Do trend extraction approaches affect causality detection in climate change studies?

Abstract

Various scientific studies have investigated the causal link between solar activity (SS) and the earth's temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM)-an advanced nonparametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades.

The relationship between electricity consumption, trade openness and economic growth in India

“…This finding of a causal link between electricity use and economic activity can be further supported by employing nonparametric causality tests recently proposed by Hassani et al (2016) and trend extraction approaches used by Huang et al (2017) in studies on climate change.…”

The effect of energy and urbanisation on carbon dioxide emissions: evidence from Ghana

“…Many studies (Salahuddin et al, 2015;Sbia et al, 2014;Al-mulali et al, 2015;Adom et al, 2012) on carbon dioxide emission have examined causality among the variables using mainly the Granger vector error correction model (VECM) and error correction model (ECM). To provide better and reliable results, some empirical causality studies have also taken into consideration structural breaks in the usual granger causality test (Huang et al, 2017;Hassan et al, 2016;Dramani et al, 2012;Altinay and Karagol, 2004;Narayan and Smyth, 2008). Furthermore, Toda and Yamamoto (1995) have also built upon the ECM and VECM to eliminate a major shortfall (the situation where the results are sensitive to the values of the nuisance parameters in finite samples).…”

Hydrological natural inflow and climate variables: Time and frequency causality analysis

“…CCM is firstly introduced in [21] that aimed at detecting the causation among time series and provide a better understanding of the dynamical systems that have not been covered by other well established methods like GC. CCM has proven to be an advanced non-parametric technique for distinguishing causation in a dynamical system that contains complex interactions in ecosystems and climate studies [21,30], more details can be found in [31,32]. Some significant rationales of embracing this advanced technique include: CCM is non-parametric approach with no restrictions of assumptions for parametric methods; CCM can distinguish statistically significant causality by considering only two key variables instead of building a complex model by incorporating many possible influential variables based on regression modelling; CCM has remarkable sensitivity at detecting causal links within complex systems whilst not being limited to linearity or non-linearity; the calculation itself is efficient and comparatively straight forward.…”