Prediction of household levels of greenhouse-gas emissions from personal automotive transportation
TL;DR: In this paper, the authors demonstrate that different subgroups in the population described by various economic and demographic characteristics have different levels of greenhouse gas emissions from personal automotive transportation, and these characteristics may be assigned a rank or importance in terms of association with emissions levels.
About: This article is published in Energy.The article was published on 1997-05-01. It has received 15 citations till now. The article focuses on the topics: Population & Greenhouse gas.
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01 Jan 2016
TL;DR: In this article, a case study of demographic influences on household energy consumption in the United States is presented, where the authors consider the effects of demographic factors such as population size, age structure, and levels of urbanization on household consumption.
Abstract: Projections of energy demand over the coming decades are critically important to understanding and anticipating future resource requirements and environmental impacts such as acid rain, local air pollution, and climate change (e.g., Nakicenovic et al. 2000). Household consumption of energy for space heating and cooling, lighting, appliances, transportation, and other energy services is a key driver of national energy demand. A number of demographic factors such as population size, age structure, and levels of urbanization have potentially important direct and indirect influences on household demand. For example, aging may have direct consequences since energy consumption tends to change over the lifespan (Yamasaki and Tominaga 1997); aging could also have indirect impacts through an associated decline in household size and consequently a loss of economies of scale in energy use at the household level. However, the treatment of population-related variables in energy projections has been essentially limited to considerations of changes in population size alone (O'Neill, MacKellar, and Lutz 2001; Gaffin 1998), even though significant changes in other factors, especially age structure, are anticipated in all regions of the world.
Improvements to the development of credible projections of energy demand through a better understanding of demographic determinants of energy use would be valuable for several reasons. First, they would clarify the outlook for the potential range of projected environmental consequences of energy-related emissions. Second, they would allow better estimates of the costs of reducing emissions, which are sensitive to baseline emissions projections. Cost estimates play a key role in the current debate over appropriate climate change policy. And third, understanding energy demand across different demographic groups can help assess the potential distributional effects of emissions-reduction efforts.
We first briefly review the principal approaches to the incorporation of demographic factors in current studies of energy use and associated greenhouse gas emissions, as well as recent work on determinants of household energy use. We then present a case study of demographic influences on household energy consumption in the United States....
222 citations
TL;DR: In this article, a case study application of the methodology involving a major survey of UK residents provides an improved understanding of the extent to which individual and household travel activity patterns, choice of transport mode, geographical location, socio-economic and other factors impact on greenhouse gas emissions.
150 citations
TL;DR: In this article, the authors examined the composition of greenhouse gas emissions from personal, non-business travel at disaggregate levels and found that the highest 20% of emitters produced 61% of emissions.
141 citations
01 Aug 2002
TL;DR: In this article, a cross-sectional analysis of car use in Austria is combined with detailed household projections to explore the sensitivity of projected car use to the specific type of demographic disaggregation employed.
Abstract: Understanding the factors driving demand for transportation in industrialised countries is important in addressing a range of environmental issues. Previous work has identified demographic factors as important influences on demand, in addition to economic factors. While some studies applied a detailed demographic composition to analyse past developments of transportation demand, or estimated parameters based on models that include demographic variables, projections for the future have never accounted for future compositional changes in the population. In this paper, we combine cross-sectional analysis of car use in Austria with detailed household projections to explore the sensitivity of projections of car use to the specific type of demographic disaggregation employed. We find that particular demographic characteristics of households can have important effects on aggregate demand through the combined effect of differences in demand across different types of households, and changes in the future composition of the population by household type. For example, the highest projected car use -- an increase of about 20 per cent between 1996 and 2046 -- is obtained if we apply the value of car use per household to the projected numbers of households. However, if we apply a composition that differentiates households by size, age and sex of the household head, car use is projected to increase by less than 3 per cent during the same time period. These findings suggest that the inclusion of demographic factors in transportation demand modelling should extend beyond their use in historical decompositions and as controls in model parameter estimation to explicit consideration of future demographic changes.
76 citations
References
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01 Jan 1993TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Abstract: This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.
37,183 citations
Book•
01 May 1974
TL;DR: In this article, the authors analyzed the distribution of worker earnings across workers and over the working age as consequences of differential investments in human capital and developed the human capital earnings function, an econometric tool for assessing rates of return and other investment parameters.
Abstract: Analyzes the distribution of worker earnings across workers and over the working age as consequences of differential investments in human capital. The study also develops the human capital earnings function, an econometric tool for assessing rates of return and other investment parameters.
8,587 citations
TL;DR: A review of the intergovernmental panel on climate change report on global warming and the greenhouse effect can be found in this paper, where the authors present chemistry of greenhouse gases and mathematical modelling of the climate system.
Abstract: Book review of the intergovernmental panel on climate change report on global warming and the greenhouse effect. Covers the scientific basis for knowledge of the future climate. Presents chemistry of greenhouse gases and mathematical modelling of the climate system. The book is primarily for government policy makers.
3,456 citations
TL;DR: The special cases of linear functions and logarithmic functions of the 7rin are developed in detail, and some examples of how the general approach can be used to analyze various types of categorical data are presented.
Abstract: Assume there are ni., i = 1, 2, *--, s, samples from s multinomial distributions each having r categories of response. Then define any u functions of the unknown true cell probabilities {7rij: i = 1, 2, * , s; j = 1, 2, * , r, where E jrij l 1 } that have derivatives up to the second order with respect to 7rij, and for which the matrix of first derivatives is of rank u. A general noniterative procedure is described for fitting these functions to a linear model, for testing the goodness-of-fit of the model, and for testing hypotheses about the parameters in the linear model. The special cases of linear functions and logarithmic functions of the 7rin are developed in detail, and some examples of how the general approach can be used to analyze various types of categorical data are presented.
1,515 citations
DOI•
01 Jan 2008
TL;DR: The Transportation Energy Data Book: Edition 11 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the Office of Transportation Technologies in the Department of Energy (DOE) as discussed by the authors.
Abstract: The Transportation Energy Data Book: Edition 11 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the Office of Transportation Technologies in the Department of Energy (DOE). Designed for use as a desk-top reference, the data book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. Each of the major transportation modes - highway, air, water, rail, pipeline - is treated in separate chapters or sections. Chapter 1 compares US transportation data with data from seven other countries. Aggregate energy use and energy supply data for all modes are presented in Chapter 2. The highway mode, which accounts for over three-fourths of total transportation energy consumption, is dealt with in Chapter 3. Topics in this chapter include automobiles, trucks, buses, fleet automobiles, federal standards, fuel economies, and household data. Chapter 4 is a new addition to the data book series, containing information on alternative fuels and alternatively-fueled vehicles. The last chapter, Chapter 5, covers each of the nonhighway modes: air,more » water, pipeline, and rail, respectively. 92 figs., 112 tabs.« less
821 citations