Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China's provinces

Yang, Wenyue; Тао, Ли; Cao, Xiaoshu

doi:10.1016/j.habitatint.2015.05.030

Cited by 161 publications

(71 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Dong

Deng

et al. 2016

Sustainability

View full text Add to dashboard Cite

show abstract

“…Researchers applying the STIRPAT frame to carbon emissions or energy use typically include data on population, income, urbanization level, urban density, and age compositions in their analyses, summarized in the appendix A1 (Boyko and Cooper, 2011;Fan et al, 2006;Hossain, 2011;Liddle, 2004;Liddle and Lung, 2010;Martínez-Zarzoso et al, 2007;Martínez-Zarzoso and Maruotti, 2011;Menz and Welsch, 2012;Norman et al, 2006;Perkins et al, 2009;Poumanyvong and Kaneko, 2010;Poumanyvonga et al, 2012;Yang et al, 2015;Zhu and Peng, 2012). The common feature in these studies is the lack of information on the urban form which may be ascribed to the deficiency of appropriate measures of urban area level spatial structure (Lee and Lee, 2014) as well as the limited variables in the STIRPAT framework.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is questionable due to its multiple facets (e.g., urban area, urban density, residential dwelling spatial structure) across countries and geographical regions. In addition, many recent empirical studies have found that some urban form variables (e.g., urban area, residential density, housing sizes and types, urban structure) can have significant impacts on environment (i.e., carbon emission, energy use) (Boyko and Cooper, 2011;Fang et al, 2015;Lee and Lee, 2014;Norman et al, 2006;Perkins et al, 2009;Reingewertz, 2012 ;Yang et al, 2015;Yin et al, 2015). Therefore, further studies with careful considerations of the different urban forms become imperative.…”

Section: Introductionmentioning

confidence: 99%

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

Liu

Huang

Kaloudis

et al. 2017

Transportation Research Part D: Transport and Environment

View full text Add to dashboard Cite

The relationship between urbanization, energy use, and CO2 emissions has been extensively studied in recent years, however little attention paid to the differences in urban forms. Previous studies implicitly assume that the urban form is homogenous across different urban areas. Such an assumption is questionable as urban form can have many different facets. This paper investigates the effects of urbanization on the road transport energy use by considering different urban forms from a dataset of (3) there is a non-linear (quadratic) relationship between road energy use per capita and urban population. This implies that an increase in total municipality population over a specific turning point can result in a decrease in road energy use per capita. However, (4) the ratio of urban residential buildings with private gardens has a negative and significant influence on road transport energy use.This implies that there may be a trade-off between compact and sprawl city development strategies, highlighting that sustainable energy use requires further investigation.

show abstract

“…A bottom-up material flow analysis model was employed for material stocks estimation. The details on model description, parameter settings, and data sources can be found in our previous studies [10,25]. Firstly, we adopted the standard Intergovernmental Panel on Climate Change (IPCC) guideline and local carbon emission coefficient to calculate China's provincial CO2 emissions from fossil fuel consumption [19].…”

Section: Materials Stocks Of Infrastructure and Co 2 Emissionsmentioning

confidence: 99%

“…Fang et al [9] selected a series of urban form indicators and found that increased urban continuity will led to reductions in CO 2 emissions and that, conversely, increased urban shape complexity and exerted a positive influence in relation to CO 2 emissions. Yang et al [10] found that socio-economic development and increased income were the primary driving factors for the growth of per capita CO 2 emissions from transportation. Besides this, some studies have indicated the giant emissions caused by buildings and transport infrastructure and their great potential in emission mitigation.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Infrastructure Stock Density on CO2 Emissions: Evidence from China Provinces

et al. 2017

View full text Add to dashboard Cite

Abstract:Infrastructure not only plays an import role in socioeconomic development, but also results in a remarkable increase of CO 2 emissions. Based on a panel data analysis of 29 provinces in China from 1995 to 2013, we investigated the relationship between socioeconomic development and CO 2 emissions with special focus on the impact of infrastructure stock density in both the country and regional scales. The results confirmed that a 1% increase of material stocks in infrastructure per built-up area would lead to a 0.11% decrease in CO 2 emissions at the country level. The effect of infrastructure stock density on CO 2 emissions varied across regions, for which elasticity was −0.34, 0.06, and 0.14 for the eastern, central, and western region, respectively. In order to achieve a low-carbon and sustainable development, it is crucial to improve the spatial compactness of infrastructure in the future. Policy implications include upgrading the economic structure to a low-carbon one for the eastern region, accelerating the development of renewable energy infrastructure, and constructing and utilizing infrastructure in an energy-efficient way for the central and western regions.

show abstract

Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China's provinces

Cited by 161 publications

References 34 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

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

The Impact of Infrastructure Stock Density on CO2 Emissions: Evidence from China Provinces

Contact Info

Product

Resources

About