运输的节能减排效果提供更科学的量化参考。     

基于熵权模糊物元模型的港区温室气体减排潜力综合评价

针对港区温室气体减排的定量化评价,本本基于熵权和模糊物元模型,通过将熵权法确定评价指标的权重系数,物元分析和所计算熵权通过欧式贴近度得到有机的结合,建立了港区温室气体减排潜力熵权模糊物元评价模型,完成了对港区不同排放源的综合评价,为科学有效的港口区域温室气体减排提供技术支持。     

基于LCA厂拌热再生施工技术环境效益计算与分析

为了评估沥青混合料厂拌热再生施工技术的环境效益,有必要量化厂拌热再生技术的能耗与温室气体排放量。基于国内外典型的沥青混合料厂拌热再生工艺,以生命周期分析方法为基础,根据边界条件,建立评价指标,分别计算厂拌热再生养护技术与铣刨重铺技术从旧路面铣刨、原材料生产、材料运输到混合料拌和、摊铺碾压的整个生命周期的能源消耗与温室气体排放。结果表明,与传统铣刨重铺相比,当回收料掺量为30%～50%时,厂拌热再生技术具有显著的环境效益,能源消耗量可节省7.6%～18.5%,温室气体排放量可降低11.9%～21.5%。     

基于生命周期的煤电废气排放清单分析

本文以煤电生命周期的废气排放为分析和评价对象,对电煤开采、运输和燃烧发电等三个环节涉及的原料、燃料和废气等进行清单分析,得出我国基于煤电生命周期的发电废气产生的数量。     

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.     

Preparing highway emissions inventories for urban scale modeling: A case study in Philadelphia

Highway emissions represent a major source of many pollutants. Use of local data to model these emissions can have a large impact on the magnitude and distribution of emissions predicted and can significantly improve the accuracy of local scale air quality modeling assessments. This paper provides a comparison of top–down and bottom–up approaches for developing emission inventories for modeling in one urban area, Philadelphia, in calendar year 1999. A bottom–up approach relies on combining motor vehicle emission factors and vehicle activity data from a travel demand model estimated at the road link level to generate hourly emissions data. This approach can result in better estimates of levels and spatial distribution of on-road motor vehicle emissions than a top–down approach that relies on more aggregated information and default modeling inputs.     

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.     

Well-to-wheel assessment of natural gas vehicles and their fuel supply infrastructures – Perspectives on gas in transport in Denmark

In this paper, potential natural gas and renewable natural gas supply pathways and natural gas vehicles (NGVs) have been selected and evaluated with regards to well-to-wheel energy expended, greenhouse gas (GHG) emissions, and regulated (air pollutant) emissions. The vehicles included in the evaluation are passenger cars, light-duty vehicles (LDVs), and heavy-duty vehicles (HDVs) for road-transport applications, and a short-range passenger vessel for maritime transport applications. The results show that, compared to conventional fuels, in both transport applications and for all vehicle classes, the use of compressed and liquefied natural gas has a 15–27% GHG emissions reduction effect per km travel. The effect becomes large, 81–211%, when compressed and liquefied renewable natural gas are used instead. The results are sensitive to the type and source of feedstock used, the type of vehicle engine, assumed methane leakage and methane slip, and the allocated energy and environmental digestate credits, in each pathway. In maritime applications, the use of liquefied natural gas and renewable natural gas instead of low sulfur marine fuels results in a 60–100% SOx and 90–96% PM emissions reduction. A 1% methane slip from a dedicated LNG passenger vessel results, on average, in 8.5% increase in net GHG emissions.     

The impact of speed post-processing methods on regional mobile emissions estimation

Average roadway segment travel speeds play an important role in estimating stabilized running vehicle emissions. Currently stabilized, or hot, running emissions are computed based on speeds produced during the travel demand modeling process. Speed data from the travel forecasting models are widely recognized as being insufficiently accurate for air quality purposes. Frequently post-processing techniques are seen as the most cost-effective means of improving the accuracy of the speed estimates. Using the Sacramento Metropolitan area, this paper focuses on the impacts of different speed post-processors on regional peak period emissions inventories. The results indicated that most post-processed speeds produce consistently and significantly higher running emissions, particularly in locations with heavy traffic. The observed differences in emissions between different types of post-processed speeds vary with congestion level, pollutant type and the underlying approach encapsulated in the speed post-processor calculations. The Sacramento case study suggests that the post-processor used to develop speeds for the purposes of calculating on-road emissions inventories can significantly influence the emissions inventories.     

