Use of BP Neural Networks to Determine China’s Regional CO<sub>2</sub> Emission Quota

Qi, Yawei; Peng, Wei‐Xia; Yan, Ran; Rao, G.U.K.

doi:10.1155/2021/6659302

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…After understanding the impact of digital technology on the carbon emissions of the manufacturing industry, the research closely related to the contents of this paper mainly has two aspects. One is the measurement of carbon emissions in HCM, where existing studies mainly calculate carbon emissions in the production process of HCM by using energy consumption [23] and constructing a lifecycle assessment (LCA) to calculate carbon emissions in the whole process of HCM from production to sales [24,25]. In addition, some scholars take direct carbon emissions [26] or embodied carbon emissions [27] as undesired outputs to measure the carbon emission efficiency of HCM.…”

Section: Literature Review and Theorymentioning

confidence: 99%

Analysis of China’s High-Carbon Manufacturing Industry’s Carbon Emissions in the Digital Process

Peng,

Lei,

Zhang

2023

Sustainability

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In order to realize the coordinated development of digitalization and low-carbon emissions, it is important to understand the carbon implications of the digitization of the high-carbon manufacturing (HCM) industry; therefore, this paper focuses on studying the formation and change mechanism of China’s HCM carbon emissions in the digital process. Specifically, based on input–output and energy data, we not only compute the carbon emissions embodied in the digital process of various HCM subsectors and analyze their temporal changes but also reveal the change mechanism by identifying their supply chain tiers and crucial transfer paths. The results show that (1) the digital process of HCM can reduce carbon emissions; (2) the carbon emissions embodied in the digital process of HCM are increasing with time and shifting from low-supply chain tiers to high-supply chain tiers; and (3) the embodied emissions, supply chain tiers, and crucial paths in the digital process of HCM show spatial heterogeneity. We suggest that attention should be paid to increasing embodied emissions in the supply chain tiers and regional differences during the acceleration of HCM digitization, followed by the implementation of appropriate digital carbon neutral policies.

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Section: Literature Review and Theorymentioning

confidence: 99%

Analysis of China’s High-Carbon Manufacturing Industry’s Carbon Emissions in the Digital Process

Peng,

Lei,

Zhang

2023

Sustainability

Self Cite

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“…Their ndings concluded the superior performance of the LSTM model in CO 2 emissions prediction, outperforming the other techniques with the minimum Root Mean Square Error (RMSE) of 60.635. In recent times, several studies have employed the FFNS and ANFIS for prediction of CO 2 emission [20][21][22]. For instance, Mutascu et al considered a single-layer, 20neuron feed-forward ANN to forecast CO 2 trend within the United States of America (USA) [23] using FFNN modeling approach.…”

Section: Introductionmentioning

confidence: 99%

Predictive analytics for environmental impact: Deep learning and AI ensemble strategies in forecasting CO2 emissions

Nazir,

Nagra,

Bhatti

et al. 2024

Preprint

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The forecasting of carbon dioxide (CO2) emission trends stands as a pivotal step towards achieving environmental sustainability. As countries grapple with the challenge of curbing escalating CO2 emissions, the significance of accurate forecasting has become increasingly pronounced in recent years. In this study, to unveil the trajectory of CO2 emissions in Pakistan, forecasting was done through advanced artificial intelligence (AI) driven Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. Rigorous data preprocessing techniques were applied to historical CO2 emissions data for Pakistan comprising of 76 points from year 1946 to 2021. Sequences were formulated to capture temporal dependencies, paving the way for model training and validation. The ANN, GRU, and LSTM models were meticulously designed, each bearing unique attributes for time series forecasting. The obtained results yielded valuable insights, epitomized by model evaluations and predictions. The ANN model did really well with a test MAE of 8.111, a test R² of 0.8614 and a test RMSE of 10.25. The GRU model, characterized by a test MAE of 7.936, a test R² of 0.8355 and a test RMSE of 11.25, proved its worth as well. In contrast, the LSTM model demonstrated excellence with a test MAE of 7.941, a test R² of 0.8586 and a test RMSE of 10.45. A novel ensemble approach, combining these three models, yielded a test MAE of 7.876, a test R2 of 0.869, and a test RMSE of 10.5043. Further, the models were employed to forecast CO2 emissions for Pakistan from the year 2022 to 2030. The insights gained from this study not only enhance our understanding of CO₂ emissions trends in Pakistan but also provide valuable guidance for global efforts to adopt cleaner lifestyles and sustainable choices, fostering a healthier planet for all.

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“…Examples of reputative AI tools are the FFNN, ANFIS and LSTM. Many recent studies used the FFNN [19,20], ANFIS [21,22] and LSTM [14,23,24] techniques to build a forecasting model of CO 2 emissions. For the FFNN modelling approach, Mutascu used the single-layer, 20-neuron feed-forward artificial neural network approach to predict CO 2 emissions in the United States of America [25].…”

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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Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota

Cited by 11 publications

References 25 publications

Analysis of China’s High-Carbon Manufacturing Industry’s Carbon Emissions in the Digital Process

Analysis of China’s High-Carbon Manufacturing Industry’s Carbon Emissions in the Digital Process

Predictive analytics for environmental impact: Deep learning and AI ensemble strategies in forecasting CO2 emissions

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

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Use of BP Neural Networks to Determine China’s Regional CO2 Emission Quota

Cited by 11 publications

References 25 publications

Analysis of China’s High-Carbon Manufacturing Industry’s Carbon Emissions in the Digital Process

Analysis of China’s High-Carbon Manufacturing Industry’s Carbon Emissions in the Digital Process

Predictive analytics for environmental impact: Deep learning and AI ensemble strategies in forecasting CO2 emissions

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

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Product

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Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota