An investigation into the relationship between China's economic development and carbon dioxide emissions

Abstract

A linear programming model is established to reflect the relationship between China's economic development and the related carbon dioxide (CO₂) emissions, which is revealed by analysing the characteristics of China's economic development. Grey prediction model, GM(1,1), is applied to forecast the parameters of the programming model, including the total amount of labour, water resources and fixed assets, and the corresponding consumption or input coefficients. The results of the models indicate that contradictions exist between China's CO₂ emissions reduction and the high economic growth rate, employment and the efficient use of fixed assets. Furthermore, it also shows that China's economic development will be challenged by overcapacity, water resource shortages and a rising unemployment rate. The policy implications of the results are discussed.

Keywords:

• development
• climate change
• industry
• economics

Acknowledgements

The authors thank two anonymous reviewers for the constructive suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Data source: http://data.stats.gov.cn/workspace/index?m=hgnd

2. Data source: http://www.zj.xinhuanet.com/jrzx/2013-10/16/c_117738617.htm

3. http://opinion.people.com.cn/n/2013/0219/c1003-20523672.html

4. http://finance.sina.com.cn/consume/puguangtai/20130510/212315425107.shtml

5. http://news.qq.com/a/20110526/000796.htm

6. http://news.xinhuanet.com/fortune/2014-10/14/c_1112814509.htm

7. http://news.163.com/15/0203/01/AHGABB4A00014SEH.html

Additional information

Funding

This work was supported by Leverhulme Trust, National Natural Science Foundation of China [grant Nos. 71573120 & 71273131], the Fundamental Research Funds for the Central Universities [grant No. NS2015084], Jiangsu Natural Science Fund [grant No. BK20130785], and Doctoral Fund of China Ministry of Education [grant No. 20133218120036].