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Abstract - Education and Skill Mismatches in Maptaphut Industrial Estate, Thailand
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Abstract: This paper examines educational and skill mismatches by using primary data from Maptaphut Industrial Estate, Rayong Province, Thailand, conducted from 226 respondents. Ordinary least squares (OLS) and unconditional quantile (UQR) regressions are used to examine the mismatches on earnings. The empirical findings are as follows: (1) returns to education of high income workers are larger than average income workers; (2) education mismatches affect earnings, such that over-education is statistically significant, whereas under-education is not significant, for both the OLS and UQR estimates; (3) UQR provides more information on size of the over-education to the returns on schooling; (4) specific training for some job positions also indicates educational and skill mismatches; and (5) firms should invest in training for workers to reduce weaknesses in educational mismatches. Keywords: Over-education, Under-education, Skill Mismatches, Specific Training, Earnings, Maptaphut Industrial Estate. |
Abstract - Development of a Code of Practice and Indicators for Quality Management of Official Statistics in Thailand
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Abstract: This paper is concerned with the National Statistical Office of Thailand, and constructs a Code of Practice and indicators for quality management of official statistics in Thailand. Official statistics for quality management from three international agencies include the United Nations Statistics Division (UNSD), European Statistical System (Eurostat), and International Monetary Fund (IMF). Factor analysis is used to determine the important indicators and their respective weights. The research shows that a Code of Practice for quality management of official statistics in Thailand should be comprised of 9 rules and 35 common indicators. Keywords: Code of Practice, Rules, Indicators, Official Statistics, Quality Management. |
Abstract - Projections of Energy Use and Carbon Emissions for Bangkok, Thailand
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Abstract: Cities around the world have developed innovative plans and strategies to reduce rampant and uncontrolled energy use and CO2 emissions. It is not surprising that many cities have adopted long-term emission reduction goals. However, a detailed analysis of energy use and the resulting carbon emissions are rare in developing countries. This paper presents historical trends of energy demand and energy-related CO2 emissions for Bangkok, Thailand. The CO2 emissions account for the use of fossil fuels only. It also presents the projections of energy use and CO2 emissions from 2010 to 2050. Using a bottom-up energy model and scenario analysis, the paper employs a Long-range Energy Alternative Planning (LEAP) system to simulate four sectors in Bangkok. The results identified by the BAU (business-as-usual) scenario indicate that total energy is expected to reach 54,560 kilotonne of oil equivalent (ktoe) with 191 million tonne CO2 (Mt-CO2) by 2050. Under the alternative scenario, if all policy interventions are implemented simultaneously, the potential for energy-savings and reduced CO2 emissions in 2050 are estimated to be 32,120 ktoe and 106.19 Mt-CO2. Other international cities may wish to apply the strategies and analytical approaches presented in this paper for developing appropriate policies and measures in their own jurisdictions. Keywords: CO2 emissions, Energy-savings, Energy modeling, Low-carbon city, Scenario analysis. |
Abstract - Multi-Fuel Allocation for Power Generation Using Genetic Algorithms
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Abstract: The ever increasing growth of energy consumption has stimulated an energy crisis, not only in terms of energy demand, but also the impact of climate change from greenhouse gas (GHG) emissions. Renewable energy sources (RES) have high potential toward sustainable development, with a wide variety of socioeconomic benefits, including diversification of energy supply and creation of domestic industry. This paper presents a solution to optimal multi-fuel allocation for the electric power generation planning problem via genetic algorithms (GA). The objective is to maximize the electric power energy output and minimize generation cost. This is a difficult problem because of its data variation and volatility. GA can provide an appropriate heuristic search method and return an actual or near optimal solution. This paper uses some heuristics during crossover and mutation for tuning the system to obtain a better candidate solution. An experimental result showed significantly improved results compared with other techniques. The results in this paper should be useful for connecting power generation with economic growth. Keywords: Multi-fuel Allocation, Power Generation, Genetic Algorithms, Power Energy. |
