Mining and Analyzing Energy Layout on Carbon Emission Intensities of Industrial Sectors

Abstract:

Energy consumption structure optimum is gradually discussed in recent literatures. Based on hierarchical clustering of optimally close to content demand of data group mine and analysis, industrial sectors layout on carbon emission intensity is researched. Computed carbon emission drawn support from IPCC methodological framework, formed carbon emission intensities of emissions divided by sectors GDP respectively, and transformed calculated figures into CDF of the continuous uniform distribution to cultivate the standardized data. Resulting of the case presents that there are two categories with types of v and inversed v after mining and analyzing 37 industrial sectors data in 2006-2011. Findings are that 39% annual max paired difference of emission intensities is appeared, and the divergence of energy consumption structure is significantly obtained, which is conducive to the whole industrial distribution of low carbon policy-making.

• Periodical: Advanced Materials Research (Volumes 807-809)
• Pages: 857-860
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.807-809.857
• Online since: September 2013
• Authors: Zuo Zhi Li
• Keywords: Carbon Emission Intensity, Hierarchical Clustering, Industrial Energy Layout, Mining

References

[1] Furen An. Strategically developed choice of the low carbon economy in China. Research on financial and economic issues, 8 (2010) 15-21.

[2] Zhixin Zhang, Qiao Xue. Low-carbon economy, industrial structure and changes in China's development mode based on the data of 1996-2009 in empirical analysis. Energy Procedia, 5 (2011) 2025-(2029).

DOI: 10.1016/j.egypro.2011.03.349

[3] Yong Liu. Relationship between industrial firms, high-carbon and low-carbon energy: An agent-based simulation approach. Applied Mathematics and Computation, 219 (2013) 7472-7479.

DOI: 10.1016/j.amc.2013.01.034

[4] U. Soytas, R. Sari, and B. Ewing. Energy consumption, income, and carbon emissions in the United States. Ecological Economics, 62 (2007) 482-489.

DOI: 10.1016/j.ecolecon.2006.07.009

[5] C. Jumbe. Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi. Energy Economy, 26 (2004) 61-68.

DOI: 10.1016/s0140-9883(03)00058-6

[6] P. Mozumder and A. Marathe. Causality relationship between electricity consumption and GDP in Bangladesh. Energy Policy, 35 (2007) 395-402.

DOI: 10.1016/j.enpol.2005.11.033

[7] Y.Y. Zhang, L.X. Zhang. Scenario analysis of urban energy saving and carbon abatement policies: A case study of Beijing city, China. Procedia Environmental Sciences, 13 (2012) 632-644.

DOI: 10.1016/j.proenv.2012.01.055

[8] J. Valenzuela and Ye Qi. Framing energy efficiency and renewable energy policies: An international comparison between Mexico and China. Energy Policy, 51 (December 2012) 128-137.

DOI: 10.1016/j.enpol.2012.03.083

[9] S. Suh. Functions, commodities and environmental impacts in an ecological economic model. Ecological Economics, 48 (2004) 451-467.

DOI: 10.1016/j.ecolecon.2003.10.013