U.S. State-Level Carbon Dioxide Emissions: A Spatial-Temporal Econometric Approach of the Environmental Kuznets Curve
AbstractOne of the major criticisms of past environmental Kuznets curve (EKC) studies is that the spatiotemporal aspects within the data have largely been ignored. By ignoring the spatial aspect of pollution emissions past estimates of the EKC implicitly assume that a region’s emissions are unaffected by events in neighboring regions (i.e., assume there are no transboundary pollution emissions between neighbors). By ignoring the spatial aspects within the data several past estimates of the EKC could have generated biased or inconsistent regression results. By ignoring the temporal aspect within the data several past estimates of the EKC could have generated spurious regression results or misspecified t and F statistics. To address this potential misspecification we estimate the relationship between state-level carbon dioxide emissions and income (GDP) accounting for both the spatiotemporal components within the data. Specifically, we estimate a dynamic spatiotemporal panel model using a newly proposed robust, spatial fixed effects model. This new estimation scheme is appropriate for panels with large N and T. Consistent with the EKC hypothesis we find the inverted-U shaped relationship between CO2 emissions and income. Further, we find adequate evidence that carbon dioxide emissions and state-level GDP are temporally and spatially dependent. These findings offer policy implications for both interstate energy trade and pollution emission regulations. These implications are particularly important for the formulation of national policies related to the 2009 Copenhagen Treaty in which the U.S. has committed to significantly reduce greenhouse gas emissions over the next twenty years.
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Bibliographic InfoPaper provided by University of Georgia, Department of Agricultural and Applied Economics in its series Faculty Series with number 96031.
Date of creation: Sep 2010
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Environmental Kuznets Curve; Carbon Dioxide; Spatial Econometrics; Panel Data Econometrics; Time Series Analysis; Environmental Economics; Pollution Economics; Environmental Economics and Policy; Q50; Q53; Q43; C01; C33;
Find related papers by JEL classification:
- Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
- Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
- Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-20 (All new papers)
- NEP-ENE-2010-11-20 (Energy Economics)
- NEP-ENV-2010-11-20 (Environmental Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- María Teresa Ramírez & Ana María Loboguerrero, 2002.
"Spatial Dependence and Economic Growth: Evidence From a Panel of Countries,"
BORRADORES DE ECONOMIA
003522, BANCO DE LA REPÚBLICA.
- María Teresa Ramírez & Ana María Loboguerero, . "Spatial Dependence and Economic Growth: Evidence from a panel of Countries," Borradores de Economia 206, Banco de la Republica de Colombia.
- Hermann Pythagore Pierre Donfouet & P. Wilner Jeanty & Eric Malin, 2013. "A Spatial Dynamic Panel Analysis of the Environmental Kuznets Curve in European Countries," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201318, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
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