Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

Wang, Chunzi; Li, Moye; Jin, Yan

doi:10.2166/wcc.2023.331

Cited by 19 publications

(3 citation statements)

References 43 publications

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“…al. combined several machine learning tools in a hybrid procedure to forecast CO 2 emissions in China [26]. They applied four two-stage procedures.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

2023

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Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the “Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models’ outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.

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“…al. combined several machine learning tools in a hybrid procedure to forecast CO 2 emissions in China [26]. They applied four two-stage procedures.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

2023

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“…The GBR model achieved the best result with an R 2 of 0.9943. Wang et al [27] used machine learning models to predict CO 2 emissions in China. They achieved the lowest prediction error with a two-stage support vector regression-artificial neural network (SVR-ANN).…”

Section: Introductionmentioning

confidence: 99%

Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms

Aydın,

Cakiroglu,

Bekdaş

et al. 2023

Sustainability

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Due to environmental impacts and the need for energy efficiency, the cement industry aims to make more durable and sustainable materials with less energy requirements without compromising mechanical properties based on UN Sustainable Development Goals 9 and 11. Carbon dioxide (CO2) emission into the atmosphere is mostly the result of human-induced activities and causes dangerous environmental impacts by increasing the average temperature of the earth. Since the production of ordinary Portland cement (PC) is a major contributor to CO2 emissions, this study proposes alkali-activated binders as an alternative to reduce the environmental impact of ordinary Portland cement production. The dataset required for the training processes of these algorithms was created using Mendeley as a data-gathering instrument. Some of the most efficient state-of-the-art meta-heuristic optimization algorithms were applied to obtain the optimal neural network architecture with the highest performance. These neural network models were applied in the prediction of carbon emissions. The accuracy of these models was measured using statistical measures such as the mean squared error (MSE) and coefficient of determination (R2). The results show that carbon emissions associated with the production of alkali-activated concrete can be predicted with high accuracy using state-of-the-art machine learning techniques. In this study, in which the binders produced by the alkali activation method were evaluated for their usability as a binder material to replace Portland cement, it is concluded that the most successful hyperparameter optimization algorithm for this study is the genetic algorithm (GA) with accurate mean squared error (MSE = 161.17) and coefficient of determination (R2 = 0.90) values in the datasets.

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“…[14] et al used a comprehensive modeling tool consisting of a time series ARIMA model to predict the total carbon dioxide emissions from 2009 to 2020 for energy consumption, and transportation carbon dioxide emissions forecasting in Malaysia. Wang [15] et al proposed a two-stage prediction method based on Support Vector Regression, Random Forest, Ridge Regression, and Artificial Neural Networks for carbon dioxide forecasting, and compared it with a single prediction method for carbon dioxide emissions forecasting. Peng [16] proposed a model-based method for predicting short-term carbon emissions from green buildings.…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Forecasting Study Based on Influence Factor Mining and Mini-Batch Stochastic Gradient Optimization

Yang,

Yuan,

Wang

et al. 2023

Energies

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With the increasing prominence of the global carbon emission problem, the accurate prediction of carbon emissions has become an increasingly urgent need. Existing carbon emission prediction methods have the problems of slow calculation speed, inaccurate prediction, and insufficient deep mining of influencing factors when dealing with large-scale data. In this study, a comprehensive carbon emission prediction method is proposed. Firstly, multiple influencing factors including economic factors and demographic factors are considered, and a pathway analysis method is introduced to mine the long-term relationship between these factors and carbon emissions. Then, indirect influence terms are added to the multiple regression equation, and the variable is used to represent the indirect influence relationship. Finally, this study proposes the PCA-PA-MBGD method, which applies the results of principal component analysis to the pathway analysis. By reducing the data dimensions and extracting the main influencing factors, and optimizing the carbon emission prediction model by using a mini-batch stochastic gradient descent algorithm, the results show that this method can process a large amount of data quickly and efficiently, and realize an accurate prediction of carbon emissions. This provides strong support for solving the carbon emission problem and offers new ideas and methods for future related research.

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Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

Cited by 19 publications

References 43 publications

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms

Carbon Emission Forecasting Study Based on Influence Factor Mining and Mini-Batch Stochastic Gradient Optimization

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