Pornphimol Winyuchakrit

Low carbon transportation in Thailand: CO2 mitigation strategy in 2050.

Nationally Appropriate Mitigation Actions (NAMAs) involve the collaboration on reduction of greenhouse gas (GHG) emissions in developing countries with suitable countermeasures relevant to the state of technological and economic conditions prevalent in the country. This study proposes appropriate GHG countermeasures in Thai transport NAMAs, which are based on the implementation of transport demand management, modal shift, fuel switching, and advanced technologies in the timeframe between 2005 and 2050. Furthermore, this study considers the impacts of CO2 mitigation through the proposed countermeasures on energy security and GHG emissions. Results of analyses on low carbon transportation are also useful to other developing countries. Finally, the concept of marginal abatement cost is employed to investigate cost-effective mitigation countermeasures.

CO2 mitigation in Thailand’s low-carbon society: The potential of renewable energy

ABSTRACT This study aims at the development of Thailand’s low-carbon society in 2030 by using the Asia-Pacific Integrated Model/Extended Snap Shot model for analysis of greenhouse gas (GHG) mitigation through renewable energy (RE) utilization. This article presents the potentials of RE in power generation, industrial, and transport sectors in Thailand for GHG mitigation in 2030. The deployment of the RE sources is used in the analyses with potentials of mini-hydro of 390 MW, wind power of 960 MW, solar power of 600 MW, biomass energy of 4,400 MW, biogas power of 144 MW, waste to power of 192 MW, bioenergy of 4,634 ktoe, and RE for thermal of 8,088 ktoe in 2030. According to the proposed development, the amount of GHG emissions is estimated based on business-as-usual (BAU) without mitigation measures and countermeasures with GHG mitigation options of adopted RE technologies available during 2005–2030. Results show that annual GHG emissions in the base year of 2005 are 185,983 kt-CO2. In 2030 the GHG emissions in the BAU scenario will increase to 563,730 kt-CO2 or 3.03 times higher than the base year 2005. The GHG emissions in the 2030 can be decreased dramatically to 443,043 kt-CO2, and accounted for 21.4% of GHG reduction by deployment of RE technologies.

Trends of energy intensity and CO2 emissions in the Thai industrial sector: The decomposition analysis

ABSTRACT This article investigates the components of CO2 emission changes in Thai industries by using the Logarithmic Mean Divisia Index (LMDI) approach. The analyses of historical trends during the period 1990–2007 show the key factors influencing CO2 emissions. Changes in CO2 emissions can be decomposed into five effects: activity, structural, energy intensity, fuel share, and emission effects. In this study Thai industries are categorized into nine subindustries: nonmetallic, chemical, food and beverage, fabricated metal, textile, basic metal, paper and pulp, wood and furniture, and others (unclassified) industries. This study considers CO2 emissions from five fuel types: coal, petroleum, natural gas, renewable energy, and electricity. In 2007, results show that CO2 emissions increased by 129.3% when compared with the 1990 level. Results also show that the nonmetallic, chemical, basic metal, and fabricated metal industries are the major CO2 emitters. Finally, results indicate that policy measures on efficiency improvement would result in CO2 mitigation in Thai industries.

Multilevel decomposition analysis of energy intensity in the Thai road transport sector

ABSTRACT This article presents the changes of aggregate energy intensity in the road transport sector in Thailand. Seven vehicle types in the Greater Bangkok (GBKK) and the provincial areas are considered during 1990–2007. Vehicle types consist of sedans, vans and pickups, motorcycles, taxis, buses, trucks, and others. The logarithmic mean Divisia multilevel decomposition method is employed to investigate the changes in aggregate energy intensity through structural effect, energy intensity effect, and fuel share effect. Results show that the aggregate energy intensity decreased by 39.6% in 2007 due to decreasing in energy intensity and structural effects. In the area level, the aggregate energy intensity decreased in the provincial area by 30.3% and in the GBKK area by 13.4%. The energy intensity effect decreased in all vehicle types, except in taxis, while the structural effect decreased in all vehicle types, except in sedans. Results could be used to formulate strategic plans of energy-efficiency measures in the transport sector.