Zia Wadud

A cointegration analysis of gasoline demand in the United States

Time-series estimation of gasoline demand elasticities often does not take into account the possibility of nonstationarity in the underlying data, which may render the parameter estimates spurious. Studies have shown that the time trending variables used to explain gasoline demand could be difference stationary and therefore, may require cointegration analysis to assess the relationship among the trending variables. In this work we use the cointegration technique to derive long-run and short-run demand elasticities of noncommercial gasoline consumption using time-series data for the USA from 1949 to 2004. We also attempt to incorporate the presence of a structural break in the data generation process of the time trending variables. Our results show that the consumption of gasoline and lifetime income have a long-term stable relationship after the second oil shock of 1978. Prior to the first oil shock of 1973, no such long-run relationship could be established through cointegration.

Air quality and climate impacts due to CNG conversion of motor vehicles in Dhaka, Bangladesh.

Dhaka had recently experienced rapid conversion of its motor vehicle fleet to run on compressed natural gas (CNG). This paper quantifies ex-post the air quality and climate benefits of the CNG conversion policy, including monetary valuations, through an impact pathway approach. Around 2045 (1665) avoided premature deaths in greater Dhaka (City Corporation) can be attributed to air quality improvements from the CNG conversion policy in 2010, resulting in a saving of around USD 400 million. Majority of these health benefits resulted from the conversion of high-emitting diesel vehicles. CNG conversion was clearly detrimental from climate change perspective using the changes in CO2 and CH4 only (CH4 emissions increased); however, after considering other global pollutants (especially black carbon), the climate impact was ambiguous. Uncertainty assessment using input distributions and Monte Carlo simulation along with a sensitivity analysis show that large uncertainties remain for climate impacts. For our most likely estimate, there were some climate costs, valued at USD 17.7 million, which is an order of magnitude smaller than the air quality benefits. This indicates that such policies can and should be undertaken on the grounds of improving local air pollution alone and that precautions should be taken to reduce the potentially unintended increases in GHG emissions or other unintended effects.

Simultaneous Modeling of Passenger and Cargo Demand at an Airport

Forecasts for passenger and cargo demands are important parameters for airport planners. Although there are a number of studies for passenger demand in an airport, the number of studies for air cargo is much smaller. Also, these two entities are often dealt with separately in the literature. However, there can be advantages in modeling them simultaneously, especially when time series data are used for estimating the demand models. A seemingly unrelated regression (SUR) framework is followed to jointly model passenger and cargo demand at the Shahjalal International Airport at Dhaka, Bangladesh. Allowing for contemporaneous correlation between the air passenger and air cargo demand models in the SUR approach allows a more efficient and reliable estimate than ordinary least squares and individual cointegration methods. Results of the simultaneous demand modeling are used to forecast passenger and cargo demand at the airport up to 2030.

Modelling Environmental and Economic Impacts of Aviation: Introducing the Aviation Integrated Modelling Project

The Aviation Integrated Modelling project is developing a policy assessment capability to enable comprehensive analyses of aviation, environment and economic interactions at local and global levels. It contains a set of inter-linked modules of the key elements relevant to this goal. These include models for aircraft/engine technologies, air transport demand, airport activity and airspace operations, all coupled to global climate, local environment and economic impact blocks. A major benefit of the integrated system architecture is the ability to model data flow and feedback between the modules. Policy assessment can be conducted by imposing policy effects on the upstream modules and following implications through the downstream modules to the output metrics, which can then be compared to a baseline case. A case study involving different evolution scenarios of the US air transportation system from 2000 to 2030 is used to show the importance of feedback and to model a sample policy scenario in order to illustrate current capabilities.

Comparison of Air Quality–Related Mortality Impacts of Different Transportation Modes in the United States

Knowledge about the environmental impacts of various transportation modes is important for understanding trade-offs that may be involved in policy options that affect different transportation modes in different ways. This paper reviews the literature on human health impacts attributed to various transportation modes, focusing on premature mortality, to carry out a comparative analysis of the modes. This research finds that the risks of premature death attributable to air pollution from rail, shipping, and aviation emissions are typically larger than the accident-related fatalities of these modes; for road transportation, air pollution–related premature mortality risks are similar in magnitude to the number of fatalities from accidents. Of the modes considered, road transportation has the largest impact on air quality health risks. Passenger and heavy road vehicles are estimated to be responsible for almost equal shares of premature mortality due to air pollution. Road transport is followed by ocean shipping, rail, and aviation, respectively, for air quality–related total health impacts. These health risks are also normalized with respect to the service delivered by each mode to account for the differences in the levels of activity among different modes. In this case, road vehicles are found to have the largest impact per unit of activity (expressed as equivalent ton-miles), with aviation ranked second on a per-ton-mile basis. Rail transportation is the most environmentally friendly mode on a per-ton-mile basis, followed closely by shipping. Despite rails relatively low air quality–related mortality risks per ton-mile as a transportation mode, passenger rail (especially Amtrak and commuter rail) may be responsible for the highest risk of air pollution–related premature deaths on a per-ton-mile basis, followed by personal road vehicles.

