Forecasting the CO2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling

Jena, Pradyot Ranjan; Managi, Shunsuke; Majhi, Babita

doi:10.3390/en14196336

Cited by 31 publications

(18 citation statements)

References 39 publications

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“…The results showed that the countries with high emissions such as China, India, Iran, Indonesia, Saudi Arabia would reach higher values in the near future and the countries with low emission levels such as Mexico, South Africa, Turkey and South Korea would follow an increasing trend, while the emiisions in countries such as America, Japan, England, France, Italy, Australia and Canada would decrease. In addition, the study highlighted that that such forecasts could guide the countries in the transition process to the green economy (Jena et al, 2021).…”

Section: Jena Et Al (2021) Carried Out Their Work On Carbon Emission ...mentioning

confidence: 99%

Forecast of Turkey's Carbon Emissions Within the Framework of the European Union’s Green Deal

Terzioğlu

Kayakuş

ERDOGAN

2023

Preprint

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The most important of such efforts is the Paris Climate Agreement signed in 2015 and the t’s Green Deal, implemented by the European Union (EU) within the framework of this Agreement. The targets stated in Grean Deal include measures affecting not only the EU countries but also third countries with which the EU has foreign trade links. For this purpose, ın this study, the carbon emission of Turkey, which has serious commercial relations with the EU, was tried to be estimated using machine learning techniques and an estimate was made for the year 2030 on the basis of the results obtained. These results were evaluated in line with the targets of the Green Deal. The R2 value of Support Vector Regression (SVR), which is applied in the model as one of the machine learning techniques, was found to be 98.4% and it was found to have the highest predictive power. This technicque is followed by Multiple Linear Regression (MLR) with a 97.6% success rate and Artificial Neural Network (ANN) with 95.8% success rate, respectively. According to the estimates made with the most successful model, SVR, Turkey's carbon emissions are expected to be 723.97 million tons (mt) of CO2 in 2030, the target year set by the EU. This level is 42% more compared to the the target that needs be achieved given the data existing in 2019. In terms of the results obtained from the study, it is thought that the study could be an exemplary model for other countries that have commercial ties with the EU.

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Section: Jena Et Al (2021) Carried Out Their Work On Carbon Emission ...mentioning

confidence: 99%

Forecast of Turkey's Carbon Emissions Within the Framework of the European Union’s Green Deal

Terzioğlu

Kayakuş

ERDOGAN

2023

Preprint

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“…The literature on climate change, gas emissions of nations, and equity is voluminous. Some of the most significant contributions are those made by Sagar [31], who studied the allocation scheme of emissions; Vaillancourt and Waaub [32], who developed a model to allocate emissions based on the equity principle; Markandya [33], who conducted an analysis of the distribution of climate change; Mattoo and Subramanian [34], who conducted a review about equity and climate change; Remuzgo et al [35], who studied the evolution of GHG emissions during 1990-2011; Alcaraz et al, who developed a model of climate justice per capita [36] and applied it to the Mediterranean [37]; and Jena et al [38], who designed a nonlinear model to forecast the CO 2 emissions of the 17 key emitting countries.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Climate Change Analysis Parameter: A Global or a National Approach Dilemma

2022

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Climate change is an issue nowadays present in almost all of the media daily, but information can be manipulated very easily. It is a fact that, in the last decades, greenhouse gas emissions have multiplied, and to tackle climate change efficiently, it is necessary to analyze their origin and their relationship with regards to countries, population, production, etc. When analyzing a country’s emissions, not only the total emissions, but also the emissions in relation to its population, production, etc., should be considered. In this paper, a new parameter (CE2N) that merges total emissions, and emissions per capita and per GDP is proposed and applied, obtaining, for the first time, a unified and universal parameter that considers the emission efficiency and total emissions at the same time and can be used in all countries. We validated this new parameter with its implementation in previous environmental models, and the results obtained showed that CE2N would help to increase the transparency and objectivity of these models, giving more weight to emission efficiency, rather than other, more subjective criteria previously used. In addition, CE2N could be implemented in future international agreements, being beneficial not only for the scientific community, but also for policymakers.

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“…They found that using an optimization algorithm to minimize the error affects the exactness of the predicted values. Jena et al [16] used multilayer ANN to forecast CO 2 emission of 17 countries that had a key role in the worldwide emission and observed an average of 96% forecasting accuracy, which was much more than the previous models.…”

Section: Introductionmentioning

confidence: 99%

Modeling CO2 Emission in Residential Sector of Three Countries in Southeast of Asia by Applying Intelligent Techniques

Sharifpur¹,

Salem²,

Buswig³

et al. 2023

Computers, Materials &Amp; Continua

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Residential sector is one of the energy-consuming districts of countries that causes CO 2 emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO 2 and other greenhouse gases. In the present work, CO 2 emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries are represented, discussed, and analyzed. Subsequently, to propose a model, electricity, natural gas, coal, and oil products consumptions are applied as inputs, and CO 2 emission is considered as the model's output. The obtained R 2 values for the generated models based on MLP and GMDH are 0.9987 and 0.9985, respectively. Furthermore, values of the Average Absolute Relative Deviation (AARD) of the regressions using the mentioned techniques are around 4.56% and 5.53%, respectively. These values reveal significant exactness of the models proposed in this article; however, making use of MLP with the optimal architecture would lead to higher accuracy.

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Forecasting the CO2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling

Cited by 31 publications

References 39 publications

Forecast of Turkey's Carbon Emissions Within the Framework of the European Union’s Green Deal

Forecast of Turkey's Carbon Emissions Within the Framework of the European Union’s Green Deal

A New Climate Change Analysis Parameter: A Global or a National Approach Dilemma

Modeling CO2 Emission in Residential Sector of Three Countries in Southeast of Asia by Applying Intelligent Techniques

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