published_or_final_version / abstract / toc / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

No abstract available. / Thesis (M.Env.Dev.)-University of Natal, Pietermaritzburg, 2002.

No description available.

e transport sector is responsible for a quarter of global CO2 emissions and the emissions continue to grow rapidly. The overall objective of this study was to calculate the following greenhouse gas emissions (GHG); CO2, CH4 and N2O from the transport sector in South Africa in the base year 2009. However, in addition to the calculations of the emissions for this base year, emissions from road transport were recalculated since 2000. The available data allowed only Tier 1 method to calculate all the GHG emissions. Vehicles per type, province and distances they travelled were used to estimate the emissions, while fuel used at various airports in the country was used to determine aviation emissions. Emissions from other modes of the transport sector were calculated using the data from the national energy balances. It was estimated that 54,296 Giga grams (Gg) of CO2 equivalent (CO2-eq) emissions were emitted in 2009. Road, off- road, aviation and rail transports accounted for 80%, 13%, 6% and 1% of the emissions, respectively. Motorcars and trucks produced more than 70% of the road transport emissions. Road transport emissions increased at approximately 2.66% per year between 2000 and 2009. Gauteng province had the highest emissions. Minibus taxis were the most efficient transport mode on the basis of load carried.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts

Improving the efficiency of traffic operations along arterials is currently a priority for many agencies as their roadway infrastructure is built out to the largest possible capacity within the urban environment. Many advanced systems are being implemented to optimize traffic signal timing. Air quality is typically not a consideration when these decisions are made. The relationship between exposure to air pollution and traffic signal timing has not been fully explored by other researchers. This study is the first research effort to combine detailed traffic signal timing data (at 5 second intervals) and air pollutant exposure data. Results show that longer green time along the busy arterial reduces pollutant levels, while increased volume per cycle increases pollutant levels. This research quantifies the factors that contribute to pedestrian, bicyclist, and transit user exposure at a busy intersection along an urban arterial. The factors include traffic signal timing, weather related variables, traffic volumes, and heavy vehicle and bus presence. Additionally, the impact of an adaptive traffic signal system on air quality is assessed to understand the implications of signal timing on air pollutant exposure.

The negative consequences of sprawling metropolitan regions have attracted attention in both academia and in practice regarding how to better design settlements and alter travel behavior in a quest to curtail vehicle emissions. Studies that have attempted to understand the nexus between land use, travel and vehicle emissions have not been able to address the issue of self-selection in a satisfactory manner. Self-selection occurs when households choose their residential location based, in part, on expected travel behavior. This non-random experience makes the use of traditional regression frameworks that strongly rely on random sampling, unsuitable. This replication study's purpose was to examine the impact of land use and travel on CO2 emissions using the Heckman (1979) sample selection model in Portland Metropolitan Area. three research questions guided this study: (1) Does self-selection to drive a motor vehicle lead to reduction in CO2 emissions? (2) Does land use and automobile travel influence the decision to drive after controlling for self-selection? (3) What land use and travel factors determine CO2 emissions after controlling for self-selection? The findings suggest driving has a statistically significant negative effect on estimated CO2 and that most land use variables significantly affect driving behavior.

No description available.