Skip to main content

Reversed STIRPAT Modeling: The Role of CO2 Emissions, Population, and Technology for a Growing Affluence

  • Conference paper
  • First Online:
Theory and Applications of Time Series Analysis and Forecasting (ITISE 2021)

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Included in the following conference series:

Abstract

The presented paper analyzes the relationship between economic growth, demographic development, and CO2 emissions for 30 industrialized countries using time-series data from 1982–2014 in the well-known IPAT/STIRPAT setting. In contrast to the general assumption of IPAT/STIRPAT modeling, which in most cases proposes a one-way causality running from the anthropogenic factors to the environment, applied Granger-causality tests indicate a reversed causal relationship. Therefore, the paper suggests to add a new perspective to the IPAT/STIRPAT approach by setting up a stochastic model that explains impacts on economic growth (affluence) by regression on population, carbon emissions (as a proxy for energy use or ecosystem services), and technology. The results confirm that GDP per capita growth rates of highly industrialized economies are significantly driven by the development of CO2 emissions, population, and energy intensity. Coefficients remain robust with or without integrating structural and energy variables and for the short- and long-run perspective.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    For a better traceability the environmental/energetic input is still denoted as I.

  2. 2.

    In contrast to Eq. (2), P and T are not expressed inversely. This does not affect the estimation results.

  3. 3.

    Generally, the STIRPAT studies treat technology differently. This paper uses energy intensity in order to stay close to existing STIRPAT literature [26]. Often, technology is approximated and assumed to be partly captured of the error term.

References

  1. Costantini, V., Martini, C.: The causality between energy consumption and economic growth: a multi-sectoral analysis using non-stationary cointegrated panel data. Energy Econ. 32(3), 591–603 (2010)

    Article  Google Scholar 

  2. Ozturk, I.: A literature survey on energy–growth nexus. Energy Policy. 38(1), 340–349 (2010)

    Article  Google Scholar 

  3. Ehrlich, P.R., Holdren, J.P.: Impact of population growth. Science. 171(3977), 1212–1217 (1971)

    Article  Google Scholar 

  4. York, R., Rosa, E.A., Dietz, T.: STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol. Econ. 46(3), 351–365 (2003)

    Article  Google Scholar 

  5. Raskin, P.: Methods for estimating the population contribution to environmental change. Ecol. Econ. 15(3), 225–233 (1996)

    Article  Google Scholar 

  6. Ehrlich, P., Holdren, J.: Impact of population growth. Popul. Resour. Environ. 3, 365–377 (1972)

    Google Scholar 

  7. Dietz, T., Rosa, E.A.: Effects of population and affluence on CO2 emissions. Proc. Natl. Acad. Sci. 94(1), 175–179 (1997)

    Article  Google Scholar 

  8. Lohwasser, J., Schaffer, A., Brieden, A.: The role of demographic and economic drivers on the environment in traditional and standardized STIRPAT analysis. Ecol. Econ. 178 (2020)

    Google Scholar 

  9. Liddle, B.: Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Popul. Environ. 35(3), 286–304 (2014)

    Article  Google Scholar 

  10. Singh, M.K., Mukherjee, D.: Drivers of greenhouse gas emissions in the United States: revisiting STIRPAT model. Environ. Dev. Sustain. 21(6), 3015–3031 (2019)

    Article  Google Scholar 

  11. Guo, X.R., Cheng, S.Y., Chen, D.S., Zhou, Y., Wang, H.Y.: Estimation of economic costs of particulate air pollution from road transport in China. Atmos. Environ. 44(28), 3369–3377 (2010)

    Article  Google Scholar 

  12. Akinloo, A.E.: Energy consumption and economic growth: evidence from 11 SubSahara African countries. Energy Econ. 30(5), 2391–2400 (2008)

    Article  Google Scholar 

  13. Bowden, N., Payne, J.E.: The causal relationship between US energy consumption and real output: a disaggregated analysis. J. Policy Model. 31(2), 180–188 (2009)

    Article  Google Scholar 

  14. Georgescu-Roegen, N.: Feasible recipes versus viable technologies. Atl. Econ. J. 12, 21–31 (1984)

    Article  Google Scholar 

  15. Hall, C.A., Klitgaard, K.: Energy and the wealth of nations: an introduction to biophysical economics. Springer (2018)

    Book  Google Scholar 

  16. Cleveland, C.J., Costanza, R., Hall, C.A., Kaufmann, R.: Energy and the US economy: a biophysical perspective. Science. 225(4665), 890–897 (1984)

