No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Regional allocation of energy intensity reduction target: The case of Henan province in China
J. Wang, W. Huang, Y. Hu, S. Chen, and J. Li, “ Analysis of China's new energy conservation policy and the provincial decomposition of the energy consumption target,” J. Renewable Sustainable Energy 6, 053117 (2014).
L. Zhou, X. Zhang, T. Qi, J. He, and X. Luo, “ Regional disaggregation of China's national carbon intensity reduction target by reduction pathway analysis,” Energy Sustainable Dev. 23, 25–31 (2014).
S. W. Yu, Y. M. Wei, and K. Wang, “ Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition,” Energy Policy 66, 630–644 (2014).
J. Wang, G. W. Ma, Y. L. Hu, Y. L. Guan, and X. L. Dong, “ Regional decomposition of an energy-saving target: The case of Sichuan province in China,” Energy Sources 8, 245–251 (2013).
M. Sun and Y. W. Tao, “ Study on the decomposition model of energy saving,” Stat. Decis. 5, 51–53 (2011) (in Chinese).
B. Lu and Z. Q. Xu, “ Study on China's energy-saving intensity target decomposition based on zero sum gains DEA model,” in Proceedings of the International Conference on Information Technology and Industrial Engineering, Wuhan, China 2013, edited by P. Y. Ren and Z. Y. Du ( WIT Press, Southampton, Boston, 2014), pp. 487–494.
Zhejiang Statistical Bureau, Zhejiang Statistical Yearbook 2014 ( China Statistics Press, Beijing, 2014).
S. Ahmad and R. M. Tahar, “ Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia,” Renewable Energy 63, 458–466 (2014).
L. Yagmur, “ Multi-criteria evaluation and priority analysis for localization equipment in a thermal power plant using the AHP (analytic hierarchy process),” Energy 94, 476–482 (2016).
S. K. Thengane, A. Hoadley, S. Bhattacharya, S. Mitra, and S. Bandyopadhyay, “ Cost-benefit analysis of different hydrogen production technologies using AHP and Fuzzy AHP,” Int. J. Hydrogen Energy 39, 15293–15306 (2014).
S. K. Lee, G. Mogi, and K. S. Hui, “ A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices,” Renewable Sustainable Energy Rev. 21, 347–355 (2013).
C. Prakash and M. K. Barua, “ Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment,” J. Manuf. Syst. 37, 599–615 (2015).
G. Xu, Y. P. Yang, S. Y. Lu, L. Li, and X. Song, “ Comprehensive evaluation of coal-fired power plants based on grey relational analysis and analytic hierarchy process,” Energy Policy 39, 2343–2351 (2011).
H. Y. Fan and X. L. Liu, “ Comparison and optimization of various non-dimensionalized methods based on comprehensive evaluation method-A case study of land development in Yongdeng county of Lanzhou city,” Hunan Agric. Sci. 17, 163–167 (2010) (in Chinese).
J. Li, MS thesis, Gansu Agricultural University, Gansu, 2012.
G. B. Lyra, J. F. Oliveira, and M. Zeri, “ Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Alagoas state, Northeast of Brazil,” Int. J. Climatol. 34, 3546–3558 (2014).
X. F. Lou and F. X. Zou, “ Energy consumption optimization of the aluminum industrial production based on K-means algorithm,” in Proceedings of the 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, Changchun, China, 24–26 August 2010 (IEEE, 2010), pp. 61–64.
Henan Statistical Bureau, Henan Statistical Yearbook 2010 ( China Statistics Press, Beijing, 2010).
Henan Statistical Bureau, Henan Statistical Yearbook 2011 ( China Statistics Press, Beijing, 2011).
Henan Statistical Bureau, Henan Statistical Yearbook 2012 ( China Statistics Press, Beijing, 2012).
Henan Statistical Bureau, Henan Statistical Yearbook 2013 ( China Statistics Press, Beijing, 2013).
Henan Statistical Bureau, Henan Statistical Yearbook 2014 ( China Statistics Press, Beijing, 2014).
Henan Statistical Bureau, Henan Statistical Yearbook 2009 ( China Statistics Press, Beijing, 2009).
Y. Tang, H. Sun, Q. Yao, and Y. B. Wang, “ The selection of key technologies by the silicon photovoltaic industry based on the Delphi method and AHP (analytic hierarchy process): Case study of China,” Energy 75, 474–482 (2014).
W. J. Yi, L. L. Zou, J. Guo, K. Wang, and Y. M. Wei, “ How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development,” Energy Policy 39, 2407–2415 (2011).
Beijing Statistical Bureau, Beijing Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
Shanghai Statistical Bureau, Shanghai Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
Guangdong Statistical Bureau, Guangdong Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
Zhejiang Statistical Bureau, Zhejiang Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
Jiangsu Statistical Bureau, Jiangsu Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
Article metrics loading...
Using a sample of 18 prefecture-level cities in Henan province, this study explored the regional allocation of energy intensity reduction targets from the following three viewpoints: equity principle with common but differentiated responsibilities; intensity reduction target fulfillment; and economic differences and reduction potential among regions. Based on a preliminary decomposition model, an analytic hierarchy process (AHP) and Ward's hierarchical clustering, an intensity allocation method is proposed. First, the preliminary regional decomposition scheme is presented via the preliminary decomposition model. Then, a multi-criteria evaluation system consisting of four layers and covering 13 evaluation indicators is developed via the AHP method, and the evaluation results are analyzed via the cluster method to further improve the preliminary scheme. As decision makers may have different preferences when allocating the reduction burden, we allocate different weights to the indicators and analyze the results using a sensitivity analysis. The clustering results indicate that the 18 regions of Henan are divided into five categories, and each category has its own significant characteristics. Regions with high obligation and potential should share the largest reduction burden. The allocation results show that seven regions, including Zhengzhou and Luoyang, are expected from 2016 to 2020 to exceed the provincial average decrease rate of 16%.
Full text loading...
Most read this month