Prediction of Annual Carbon Emissions Based on Carbon Footprints in Various Omani Industries to Draw Reduction Paths with LSTM-GRU Hybrid Model

Chen Wang, Xiaomin Zhang (), Zekai Nie and Sarita Gajbhiye Meshram
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Chen Wang: School of Humanities and Law, Chengdu University of Technology, Chengdu 610059, China
Xiaomin Zhang: School of Marxism, Central University of Finance and Economics, Beijing 100081, China
Zekai Nie: Faculty of Business and Communications, INTI International University, Selangor 43300, Malaysia
Sarita Gajbhiye Meshram: WRAM Research Lab Pvt., Ltd., Nagpur 440027, India

Sustainability, 2025, vol. 17, issue 11, 1-19

Abstract: Despite global efforts to address climate change, carbon dioxide (CO 2 ) emissions are still on the rise. While carbon dioxide is essential for life on Earth, its increasing concentration due to human activities poses severe environmental and health risks. Therefore, accurately and efficiently predicting CO 2 emissions is essential. Hence, this research delves deeply into the prediction of CO 2 emissions by examining various deep learning models utilizing time series data to identify carbon dioxide levels in Oman. First, four important production materials of Oman (oil, gas, cement, and flaring), which have a great impact on CO 2 emissions, were selected. Then, the time series related to the release of CO 2 was collected from 1964 to 2022. After data collection, preprocessing was performed, in which outliers were removed and corrected, and data that had not been measured were completed using interpolation. Then, by dividing the data into two sections, education (1946–2004) and test (2022–2005) and creating scenarios, predictions were made. By creating four scenarios and modeling with two independent GRU and LSTM models and a hybrid LSTM-GRU model, annual carbon was predicted for Oman. The results were evaluated with three criteria: root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (r). The evaluations showed that the hybrid LSTM-GRU model with an error of 2.104 tons has the best performance compared to the rest of the models. By identifying key contributors to carbon footprints, these models can guide targeted interventions to reduce emissions. They can highlight the impact of industrial activities on per capita emissions, enabling policymakers to design more effective strategies. Therefore, in order to reduce pollution and increase the productivity of factories, using an advanced hybrid model, it is possible to identify the carbon footprint and make accurate predictions for different countries.

Keywords: carbon emission prediction; hybrid combination models; carbon footprint; GRU; LSTM; greenhouse gases; environmental policy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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