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Detection and attribution of climate change through econometric methods

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Abstract

An ever-growing body of evidence regarding observed changes in the climate system has been gathered over the last three decades, and large modeling efforts have been carried to explore how climate may evolve during the present century. The impacts from both observed weather and climate endured during the twentieth century and the magnitude of the potential future impacts of climate change have made this phenomenon of high interest for the policy-makers and the society at large. Two fundamental questions arise for understanding the nature of this problem and the appropriate strategies to address it: Is there a long-term warming signal in the observed climate, or is it the product of natural variability alone? If so, how much of this warming signal can be attributed to anthropogenic activities? As discussed in this review, these questions are intrinsically related to the study of the time-series properties of climate and radiative forcing variables and of the existence of common features such as secular co-movements. This paper presents a brief summary of how detection and attribution studies have evolved in the climate change literature and an overview of the time-series and econometric methods that have been applied for these purposes.

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Notes

  1. Multicointegration extends the cointegration methodology to integrated variables of higher order than one and to variables integrated of different order.

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Authors and Affiliations

  1. Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Ciudad Universitaria, Circuito Exterior, 0451 , Mexico, DF, Mexico

    Francisco Estrada

  2. Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands

    Francisco Estrada

  3. Department of Economics, Boston University, 270 Bay State Rd, Boston, MA, 02215, USA

    Pierre Perron

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In celebration of “Mathematics of Planet Earth-2013”.

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Estrada, F., Perron, P. Detection and attribution of climate change through econometric methods. Bol. Soc. Mat. Mex. 20, 107–136 (2014). https://doi.org/10.1007/s40590-014-0009-7

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