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SUMMARYThis study determines the factors responsible for the growth of transport sector CO 2 emissions in 20 Latin American and Caribbean (LAC) countries during the 1980-2005 period by decomposing the emissions growth into components associated with changes in fuel mix (FM), modal shift and economic growth, as well as changes in emission coefficients (EC) and transportation energy intensity (EI). The key finding of the study is that economic growth and the changes in transportation EI are the principal factors driving transport sector CO 2 emission growth in the countries considered. While economic growth is responsible for the increasing trend of transport sector CO 2 emissions in Argentina, Brazil, Costa Rica, Peru and Uruguay, the transportation EI effect is driving CO 2 emissions in Bolivia, the Caribbean, Cuba, Ecuador, Guatemala, Honduras, Other Latin America, Panama and Paraguay. Both economic activity (EA) and EI effects are found responsible for transport sector CO 2 emissions growth in the rest of the Latin American countries. In order to limit CO 2 emissions from the transportation sector in LAC countries, decoupling of the growth of CO 2 emissions from economic growth is necessary; this can be done through policy instruments to promote fuel switching, modal shifting and reductions in transport sector EI.

Abstract:The magnitude and frequency of hydrological events are expected to increase in coming years due to climate change in megacities of Asia. Intensity-Duration-Frequency (IDF) curves represent essential means to study effects on the performance of drainage systems. Therefore, the need for updating IDF curves comes from the necessity to gain better understanding of climate change effects. The present paper explores an approach based on spatial downscaling-temporal disaggregation method (DDM) to develop future IDFs using stochastic weather generator, Long Ashton Research Station Weather Generator (LARS-WG) and the rainfall disaggregation tool, Hyetos. The work was carried out for the case of Bangkok, Thailand. The application of LARS-WG to project extreme rainfalls showed promising results and nine global climate models (GCMs) were used to estimate changes in IDF characteristics for future time periods of 2011-2030 and 2046-2065 under climate change scenarios. The IDFs derived from this approach were corrected using higher order equation to mitigate biases. IDFs from all GCMs showed increasing intensities in the future for all return periods. The work presented demonstrates the potential of this approach in projecting future climate scenarios for urban catchment where long term hourly rainfall data are not readily available.