Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China-Reference-Cited by-同舟云学术

Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China

Published:2016-05 Issue: Volume:122 Page:144-153
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Sun Wei,Liu Mohan^ORCID

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference35 articles.

1. World Energy Outlook 2013;Birol,2013

2. A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and environmental life cycle perspective;Von Blottnitz;J. Clean. Prod.,2007

3. LIBSVM: a library for support vector machines;Chang;ACM Trans. Intell. Syst. Technol. TIST,2011

4. 2006 IPCC Guidelines for National Greenhouse Gas Inventories;Change, I.P.O.C,2006

5. Analysis on the carbon emissions from forestry Industry in Jiangsu Province based on logistic model;Ding;Ecol. Econ.,2014

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