Prediction of GDP growth rate based on carbon dioxide (CO2) emissions

Marjanović, Vladislav; Milovančević, Miloš; Mladenović, Igor

doi:10.1016/j.jcou.2016.07.009

Cited by 79 publications

(30 citation statements)

References 19 publications

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“…Sarkodie and Owusu [62] reported that, for Ghana, if energy-use, GDP, and population increases by 1%, then CO2 emissions also increase by 0.58%, 0.73%, and 1.30% respectively. Marjanovic et al [26] reported a novel way of predicting the GDP using CO 2 emissions from solid, liquid and gaseous fuel by using ELM. The present paper reports a DATL based GDP (termed DATL-GDP) prediction mechanism using CO2 emissions of developed and developing economies.…”

Section: Literature Surveymentioning

confidence: 99%

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A novel GDP prediction technique based on transfer learning using CO2 emission dataset

Kumar

Muhuri

2019

Applied Energy

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In the last 150 years, CO2 concentration in the atmosphere has increased from 280 parts per million to 400 parts per million. This has caused an increase in the average global temperatures by nearly 0.7 °C due to the greenhouse effect. However, the most prosperous states are the highest emitters of greenhouse gases (especially CO2). This indicates a strong relationship between gaseous emissions and the gross domestic product (GDP) of the states. Such a relationship is highly volatile and nonlinear due to its dependence on the technological advancements and constantly changing domestic and international regulatory policies and relations. To analyse such vastly nonlinear relationships, soft computing techniques has been quite effective as they can predict a compact solution for multi-variable parameters without any explicit insight into the internal system functionalities. This paper reports a novel transfer learning based approach for GDP prediction, which we have termed as 'Domain Adapted Transfer Learning for GDP Prediction. In the proposed approach per capita GDP of different nations is predicted using their CO2 emissions via a model trained on the data of any developed or developing economy. Results are comparatively presented considering three well-known regression methods such as Generalized Regression Neural Network, Extreme Learning Machine and Support Vector Regression. Then the proposed approach is used to reliably estimate the missing per capita GDP of some of the war-torn and isolated countries.

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Section: Literature Surveymentioning

confidence: 99%

“…CO2 is the most common by-product of these fuels. Hence, the fuel consumption trend of a nation can effectively predict its GDP [26]. The GDP estimation of a country can indicate its living standard and help in developing better economic policies.…”

Section: Introductionmentioning

confidence: 99%

A novel GDP prediction technique based on transfer learning using CO2 emission dataset

Kumar

Muhuri

2019

Applied Energy

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“…The main factors behind climate change include increasing CO 2 emissions along with other greenhouse gases (GHGs) in the atmosphere 2 . Globally, fossils fuels are being used for fulfilling the energy demand and are the major source of CO 2 emissions and other pollutants 3 . Amongst the GHGs, CO 2 contributes approximately 63% to the greenhouse effect, making it a dominant contributor toward global warming 4 …”

Section: Introductionmentioning

confidence: 99%

Forecasting CO₂ emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor

et al. 2020

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The forecast of CO 2 emissions is very crucial, especially for Pakistan as it is one of the top victims of climate change. A univariate model, (ARIMA) autoregressive integrated moving average (ARIMA), was used to forecast CO 2 emissions for Pakistan. The CO 2 emissions scenarios were developed for Pakistan till 2020, forecasting them further to 2030. The scenarios developed include China-Pakistan Economic Corridor (CPEC) scenario, where CO 2 emissions from high-priority energy projects under the CPEC were considered. The scenarios attempt to estimate the impactful emission reduction percentage, which the country needs to adopt along with other necessary changes in the existing policies of the country. The forecast results clearly indicate that the emissions are bound to increase under business as usual and CPEC scenarios and the country would fail to meet the Nationally Determined Contributions pledged at COP21. In other scenarios, where we assumed the country has adopted mitigatory strategies to curb the emissions, the forecast shows decreased CO 2 emissions for Pakistan.

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“…Zhao et al [35] combined the mixed data sampling regression model with BPNN to predict CO 2 emissions. Literature [36] proposed extreme learning machine (ELM) to overcome the shortcomings of the BP neural network. This method not only reduces the risk of falling into local optimum, but also greatly improves the learning speed and generalization ability of the network.…”

Section: Introductionmentioning

confidence: 99%

Predicting and Analyzing CO2 Emissions Based on an Improved Least Squares Support Vector Machine

Sun¹,

Huang²,

Wang³

2019

Pol. J. Environ. Stud.

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Carbon dioxide (CO 2) is the main cause of the greenhouse effect. With the rapid development of the economy in China, CO 2 emissions have increased dramatically. To reduce CO 2 emissions, ensure the sustainability of China's economy and implement the Paris International Convention, it is important to investigate the main factors affecting CO 2 emissions and use those factors to accurately forecast CO 2 emissions. In order to achieve accurate prediction of CO 2 , this paper proposes a CO 2 emission prediction model based on principal component analysis (PCA) and particle swarm optimization for least squares support vector machine (PSO-LSSVM). Through data 1990-2016 in Hebei Province of China, this paper identifies 24 influencing factors though the bivariate correlation analysis. After applying PCA to reduce the dimensions of the influencing factors, two principal components were extracted as input variables. Then the parameters of the LSSVM model are obtained by PSO and the forecast model is established. By comparing the prediction results with actual values, it is proved that the prediction error of the PSO-LSSVM prediction model is 0.663%, which is smaller than that of the traditional BPNN and LSSVM models.

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Prediction of GDP growth rate based on carbon dioxide (CO2) emissions

Cited by 79 publications

References 19 publications

A novel GDP prediction technique based on transfer learning using CO2 emission dataset

A novel GDP prediction technique based on transfer learning using CO2 emission dataset

Forecasting CO₂ emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor

Predicting and Analyzing CO2 Emissions Based on an Improved Least Squares Support Vector Machine

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Prediction of GDP growth rate based on carbon dioxide (CO2) emissions

Cited by 79 publications

References 19 publications

A novel GDP prediction technique based on transfer learning using CO2 emission dataset

A novel GDP prediction technique based on transfer learning using CO2 emission dataset

Forecasting CO2 emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor

Predicting and Analyzing CO2 Emissions Based on an Improved Least Squares Support Vector Machine

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Forecasting CO₂ emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor