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

2010 ◽  
Vol 104 (3-4) ◽  
pp. 325-335 ◽  
Author(s):  
Evan Kodra ◽  
Snigdhansu Chatterjee ◽  
Auroop R. Ganguly
Keyword(s):  
Carbon Dioxide ◽  
Time Series ◽  
Granger Causality ◽  
Global Average

Comparative Study on BRIC Carbon Dioxide Emissions - Econometric Empirical Test Based on Cross-Century Time Series Data

2012 ◽  
Vol 616-618 ◽  
pp. 1512-1515
Author(s):  
Wei Hua Du
Keyword(s):  
Carbon Dioxide ◽  
Time Series ◽  
Carbon Dioxide Emissions ◽  
Time Series Data ◽  
Comparative Statics ◽  
Total Carbon ◽  
Series Data ◽  
Per Capita Gdp ◽  
Bric Countries ◽  
Per Capita

Take for example the BRIC economies: Brazil, Russia, India and China. We investigated the time series data on the relationship between carbon dioxide emission and economic growth in these fast-growing developing countries by both comparative statics and comparative dynamics. The results show that there is the monotonic relationship between total carbon dioxide emissions, carbon dioxide emissions per capita and per capita GDP in any one of the BRIC countries. And there is decreasing relationship between the carbon dioxide emissions per unit GDP and per capita GDP.

scholarly journals Recent sea level rise on Ireland's east coast based on multiple tide gauge analysis

2020 ◽  
Author(s):  
Amin Shoari Nejad ◽  
Andrew C. Parnell ◽  
Alice Greene ◽  
Brian P. Kelleher ◽  
Gerard McCarthy
Keyword(s):  
Time Series ◽  
Sea Level Rise ◽  
Sea Level ◽  
Continuous Time ◽  
East Coast ◽  
Tide Gauge ◽  
Tide Gauges ◽  
Missing Value ◽  
Global Average ◽  
Good Agreement

Abstract. We analysed multiple tide gauges from the east coast of Ireland over the period 1938–2018. We validated the different time series against each other and performed a missing value imputation exercise, which enabled us to produce a homogenised record. The recordings of all tide gauges were found to be in good agreement between 2003–2015, though this was markedly less so from 2016 to the present. We estimate the sea level rise in Dublin port for this period at 10 mm yr−1. The rate over the longer period of 1938–2015 was 1.67 mm yr−1 which is in good agreement with the global average. We found that the rate of sea level rise in the longer term record is cyclic with some extreme upward and downward trends. However, starting around 1980, Dublin has seen significantly higher rates that have been always positive since 1996, and this is mirrored in the surrounding gauges. Furthermore, our analysis indicates an increase in sea level variability since 1980. Both decadal rates and continuous time rates are calculated and provided with uncertainties in this paper.

scholarly journals A study of different‑scale relationship between changes of the surface air temperature and the СО2 concentration in the atmosphere

2016 ◽  
Vol 56 (4) ◽  
pp. 533-544 ◽  
Author(s):  
N. V. Vakulenko ◽  
V. M. Kotlyakov ◽  
F. Parrenin ◽  
D. M. Sonechkin
Keyword(s):  
Carbon Dioxide ◽  
Time Series ◽  
Carbon Dioxide Concentration ◽  
Ice Cores ◽  
Glacial Maximum ◽  
Temperature Variations ◽  
The Holocene ◽  
The Antarctic ◽  
The Last Glacial Maximum ◽  
The Last Glacial

