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Does Climate Sensitivity Differ Across Regions?

Author

Listed:
  • Heather Anderson
  • Jiti Gao
  • Farshid Vahid
  • Wei Wei
  • Yang Yang

Abstract

Global mean surface temperature has been increasing in response to growing greenhouse gas concentrations (IPCC, 2021). While Earth is getting warmer overall, regions that differ in local geographical features experience unequal increases in temperature. In this paper, we develop a dynamic varying-coefficient panel data model and use it to measure regional climate sensitivity, defined as the increase in temperature in that region, following a doubling of CO2 concentration. The inference method proposed in this paper is capable of accommodating heterogeneous co-integrating relationships between global and local variables, and it allows comoving climate time series to possess both stochastic and deterministic trending components. Using observational data of mean surface temperatures, solar radiation, and carbon dioxide concentrations between 1959-2017, our model provides an estimate of a 3.7C increase for average climate sensitivity. Moreover, our estimates indicate that high-latitude regions in the Northern Hemisphere are most vulnerable to global warming.

Suggested Citation

  • Heather Anderson & Jiti Gao & Farshid Vahid & Wei Wei & Yang Yang, 2023. "Does Climate Sensitivity Differ Across Regions?," Monash Econometrics and Business Statistics Working Papers 7/23, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2023-7
    as

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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/2023/wp07-2023.pdf
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    References listed on IDEAS

    as
    1. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(1), pages 95-131, April.
    2. Chaohua Dong & Jiti Gao & Bin Peng, 2021. "Varying-Coefficient Panel Data Models With Nonstationarity and Partially Observed Factor Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 700-711, July.
    3. Feng, Guohua & Gao, Jiti & Peng, Bin & Zhang, Xiaohui, 2017. "A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks," Journal of Econometrics, Elsevier, vol. 196(1), pages 68-82.
    4. Sun, Yiguo & Cai, Zongwu & Li, Qi, 2016. "A Consistent Nonparametric Test On Semiparametric Smooth Coefficient Models With Integrated Time Series," Econometric Theory, Cambridge University Press, vol. 32(4), pages 988-1022, August.
    5. Robert Kaufmann & Heikki Kauppi & Michael Mann & James Stock, 2013. "Does temperature contain a stochastic trend: linking statistical results to physical mechanisms," Climatic Change, Springer, vol. 118(3), pages 729-743, June.
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    More about this item

    Keywords

    climate sensitivity; dynamic panel; varying-coefficient model; cointegration;
    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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