Social acceptance of green energy determinants using principal component analysis

被引:39
|
作者
Bhowmik, Chiranjib [1 ]
Bhowmik, Sumit [1 ]
Ray, Amitava [2 ]
机构
[1] Natl Inst Technol Silchar, Dept Mech Engn, Silchar 788010, Assam, India
[2] Jalpaiguri Govt Engn Coll, Jalpaiguri 735102, W Bengal, India
关键词
Social acceptance; Green energy determinants; Green sources; Principal component analysis; Policy; MULTICRITERIA DECISION-MAKING; ANALYTIC HIERARCHY PROCESS; RENEWABLE ENERGY; PUBLIC ACCEPTANCE; SUSTAINABLE DEVELOPMENT; SOLAR-ENERGY; WIND ENERGY; TECHNOLOGIES; SELECTION; POWER;
D O I
10.1016/j.energy.2018.07.093
中图分类号
O414.1 [热力学];
学科分类号
摘要
This research aims to explore the social acceptability of green energy determinants in a specific region to push forward the community acceptance. None of the previously cited research work dealt with the green energy determinants acceptance using principal component analysis for a particular area. A face to face interview and a randomized survey is conducted to show the community acceptance of green energy. This research led a survey (n = 482) among various government and non-government organizations, universities, colleges, schools, offices and some door to door visits. This study assumes that these stakeholders would play a vital role in the deployment of green energy in the future. The results of the analysis show that 74% of respondents are aware of green energy. Results also show that land requirement, share of dirty fuels, consumption of commercial energies, income inequality, depletion of local resources, foreign direct investment, and technology transfer are the most influencing parameters identified by principal component analysis. This research also divulges some policies for future energy sources adoption with the support of local participation. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1030 / 1046
页数:17
相关论文
共 50 条
  • [21] Sensor validation using principal component analysis
    Kerschen, G
    De Boe, P
    Golinval, JC
    Worden, K
    SMART MATERIALS & STRUCTURES, 2005, 14 (01): : 36 - 42
  • [22] Analysis of EEG using principal component approach
    Padmasai, Y.
    SubbaRao, K.
    Rao, C. Raghavendra
    Jayalakshmi, S. Sita
    2007 14TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS, VOLS 1-4, 2007, : 134 - +
  • [23] Gait recognition using principal component analysis
    Zhang, Yupu
    Wang, Zhen
    International Journal of Advancements in Computing Technology, 2012, 4 (22) : 600 - 607
  • [24] Principal Component Analysis Using LISREL 8
    Dolan, Conor
    STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 1996, 3 (04) : 307 - 322
  • [25] Combustion modeling using principal component analysis
    Sutherland, James C.
    Parente, Alessandro
    PROCEEDINGS OF THE COMBUSTION INSTITUTE, 2009, 32 : 1563 - 1570
  • [26] Trajectory Learning Using Principal Component Analysis
    Osman, Asmaa A. E.
    El-Khoribi, Reda A.
    Shoman, Mahmoud E.
    Shalaby, M. A. Wahby
    RECENT ADVANCES IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 1, 2017, 569 : 174 - 183
  • [27] Face Recognition Using Principal Component Analysis
    Kaur, Ramandeep
    Himanshi, Er.
    2015 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2015, : 585 - 589
  • [28] Enhanced coherence using principal component analysis
    Liu, Zhining
    Song, Chengyun
    Cai, Hanpeng
    Yao, Xingmiao
    Hu, Guangmin
    INTERPRETATION-A JOURNAL OF SUBSURFACE CHARACTERIZATION, 2017, 5 (03): : T351 - T359
  • [29] Speaker recognition using principal component analysis
    Ding, PL
    Zhang, LM
    8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 1392 - 1397
  • [30] Principal component analysis using QR decomposition
    Alok Sharma
    Kuldip K. Paliwal
    Seiya Imoto
    Satoru Miyano
    International Journal of Machine Learning and Cybernetics, 2013, 4 : 679 - 683