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

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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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Notes

  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.

References

  • Aldy, J. E. (2005). An environmental Kuznets curve analysis of US state-level carbon dioxide emissions. The Journal of Environment & Development,14(1), 48–72.

    Article  Google Scholar 

  • Auffhammer, M., & Steinhauser, R. (2007). The future trajectory of us CO2 emissions: The role of state versus aggregate information. Journal of Regional Science,47(1), 47–61.

    Article  Google Scholar 

  • Boden, T. A., Marland, G., & Andres, R. J. (2015). National CO2 emissions from fossil-fuel burning, cement manufacture, and gas flaring: 1751–2011, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy. https://doi.org/10.3334/cdiac/00001_v2015.

  • Brulle, R. J., Carmichael, J., & Jenkins, J. C. (2012). Shifting public opinion on climate change: an empirical assessment of factors influencing concern over climate change in the US, 2002–2010. Climatic Change,114(2), 169–188.

    Article  Google Scholar 

  • Carley, S., & Browne, T. R. (2013). Innovative US energy policy: A review of states’ policy experiences. Wiley Interdisciplinary Reviews: Energy and Environment,2(5), 488–506.

    Article  Google Scholar 

  • Clement, M. T., & Elliott J. R. (2012). Growth machines and carbon emissions: A county-level analysis of how US place-making contributes to global climate change. In G. H. William (Ed.) Urban areas and global climate change (research in urban sociology, vol. 12), Emerald Group Publishing Limited, pp 29–50.

  • Dietz, T., Frank, K. A., Whitley, C. T., Kelly, J., & Kelly, R. (2015). Political influences on greenhouse gas emissions from US states. Proceedings of the National Academy of Sciences,112(27), 8254–8259.

    Article  CAS  Google Scholar 

  • Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences USA,94(1), 175–179.

    Article  CAS  Google Scholar 

  • Eberhardt, M. (2012). Estimating panel time-series models with heterogeneous slopes. The Stata Journal,12(1), 61–71.

    Article  Google Scholar 

  • Eberhardt, M., & Bond, S. (2009). Cross-section dependence in nonstationary panel models: A novel estimator. MPRA Paper 17692, University Library of Munich. http://mpra.ub.uni-muenchen.de/17692/1/MPRA paper 17692.pdf.

  • Eberhardt, M., & Teal, F. (2010). Productivity analysis in global manufacturing production. Discussion Paper 515, Department of Economics, University of Oxford. http://www.economics.ox.ac.uk/research/WP/pdf/paper515.pdf.

  • Ehrlich, P. R., & Holdren, J. P. (1971). Impact of population growth. Science, New Series,171(3977), 1212–1217.

    CAS  Google Scholar 

  • Fan, Y., Liu, L.-C., Wu, G., & Wei, Y.-M. (2006). Analyzing impact factors of CO2 emissions using the STIRPAT model. Environmental Impact Assessment Review,26, 377–395.

    Article  Google Scholar 

  • FAO (2013). FAO statistical yearbook 2013. Rome.

  • Food and Agriculture Organization of the United Nations (FAO). (2011). World Livestock 2011—livestock in food security. Rome.

  • Gerber, P. J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., et al. (2013). Tackling climate change through livestock: A global assessment of emissions and mitigation opportunities. Rome: FAO.

    Google Scholar 

  • Gilmore, E. A., & St. Clair, T. (2017). Budgeting for climate change: obstacles and opportunities at the US state level. Climate Policy. https://doi.org/10.1080/14693062.2017.1366891

    Article  Google Scholar 

  • Itkonen, J. V. (2012). Problems estimating the carbon Kuznets curve. Energy,39(1), 274–280.

    Article  Google Scholar 

  • Jaforullah, M., & King, A. (2017). The econometric consequences of an energy consumption variable in a model of CO2 emissions. Energy Economics,63, 84–91.

    Article  Google Scholar 

  • Jorgenson, A. (2006). Global warming and the neglected greenhouse gas: A cross-national study of the social causes of methane emissions intensity, 1995. Social Forces,84(3), 1779–1798.

    Article  Google Scholar 

  • Jorgenson, A. K., Schor, J. B., Huang, X., & Fitzgerald, J. (2016). Income inequality and residential carbon emissions in the United States: A preliminary analysis. Human Ecology Review,22(1), 93–105.

    Google Scholar 

  • League of Conservation Voters (LCV). Scorecards retrieved from: http://scorecard.lcv.org/

  • Liddle, B. (2015). What are the carbon emissions elasticities for income and population? Bridging STIRPAT and EKC via robust heterogeneous panel estimates. Global Environmental Change,31, 62–73.

    Article  Google Scholar 

  • Liddle, B., & Lung, S. (2010). Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Population and Environment,31(5), 317–343.

