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Global renewable energy development: Influencing factors, trend predictions and countermeasures
Promoting the development and utilisation of renewable energy is the current trend of energy policy in various regions. First, we divide the world into seven regions based on the Engineering News-Record (ENR) regional classification—Asia-Pacific, Middle East, Canada, the United States, Latin America...
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Published in: | Resources policy 2019-10, Vol.63, p.101470, Article 101470 |
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container_title | Resources policy |
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creator | Xu, Xiaofeng Wei, Zhifei Ji, Qiang Wang, Chenglong Gao, Guowei |
description | Promoting the development and utilisation of renewable energy is the current trend of energy policy in various regions. First, we divide the world into seven regions based on the Engineering News-Record (ENR) regional classification—Asia-Pacific, Middle East, Canada, the United States, Latin America, Europe and Africa—and analyse the status of renewable energy from political, technical, economic and social perspectives. Second, an integration forecasting model is proposed, which includes differential autoregressive integrated moving average model (ARIMA), neural network model (NNM), support vector machine model (SVM), and predicts the development prospects of renewable resources by one-way regression in various regions. Finally, corresponding countermeasures are proposed for these seven regions.
•The worldwide status of renewable energy is studied from political, technical, economic and social perspectives.•An integration forecasting model is proposed combining ARIMA, NNM with SVM.•The development prospects of renewable resources by one-way regression is predicted in various regions.•Corresponding countermeasures are proposed for the global seven regions. |
doi_str_mv | 10.1016/j.resourpol.2019.101470 |
format | article |
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•The worldwide status of renewable energy is studied from political, technical, economic and social perspectives.•An integration forecasting model is proposed combining ARIMA, NNM with SVM.•The development prospects of renewable resources by one-way regression is predicted in various regions.•Corresponding countermeasures are proposed for the global seven regions.</description><identifier>ISSN: 0301-4207</identifier><identifier>EISSN: 1873-7641</identifier><identifier>DOI: 10.1016/j.resourpol.2019.101470</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Autoregressive models ; Classification ; Development countermeasure ; Economic analysis ; Economic forecasting ; Economic models ; Energy development ; Energy policy ; Forecasting ; Forecasting model ; Neural networks ; News ; Prospects ; Regression analysis ; Renewable energy ; Renewable resources ; Statistical analysis ; Support vector machines ; Trend analysis</subject><ispartof>Resources policy, 2019-10, Vol.63, p.101470, Article 101470</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Oct 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-b90a9f3cfa62ddb3fd1125afdeeb040102951b51c0b94cfee4cafeb57849bae33</citedby><cites>FETCH-LOGICAL-c376t-b90a9f3cfa62ddb3fd1125afdeeb040102951b51c0b94cfee4cafeb57849bae33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27866,27924,27925,33223</link.rule.ids></links><search><creatorcontrib>Xu, Xiaofeng</creatorcontrib><creatorcontrib>Wei, Zhifei</creatorcontrib><creatorcontrib>Ji, Qiang</creatorcontrib><creatorcontrib>Wang, Chenglong</creatorcontrib><creatorcontrib>Gao, Guowei</creatorcontrib><title>Global renewable energy development: Influencing factors, trend predictions and countermeasures</title><title>Resources policy</title><description>Promoting the development and utilisation of renewable energy is the current trend of energy policy in various regions. First, we divide the world into seven regions based on the Engineering News-Record (ENR) regional classification—Asia-Pacific, Middle East, Canada, the United States, Latin America, Europe and Africa—and analyse the status of renewable energy from political, technical, economic and social perspectives. Second, an integration forecasting model is proposed, which includes differential autoregressive integrated moving average model (ARIMA), neural network model (NNM), support vector machine model (SVM), and predicts the development prospects of renewable resources by one-way regression in various regions. Finally, corresponding countermeasures are proposed for these seven regions.
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•The worldwide status of renewable energy is studied from political, technical, economic and social perspectives.•An integration forecasting model is proposed combining ARIMA, NNM with SVM.•The development prospects of renewable resources by one-way regression is predicted in various regions.•Corresponding countermeasures are proposed for the global seven regions.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.resourpol.2019.101470</doi></addata></record> |
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source | International Bibliography of the Social Sciences (IBSS); ScienceDirect Journals; PAIS Index |
subjects | Autoregressive models Classification Development countermeasure Economic analysis Economic forecasting Economic models Energy development Energy policy Forecasting Forecasting model Neural networks News Prospects Regression analysis Renewable energy Renewable resources Statistical analysis Support vector machines Trend analysis |
title | Global renewable energy development: Influencing factors, trend predictions and countermeasures |
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