Skip to main content
banner image
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.
/content/aip/journal/jrse/8/5/10.1063/1.4962416
1.
NBS (National Bureau of Statistics) and NDRC (National Development and Reform Commission), http://bgt.ndrc.gov.cn/zcfb/201106/t20110610_500045.html for Communiqué on the achievements of energy conservation targets by region in 11th FYP, 2011.
2.
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).
http://dx.doi.org/10.1063/1.4897943
3.
NDRC, http://www.sdpc.gov.cn/fzgggz/fzgh/ghwb/gjjh/ for The 13th Five-Year Plan for national economic and social development of the People's Republic of China, 2016.
4.
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, 2531 (2014).
http://dx.doi.org/10.1016/j.esd.2014.07.003
5.
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, 630644 (2014).
http://dx.doi.org/10.1016/j.enpol.2013.11.025
6.
Y. J. Zhang, A. D. Wang, and Y. B. Da, “ Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method,” Energy Policy 74, 454464 (2014).
http://dx.doi.org/10.1016/j.enpol.2014.08.006
7.
C. Wei, J. Ni, and L. Du, “ Regional allocation of carbon dioxide abatement in China,” China Econ. Rev. 23, 552565 (2012).
http://dx.doi.org/10.1016/j.chieco.2011.06.002
8.
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, 245251 (2013).
http://dx.doi.org/10.1080/15567249.2012.687801
9.
H. Bi, “ Alternative method based on the factors of regional energy saving goal decomposition model research,” Appl. Energy Technol. 5, 48 (2014) (in Chinese).
http://dx.doi.org/10.3969/j.issn.1009-3230.2014.05.002
10.
M. Sun and Y. W. Tao, “ Study on the decomposition model of energy saving,” Stat. Decis. 5, 5153 (2011) (in Chinese).
11.
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. 487494.
12.
Zhejiang Statistical Bureau, Zhejiang Statistical Yearbook 2014 ( China Statistics Press, Beijing, 2014).
13.
Zhejiang Province Economic and Information Commission, http://www.zjjxw.gov.cn/jxdt/zxdt/tpxw/2014/09/11/2014091100038.shtml for Status and utilization of energy in Zhejiang Province in 2013, 2014.
14.
Z. Liu, X. L. Zhang, and D. Zhang, “ The target decomposition of renewable energy in Europe Union and its inspiration to China's provincial planning,” China Min. Mag. 104(14), 55585571 (2009) (in Chinese).
http://dx.doi.org/10.3969/j.issn.1004-4051.2009.09.017
15.
T. L. Saaty, “ Exploring optimization through hierarchies and ratio scales,” Socio-Econ. Plann. Sci. 20, 355360 (1986).
http://dx.doi.org/10.1016/0038-0121(86)90047-9
16.
O. S. Vaidya and S. Kumar, “ Analytic hierarchy process: An overview of applications,” Eur. J. Oper. Res. 169, 129 (2006).
http://dx.doi.org/10.1016/j.ejor.2004.04.028
17.
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, 458466 (2014).
http://dx.doi.org/10.1016/j.renene.2013.10.001
18.
T. Kurka, “ Application of the analytic hierarchy process to evaluate the regional sustainability of bioenergy developments,” Energy 62, 393402 (2013).
http://dx.doi.org/10.1016/j.energy.2013.09.053
19.
E. Özgür and B. Kılkış, “ An energy source policy assessment using analytical hierarchy process,” Energy Convers. Manage. 63, 245252 (2012).
http://dx.doi.org/10.1016/j.enconman.2012.01.040
20.
L. Yagmur, “ Multi-criteria evaluation and priority analysis for localization equipment in a thermal power plant using the AHP (analytic hierarchy process),” Energy 94, 476482 (2016).
http://dx.doi.org/10.1016/j.energy.2015.11.011
21.
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, 1529315306 (2014).
http://dx.doi.org/10.1016/j.ijhydene.2014.07.107
22.
B. Jovanović, J. Filipović, and V. Bakić, “ Prioritization of manufacturing sectors in Serbia for energy management improvement—AHP method,” Energy Convers. Manage. 98, 225235 (2015).
http://dx.doi.org/10.1016/j.enconman.2015.03.107
23.
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, 347355 (2013).
http://dx.doi.org/10.1016/j.rser.2012.12.067
24.
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, 599615 (2015).
http://dx.doi.org/10.1016/j.jmsy.2015.03.001
25.
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, 23432351 (2011).
http://dx.doi.org/10.1016/j.enpol.2011.01.054
26.
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, 163167 (2010) (in Chinese).
http://dx.doi.org/10.3969/j.issn.1006-060X.2010.17.052
27.
J. Li, MS thesis, Gansu Agricultural University, Gansu, 2012.
28.
J. H. Ward, “ Hierarchical grouping to optimize an objective function,” J. Am. Stat. Assoc. 58(301), 236244 (1963).
http://dx.doi.org/10.1080/01621459.1963.10500845
