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Drivers of greenhouse gas emissions in the United States: revisiting STIRPAT model

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  • Mahendra Kumar Singh

    (Indira Gandhi Institute of Development Research)

  • Deep Mukherjee

    (Indian Institute of Technology Kanpur)

Abstract

The challenge of reducing emissions of greenhouse gases (GHG) has stimulated great attention among policymakers and scholars in recent past, and a number of STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) studies on carbon emissions have been conducted. This paper contributes to that literature by: (i) studying per capita GHG emissions in the United States (US) adopting STIRPAT modeling framework; (ii) employing new explanatory factors like cattle population density, political willingness to address environmental problems, and educational attainment; and (iii) investigating whether emissions elasticities of various factors vary within the US or not. State-level panel data over the period 1990–2014 are used, and partitioning of the sample is done with respect to two controlling factors: an indicator of political support to environmentalism and educational attainment. Results of heterogeneous slope parameters panel data models indicate that cattle density and affluence are major drivers of per capita GHG emissions in the continental US. We find strong evidence of heterogeneity in emissions elasticities across partitioned samples. Our grouping analysis suggests that in a diverse country like US, policymakers should not focus on the average relationships dictated by a single STIRPAT equation, but should account for regional differences if they want accuracy and higher effectiveness in climate policymaking.

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  • Mahendra Kumar Singh & Deep Mukherjee, 2019. "Drivers of greenhouse gas emissions in the United States: revisiting STIRPAT model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(6), pages 3015-3031, December.
    • Handle: RePEc:spr:endesu:v:21:y:2019:i:6:d:10.1007_s10668-018-0178-z
    DOI: 10.1007/s10668-018-0178-z
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