A concept of the anthropogenic origin of the current global climate warming assumes that growth of concentration of the atmospheric carbon dioxide and other greenhouse gases is of great concern in this process. However, all earlier performed analyses of the Antarctic ice cores, covering the time interval of several glacial cycles for about 1 000 000 years, have demonstrated that the carbon dioxide concentration changes had a certain lag relative to the air temperature changes by several hundred years during every beginning of the glacial terminations as well as at endings of interglacials. In contrast to these findings, a recently published careful analysis of Antarctic ice cores (Parrenin et al., 2013) had shown that both, the carbon dioxide concentration and global temperature, varied almost synchronously during the transition from the last glacial maximum to the Holocene. To resolve this dilemma, a special technique for analysis of the paleoclimatic time series, based on the wavelets, had been developed and applied to the same carbon dioxide concentration and temperature time series which were used in the above paper of Parrenin et al., 2013. Specifically, a stack of the Antarctic δ18O time series (designated as ATS) and the deuterium Dome C – EPICA ones (dD) were compared to one another in order to: firstly, to quantitatively estimate differences between time scales of these series; and, secondly, to clear up the lead–lag relationships between different scales variations within these time series. It was found that accuracy of the mutual ATS and dD time series dating lay within the range of 80–160 years. Perhaps, the mutual dating of the temperature and carbon dioxide concentration series was even worse due to the assumed displacement of air bubbles within the ice. It made us to limit our analysis by the time scales of approximately from 800 to 6000 years. But it should be taken into account that any air bubble movement changes the time scale of the carbon dioxide series as a whole. Therefore, if a difference between variations in any temperature and the carbon dioxide time series is found to be longer than 80–160 years, and if these variations are timescale‑dependent, it means that the bubble displacements are not essential, and so these advancing and delays are characteristic of the time series being compared. Our wavelet‑based comparative and different‑scale analysis confirms that the relationships between the carbon dioxide concentration and temperature variations were essentially timescale‑dependent during the transition from the last glacial maximum to the Holocene. The carbon dioxide concentration variations were ahead of the temperature ones during transition from the glacial maximum to the Boelling – Alleroud warming as well as from the Young Drias cooling to the Holocene optimum. However, the temperature variations were ahead during the transition from the Boelling – Alleroud warming to the Young Drias cooling and during the transition from the Holocene optimum to the present‑day climate.

scholarly journals Technical Note: Long-term memory effect in the atmospheric CO<sub>2</sub> concentration at Mauna Loa

2006 ◽  
Vol 6 (6) ◽  
pp. 11957-11970 ◽  
Author(s):  
C. Varotsos ◽  
M.-N. Assimakopoulos ◽  
M. Efstathiou
Keyword(s):  
Carbon Dioxide ◽  
Time Series ◽  
Power Law ◽  
Carbon Dioxide Concentration ◽  
Global Climate Models ◽  
Fluctuation Analysis ◽  
Original Time Series ◽  
Mauna Loa ◽  
Carbon Dioxide Concentrations ◽  
Original Time

Abstract. The monthly mean values of the atmospheric carbon dioxide concentration derived from in-situ air samples collected at Mauna Loa Observatory, Hawaii, during 1958–2004 (the longest continuous record available in the world) are analyzed by employing the detrended fluctuation analysis to detect scaling behavior in this time series. The main result is that the fluctuations of carbon dioxide concentrations exhibit long-range power-law correlations (long memory) with lag times ranging from four months to eleven years, which correspond to 1/f noise. This result indicates that random perturbations in the carbon dioxide concentrations give rise to noise, characterized by a frequency spectrum following a power-law with exponent that approaches to one; the latter shows that the correlation times grow strongly. This feature is pointing out that a correctly rescaled subset of the original time series of the carbon dioxide concentrations resembles the original time series. Finally, the power-law relationship derived from the real measurements of the carbon dioxide concentrations could also serve as a tool to improve the confidence of the atmospheric chemistry-transport and global climate models.

scholarly journals Risks and the influence of negative interest rates on economic activity: a case study of Sweden, Denmark, and Switzerland

2020 ◽  
Vol 15 (1) ◽  
pp. 30-41
Author(s):  
Liběna Černohorská ◽  
Darina Kubicová
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Granger Causality ◽  
Economic Activity ◽  
Central Banks ◽  
Time Interval ◽  
Short Term ◽  
Stock Indices ◽  
Negative Interest Rates ◽  
The Impact

