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dc.contributor.authorPao, Hsiao-Tienen_US
dc.contributor.authorFu, Hsin-Chiaen_US
dc.contributor.authorTseng, Cheng-Lungen_US
dc.date.accessioned2014-12-08T15:22:44Z-
dc.date.available2014-12-08T15:22:44Z-
dc.date.issued2012-04-01en_US
dc.identifier.issn0360-5442en_US
dc.identifier.urihttp://hdl.handle.net/11536/16058-
dc.description.abstractAnalyses and forecasts of carbon emissions, energy consumption and real outputs are key requirements for clean energy economy and climate change in rapid growth market such as China. This paper employs the nonlinear grey Bernoulli model (NGBM) to predict these three indicators and proposes a numerical iterative method to optimize the parameter of NGBM. The forecasting ability of NGBM with optimal parameter model, namely NGBM-OP has remarkably improved, compared to the GM and ARIMA. The MAPEs of NGBM-OP for out-of-sample (2004-2009) are ranging from 1.10 to 6.26. The prediction results show that China's compound annual emissions, energy consumption and real GDP growth is set to 4.47%, -0.06% and 6.67%, respectively between 2011 and 2020. The co-integration results show that the long-run equilibrium relationship exists among these three indicators and emissions appear to be real output inelastic and energy consumption elastic. The estimated values cannot support an EKC hypothesis, and real output is significantly negative impact on emissions. In order to promote economic and environmental quality, the results suggest that China should adopt the dual strategy of increasing energy efficiency, reducing the loss in power transmission and distribution and stepping up energy conservation policies to reduce any unnecessary wastage of energy. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectGrey prediction modelen_US
dc.subjectNonlinear grey Bernoulli modelen_US
dc.subjectCo-integration techniqueen_US
dc.subjectCO2 emissionsen_US
dc.subjectChinaen_US
dc.titleForecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey modelen_US
dc.typeArticleen_US
dc.identifier.journalENERGYen_US
dc.citation.volume40en_US
dc.citation.issue1en_US
dc.citation.epage400en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department管理科學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Management Scienceen_US
dc.identifier.wosnumberWOS:000303146000039-
dc.citation.woscount30-
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