    Article  Google Scholar 

  17. Apergis, N., Payne, J.E.: Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy Policy. 38(1), 650–655 (2010)

    Article  Google Scholar 

  18. Lee, C.C., Chang, C.P., Chen, P.F.: Energy-income causality in OECD countries revisited: the key role of capital stock. Energy Econ. 30(5), 2359–2373 (2008)

    Article  Google Scholar 

  19. Wooldridge, J.: Introductory econometrics: a modern approach (with economic applications online, econometrics data sets with solutions manual web site printed access card). MIT press (2015)

    Google Scholar 

  20. Lee, C.C., Chang, C.P.: Energy Consumption and GDP revisited: a panel analysis of developed and developing. Energy Econ. 29, 1206–1223 (2007)

    Article  Google Scholar 

  21. Stern, D.I.: A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Econ. 22(2), 267–283 (2000)

    Article  Google Scholar 

  22. Stern, D.I.: Energy and economic growth in the USA: a multivariate approach. Energy Econ. 15(2), 137–150 (1993)

    Article  Google Scholar 

  23. Hamilton, J.D.: Oil and the macroeconomy since World War II. J. Polit. Econ. 91(2), 228–248 (1983)

    Article  Google Scholar 

  24. IMF: World Economic Outlook, October 2015. International Monetary Fund (2016)

    Google Scholar 

  25. Boden, T., Marland, G., Andres, R.: Global, Regional, and National Fossil-Fuel CO2 Emissions in Trends. Carbon Dioxide Information Analysis Centre (CDIAC), UK (2015)

    Google Scholar 

  26. Vélez-Henao, J.A., Vivanco, D.F., Hernández-Riveros, J.A.: Technological change and the rebound effect in the STIRPAT model: a critical view. Energy Policy. 129, 1372–1381 (2019)

    Article  Google Scholar 

  27. Feenstra, R.C., Inklaar, R., Timmer, M.P.: The next generation of the penn world table. Am. Econ. Rev. 105(10), 3150–3182 (2015)

    Article  Google Scholar 

  28. The World Bank: Population ages 15–64 (% of total), urban population (% of total), energy intensity level of primary energy (in MJ per US$ 2011), renewable energy consumption (% of total) and electricity production from nuclear sources (% of total). Data retrieved from World Bank Open Data, http://data.worldbank.org (2018)

  29. Liddle, B.: Consumption-driven environmental impact and age structure change in OECD countries: a cointegration-STIRPAT analysis. Demogr. Res. 24, 749–770 (2011)

    Article  Google Scholar 

  30. Turok, I., McGranahan, G.: Urbanization and economic growth: the arguments and evidence for Africa and Asia. Environ. Urban. 25(2), 465–482 (2013)

    Article  Google Scholar 

  31. Gygli, S., Haelg, F., Potrafke, N., Sturm, J.E.: The KOF globalisation index–revisited. Rev. Int. Organ. 14(3), 543–574 (2019)

    Article  Google Scholar 

  32. Chang, C.P., Lee, C.C.: Globalization and economic growth: a political economy analysis for OECD countries. Glob. Econ. Rev. 39(2), 151–173 (2010)

    Article  Google Scholar 

  33. Cervellati, M., Sunde, U.: Life expectancy and economic growth: the role of the demographic transition. J. Econ. Growth. 16(2), 99–133 (2011)

    Article  Google Scholar 

  34. Reher, D.S.: The demographic transition revisited as a global process. Popul. Space Place. 10(1), 19–41 (2004)

    Article  Google Scholar 

  35. Nguyen, H.M.: The relationship between urbanization and economic growth: an empirical study on ASEAN countries. Int. J. Soc. Econ. (2018)

    Google Scholar 

  36. Grossman, G.M., Helpman, E.: Globalization and growth. Am. Econ. Rev. 105(5), 100–104 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Johannes Lohwasser .

Editor information

Editors and Affiliations

Appendix

Appendix

Table 3 Results of the Kao- and Pedroni Cointegration Tests
Table 4 Determinants of GDP per Capita for the Long-run (FMOLS)

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lohwasser, J., Schaffer, A., Brökel, T. (2023). Reversed STIRPAT Modeling: The Role of CO2 Emissions, Population, and Technology for a Growing Affluence. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis and Forecasting. ITISE 2021. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-14197-3_21

Download citation

Publish with us

Policies and ethics