Thesis. 1978. M.Arch.--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography: leaves 182-186. / M.Arch.

University of Technology, Sydney. Faculty of Engineering. / This research has analysed the economy-wide impacts of carbon tax as a policy option to reduce the rate of growth of carbon-dioxide emissions from the electricity sector in Australia. These impacts are analysed for energy and non energy sectors of the economy. An energy-oriented Input–Output framework, with ‘flexible’ production functions, based on Translog and Cobb-Douglas formulations, is employed for the analysis of various impacts. Further, two alternative conceptions of carbon tax are considered in this research, namely, based on Polluter Pays Principle (PPP) and Shared Responsibility Principle (SRP). In the first instance, the impacts are analysed, for the period 2005–2020, for tax levels of $10 and $20 per tonne of CO2, in a situation of no a-priori limit on CO2 emissions. The analysis shows that CO2 emissions from the electricity sector, when carbon tax is based on PPP, would be 211 and 152 Mt, for tax levels of $10 and $20, respectively (as compared to 250 Mt in the Base Case scenario, that is, the business-as-usual-case). The net economic costs, corresponding with these tax levels, expressed in present value terms, would be $27 and $49 billion, respectively, over the period 2005-2020. These economic costs are equivalent to 0.43 and 0.78 per cent of the estimated GDP of Australia. Further, most of the economic burden, in this instance, would fall on the electricity sector, particularly coal-fired electricity generators – large consumers of direct fossil fuel. On the other hand, in the case of a carbon tax based on SRP, CO2 emissions would be 172 and 116 Mt, for tax levels of $10 and $20, respectively. The corresponding net economic costs would be $47 (0.74 per cent of GDP) and $84 (1.34 per cent of GDP) billion, respectively, with significant burden felt by the commercial sector – large consumers of indirect energy and materials whose production would contribute to CO2 emissions. Next, the impacts are analysed by placing an a-priori limit on CO2 emissions from the electricity sector – equivalent to 108 per cent of the 1990 level (that is, 138 Mt), by the year 2020. Two cases are analysed, namely, early action (carbon tax introduced in 2005) and deferred action (carbon tax introduced in 2010). In the case of early action, the analysis suggests, carbon tax of $25 and $15, based on PPP and SRP, respectively, would be required to achieve the above noted emissions target. The corresponding tax levels in the case of deferred action are $51 and $26, respectively. This research also shows that the net economic costs, in the case of early action, would be $32 billion (for PPP) and $18 billion (for SRP) higher than those in the case of deferred action. However, this research has demonstrated, that this inference is largely due to the selection of particular indicator (that is, present value) and the relatively short time frame (that is, 2005–2020) for analysis. By extending the time frame of the analysis to the year 2040, the case for an early introduction of carbon tax strengthens. Overall, the analysis in this research suggests that an immediate introduction of carbon tax, based on SRP, is the most attractive approach to reduce the rate of growth of CO2 emissions from the electricity sector and to simultaneously meet economic and social objectives. If the decision to introduce such a tax is deferred, it would be rather difficult to achieve not only environmental objectives but economic and social objectives as well.

No description available.

A procedure for model-assisted climate impact assessment is developed. The approach combines data from observations and atmospheric general circulation models (GCNs), and provides the basis for a potentially valuable means of using information derived from GCMs for climate impact assessments on local scales. The first component of this procedure is an extension of the 'climate inverse' method of Kim al. (1984). Daily mesoscale temperature and precipitation values are stochastically specifed on the basis of observational data representing the average over an area corresponding to a GCN grid element. Synthetic local data sets generated in this manner resemble the corresponding observations with respect to various spatial and temporal statistical measures. A method for extrapolation to grid-scale 'scenarios' of a changed climate on the basis of control and experimental integrations of a GCM, in conjunction with observational data, is also presented. The statistical characteristics of daily time series from each of these data sources are portrayed in terms of the parameters of a multivariate time-domain stochastic model. Significant differences between the model data sets are applied to the corresponding parameters derived from the observations, and synthetic data Bets representing the inferred changed climate are generated using Monte-Carlo simulations. The use of the procedure is illustrated in a case study. The potential climatic impacts of a doubling of atmospheric carbon dioxide concentrations on three important North American grain cropping regions is investigated using two 'physiological' crop models. Although the specific results must be interpreted with caution, they are moderately optimistic and demonstrate possible means by which agricultural production may adapt to climatic changes. / Graduation date: 1987

Graduation date: 1955

Thesis (M.S.)--Oregon State University, 1984. / Typescript (photocopy). Includes bibliographical references (leaves 88-90). Also available on the World Wide Web.