The purpose of this paper is to analyze the impact of negative interest rates on economic activity in a selected group of countries, in particular Sweden, Denmark, and Switzerland, for the period 2009–2018. The central banks of these countries were among the first to implement negative interest rates to revive the economic growth. Therefore, this study analyzed long- and short-term relationships between interest rates announced by central banks and gross domestic product and blue chip stock indices. Time series analysis was conducted using Engle-Granger cointegration analysis and Granger causality testing to identify long- and short-term relationship. The first step, using the Akaike criteria, was to determine the optimal delay of the entire time interval for the analyzed periods. Time series that seem to be stationary were excluded based on the results of the Dickey-Fuller test. Further testing continued with the Engle-Granger test if the conditions were met. It was designed to identify co-integration relationships that would show correlation between the selected variables. These tests showed that at a significance level of 0.05, there is no co-integration between any time series in the countries analyzed. On the basis of these analyses, it was determined that there were no long-term relationships between interest rates and GDP or stock indices for these countries during the monitored time period. Using Granger causality, the study only confirmed short-term relationship between interest rates and GDP for all examined countries, though not between interest rates and the stock indices. Acknowledgment&amp;nbsp;The paper has been created with the financial support of The Czech Science Foundation GACR 18-05244S – Innovative Approaches to Credit Risk Management.

Analyzing Granger Causality in Climate Data with Time Series Classification Methods

2017 ◽  
pp. 15-26 ◽  
Author(s):  
Christina Papagiannopoulou ◽  
Stijn Decubber ◽  
Diego G. Miralles ◽  
Matthias Demuzere ◽  
Niko E. C. Verhoest ◽  
...  
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Classification ◽  
Classification Methods ◽  
Climate Data

HUBUNGAN KAUSALITAS HARGA JUAL DAN HARGA SEWA APARTEMEN DI JABODETABEK

2021 ◽  
Vol 1 (1) ◽  
pp. 93-105
Author(s):  
Zainal Zawir Simon ◽  
Effendy Zain ◽  
Zulihar Zulihar
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Investment Analysis ◽  
Series Data ◽  
Analysis Tool ◽  
Selling Price ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Causality Relationship

Abstrak Penelitian ini bertujuan untuk mengetahui hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Data yang dipergunakan adalah data  time series dalam bentuk kuartalan untuk periode 2007:1-2018:3 dan alat analisis yang dipergunakan adalah analisa kausalitas Granger. Hasil penelitian menunjukkan bahwa tidak terdapat hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Dengan kata lain perubahan harga jual  tidak mempengaruhi harga sewa. Sebaliknya harga sewa juga tidak mempengaruhi harga jual apartemen. Dengan demikian Investor diharapkan dalam melakukan analisis investasinya memasukkan faktor-faktor lain yang dapat mempengaruhi harga jual dan harga sewa untuk apartemen, agar terlepas dari pandangan bahwa harga jual mempengaruhi harga sewa dan sebaliknya.Kata Kunci : Harga Jual apartemen, Harga Sewa Apartemen, Data Runtut Waktu, Analisa Kausalitas GrangerABSTRACTThis study aims to determine the causality relationship between the selling price of apartments and apartment rental prices in the Greater Jakarta area. The data used are time series data in quarterly form for the period 2007: 1-2018: 3 and the analysis tool used is the Granger causality analysis. The results showed that there was no causality relationship between apartment selling prices and apartment rental prices in the Greater Jakarta area. In other words, changes in selling prices do not affect rental prices. Conversely the rental price also does not affect the selling price of the apartment. Thus Investors are expected to carry out investment analysis to include other factors that can affect the selling price and rental price for an apartment, so that regardless of the view that the selling price affects the rental price and vice versa.Keywords : Selling Price of apartments, rental prices apartments, time series data, Granger Causality Analysis

Stochastic analysis of time-series related to ocean acidification

2021 ◽  
Author(s):  
Georgios Vagenas ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Carbon Dioxide ◽  
Time Series ◽  
Ocean Acidification ◽  
Atmospheric Carbon Dioxide ◽  
Anthropogenic Activities ◽  
Time Lag ◽  
Carbon Dioxide Concentration ◽  
Carbon Dioxide Partial Pressure ◽  
Chemical Transformations ◽  
Mauna Loa

