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Global Temperatures and Greenhouse Gases: A Common Features Approach

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  • Li Chen
  • Jiti Gao
  • Farshid Vahid

Abstract

We propose a common features approach and establish that global temperatures and greenhouse gases share a common trend without conditioning on the exact nature of their trends. We also find deterministic cycles in global temperature series with a period of 72 years whose amplitude is non-negligible compared to the warming effect caused by anthropogenic greenhouse gas emissions. We also explore the direction of causation in the co-trending relationship by establishing an error correction mechanism, which shows that greenhouse gas emissions do not respond to the equilibrium errors whereas the global temperature responds to equilibrium errors so as to maintain the co-trending relationship. Finally, we forecast future temperatures conditional on representative concentration pathways (RCP) of greenhouse gases considered by the intergovernmental panel on climate change (IPCC). Our forecasts show that the cyclical component could play a significant role in the next 30 years in such a way that would make the politics of effective emission control policy making more challenging.

Suggested Citation

  • Li Chen & Jiti Gao & Farshid Vahid, 2019. "Global Temperatures and Greenhouse Gases: A Common Features Approach," Monash Econometrics and Business Statistics Working Papers 23/19, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2019-23
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp23-2019.pdf
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    Cited by:

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    2. Liang Chen & Juan J. Dolado & Jesús Gonzalo & Andrey Ramos, 2023. "Heterogeneous predictive association of CO2 with global warming," Economica, London School of Economics and Political Science, vol. 90(360), pages 1397-1421, October.
    3. Gadea Rivas, María Dolores & Ramos, Andrey, 2023. "Trends in temperature data: micro-foundations of their nature," UC3M Working papers. Economics 39045, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    5. Anderson, Heather M. & Gao, Jiti & Turnip, Guido & Vahid, Farshid & Wei, Wei, 2023. "Estimating the effect of an EU-ETS type scheme in Australia using a synthetic treatment approach," Energy Economics, Elsevier, vol. 125(C).
    6. Yu, Deshui & Huang, Difang & Chen, Li, 2023. "Stock return predictability and cyclical movements in valuation ratios," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 36-53.

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    More about this item

    Keywords

    climate change; cycle; endogeneity; trending behaviour.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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