Effective policies for renewable energy—the example of China's wind power—lessons for China's photovoltaic power

Abstract:The transport sector is the major green-house gas emitter and most rapidly growing sector in terms of consuming energy in China. Understanding the driving forces behind carbon emission is a prerequisite for reducing carbon emissions and finding a balance between economic growth and carbon emissions. The purpose of this paper is to identify the impact of the factors which influence the level of carbon emissions from the transportation sector in the Beijing-Tianjin-Hebei (BTH) area, China, using decomposition model, combined with a decoupling elasticity index. The results of our study indicate that: (1) changes in the level of carbon emissions from the transportation sector are not always synchronized with changes in economic growth. (2) The decoupling state between the carbon emissions and economic growth of Tianjin and Beijing can be roughly divided into two phases. The first phase was during the 2005 to 2009 period, when the decoupling state was pessimistic. The second phase was from 2009 to 2013, when the decoupling state became better overall and was mainly dominated by weak decoupling. Conversely, the decoupling state of Hebei was mainly weak during this period. (3) Economic growth and population size play positive roles in increasing the levels of transportation-related carbon emissions in BTH. However, the energy structure is a negative force. The effect of energy intensity always plays a negative role in Tianjin and Hebei, but positive in Beijing. The industrial structure effect shows a fluctuating trend, but the cumulative effect value is negative, and negative interaction is prominent. Finally, this paper gives some suggestions on how to develop low-carbon transport in BTH area.

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Zhu

2017

“…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

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.

“…Carbon dioxide emissions data in previous studies (e.g., [52][53][54][55][56][57][58][59]) were supplied by the Carbon Dioxide Information Analysis Center (CDIAC) of the Oak Ridge National Laboratory, U.S. Department of Energy [60]. Given that CDIAC provides national-level data of carbon emission, the provincial-level carbon emission data in those studies were estimated according to the percentage of the provincial energy consumption of China's total energy consumption.…”

Section: Data Sourcementioning

confidence: 99%

Inequality of Carbon Intensity: Empirical Analysis of China 2000–2014

Jiang

2017

Abstract:On the 3 September 2016, China officially ratified the Paris agreement as the main global producer of carbon emissions. A key of China's commitment is to reduce its carbon intensity by 60-65% between 2005 and 2030. An improved understanding of the inequality of carbon intensity at national-, inter-regional-, and intra-regional scale is a prerequisite for the development of a more cost-effective carbon intensity reduction policy. In this study, we used the Dagum Gini coefficient and its subgroup decomposition method to quantify China's inequality of carbon intensity between 2000 and 2014 based on available and updated data. The results show: (i) The Gini coefficient indicates a rising inequality of the carbon intensity at both national and sub-national scale, suggesting accelerated inequality of carbon intensity at national-, inter-regional, and intra-regional-scale.(ii) The Gini coefficient indicates a rising trend of intra-regional carbon intensity in Central and Western China, while the trend declines for Eastern China. (iii) The Gini coefficient indicates rising carbon intensity between Eastern and Central China, Western and Eastern China, and Central and Western China. (iv) Transvariation intensity occupies a leading role in the increasing national-level carbon intensity Gini coefficient. Ultimately, several policy recommendations are provided.