&lt;p&gt;Since the pre-industrial era at the end of the 18&lt;sup&gt;th&lt;/sup&gt; century, the atmospheric carbon dioxide concentration (CO&lt;sub&gt;2&lt;/sub&gt;) has increased by 47.46% from the level of 280 ppmv (parts per million volume) to 412.89 ppmv (Mauna Loa &amp;#8211; NOAA Station, November 2020). These increased concentrations caused by natural &amp; anthropogenic activities, interact with the aquatic environment which acts as a safety valve. Nevertheless, the absorbed CO&lt;sub&gt;2 &lt;/sub&gt;amounts undergo chemical transformations, resulting in increasing ionized concentrations that can significantly reduce the water&amp;#8217;s pH, a process described as ocean acidification. Here, we use the HOT (Hawaii-Ocean-Time series) to perform time series analysis for temperature, carbon dioxide partial pressure and pH. More specifically, we analyze their temporal changes in month and annual time lag. Then, we proceed in comparisons with relevant studies on atmospheric data to evaluate the produced results. Finally, we make an effort to disentangle the results with simplified assumptions connected with the observed impact of ocean acidification on the aquatic ecosystems.&lt;/p&gt;

scholarly journals Comparison of eddy covariance CO<sub>2</sub> and CH<sub>4</sub> fluxes from mined and recently rewetted sections in a northwestern German cutover bog

2020 ◽  
Vol 17 (10) ◽  
pp. 2853-2874 ◽  
Author(s):  
David Holl ◽  
Eva-Maria Pfeiffer ◽  
Lars Kutzbach
Keyword(s):  
Carbon Dioxide ◽  
Land Use ◽  
Time Series ◽  
Eddy Covariance ◽  
Relative Difference ◽  
Plant Litter ◽  
Absolute Difference ◽  
Data Set ◽  
Ch4 Flux ◽  
The Impact

Abstract. With respect to their role in the global carbon cycle, natural peatlands are characterized by their ability to sequester atmospheric carbon. This trait is strongly connected to the water regime of these ecosystems. Large parts of the soil profile in natural peatlands are water saturated, leading to anoxic conditions and to a diminished decomposition of plant litter. In functioning peatlands, the rate of carbon fixation by plant photosynthesis is larger than the decomposition rate of dead organic material. Over time, the amount of carbon that remains in the soil and is not converted back to carbon dioxide grows. Land use of peatlands often goes along with water level manipulations and thereby with alterations of carbon flux dynamics. In this study, carbon dioxide (CO2) and methane (CH4) flux measurements from a bog site in northwestern Germany that has been heavily degraded by peat mining are presented. Two contrasting types of management have been implemented at the site: (1) drainage during ongoing peat harvesting on one half of the central bog area and (2) rewetting on the other half that had been taken out of use shortly before measurements commenced. The presented 2-year data set was collected with an eddy covariance (EC) system set up on a central railroad dam that divides the two halves of the (former) peat harvesting area. We used footprint analysis to split the obtained CO2 and CH4 flux time series into data characterizing the gas exchange dynamics of both contrasting land use types individually. The time series gaps resulting from data division were filled using the response of artificial neural networks (ANNs) to environmental variables, footprint variability, and fuzzy transformations of seasonal and diurnal cyclicity. We used the gap-filled gas flux time series from 2 consecutive years to evaluate the impact of rewetting on the annual vertical carbon balances of the cutover bog. Rewetting had a considerable effect on the annual carbon fluxes and led to increased CH4 and decreased CO2 release. The larger relative difference between cumulative CO2 fluxes from the rewetted (13±6 mol m−2 a−1) and drained (22±7 mol m−2 a−1) section occurred in the second observed year when rewetting apparently reduced CO2 emissions by 40 %. The absolute difference in annual CH4 flux sums was more similar between both years, while the relative difference of CH4 release between the rewetted (0.83±0.15 mol m−2 a−1) and drained (0.45±0.11 mol m−2 a−1) section was larger in the first observed year, indicating a maximum increase in annual CH4 release of 84 % caused by rewetting at this particular site during the study period.

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