Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8117809 | Renewable and Sustainable Energy Reviews | 2015 | 19 Pages |
Abstract
This study investigates the effects of public policy supports on the export performance of bioenergy technologies; it uses panel data from 18 countries from the 1992 to 2008 period. Panel unit-root and cointegration tests are applied, taking into account the results of structural-break tests for each time series and testing for the presence in the panel of cross-sectional dependence. Time-series data on public supports and exports are integrated and cointegrated. The results of dynamic ordinary least squares indicate that in the long term, public R&D expenditures have a positive effect on the exports, the contribution of bioenergy to the total energy supply has a negative effect on the exports, and GDP has a positive effect on the exports. The contribution of bioenergy to total energy supply responds to deviations in the previous period from the long-term equilibrium. Additionally, Blundell-Bond system generalized methods of moments estimations are made, to determine dynamic causality in a panel vector error correction mechanism setting. Evidence of a positive strong and short-term relationship from exports to R&D expenditures, and of a positive short-term causality from exports to the contribution of bioenergy to total energy supply, is found. A positive strong bidirectional relationship between GDP and exports is also uncovered. There is a positive strong, bidirectional, and short-term relationship between GDP and the contribution of bioenergy to total energy supply. Finally, some policy implications based on the results of this study are offered.
Keywords
ADFrSCACADFDOLSQMLFMOLSVECMECMPMGFully Modified Ordinary Least SquaresRADGMMOLSCUSUMRCACausality analysisGDPordinary least squaresQuasi maximum likelihoodGeneralized method of momentsBioenergy policyExport performanceError-correction modelRevealed comparative advantageDynamic PanelCresDynamic ordinary least squaresPooled mean group
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Bongsuk Sung,