Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Debone, Daniela; Martins, Tiago Dias; Miraglia, Simone Georges El Khouri

doi:10.3390/cli10010009

Cited by 3 publications

(1 citation statement)

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“…Especially in the context of global warming, it is particularly important to divide the emission responsibilities of each country, and the carbon embodied in trade has thus become a subject of research (Bhatti et al, 2021a;Bhatti et al, 2022;Yuan et al, 2022). Debone et al explored the relationship between Brazil's economic structure changes and carbon emissions and found that there was a significant correlation between those factors (Debone et al, 2022). In particular, the increase in Brazil's carbon emissions in the 1990s was largely attributable to the transition to energy-intensive production in its production, in which 6.6% of energy use and 7.1% of carbon emissions were caused by the export sector.…”

Section: Introductionmentioning

confidence: 99%

The Nexus Between CO2 Emission, Economic Growth, Trade Openness: Evidences From Middle-Income Trap Countries

Galvan

Bhatti²,

Campo³

et al. 2022

Front. Environ. Sci.

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In recent years, the carbon dioxide emissions generated by the massive consumption of fossil energy have been increasing year by year, resulting in more and more obvious greenhouse effect, and the occurrence of climate disasters around the world has become more and more frequent. This study analyses the long-term effects of economic growth, trade, foreign direct investment (FDI) and gross domestic product (GDP) on carbon dioxide (CO2) emissions in Latin American countries that are in the middle-income trap (MIT). Using annual time-series data for the period 2000–2020, the results of middle-income countries of Latin America are compared with higher-income countries (Singapore, the United States, and South Korea) and an upper-middle-income country, China. Specifically, we examine the role of sector value addition to GDP on the CO2 emission nexus for middle-income economies, controlling for the effects on GDP, FDI, and trade. Using regression and path analysis (multiple regression) we find that for higher-income countries FDI, GDP and trade are the important variables that have a strong positive impact on CO2 emission, but that positive impact is weak in MIT countries, which makes this study significant as it highlights important variables needed for MIT countries to remain focused. Autoregressive distributed lag (ARDL) model results also explore that FDI, GDP growth and trade variables can significantly accelerate the environmental quality by CO2 emission, while tourism/travel services and education do not much impact the environment. Hence, our paper provides solid ground for developing a sustainable and pro-growth policy for MIT countries because they are plagued by the decline or stagnation of economic growth.

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Section: Introductionmentioning

confidence: 99%

The Nexus Between CO2 Emission, Economic Growth, Trade Openness: Evidences From Middle-Income Trap Countries

Galvan

Bhatti²,

Campo³

et al. 2022

Front. Environ. Sci.

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The effects of economic growth and fossil fuel consumption to climate change: Evidence from Mediterranean Europe by robust estimators

Alsayed¹,

Sek²,

Arıç³

et al. 2023

Economics and Policy of Energy and the Environment

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Climate change and global warming during the recent decades are posing formidable chal- lenges to ecosystems. Nevertheless, changing the climate system due to extreme weather events such as cold spells, high temperatures, droughts, and heat waves have been recorded all over the world. Particularly, it has become less accurate to predict the weather in some European regions using a short time series without considering the extreme values events in the estimated model. Thus, forecasting the behaviour of climate needs more accurate statisti- cal techniques to be used. The main objective of this experimental study is to detect the best robust scale or robust location estimator to model the relationship between CO2 emissions, fossil fuel consumption and gross domestic product by considering the influence of different types of extreme weather events in the panel data of Mediterranean Europe countries over the period 1960-2020. The findings show that the MM-estimator is the best robust estimator han- dling data with high efficiency and high breakdown point with the existence of different types of extreme weather events. In conclusion, the robust MM-estimator could be used to provide an innovative integrated climate-economic approach for the accurate prediction of carbon emissions.

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Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model

Al-Nefaie

Aldhyani

2023

Sustainability

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Burning fossil fuels results in emissions of carbon dioxide (CO2), which significantly contributes to atmospheric changes and climate disturbances. Consequently, people are becoming concerned about the state of the environment, and governments are required to produce precise projections to develop efficient preventive measures. This study makes a significant contribution to the area by modeling and predicting the CO2 emissions of vehicles using advanced artificial intelligence. The model was constructed using the CO2 emission by vehicles dataset from Kaggle, which includes different parameters, namely, vehicle class, engine size (L), cylinder transmission, fuel type, fuel consumption city (L/100 km), fuel consumption hwy (L/100 km), fuel consumption comb (L/100 km), fuel consumption comb (mpg), and CO2 emissions (g/km). To forecast the CO2 emissions produced by vehicles, a deep learning long short-term memory network (LSTM) model and a bidirectional LSTM (BiLSTM) model were developed. Both models are efficient. Throughout the course of the investigation, the researchers employed four statistical assessment metrics: the mean square error (MSE), the root MSE (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Based on the datasets of experiments carried out by Kaggle, the LSTM and BiLSTM models were created and implemented. The data were arbitrarily split into two phases: training, which included 80% of the total data, and testing, which comprised 20% of the total data. The BiLSTM model performed best in terms of accuracy and achieved high prediction values for MSE and RMSE. The BiLSTM model has the greatest attainable (R2 = 93.78). In addition, R% was used to locate a connection between the dataset’s characteristics to ascertain which characteristics had the highest level of association with CO2 emissions. An original strategy for the accurate forecasting of carbon emissions was developed as a result of this work. Consequently, policymakers may use this work as a potentially beneficial decision-support tool to create and execute successful environmental policies.

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Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Cited by 3 publications

References 98 publications

The Nexus Between CO2 Emission, Economic Growth, Trade Openness: Evidences From Middle-Income Trap Countries

The Nexus Between CO2 Emission, Economic Growth, Trade Openness: Evidences From Middle-Income Trap Countries

The effects of economic growth and fossil fuel consumption to climate change: Evidence from Mediterranean Europe by robust estimators

Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model

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