Modeling and Forecasting Passenger Demand for a New Domestic Airport with Limited Data

Because adequate information on peoples travel behavior is often unavailable in developing countries, demand modeling for a new airport is difficult. This study presents a simple modeling approach that uses limited aggregate information about a country to generate a forecast for passenger patronage in a new airport. The model uses aggregate time-series information on the national level and patronage at other airports to develop a gravity-type demand model for the country. The demand model incorporates the possible competition with a substitute mode, which is often the case for air travel demand in a small country. Demand for the specific new airport is then calibrated by using the parameters from this demand model and a small passenger survey. This modeling approach is applied to forecast the demand for a new airport in a divisional capital in Bangladesh, for which no regional data on gross domestic product or population are available. The modeling approach can be useful to researchers and practitioners when extensive data about air travel behavior are not available.

Review of Oil Demand Restraint Policies for Heavy-goods Vehicles

Abstract Supply disruptions in the petroleum sector are a major cause for concern as these can lead to large transport fuel price spikes and physical shortages of supply. Transport demand restraint measures can play a role in mitigating the impact of supply shortages by providing increased flexibility for consumers. Heavy-goods vehicles and road freight are highly dependent on fuel availability to maintain the flow of goods within the economy. This article examines measures for reducing fuel consumption in road freight with a focus on potential emergency management measures and the feasibility of fast implementation of demand-side restraints. These include operational, logistical, and rationing allocation policies, which are examined based on their feasibility to be implemented quickly at the onset of a crisis. While no quantitative results are derived, a list of potential demand restraint policies and their likely effectiveness and feasibility is presented based on the review.

Fully automated vehicles: A cost of ownership analysis to inform early adoption

Vehicle automation and its adoption by the vehicle purchasers is an active area of research among transportation academics. So far, most of the interest in the adoption of fully automated, driverless vehicles has focussed on private vehicles alone, yet full automation could be beneficial for commercial vehicles too. This paper identifies the vehicle sectors that will likely be the earliest adopters of full automation. Total cost of ownership analysis is used to compare the costs (and benefits) of vehicle automation for private vehicles among different income groups and commercial vehicles in the taxi and freight sectors in the UK. Commercial operations clearly benefit more from automation because the driver costs can be reduced substantially through automation. Among private users, households with the highest income will benefit more from automation because of their higher driving distances and higher perceived value of time, which can be used more productively through full automation.

Using meta-regression to determine Noise Depreciation Indices for Asian airports

The external costs of aviation noise are an important input in policy assessment for cost-benefits analysis. The Noise Depreciation Index (NDI) is used to capture the externality costs through measuring the depreciation of property prices (PPs) exposed to aviation noise. This paper summarizes existing studies on NDI and examines the underlying differences in order to transfer these NDI values to other parts of the world, where NDI estimates are not directly available. We find that higher wealth, expressed in terms of PPs, relative PPs or income, result in higher values of NDI. This means that wealthier households de-value the PPs more than the average in the presence of aircraft noise. The income dependence allows the NDI estimates to be transferred to other locations using local PPs or income for cost–benefit analysis. Estimates of NDI for some Asian countries using the meta-regression results are also provided.

Econometric modeling and forecasting of natural gas demand for power sector in Bangladesh

Natural gas is the principal indigenous resource for power generation in Bangladesh. Use of other conventional fossil fuels like petroleum and coal are quite insignificant with respect to the use of natural gas. The gas reserve of Bangladesh is depleting fast because of this heavy dependence on gas. Therefore, it is essential to have a realistic assessment of the gas demand pattern in the near future. In this paper the impacts of economic variables like gas price, per capita Gross Domestic Product (GDP) on gas demand were investigated by employing advanced econometric modeling technique. This research uses time series data for modeling the gas demand in Power sector and forecasting the demand up to 2025. Various statistical tests were employed to choose the correct functional form. Results show no significant impact of price on gas demand. Moreover, the forecast obtained from the analysis shows that the gas demand will increase rapidly in future. This research thus concludes that gas demand cannot possibly be controlled by price management and also emphasizes on strengthening the supply side to meet the growing gas demand in the near future.