29.
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, 35463558 (2014).
http://dx.doi.org/10.1002/joc.3926
30.
L. F. Hu, “ A comparison of five kinds of hierarchical clustering methods,” Stat. Sci. Pract. 4, 1113 (2007).
http://dx.doi.org/10.3969/j.issn.1674-8905.2007.04.004
31.
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. 6164.
32.
Henan Statistical Bureau, Henan Statistical Yearbook 2010 ( China Statistics Press, Beijing, 2010).
33.
Henan Statistical Bureau, Henan Statistical Yearbook 2011 ( China Statistics Press, Beijing, 2011).
34.
Henan Statistical Bureau, Henan Statistical Yearbook 2012 ( China Statistics Press, Beijing, 2012).
35.
Henan Statistical Bureau, Henan Statistical Yearbook 2013 ( China Statistics Press, Beijing, 2013).
36.
Henan Statistical Bureau, Henan Statistical Yearbook 2014 ( China Statistics Press, Beijing, 2014).
37.
The People's Government of Henan Province, http://www.henan.gov.cn/zw/zwgk/system/2013/06/19/010402381.shtml for Communique on the medium-long term development planning of energy in Henan province (2012–2030), 2013.
38.
The People's Government of Henan Province, http://www.henan.gov.cn/zwgk/system/2012/01/10/010286089.shtml for Comprehensive working plan for energy conservation and emission reduction in Henan province during the 12th Five-Year Plan period, 2012.
39.
The People's Government of Henan Province, http://www.henan.gov.cn/zwgk/system/2014/10/15/010501767.shtml for Action plan of energy saving and low carbon development in Henan province in 2014–2015, 2014.
40.
Henan Statistical Bureau, http://www.ha.stats.gov.cn/hntj/tjfw/tjgb/sxsgb/A06200704index_5.htm for Bulletin of national economic and social development statistics of cities in 2010, 2011.
41.
Henan Statistical Bureau, http://www.ha.stats.gov.cn/hntj/tjfw/tjgb/sxsgb/A06200704index_4.htm for Bulletin of national economic and social development statistics of cities in 2011, 2012.
42.
Henan Statistical Bureau, http://www.ha.stats.gov.cn/hntj/tjfw/tjgb/sxsgb/A06200704index_3.htm for Bulletin of national economic and social development statistics of cities in 2012, 2013.
43.
Henan Statistical Bureau, http://www.ha.stats.gov.cn/hntj/tjfw/tjgb/sxsgb/A06200704index_2.htm for Bulletin of national economic and social development statistics of cities in 2013, 2014.
44.
Henan Statistical Bureau, http://www.ha.stats.gov.cn/hntj/tjfw/tjgb/sxsgb/A06200704index_1.htm for 2015, Bulletin of national economic and social development statistics of cities in 2014, 2015.
45.
Henan Statistical Bureau, Henan Statistical Yearbook 2009 ( China Statistics Press, Beijing, 2009).
46.
World Bank, see http://data.worldbank.org.cn/ for Word Bank WDI (World Development Indicators) Database.
47.
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, 474482 (2014).
http://dx.doi.org/10.1016/j.energy.2014.08.003
48.
The People's Government of Henan Province, http://www.henan.gov.cn/zwgk/system/2015/04/22/010545612.shtml for Energy development planning of Henan province during the 12th FYP period, 2012.
49.
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, 24072415 (2011).
http://dx.doi.org/10.1016/j.enpol.2011.01.063
50.
Beijing Statistical Bureau, Beijing Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
51.
Shanghai Statistical Bureau, Shanghai Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
52.
Guangdong Statistical Bureau, Guangdong Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
53.
Zhejiang Statistical Bureau, Zhejiang Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
54.
Jiangsu Statistical Bureau, Jiangsu Statistical Yearbook 2015 ( China Statistics Press, Beijing, 2015).
http://aip.metastore.ingenta.com/content/aip/journal/jrse/8/5/10.1063/1.4962416
Loading
/content/aip/journal/jrse/8/5/10.1063/1.4962416
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aip/journal/jrse/8/5/10.1063/1.4962416
2016-09-08
2016-10-01

Abstract

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%.

Loading

Full text loading...

/deliver/fulltext/aip/journal/jrse/8/5/1.4962416.html;jsessionid=CPLa45s5c3_-I_0pvV2XgpPZ.x-aip-live-06?itemId=/content/aip/journal/jrse/8/5/10.1063/1.4962416&mimeType=html&fmt=ahah&containerItemId=content/aip/journal/jrse
true
true

Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
/content/realmedia?fmt=ahah&adPositionList=
&advertTargetUrl=//oascentral.aip.org/RealMedia/ads/&sitePageValue=jrse.aip.org/8/5/10.1063/1.4962416&pageURL=http://scitation.aip.org/content/aip/journal/jrse/8/5/10.1063/1.4962416'
Right1,Right2,Right3,