Skip to main content

Advertisement

SpringerLink
Log in

Exploring spatial patterns of carbon emissions in the USA: a geographically weighted regression approach

  • Original Paper
  • Published:
Population and Environment Aims and scope Submit manuscript

Abstract

This paper uses US county-level data and the Stochastic Impacts by Regression on Population, Affluence, and Technology framework to explore the extent of geographical variability in the process that links total emissions of carbon dioxide to measures of population, affluence, and technology. Geographically weighted regression models show that there is strong evidence of spatial heterogeneity in the estimated elasticities of emissions. While this research cannot explain these regional patterns, it may provide a useful starting point to examine in detail the nature of regional variation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Carson (2010) discusses the IPAT identity in the context of the controversy generated by Ehrlich’s The Population Bomb, of 1968, and the Club of Rome’s Limits to Growth of 1972. Brander (2007) argues that these issues can be traced back to Malthus and the “Malthusian trap” hypothesis.

  2. I thank an anonymous referee for this point.

  3. To calculate these variables, I use weather station data and geoprocessing tools to calculate average temperatures in counties with multiple weather stations and to assign the temperature of the weather station closest to those counties without weather stations. To convert these variables to logs, temperatures below zero are set to zero. There might be weather variability within counties that these measures do not capture. Also, researchers have documented problems with weather station data, for example, Christy (2012). Thus, these variables are imperfect proxies of climatic determinants of emissions. The results are robust to omitting these two variables.

  4. For this test of geographical variability, the AICc of the GWR model is compared to the AICc of a model in which one of the coefficients is assumed to be global and not to vary locally while all other coefficients are allowed to vary locally. If the original GWR model fits the data better than the model with a global coefficient, there is evidence that this coefficient does in fact vary over the area of study (Nakaya et al. 2012).

  5. I thank an anonymous referee for these insights about the extraction sector.

References

  • Anselin, L. (1988). Spatial econometrics: Methods and models. Norwell, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Brander, J. A. (2007). Sustainability: Malthus revisited? Canadian Journal of Economics, 40(1), 1–38.

    Article  Google Scholar 

  • Brunsdon, C., McClatchey, J., & Unwin, D. J. (2001). Spatial variation in the average rainfall-altitude relationship in Great Britain: An approach using geographically weighted regression. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(4), 455–466.

    Article  Google Scholar 

  • Carson, R. T. (2010). The environmental Kuznets curve: Seeking empirical regularity and theoretical structure. Review of Environmental Economics and Policy, 4(1), 3–23.

    Article  Google Scholar 

  • Christy, J. R. (2012). Searching for information in 133 years of California snowfall observations. Journal of Hydrometeorology, 13(3), 895–912.

    Article  Google Scholar 

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

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

  • Fischer, M. M. (2009). Neural networks for spatial analysis. In S. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis. Thousand Oaks, California: SAGE Publications Inc.

    Google Scholar 

  • Fotheringham, S. A. (2009). Geographically weighted regression. In S. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis. Thousand Oaks, California: SAGE Publications Inc.

    Google Scholar 

  • Fotheringham, S. A., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. West Sussex: Wiley.

    Google Scholar 

  • Gurney, K. R., Mendoza, D., Zhou, Y., Fischer, M., de la Rue du Can, S., Geethakumar, S., et al. (2009). The vulcan project: High resolution fossil fuel combustion CO2 emissions fluxes for the United States. Environmental Science and Technology, 43(14), 5535–5541.

    Article  Google Scholar 

  • Huang, Y., & Leung, Y. (2002). Analysing regional industrialisation in Jiangsu province using geographically weighted regression. Journal of Geographical Systems, 4, 233–249.

    Article  Google Scholar 

  • Kalogirou, S., & Hatzichristos, T. (2007). A spatial modeling framework for income estimation. Spatial Economic Analysis, 2(3), 297–316.

    Article  Google Scholar 

  • Lawson, A. B., & Banerjee, S. (2009). Bayesian spatial analysis. In S. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis. Thousand Oaks, California: SAGE Publications Inc.

    Google Scholar 

  • LeSage, J., & Kelley Pace, R. (2009). Introduction to spatial econometrics. Boca Raton, FL: CRC Press, Taylor & Francis Group.

    Book  Google Scholar 

  • Liddle, B. (2004). Demographic dynamics and per capita environmental impact: Using panel regressions and household decomposition to examine population and transport. Population and Environment, 26(1), 23–39.

    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, 317–343.

    Article  Google Scholar 

  • List, J. A., & Gallet, C. A. (1999). The environmental Kuznets curve: Does one size fit all? Ecological Economics, 31, 409–423.

    Article  Google Scholar 

  • Lloyd, C. D. (2007). Local models for spatial analysis. Boca Raton, FL: CRC Press, Taylor & Francis Group.

    Google Scholar 

  • Maddison, D. (2006). Environmental Kuznets curves: A spatial econometric approach. Journal of Environmental Economics and Management, 51, 218–230.

    Article  Google Scholar 

  • Mennis, J. L., & Jordan, L. (2005). The distribution of environmental equity: Exploring spatial nonstationarity in multivariate models of air toxic releases. Annals of the Association of American Geographers, 95(2), 249–268.

    Article  Google Scholar 

  • Mittal, V., Kamakura, W. A., & Govind, R. (2004). Geographic patterns in customer service and satisfaction: An empirical investigation. Journal of Marketing, 68, 48–62.

    Article  Google Scholar 

  • Nakaya, T., Charlton, M., Lewis, P. (2012). Fotheringham Stewart, and Chris Brunsdon, GWR4 User Manual.

  • Neumayer, E. (2002). Can natural factors explain any cross-country differences in carbon dioxide emissions? Energy Policy, 30, 7–12.

    Article  Google Scholar 

  • O’Sullivan, D., & Unwin, D. J. (2010). Geographic information analysis. New Jersey: Wiley.

  • Partridge, M. D., Rickman, D. S., Ali, K., & Rose Olfert, M. (2008). The geographic diversity of U.S. nonmetropolitan growth dynamics: A geographically weighted regression approach. Land Economics, 84(2), 241–266.

    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, 731–739.

    Article  Google Scholar 

  • Rupasingha, A., Goetz, S. J., Debertin, D. L., & Pagoulatos, A. (2004). The environmental Kuznets curve for US counties: A spatial econometric analysis with extensions. Papers in Regional Science, 83, 407–424.

    Article  Google Scholar 

  • Shi, A. (2003). The impact of population pressure on global carbon dioxide emissions, 1975–1996: Evidence from pooled cross-country data. Ecological Economics, 44, 29–42.

    Article  Google Scholar 

  • Tribbia, J.L. (2012) What’s so local about global climate change? Testing social theories of environmental degradation to quantify the demographic, economic, and governmental factors associated with energy consumption and CO2 emissions in U.S. metropolitan areas and counties. Dissertation University of Colorado.

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hamilton College, 198 College Hill Road, Clinton, NY, 13323, USA

    Julio Videras

Authors
  1. Julio Videras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julio Videras.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Videras, J. Exploring spatial patterns of carbon emissions in the USA: a geographically weighted regression approach. Popul Environ 36, 137–154 (2014). https://doi.org/10.1007/s11111-014-0211-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11111-014-0211-6

Keywords

Search

Navigation