Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model

Xu, Bin; Lin, Boqiang

doi:10.1016/j.apenergy.2015.10.039

Cited by 129 publications

(50 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because, on the one hand, the larger p is, the more obvious dynamic characteristics are reflected by the model. However, on the other hand, the larger lag length means the more parameters to be estimated and the lower degrees of freedom [35]. Therefore, it is necessary to find a balance between the lag length and degrees of freedom.…”

Section: Var Modelmentioning

confidence: 99%

Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model

Deng

Dong

2016

Sustainability

View full text Add to dashboard Cite

Coal consumption and carbon dioxide emissions from coal combustion in China are attracting increasing attention worldwide. Between 1990 and 2013, the coal consumption in Shandong Province increased by approximately 5.29 times. Meanwhile, the proportion of coal consumption of Shandong Province to China rose from 7.6% to 10.8%, and to the world, it rose from 1.8% to 5.5%. Identifying the drivers of coal consumption in Shandong Province is vital for developing effective environmental policies. This paper uses the Vector Autoregression model to analyze the influencing factors of coal consumption in Shandong Province. The results show that industrialization plays a dominant role in increasing coal consumption. Conversely, coal efficiency is the key factor to curtailing coal consumption. Although there is a rebound effect of coal efficiency in the short term, from a long-term perspective, coal efficiency will reduce coal consumption gradually. Both economic growth and urbanization have a significant effect on coal consumption in Shandong Province. In addition, the substitution effect of oil to coal has not yet met expectations. These findings are important for relevant authorities in Shandong in developing appropriate policies to halt the growth of coal consumption.

show abstract

Section: Var Modelmentioning

confidence: 99%

Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model

Deng

Dong

2016

Sustainability

View full text Add to dashboard Cite

show abstract

“…Since most economic variables are non-stationary sequences (Gallagher et al 2015), it is necessary to implement a stationary test before establishing the model. The standard method of checking sequence stationary is the unit root test (Xu and Lin 2016;Blanco et al 2013). Table 2 provides the results of unit root tests based on the augmented Dickey-Fuller (ADF) test methods (Dickey and Fuller 1979).…”

Section: Stationary Tests Of International Crude Price (Ic) and Localmentioning

confidence: 99%

“…The impulse response function is used to investigate the dynamic effects on the system when a variable is subjected to a certain impact (Xu and Lin 2016). It can be used to analyze the time profile of effects of shocks on the future behavior of international crude oil prices and China's refined oil prices.…”

Section: Impulse Response Functions Of Ic and Rdmentioning

confidence: 99%

The relationship between international crude oil prices and China’s refined oil prices based on a structural VAR model

et al. 2016

View full text Add to dashboard Cite

“…The Vector Autoregressive Model (VAR) model based on economic theory can be used to predict the dynamic relationship between energy and the economy because it can provide a more accurate forecast value [26]. Bin et al [27] used the Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in China's iron and steel industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the Evolution Mechanism and Development Forecasting of China’s Power Supply Structure Clean Development

Zhang

Yun

et al. 2017

Sustainability

View full text Add to dashboard Cite

Abstract:The clean development of China's power supply structure has become a crucial strategic problem for the low-carbon, green development of Chinese society. Considering the subsistent developments of optimized allocation of energy resources and efficient utilization, the urgent need to solve environmental pollution, and the continuously promoted power market-oriented reform, further study of China's power structure clean development has certain theoretical value. Based on the data analysis, this paper analyzes the key factors that influence the evolution process of the structure with the help of system dynamics theory and carries out comprehensive assessments after the construction of the structure evaluation system. Additionally, a forecasting model of the power supply structure development based on the Vector Autoregressive Model (VAR) has been put forward to forecast the future structure. Through the research of policy review and scenario analysis, the paths and directions of structure optimization are proposed. In this paper, the system dynamics, vector autoregressive model (VAR), policy mining, and scenario analysis methods are combined to systematically demonstrate the evolution of China's power structure, and predict the future direction of development. This research may provide a methodological and practical reference for the analysis of China's power supply structure optimization development and for theoretical studies.

show abstract

Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model

Cited by 129 publications

References 87 publications

Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model

Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model

The relationship between international crude oil prices and China’s refined oil prices based on a structural VAR model

Study on the Evolution Mechanism and Development Forecasting of China’s Power Supply Structure Clean Development

Contact Info

Product

Resources

About