Differences in regional emissions in China's transport sector: Determinants and reduction strategies

Xu, Bin; Lin, Boqiang

doi:10.1016/j.energy.2015.12.016

Cited by 89 publications

(42 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the studies on the urbanization effect on energy-related CO 2 emission in Chinese provinces are incomplete and fragmented. This fragmentation likely stems from the fact that researchers are interested in a broad range of specific questions in particular research contexts and have tackled a variety of sector-specific [6,9,17,18,25,27] and region-specific research [17,18,21,24,32,33], for example, the carbon emission trading in Guangdong Province [20], the energy efficiency in Shandong Province [7], and the industrial restructuring in Jiangsu Province [29]. The fragmented research landscape raises up many fundamental questions for scholars of this field.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the effects of urbanization and industrialization on carbon dioxide emission: Evidence from China's provincial regions

Ding

2017

Energy

128

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Examining the effects of urbanization and industrialization on carbon dioxide emission: Evidence from China's provincial regions

Ding

2017

Energy

128

View full text Add to dashboard Cite

“…According to the results shown in Table 7, there was a cointegration relationship between each benefit and coupling coordination degree among three benefits at different confidence levels. In addition, the cointegration relationship between coupling coordination degree among three benefits and all of the explanatory variables was checked by the KAO panel test [55]. The result was revealed in Table 8 which demonstrated the significance of ADF test statistics.…”

Section: Results Of Panel Regressionmentioning

confidence: 88%

“…To examine the stability of the variable sequences, we must test whether the variables in the model have unit root at first. Fisher-ADF test, Fisher-PP test, and IPS test are the three main test methods which are widely used [54,55]. Therefore, this paper applied them to conduct the panel unit root test.…”

Section: Changes Of Benefit Index Valuementioning

confidence: 99%

Analyzing the Coupling Coordination among Economic, Social, and Environmental Benefits of Urban Infrastructure: Case Study of Four Chinese Autonomous Municipalities

Sun

Cui

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Urban infrastructure is a necessary condition for urban development. Its use generates three benefits which are economic, social, and environmental benefits of urban infrastructure. They are the positive impacts on urban economy, society, and environment generated by the use of urban infrastructure, respectively. This paper evaluates the coupling coordination among these three benefits taking four Chinese autonomous municipalities as examples. These four cities have large-scale urban infrastructure but its basic function has not been fully fulfilled. Whether three benefits of urban infrastructure have been developed in harmony or not was unclear. We analyzed the coordinated development among three benefits by constructing coupling coordination degree model and studied the impacts of three benefits on their coupling coordination degree using panel regression model. The results showed that the levels of coordinated development among three benefits of urban infrastructure were low in these four cities and the impact of social benefit on their coordinated development was largest. Therefore, urban infrastructure social benefit needs to be improved mainly in these four cities.

show abstract

“…Previous studies on the amount of CO 2 emissions include factors that affect the amount of emissions [1][2][3][4] and carbon emission amount forecasting [2,5]. Previous studies on factors that affect the amount of carbon emissions considered different factors.…”

mentioning

confidence: 99%

“…Thus, their study did not consider other factors that could cause and increase the amount of carbon emissions. Xu and Lin [4] primarily analyzed the carbon emissions produced by China's transportation industry. They used panel data to analyze the impact of the number of automobiles in different areas on the amount of CO 2 emissions.…”

mentioning

confidence: 99%

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Lin

Liu

2018

Energies

View full text Add to dashboard Cite

Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analyzed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.

show abstract

Differences in regional emissions in China's transport sector: Determinants and reduction strategies

Cited by 89 publications

References 59 publications

Examining the effects of urbanization and industrialization on carbon dioxide emission: Evidence from China's provincial regions

Examining the effects of urbanization and industrialization on carbon dioxide emission: Evidence from China's provincial regions

Analyzing the Coupling Coordination among Economic, Social, and Environmental Benefits of Urban Infrastructure: Case Study of Four Chinese Autonomous Municipalities

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Contact Info

Product

Resources

About