Next Article in Journal
Transition Governance towards a Bioeconomy: A Comparison of Finland and The Netherlands
Previous Article in Journal
The Non-Linear Effect of Chinese Financial Developments on Energy Supply Structures
Open AccessArticle

Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

1
Institute of Climate Change and Public Policy, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Sustainability 2016, 8(10), 1018; https://doi.org/10.3390/su8101018
Received: 13 June 2016 / Revised: 25 September 2016 / Accepted: 28 September 2016 / Published: 13 October 2016
According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015–2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China. View Full-Text
Keywords: carbon emissions; GM (1, 1); LMDI decomposition analysis model; trend prediction carbon emissions; GM (1, 1); LMDI decomposition analysis model; trend prediction
Show Figures

Figure 1

MDPI and ACS Style

Tang, D.; Ma, T.; Li, Z.; Tang, J.; Bethel, B.J. Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020. Sustainability 2016, 8, 1018. https://doi.org/10.3390/su8101018

AMA Style

Tang D, Ma T, Li Z, Tang J, Bethel BJ. Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020. Sustainability. 2016; 8(10):1018. https://doi.org/10.3390/su8101018

Chicago/Turabian Style

Tang, Decai; Ma, Tingyu; Li, Zhijiang; Tang, Jiexin; Bethel, Brandon J. 2016. "Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020" Sustainability 8, no. 10: 1018. https://doi.org/10.3390/su8101018

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop