IDEAS home Printed from https://ideas.repec.org/a/sae/amsocr/v88y2023i3p418-453.html
   My bibliography  Save this article

Guns versus Climate: How Militarization Amplifies the Effect of Economic Growth on Carbon Emissions

Author

Listed:
  • Andrew K. Jorgenson
  • Brett Clark
  • Ryan P. Thombs
  • Jeffrey Kentor
  • Jennifer E. Givens
  • Xiaorui Huang
  • Hassan El Tinay
  • Daniel Auerbach
  • Matthew C. Mahutga

Abstract

Building on cornerstone traditions in historical sociology, as well as work in environmental sociology and political-economic sociology, we theorize and investigate with moderation analysis how and why national militaries shape the effect of economic growth on carbon pollution. Militaries exert a substantial influence on the production and consumption patterns of economies, and the environmental demands required to support their evolving infrastructure. As far-reaching and distinct characteristics of contemporary militarization, we suggest that both the size and capital intensiveness of the world’s militaries enlarge the effect of economic growth on nations’ carbon emissions. In particular, we posit that each increases the extent to which the other amplifies the effect of economic growth on carbon pollution. To test our arguments, we estimate longitudinal models of emissions for 106 nations from 1990 to 2016. Across various model specifications, robustness checks, a range of sensitivity analyses, and counterfactual analysis, the findings consistently support our propositions. Beyond advancing the environment and economic growth literature in sociology, this study makes significant contributions to sociological research on climate change and the climate crisis, and it underscores the importance of considering the military in scholarship across the discipline.

Suggested Citation

  • Andrew K. Jorgenson & Brett Clark & Ryan P. Thombs & Jeffrey Kentor & Jennifer E. Givens & Xiaorui Huang & Hassan El Tinay & Daniel Auerbach & Matthew C. Mahutga, 2023. "Guns versus Climate: How Militarization Amplifies the Effect of Economic Growth on Carbon Emissions," American Sociological Review, , vol. 88(3), pages 418-453, June.
  • Handle: RePEc:sae:amsocr:v:88:y:2023:i:3:p:418-453
    DOI: 10.1177/00031224231169790
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00031224231169790
    Download Restriction: no

    File URL: https://libkey.io/10.1177/00031224231169790?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    2. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    3. Swapna Pathak, 2020. "Ecological footprints of war: an exploratory assessment of the long-term impact of violent conflicts on national biocapacity from 1962–2009," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 10(4), pages 380-393, December.
    4. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    5. Richard York, 2012. "Asymmetric effects of economic growth and decline on CO2 emissions," Nature Climate Change, Nature, vol. 2(11), pages 762-764, November.
    6. Arthur P. J. Mol, 2003. "Globalization and Environmental Reform: The Ecological Modernization of the Global Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262632845, December.
    7. Kentor, Jeffrey & Clark, Rob & Jorgenson, Andrew, 2023. "The hidden cost of global economic integration: How foreign investment drives military expenditures," World Development, Elsevier, vol. 161(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongfu Huang, 2011. "Private investment and financial development in a globalized world," Empirical Economics, Springer, vol. 41(1), pages 43-56, August.
    2. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    3. Hailemariam, Abebe & Ivanovski, Kris & Dzhumashev, Ratbek, 2022. "Does R&D investment in renewable energy technologies reduce greenhouse gas emissions?," Applied Energy, Elsevier, vol. 327(C).
    4. Guowei Cui & Milda NorkutÄ— & Vasilis Sarafidis & Takashi Yamagata, 2022. "Two-stage instrumental variable estimation of linear panel data models with interactive effects [Eigenvalue ratio test for the number of factors]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 340-361.
    5. repec:cep:stiecm:/2014/577 is not listed on IDEAS
    6. Sebastian Kripfganz & Vasilis Sarafidis, 2021. "Instrumental-variable estimation of large-T panel-data models with common factors," Stata Journal, StataCorp LP, vol. 21(3), pages 659-686, September.
    7. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    8. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Ardjouma Sombie, 2023. "An empirical analysis using new instrumental variable methods of distributional effects of corruption on public expenditures in developing countries," SN Business & Economics, Springer, vol. 3(3), pages 1-26, March.
    10. Juodis, Artūras & Sarafidis, Vasilis, 2022. "An incidental parameters free inference approach for panels with common shocks," Journal of Econometrics, Elsevier, vol. 229(1), pages 19-54.
    11. Ovidijus Stauskas, 2023. "Complete Theory for CCE Under Heterogeneous Slopes and General Unknown Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 283-303, April.
    12. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    13. Su, Liangjun & Yang, Zhenlin, 2015. "QML estimation of dynamic panel data models with spatial errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 230-258.
    14. Yanbo Liu & Peter C. B. Phillips & Jun Yu, 2023. "A Panel Clustering Approach To Analyzing Bubble Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1347-1395, November.
    15. Norkutė, Milda & Westerlund, Joakim, 2021. "The factor analytical approach in near unit root interactive effects panels," Journal of Econometrics, Elsevier, vol. 221(2), pages 569-590.
    16. Kazuhiko Hayakawa & M. Hashem Pesaran & L. Vanessa Smith, 2014. "Transformed Maximum Likelihood Estimation of Short Dynamic Panel Data Models with Interactive Effects," CESifo Working Paper Series 4822, CESifo.
    17. Artūras Juodis & Vasilis Sarafidis, 2022. "A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 1-15, January.
    18. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    19. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.
    20. Ignace De Vos & Gerdie Everaert & Vasilis Sarafidis, 2021. "A method for evaluating the rank condition for CCE estimators," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1013, Ghent University, Faculty of Economics and Business Administration.
    21. Hugo Freeman & Martin Weidner, 2021. "Linear Panel Regressions with Two-Way Unobserved Heterogeneity," Papers 2109.11911, arXiv.org, revised Aug 2022.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:amsocr:v:88:y:2023:i:3:p:418-453. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.