Forecasting European Union Co2 Emissions Using Autoregressive Integrated Moving Average-Autoregressive Conditional Heteroscedasticity Models

Dritsaki, Melina; Dritsaki, Chaido

doi:10.32479/ijeep.9186

Cited by 20 publications

(15 citation statements)

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“…The authors forecasted CO 2 emissions for six years (2015-2020) and used an autoregressive integrated moving average (ARIMA) (1,1,1)-autoregressive conditional heteroscedasticity (ARCH) (1) model combined with the linear ARIMA model and the conditional variance of the ARCH model. Their findings support the fact that the year 2020 presented a considerable decrease in CO 2 emissions, reaching 33.8% less than in the year 1990 (Kyoto Protocol) [44].…”

Section: Brief Overview Of Ghg Emissions In the European Unionsupporting

confidence: 59%

“…In the same context, Dritsaki and Dritsaki [44] investigated the optimum model to forecast CO 2 emissions in the EU-28 based on annual data (from 1960 until 2014). The authors forecasted CO 2 emissions for six years (2015-2020) and used an autoregressive integrated moving average (ARIMA) (1,1,1)-autoregressive conditional heteroscedasticity (ARCH) (1) model combined with the linear ARIMA model and the conditional variance of the ARCH model.…”

Section: Brief Overview Of Ghg Emissions In the European Unionmentioning

confidence: 99%

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Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania

Moiceanu

Dincă

2021

Sustainability

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Greenhouse gases (GHG), such as carbon dioxide, methane, nitrous oxide, and other gases, are considered to be the main cause of global climate change, and this problem has received significant global attention. Carbon dioxide has been considered the most significant gas contributing to global climate change. Our paper presents an analysis of the greenhouse gas emissions in Romania along with a forecast for the years to come. For the study, data from the National Institute of Statistics and Eurostat were gathered and used for the analysis in order to present the results. To obtain the results, the data gathered were analyzed using forecasting methods that can be of help in solving some uncertainties that surround the future. The greenhouse gas (GHG) emissions trends in Romania were analyzed both for linear and exponential function methods. The obtained results showed that the linear function analysis of total GHG emissions in Romania had a forecast accuracy higher than the exponential function method. From the analytical methods used we can draw the conclusion that the emissions are on a descending scale and choosing a proper method is important in analyzing data.

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Section: Brief Overview Of Ghg Emissions In the European Unionsupporting

confidence: 59%

Section: Brief Overview Of Ghg Emissions In the European Unionmentioning

confidence: 99%

Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania

Moiceanu

Dincă

2021

Sustainability

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“…The ARIMA forecasting equation for a stationary time series is a linear equation like regression where the predictors consist of the lags of dependent variable as well as the lags of forecast errors. Thus, theformof ARIMA equation will be (Dritsaki and Dritsaki, 2020):…”

Section: Arima Modelsmentioning

confidence: 99%

Oil Consumption Forecasting Using Arima Models: An Empirical Study for Greece

Dritsaki

Niklis

Stamatiou

2021

IJEEP

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Oil is considered one of the most widely used commodity worldwide and one of the most important goods for a country's productivity. Even if the effect of renewable energy sources tries to replace the consumption of fossil fuels, such as oil, nonetheless the level of worldwide oil consumption hasn't changed. Forecasting oil consumption plays an important role on the designing of energy strategies for policy makers. This paper aims at modeling and forecasting oil consumption in Greece using Box-Jenkins methodology during 960-2020. Forecasting oil consumption was accomplished both with static and dynamic procedure, in and out-of-sample using various forecasting criteria. The results of our paper present a downturn in oil consumption for the following years due to two basic factors. The first is referred to Covid-19 pandemia where economic activity of the country decreased as well as business revenues. The second is the efforts made by the country to replace, oil consumption with other energy forms such as natural gas and mostly renewable sources like sun and wind. With these actions taken, the country -member of EU is consistent with the regulations signed to Kyoto protocol where there are commitments for CO 2 reduction emissions and improvement of energy use.

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“…It is generally accepted that economic variables follow nonlinear processes [11]; non-linearity represents a major difficulty when modeling the dynamics of time series describing those economic (and financial) variables' evolution [12,13]. To deal with those kinds of complex problems, classical linear forecasting tools such as ARIMA are used along with other forecasting tools in [14] a Fourier Series Expansion optimized with Particle Swarm Optimization (PSO) was used to refine the predictions provided by a seasonal ARIMA to forecast electricity consumption, while in [15] ARIMA was combined with Autoregressive Conditional Heteroscedasticity (ARCH) to forecast CO 2 emissions in Europe. The hybrid models clearly outperformed the basic ARIMA.…”

Section: Introductionmentioning

confidence: 99%

“…The ARCH and Generalized ARCH (GARCH) models have proved very useful for financial time series analysis. Thus, they have been also used to forecast CO 2 allowance prices, sometimes without any other tool [16], where a modification of its basic structure (fractionally integrated asymmetric power GARCH) is used, integrated into another forecasting model such as Markov chains [4,13] or by forming a hybrid model with other forecasting tool such as ARIMA [15].…”

Section: Introductionmentioning

confidence: 99%

Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study

et al. 2021

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European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.

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Forecasting European Union Co2 Emissions Using Autoregressive Integrated Moving Average-Autoregressive Conditional Heteroscedasticity Models

Cited by 20 publications

References 0 publications

Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania

Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania

Oil Consumption Forecasting Using Arima Models: An Empirical Study for Greece

Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study

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