Stochastic weather generators create multiple series of synthetic daily weather (precipitation, maximum temperature, etc.), and ideally these series will have statistical properties similar to those of the input historical data. The synthetic output has many applications and for example, can be used in sectors such as agriculture and hydrology. This work used a ?hybrid? weather generator which consists of a parametric Markov chain for generating precipitation occurrence and a nonparametric k-nearest neighbor method for generating values of maximum temperature, minimum temperature, and precipitation. The hybrid weather generator was implemented and validated for use at 11 different locations in the Southeastern United States. A total of 36 graphic diagnostics were used to assess the model?s performance. These diagnostics revealed that the weather generator successfully created synthetic series with most statistical properties of the historical data including extreme wet and dry spell lengths and days of first and last freeze. Climate forecasts are typically provided for seasons or months. Alternatively, process models used for risk assessment often operate at daily time scales. If climate forecasts were incorporated into the daily weather input for process models, stakeholders could then use these models to assess possible impacts on their sector of interest due to anticipated changes in climate conditions. In this work, an ?ad hoc? resampling approach was developed to create sets of daily synthetic weather series consistent with seasonal climate forecasts in the Southeastern United States. In this approach, the output of the hybrid weather generator was resampled based on forecasts in two different formats: the commonly used tercile format and a probability distribution function. This resampling approach successfully created sets of synthetic series which reflected different forecast scenarios (i.e. wetter or drier conditions). Distributions of quarterly total precipitation from the resampled synthetic series were found to be shifted with respect to the corresponding historical distributions, and in some cases, the occurrence and intensity statistics of precipitation in the new weather series had changed with respect to the historical values.

Thesis (M.S.)--University of Montana, 2006. / Mode of access: Internet. Title from title screen. Description based on contents viewed Feb. 13, 2007. Includes bibliographical references (p. 118-121).

Hourly precipitation data from thirty cooperative stations in Kentucky from 1965 to 1996 were used to determine the diurnal distribution of precipitation rates. Descriptive summaries for the diurnal distribution for each climate division in Kentucky and for Kentucky as a whole were calculated. In each case, the trends were similar. Precipitation rates increased into the afternoon and then decreased until sunrise. A stochastic model was developed to estimate mean seasonal precipitation rates in Kentucky by using regional and localized parameters. More than half of the variation (r2 = 0.57) in precipitation rates can be explained by the following variables: 1) Distance away from the moisture source, the Gulf of Mexico; 2) Roughness of topography; 3) Degree of urbanization. Precipitation rates decrease in a northeasterly direction across Kentucky as air moves farther away from the Gulf of Mexico along its path of migration. The maritime tropical air mass migrating out of the Gulf of Mexico loses its water vapor over its path of migration. As a result, less water vapor is available for precipitation processes in areas farther away. As a precipitation event moves over rougher terrain and more urbanized areas, precipitation rates decrease as well. A rougher terrain absorbs more solar radiation because it has more surface area. An urbanized area absorbs more solar radiation because of the urban structures (e.g., buildings, asphalt, roofs). As a result, both will radiate more heat causing the air to be buoyant at the surface to either enhance convection or increase vertical air motions. Increased vertical air motions will cause an increase in air resistance acting upon precipitation falling, thereby, causing a decrease in the amount falling to the surface per hour.

An agroclimatic risk assessment for agricultural production across the Canadian prairie provinces has been assembled to address challenges related to frost, heat units, and moisture. Based on 30 years of daily climate data from 1971 though 2000 from 230 climate stations, the assessment provides a series of risk maps depicting the likelihood to achieve certain thresholds of frost dates or durations, heat unit accumulations, growing season rainfall, crop water demand (CWD), and crop water deficits. Maps for each parameter provide a spatial representation of 50%, 25%, and 10% risks and the coefficient of variation. / October 2007