Trends in Thailand CO2 emissions in the transportation sector and Policy Mitigation

Ratanavaraha, Vatanavongs; Jomnonkwao, Sajjakaj

doi:10.1016/j.tranpol.2015.01.007

Cited by 52 publications

(22 citation statements)

References 32 publications

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“…Researchers have analyzed the carbon emissions of the transportation sector from various perspectives. Several studies have made creditable attempts to accurately calculate transportation-related carbon emissions and build models of the influencing factors [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Chandran et al [25] introduced a co-integration analysis and Granger causality analysis to study the influence of energy-related CO 2 emissions in the transportation sector on five Association of Southeast Asian Nations (ASEAN) countries.…”

Section: Introductionmentioning

confidence: 99%

“…This study concluded that improving energy efficiency will reduce CO 2 emissions, but increasing the total number of private vehicles and promoting the progress of urbanization will significantly increase CO 2 emissions. Ratanavaraha et al [29] considered five independent variables, namely (1) the size of the population, (2) gross domestic product (GDP) and the number of (3) small, (4) medium and (5) large-sized registered vehicles, and employed four different measurement techniques (log-linear regression, path analysis, time series and curve estimation) to forecast the carbon emissions coming from Thailand's transportation sector. The researchers claimed that the primary means of reducing carbon emissions will be to improve the energy efficiency of motor vehicles and to transform the current highway freight-based mode of transportation.…”

Section: Introductionmentioning

confidence: 99%

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Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Dong

Deng

et al. 2016

Sustainability

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Abstract:With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area's transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang's transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region's energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang's transportation sector during the period from 1989 to 2012. We also analyze the transportation sector's trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang's transportation sector. The results indicate the following: (1) the total carbon emissions and per capita carbon emissions of Xinjiang's transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2) the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an "S"-pattern trend within the study period; (3) population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important implications for future efforts to reduce the growth of transportation-based carbon dioxide emissions in Xinjiang and for any effort to construct low-carbon and sustainable environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Dong

Deng

et al. 2016

Sustainability

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“…The multiple regression model is an effective statistical technique used for exploring the relationship between a dependent variable and a series of independent variables, so that the response of one variable can be predicted from changes to the others [31]. Many studies have adopted regression models to make forecasts for transport demand and energy demands in the transportation sector, since it is considered to be an accurate approach used to identify the relationships between transport demand or fuel consumption and their relative inputs [17,32,33]. The Log-linear regression model generally describes the dependent and the independent variables in logarithm form.…”

Section: Marine Transport Turnovermentioning

confidence: 99%

Trends in CO2 Emissions from China-Oriented International Marine Transportation Activities and Policy Implications

Yang

Xing

2017

Energies

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Abstract:The demand for marine transportation and its associated CO 2 emissions are growing rapidly as a result of increasing international trade and economic growth. An activity-based approach is developed for forecasting CO 2 emissions from the China-oriented international seaborne trade sector. To accurately estimate the aggregated emissions, CO 2 emissions are calculated individually for five categories of vessels: crude oil tanker, product tanker, chemical tanker, bulk carrier, and container. A business-as-usual (BAU) scenario was developed to describe the current situation without additional mitigation policies, whilst three alternative scenarios were developed to describe scenarios with various accelerated improvements of the key factors. The aggregated CO 2 emissions are predicted to reach 419.97 Mt under the BAU scenario, and 258.47 Mt under the optimal case, AD3. These predictions are 4.5 times and 2.8 times that of the aggregated emissions in 2007. Our analysis suggests that regulations for monitoring, reporting, and verifying the activities of vessels should be proposed, in order to quantify the CO 2 emissions of marine transportation activities in Chinese territorial waters. In the long-term future, mitigation policies should be employed to reduce CO 2 emissions from the marine trade sector and to address the climatic impact of shipping.

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“…In the United States, a life cycle analysis was performed for the consumption and pollution of aircraft and intercity bus emissions [14] using three independent variables, i.e., the size of the population; gross domestic product (GDP); the number of small-, medium-, and large-sized registered vehicles. The future carbon emissions of the transport sector were predicted for Thailand [15]. Estimates of carbon emissions from medium and heavy-duty vehicles have been predicted in Korea [16].…”

Section: Introductionmentioning

confidence: 99%

Ecological Pressure of Carbon Footprint in Passenger Transport: Spatio-Temporal Changes and Regional Disparities

Wang

Sun

et al. 2018

Sustainability

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Abstract:Passenger transport has become a significant producer of carbon emissions in China, thus strongly contributing to climate change. In this paper, we first propose a model of ecological pressure of the carbon footprint in passenger transport (EP cfpt ). In the model, the EP cfpt values of all the provinces and autonomous regions of China are calculated and analyzed during the period of 2006-2015. For the outlier EP cfpt values of Beijing, Shanghai and Tianjin, the research areas are classified into two scenarios: the first scenario (all the provinces and autonomous regions) and the second scenario (not including Beijing, Shanghai and Tianjin). The global spatial autocorrelation analysis of the first scenario shows that the EP cfpt might be randomly distributed, while it shows positive spatial autocorrelation in the second scenario. Furthermore, we carry out the local spatial autocorrelation analysis of the second scenario, and find that the low aggregation areas are the most common type and are mainly located in the west of China. Then the disparities in EP cfpt between China's Eight Comprehensive Economic Zones are further analyzed. Finally, we put forward a number of policy recommendations in relation to the spatio-temporal changes and the regional disparities of EP cfpt in China. This study provides related references for proposing effective policy measures to reduce the ecological pressure of carbon emissions from the passenger transport sector.

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Trends in Thailand CO2 emissions in the transportation sector and Policy Mitigation

Cited by 52 publications

References 32 publications

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Trends in CO2 Emissions from China-Oriented International Marine Transportation Activities and Policy Implications

Ecological Pressure of Carbon Footprint in Passenger Transport: Spatio-Temporal Changes and Regional Disparities

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