    Article  Google Scholar 

  • Marcotullio, P. J., Sarzynski, A., Albrecht, J., Schulz, N., & Garcia, J. (2013). The geography of global urban greenhouse gas emissions: An exploratory analysis. Climatic Change,121(4), 621–634.

    Article  Google Scholar 

  • Mather, M., Jacobsen, L. A., & Pollard, K. M. (2015). Aging in the United States. Population Bulletin,70(2), 1–18.

    Google Scholar 

  • McDermott, M. (2009). Know your energy: The 5 states that supply 73% of US coal. Retrieved from: https://www.treehugger.com/corporate-responsibility/know-your-energy-the-5-states-that-supply-73-of-us-coal.html

  • Menz, T., & Welsch, H. (2012). Population aging and carbon emissions in OECD countries: Accounting for life-cycle and cohort effects. Energy Economics,34, 842–849.

    Article  Google Scholar 

  • O’Neill, B. C., Liddle, B., Jiang, L., Smith, K. R., Pachauri, S., Dalton, M., et al. (2012). Demographic change and carbon dioxide emissions. Lancet,380, 157–164.

    Article  Google Scholar 

  • Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics,68(1), 79–113.

    Article  Google Scholar 

  • Progressive Dairyman. (2017). 2016 US dairy statistics. Retrieved from: https://www.progressivedairy.com/site/stats/us-dairy-stats

  • Ripple, W. J., Smith, P., Haberl, H., Montzka, S. A., McAlpine, C., & Boucher, D. H. (2014). Ruminants, climate change and climate policy. Nature Climate Change,4(1), 2–5.

    Article  CAS  Google Scholar 

  • Roberts, T. D. (2011). Applying the STIRPAT model in a post-Fordist landscape: Can a traditional econometric model work at the local level? Applied Geography,31(2), 731–739.

    Article  Google Scholar 

  • Sadorsky, P. (2014). The effect of urbanization on CO2 emissions in emerging economies. Energy Economics,41, 147–153.

    Article  Google Scholar 

  • Shafiei, S., & Salim, R. A. (2014). Non–renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis. Energy Policy,66, 547–556.

    Article  CAS  Google Scholar 

  • Sivak, M. (2008). Where to live in the United States: combined energy demand for heating and cooling in the 50 largest metropolitan areas. Cities,25(6), 396–398.

    Article  Google Scholar 

  • Squalli, J. (2017). Renewable energy, coal as a baseload power source, and greenhouse gas emissions: Evidence from US state-level data. Energy, 127, 479–488.

    Article  CAS  Google Scholar 

  • Turner, A. J., Jacob, D. J., Benmergui, J., Wofsy, S. C., Maasakkers, J. D., Butz, A., et al. (2016). A large increase in US methane emissions over the past decade inferred from satellite data and surface observations. Geophysical Research Letters,43(5), 2218–2224.

    Article  CAS  Google Scholar 

  • US Department of State. (2014). United States climate action report 2014. Retrieved from: https://www.globalchange.gov/browse/reports/us-climate-action-report-2014

  • US Energy Information Administration. (2012). Annual energy review 2011. Retrieved from: https://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf

  • US Environmental Protection Agency. (2016). Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2014. Retrieved from: https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2014

  • Videras, J. (2014). Exploring spatial patterns of carbon emissions in the USA: A geographically weighted regression approach. Population and Environment,36(2), 137–154.

    Article  Google Scholar 

  • Wei, T. (2011). What STIRPAT tells about effects of population and affluence on the environment? Ecological Economics,72, 70–74.

    Article  Google Scholar 

  • World Resources Institute. (2017). Retrieved from: http://www.wri.org/resources/data-sets/cait-country-greenhouse-gas-emissions-data

  • York, R., Rosa, E. A., & Dietz, T. (2003a). a. A rift in modernity? Assessing the anthropogenic sources of global climate change with the STIRPAT model. International Journal of Sociology and Social policy,23(10), 31–51.

    Article  Google Scholar 

  • York, R., Rosa, E. A., & Dietz, T. (2003b). b. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics,46(3), 351–365.

    Article  Google Scholar 

  • Zagheni, E. (2011). The leverage of demographic dynamics on carbon dioxide emissions: Does age structure matter? Demography,48(1), 371–399.

    Article  Google Scholar 

  • Zahran, S., Brody, S. D., Grover, H., & Vedlitz, A. (2006). Climate change vulnerability and policy support. Society and Natural Resources,19, 771–789.

    Article  Google Scholar 

  • Zhu, Q., & Peng, X. (2012). The impacts of population change on carbon emissions in China during 1978–2008. Environmental Impact Assessment Review,36, 1–8.

    Article  Google Scholar 

Download references

Acknowledgements

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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  1. Indira Gandhi Institute of Development Research, Mumbai, 400065, India

    Mahendra Kumar Singh

  2. Department of Economic Sciences, Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Deep Mukherjee

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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). https://doi.org/10.1007/s10668-018-0178-z

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