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

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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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  1. List of ten states: Oklahoma, Rhode Island, Illinois, Arkansas, Missouri, South Dakota, Mississippi, Iowa, Nebraska, and North Dakota.

  2. Ehrlich and Holdren (1971) introduced a mathematical identity I ≡ PAT (I: environmental impact; P: population; A: affluence; T: technology) which has been used as a modeling framework for analyzing the main drivers of anthropogenic environmental impacts. In 1990s, scholars reformulated IPAT model to its stochastic cousin named STIRPAT which allows both hypothesis testing as well as relaxes the implicit assumption of proportionality to conduct empirical research.

  3. Heating degree days (HDD) and cooling degree days (CDD) are used in calculations pertaining to building energy consumption (US Energy Information Administration 2012).

  4. We call a state ‘Strongly Republican’ if the state is carried by the Republican Party in at least three of the four presidential elections (years: 2000, 2004, 2008, 2012).

  5. Details of Median test results are as follows. Results from environmentalism-based partition: (a) Year = 1990, χ2(1) = 6.4; Probability > χ2 = 0.01; (b) Year = 2014, χ2(1) = 6.4; Probability > χ2 = 0.01. Results from educational attainment-based partition: (a) Year = 1990, χ2(1) = 3.6, Probability > χ2 = 0.05; (b) Year = 2014, χ2(1) = 6.4, Probability > χ2 = 0.01.

  6. EERS emphasizes on long-term energy savings target by achieving certain percentage reduction in the total energy sales from energy efficiency measures. RPS requires that electric utilities are supposed to produce certain percentage of the total electricity generated from renewable sources. Interested reader may refer to Carley and Browne (2013) for details.


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We are immensely grateful to two anonymous referees for valuable comments and suggestions on the earlier version of this article. Remaining errors, if any, are our own.

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Correspondence to Deep Mukherjee.

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Singh, M.K., Mukherjee, D. Drivers of greenhouse gas emissions in the United States: revisiting STIRPAT model. Environ Dev Sustain 21, 3015–3031 (2019).

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