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The relationship between temperature and CO 2 emissions: evidence from a short and very long dataset

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  • David G. McMillan
  • Mark E. Wohar

Abstract

The debate regarding rising temperatures and CO 2 emissions has attracted the attention of economists employing recent econometric techniques. This article extends the previous literature using a dataset that covers 800 000 years, as well as a shorter dataset, and examines the interaction between temperature and CO 2 emissions. Unit root tests reveal a difference between the two datasets. For the long dataset, all tests support the view that both temperature and CO 2 are stationary around a constant. For the short dataset, temperature exhibits trend-stationary behaviour, while CO 2 contains a unit root. This result is robust to nonlinear trends or trend breaks. Modelling the long dataset reveals that while contemporaneous CO 2 appears positive and significant in the temperature equation, including lags results in a joint effect that is near zero. This result is confirmed using a different lag structure and Vector Autoregressive (VAR) model. A Generalized Method of Moments (GMM) approach to account for endogeneity suggests an insignificant relationship. In sum, the key result from our analysis is that CO 2 has, at best, a weak relationship with temperature, while there is no evidence of trending when using a sufficiently long dataset. Thus, as a secondary result we highlight the danger of using a small sample in this context.

Suggested Citation

  • David G. McMillan & Mark E. Wohar, 2013. "The relationship between temperature and CO 2 emissions: evidence from a short and very long dataset," Applied Economics, Taylor & Francis Journals, vol. 45(26), pages 3683-3690, September.
  • Handle: RePEc:taf:applec:v:45:y:2013:i:26:p:3683-3690
    DOI: 10.1080/00036846.2012.729955
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    Cited by:

    1. Huang, Xu & Hassani, Hossein & Ghodsi, Mansi & Mukherjee, Zinnia & Gupta, Rangan, 2017. "Do trend extraction approaches affect causality detection in climate change studies?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 604-624.
    2. Yang, Tianle & Li, Fangmin & Du, Min & Huang, Miao & Li, Yinuo, 2023. "Impacts of alternative energy production innovation on reducing CO2 emissions: Evidence from China," Energy, Elsevier, vol. 268(C).
    3. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.

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