Global Warming and Global Dioxide Emission: An Empirical Study

Sun, Liguang; Wang, Muhong

doi:10.1006/jema.1996.0025

Cited by 60 publications

(58 citation statements)

References 16 publications

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“…At our knowledge, the first paper dealing with this problem was written by Sun and Wang [16]. They analyzed time series of global CO 2 emissions and global temperature anomalies, finding a strong numerical evidence that the increase in CO 2 emissions causes global temperature change.…”

Section: Granger Analyses In Specific Climatic Problemsmentioning

confidence: 99%

Granger Causality Analyses for Climatic Attribution

Attanasio¹,

Pasini²,

Triacca³

2013

ACS

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This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and particular attention is paid to the direct role of anthropogenic and natural forcings, and to the influence of patterns of natural variability. By analyzing both in-sample and out-of-sample results, clear evidences are obtained (e.g., the major role of greenhousegases radiative forcing in driving temperature, a recent causal decoupling between solar irradiance and temperature itself) together with interesting prospects of further research.

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Section: Granger Analyses In Specific Climatic Problemsmentioning

confidence: 99%

Granger Causality Analyses for Climatic Attribution

Attanasio¹,

Pasini²,

Triacca³

2013

ACS

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“…This work attempts to show evidence for causality going from soil moisture to precipitation, using a form of GC involving a Markov model (Salvucci et al 2002). Sun and Wang (1996), using a classic GC partial F test, suggest that CO 2 Granger causes global temperature. Smirnov and Mokhov (2009) introduce the idea of long-term Granger causality and apply it to temperature and CO 2 ; their method involves empirical modeling, extending the concept of GC to deal with longer-term behavior.…”

Section: Granger Causality Overviewmentioning

confidence: 99%

“…Substantial advances have been made in climate change detection and attribution through the analysis of climate model outputs (e.g., Hasselmann 1979;Santer et al 1991;Santer et al 1993;Hegerl et al 1996;Hegerl et al 1997;Meehl et al 2007;Barnett et al 2008). A few detection and attribution studies have also relied solely on observations (e.g., Tol and De Vos 1993;Sun and Wang 1996;Tol and De Vos 1998;Triacca 2005). Past work attempting to statistically assess Granger causality from observed emissions to globally averaged temperature observations has varied in both method and results (e.g., Sun and Wang 1996;Triacca 2005).…”

Section: Introductionmentioning

confidence: 99%

“…A few detection and attribution studies have also relied solely on observations (e.g., Tol and De Vos 1993;Sun and Wang 1996;Tol and De Vos 1998;Triacca 2005). Past work attempting to statistically assess Granger causality from observed emissions to globally averaged temperature observations has varied in both method and results (e.g., Sun and Wang 1996;Triacca 2005).…”

Section: Introductionmentioning

confidence: 99%

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Exploring Granger causality between global average observed time series of carbon dioxide and temperature

Kodra

Chatterjee

Ganguly

2010

Theor Appl Climatol

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Detection and attribution methodologies have been developed over the years to delineate anthropogenic from natural drivers of climate change and impacts. A majority of prior attribution studies, which have used climate model simulations and observations or reanalysis datasets, have found evidence for human-induced climate change. This papers tests the hypothesis that Granger causality can be extracted from the bivariate series of globally averaged land surface temperature (GT) observations and observed CO 2 in the atmosphere using a reverse cumulative Granger causality test. This proposed extension of the classic Granger causality test is better suited to handle the multisource nature of the data and provides further statistical rigor. The results from this modified test show evidence for Granger causality from a proxy of total radiative forcing (RC), which in this case is a transformation of atmospheric CO 2 , to GT. Prior literature failed to extract these results via the standard Granger causality test. A forecasting test shows that a holdout set of GT can be better predicted with the addition of lagged RC as a predictor, lending further credibility to the Granger test results. However, since second-order-differenced RC is neither normally distributed nor variance stationary, caution should be exercised in the interpretation of our results.

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“…For example in Gil-Alana (2004) and Bentzen (2007) to investigate possible relationships between oil price and other macroeconomic variables. Sun and Wang (1996) applied both approaches to confirm the dynamic relationship between global surface temperature and carbon dioxide (CO 2 ). Generally, in applied economics, we have the accounts of Auerbach and Rutner (1976); Jones and Uri (1987); Erol and Balkan (1991); Dropsy (1996);Gelper, Lemmens and Croux (2007); Ashley and Ye (2012) and Nachane and Dubey (2013).…”

mentioning

confidence: 99%

Do sunspot numbers cause global temperatures? Evidence from a frequency domain causality test

2014

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This paper applies the causality test in the frequency domain, developed by Breitung and Candelon (2006), to analyze whether sunspot numbers (used as a partial approximation to solar irradiance) cause global temperatures, using monthly data covering the time period 1880:1-2013:9. While, standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers does not cause global temperatures for both full and sub-samples (identified based on tests of structural breaks), the frequency domain causality test detects predictability for both the full-sample and the last sub-sample at short (2 to 2.6 months) and long (10.3 months and above) cycle lengths respectively. Our results highlight the importance of analyzing causality using the frequency domain test, which, unlike the time domain Granger causality test, allows us to decompose causality by different time horizons, and hence, could detect predictability at certain cycle lengths even when the time domain causality test might fail to pick up any causality. Further, given the wide-spread discussion in the literature, that results for the full-sample causality, irrespective of whether it is in time or frequency domains, cannot be relied upon when there are structural breaks present, and one needs to draw inference regarding causality from the sub-samples, we can conclude that there has been an emergence of causality running from sunspot numbers to global temperatures only recently at cycle length of 10.3 months and above.

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Global Warming and Global Dioxide Emission: An Empirical Study

Cited by 60 publications

References 16 publications

Granger Causality Analyses for Climatic Attribution

Granger Causality Analyses for Climatic Attribution

Exploring Granger causality between global average observed time series of carbon dioxide and temperature

Do sunspot numbers cause global temperatures? Evidence from a frequency domain causality test

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