The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector

Lu, I. J.; Lewis, Charles; Lin, Sue J.

doi:10.1016/j.enpol.2009.03.039

Cited by 89 publications

(33 citation statements)

References 17 publications

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“…1), annual data for POP and GDP must be forecasted for the study planning horizon (2025) using first order Grey model (GM(1,1)) given by Refs. [46,47] dx ð1Þ dt þ ax ð1Þ ¼ b…”

Section: Forecasts Of Pop and Gdpmentioning

confidence: 99%

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

Sadri

Ardehali

Amirnekooei

2014

Energy

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“…1), annual data for POP and GDP must be forecasted for the study planning horizon (2025) using first order Grey model (GM(1,1)) given by Refs. [46,47] dx ð1Þ dt þ ax ð1Þ ¼ b…”

Section: Forecasts Of Pop and Gdpmentioning

confidence: 99%

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

Sadri

Ardehali

Amirnekooei

2014

Energy

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“…However, Lu et al [45] have estimated the development trends of the number of motor vehicles, vehicular energy consumption and CO 2 emissions in Taiwan during 2007-2025. They have adopted simulation of different economic growth scenarios in order to explore the influence of economic growth on energy consumption.…”

Section: Empirical Literature Reviewmentioning

confidence: 99%

Indicators for sustainable energy development: A multivariate cointegration and causality analysis from Tunisian road transport sector

Abdallah

Belloumi

Wolf³

2013

Renewable and Sustainable Energy Reviews

109

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. Empirical results show that road transport-related energy consumption, transport value added, transport CO 2 emissions and road infrastructure are mutually causal in the long-run. These results do not support the hypothesis of neutrality between energy and income for the Tunisian road transport sector. Also, there is a unidirectional causality running from fuel price to road transport-related energy consumption with no feedback in both the short and long runs. The fuel price and the road infrastructure are significant in the causal chain. Though the estimated long-run cointegrated equation, we conclude that there is an inverse N-shaped relation between transport value added and transport CO 2 emissions, with income turning point respectively equal to 75 and 579 (constant 2000 TND prices). Therefore, our empirical findings refute the hypothesis of an inverted U-shaped EKC for transport CO 2 emissions in Tunisia.The findings of this study have a number of policy implications. Economic growth, environmental degradation, energy and transportation policies should be recognized in order to improve the energy efficiency in transport sector. All measures that should reduce petroleum consumption and greenhouse gas emissions without affecting economic growth may be undertaken. The policymakers in Tunisia should plan urban transport, relocate production units, improve fuel-efficient vehicles and reinforce legislation on controlling emissions in order to copying with policies based on low-carbon development and climate-resilient strategies.

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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%

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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The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector

Cited by 89 publications

References 17 publications

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN

Indicators for sustainable energy development: A multivariate cointegration and causality analysis from Tunisian road transport sector

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

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