Marginal costs of freeway traffic congestion with on-road pollution exposure externality

The health cost of on-road air pollution exposure is a component of traffic marginal costs that has not previously been assessed. The main objective of this paper is to introduce on-road pollution exposure as an externality of traffic, particularly important during traffic congestion when on-road pollution exposure is highest. Marginal private and external cost equations are developed that include on-road pollution exposure in addition to time, fuel, and pollution emissions components. The marginal external cost of on-road exposure includes terms for the marginal vehicle’s emissions, the increased emissions from all vehicles caused by additional congestion from the marginal vehicle, and the additional exposure duration for all travelers caused by additional congestion from the marginal vehicle. A sensitivity analysis shows that on-road pollution exposure can be a large portion (18%) of marginal social costs of traffic flow near freeway capacity, ranging from 4% to 38% with different exposure parameters. In an optimal pricing scenario, excluding the on-road exposure externality can lead to 6% residual welfare loss because of sub-optimal tolls. While regional pollution generates greater costs in uncongested conditions, on-road exposure comes to dominate health costs on congested freeways because of increased duration and intensity of exposure. The estimated marginal cost and benefit curves indicate a theoretical preference for price controls to address the externality problem. The inclusion of on-road exposure costs reduces the magnitudes of projects required to cover implementation costs for intelligent transportation system (ITS) improvements; the net benefits of road-pricing ITS systems are increased more than the net benefits of ITS traffic flow improvements. When considering distinct vehicle classes, inclusion of on-road exposure costs greatly increases heavy-duty vehicle marginal costs because of their higher emissions rates and greater roadway capacity utilization. Lastly, there are large uncertainties associated with the parameters utilized in the estimation of health outcomes that are a function of travel pollution intensity and duration. More research is needed to develop on-road exposure modeling tools that link repeated short-duration exposure and health outcomes.     

Mobility and environment improvement of signalized networks through Vehicle-to-Infrastructure (V2I) communications

Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.     

Optimal fleet conversion policy from a life cycle perspective

Vehicles typically deteriorate with accumulating mileage and emit more tailpipe air pollutants per mile. Although incentive programs for scrapping old, high-emitting vehicles have been implemented to reduce urban air pollutants and greenhouse gases, these policies may create additional sales of new vehicles as well. From a life cycle perspective, the emissions from both the additional vehicle production and scrapping need to be addressed when evaluating the benefits of scrapping older vehicles. This study explores an optimal fleet conversion policy based on mid-sized internal combustion engine vehicles in the US, defined as one that minimizes total life cycle emissions from the entire fleet of new and used vehicles. To describe vehicles' lifetime emission profiles as functions of accumulated mileage, a series of life cycle inventories characterizing environmental performance for vehicle production, use, and retirement was developed for each model year between 1981 and 2020. A simulation program is developed to investigate ideal and practical fleet conversion policies separately for three regulated pollutants (CO, NMHC, and NO_x) and for CO₂. According to the simulation results, accelerated scrapping policies are generally recommended to reduce regulated emissions, but they may increase greenhouse gases. Multi-objective analysis based on economic valuation methods was used to investigate trade-offs among emissions of different pollutants for optimal fleet conversion policies.     

Electric vehicles for greenhouse gas reduction in China: A cost-effectiveness analysis

There have been ongoing debates over whether battery electric vehicles contribute to reducing greenhouse gas emissions in China’s context, and if yes, whether the greenhouse gas emissions reduction compensates the cost increment. This study informs such debate by examining the life-cycle cost and greenhouse gas emissions of conventional vehicles, hybrid electric vehicles and battery electric vehicles, and comparing their cost-effectiveness for reducing greenhouse gas emissions. The results indicate that under a wide range of vehicle and driving configurations (range capacity, vehicle use intensity, etc.), battery electric vehicles contribute to reducing greenhouse gas emissions compared with conventional vehicles, although their current cost-effectiveness is not comparable with hybrid electric vehicles. Driven by grid mix optimization, power generation efficiency improvement, and battery cost reduction, the cost-effectiveness of battery electric vehicles is expected to improve significantly over the coming decade and surpass hybrid electric vehicles. However, considerable uncertainty exists due to the potential impacts from factors such as gasoline price. Based on the analysis, it is recommended that the deployment of battery electric vehicles should be prioritized in intensively-used fleets such as taxis to realize high cost-effectiveness. Technology improvements both in terms of power generation and vehicle electrification are essential in improving the cost-effectiveness of battery electric vehicles.     

A bi-level model for GHG emission charge based on a continuous distribution of travelers’ value of time (VOT)

Transportation is an important source of greenhouse gas (GHG) emissions. In this paper, we develop a bi-level model for GHG emission charge based on continuous distribution of the value of time (VOT) for travelers. In the bi-level model framework, a policy maker (as the leader) seeks an optimal emission charge scheme, with tolls differentiated across travel modes (e.g., bus, motorcycles, and cars), to achieve a given GHG reduction target by shifting the proportions of travelers taking different modes. In response, travelers (as followers) will adjust their travel modes to minimize their total travel cost. The resulting mode shift, hence the outcome of the emission charge policy, depends on travelers’ VOT distribution. For the solution of the bi-level model, we integrate a differential evolution algorithm for the upper level and the “all or nothing” traffic assignment for the lower level. Numerical results from our analysis suggest important policy implications: (1) in setting the optimal GHG emission charge scheme for the design of transportation GHG emission reduction targets, policy makers need to be equipped with rigorous understanding of travelers’ VOT distribution and the tradeoffs between emission reduction and system efficiency; and (2) the optimal emission charge scheme in a city depends significantly on the average value of travelers’ VOT distribution—the optimal emission charge can be designed and implemented in consistency with rational travel flows. Further sensitivity analysis considering various GHG reduction targets and different VOT distributions indicate that plausible emission toll schemes that encourage travelers to choose greener transportation modes can be explored as an efficient policy instrument for both transportation network performance improvement and GHG reduction.