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Trends in distributional characteristics : Existence of global warming

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  • Gadea Rivas, María Dolores
  • Gonzalo, Jesús

Abstract

Does global warming exist? The answer to this question is the starting point for all the other issues involved in climate change analysis. In this paper, global warming is defined as an increasing trend in certain distributional characteristics (moments, quantiles, etc) of global temperatures, and not only on the average. Temperatures are seen as a functional stochastic process from which we obtain distributional characteristics as time series objects. We present a simple robust trend test and prove that it is able to detect the existence of an unknown trend component (deterministic or stochastic) in these characteristics. Applying this trend test to daily Central England temperatures (1772-2016) and to Global cross-sectional temperatures (1880-2015), we obtain the same strong conclusions: (i) there is an increasing trend in all the distributional characteristics (time series and cross-sectional) and this trend is larger in the lower quantiles than in the mean, median and upper quantiles; (ii) there is a negative trend in the characteristics measuring dispersion (lower temperatures approach the median faster than the higher ones).The paper concludes by clearly answering the opening question in the afirmative and showing that global-local warming is not only a phenomenon of an increase in the average temperature but also of a larger increase in the lower temperatures producing a decreasing dispersion. This type of warming has more serious consequences than the one found by using only the average.

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  • Gadea Rivas, María Dolores & Gonzalo, Jesús, 2017. "Trends in distributional characteristics : Existence of global warming," UC3M Working papers. Economics 24121, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:24121
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    Cited by:

    1. Harry Haupt & Markus Fritsch, 2022. "Quantile Trend Regression and Its Application to Central England Temperature," Mathematics, MDPI, vol. 10(3), pages 1-20, January.
    2. Maria Dolores Gadea & Jesus Gonzalo & Andrey Ramos, 2023. "Trends in Temperature Data: Micro-foundations of Their Nature," Papers 2312.06379, arXiv.org.
    3. Gadea Rivas, Marta Dolores & Gonzalo, Jesús, 2022. "Climate change heterogeneity: a new quantitative approach," UC3M Working papers. Economics 35442, Universidad Carlos III de Madrid. Departamento de Economía.
    4. 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.
    5. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2021. "A tale of three cities: climate heterogeneity (special issue of SERIES in homage to Juan J. Dolado)," UC3M Working papers. Economics 32200, Universidad Carlos III de Madrid. Departamento de Economía.
    6. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Ardia, David & Bluteau, Keven & Tran, Thien Duy, 2022. "How easy is it for investment managers to deploy their talent in green and brown stocks?," Finance Research Letters, Elsevier, vol. 48(C).
    8. Matei Demetrescu & Robinson Kruse-Becher, 2021. "Is U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models," CREATES Research Papers 2021-07, Department of Economics and Business Economics, Aarhus University.

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    More about this item

    Keywords

    Climate change;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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