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A platform for research: civil engineering, architecture and urbanism
Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand
Abstract The aim of this research is to forecast $ CO_{2} $emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate $ CO_{2} $emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in $ CO_{2} $emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.
Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand
Abstract The aim of this research is to forecast $ CO_{2} $emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate $ CO_{2} $emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in $ CO_{2} $emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.
Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand
Sutthichaimethee, Pruethsan (author) / Ariyasajjakorn, Danupon (author)
2018
Article (Journal)
English
Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand
| DOAJ | 2018
The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions
| DOAJ | 2020