Time Series Data Analysis And Prediction Of CO2 Emissions

Tanania, Vartika; Shukla, Supriya; Singh, Shambhavi

doi:10.1109/confluence47617.2020.9058001

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Moreover, ref. [24] analyzes and forecasts the emissions from CO 2 using the dataset of the years 1995 to 2018 from the Indian region. CO 2 emissions in the Arabian region are presented in [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting CO2 Emission Footprint Using AI through Machine Learning

Yang

Noman

2022

Atmosphere

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Adequate CO2 is essential for vegetation, but industrial chimneys and land, space and oceanic vehicles exert tons of excessive CO2 and are mostly responsible for the greenhouse effect, global warming and climate change. Due to COVID-19, CO2 emission was in 2020 at its lowest level compared to prior decades. However, it is unknown how long it will take to reduce CO2 emission to a tolerable point. Furthermore, it is also unknown to what extent it can increase or change in the future. Accurate forecasting of CO2 emissions has real significance for choosing the optimum ways of reducing CO2 emissions. Although some existing models have noticeable CO2 emission forecasting accuracy, the models implemented in this work have more efficacy in prediction due to incorporating COVID-19’s effect on CO2 emission. This paper implements four prediction models using SARIMA (SARIMAX) based on ARIMA. The four models are based on the time period of the surge of the COVID-19 pandemic. The main objective of this work is to compare these four models to suggest an effective model to predict the total CO2 emissions for the future. The study forecasts global total CO2 emission from 2022 to 2027 for near future prediction, 2022 to 2054 for future prediction and 2022 to 2072 for far future prediction. Among the various error measures, mean absolute percentage error (MAPE) is chosen for accuracy comparison. The calculation yields different accuracy for the four SARIMAX models. The MAPEs for the four methods are: pre-COV (MAPE: 0.32), start-COV (MAPE: 0.28), trans-COV (MAPE: 0.19), post-COV (MAPE: 0.09). The MAPE value is relatively low for post-COV (MAPE: 0.09). Hence, it can be inferred that post-COV are suitable models to forecast the global total CO2 emission for the future. The post-COV predictions for the global total CO2 emission for the years 2022 to 2027 are: 36,218.59, 36,733.69, 37,238.29, 37,260.88, 37,674.01 and 37,921.47 million tons (MT). This study successfully predicts CO2 emission either for the COVID-19 period or the post-COVID-19 normal periods. The Machine Learning (ML) method used in this study has shown good agreement with the IPCC model in predicting the past emissions, the current emissions due to COVID-19 and the emissions of the upcoming future. These prediction results can be an asset for the decision support system to develop a suitable policy for global CO2 emission reduction. For future research, a number of other external influence variables responsible for CO2 emission can be added for finer forecasts. This research is an original work in predicting COVID-19-affected CO2 emission using AI through the ML methodology.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting CO2 Emission Footprint Using AI through Machine Learning

Yang

Noman

2022

Atmosphere

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“…At present, carbon emission forecasting methods can be divided into traditional statistical models and machine learning models. Traditional statistical models mainly include multiple linear regression model (Tanania et al, 2020), autoregressive integrated moving average model (Yang and O'Connell, 2020), logistic model (Ma et al, 2017), grey forecast model, etc. Grey forecast model can effectively handle problems of poor information and uncertainty for small sample forecasting, so it is introduced to forecast carbon emission (Deng, 1982;Xie and Liu, 2009).…”

Section: Introductionmentioning

confidence: 99%

The assessment and forecasting of carbon emission for Gansu-Qinghai-Shaanxi of China

Zhu

Zhang

et al. 2023

Preprint

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The assessment of regional low carbon development level can fully reflect the current situation of low carbon development, and the carbon emission forecasting can reveal the future trend of carbon emission and pressure of emission reduction. Therefore, it is of great significance to carry out the assessment of low carbon development level and carbon emission forecasting in Gansu, Qinghai and Shaanxi provinces for regional green economy development. Therefore, this research constructs an index system to objectively reflect the low carbon development level of Gansu, Qinghai and Shaanxi provinces, and proposes IE-TOPSIS comprehensive assessment method to assess the regional low carbon development level from 2010 to 2020. It is showed that low carbon development level of the three provinces over the past decade has an increasing trend. Furthermore, this research establishes a GRA-DPC-MAPE combined forecasting model for carbon emission forecasting of Gansu, Qinghai and Shaanxi. GRA-DPC-MAPE model has higher forecast accuracy than individual model SVR, PSO-SVR, ELM and Elman. It is showed that carbon emission of Gansu and Shaanxi provinces will continue to increase from 2021 to 2025 under the current development.

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“…V. Tanania, S. Shukla and S. Singh [13] the study employs various techniques of time series analysis, such as trend analysis, seasonal decomposition, and autocorrelation analysis, to gain insights into the historical patterns and underlying dynamics of CO2 emissions. Additionally, the authors apply predictive models, such as ARIMA or SARIMA, to forecast future CO2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Emission of Greenhouse Gases in the UK using SARIMA Model

Lalwani,

Shetty

2023

Preprint

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Earth’s climate change is an important and urgent issue, and most researchers believe it is caused by greenhouse gas emissions. Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) trap heat in the atmosphere, raising the temperature of our planet Human activity is the main source of greenhouse gases, including the burning of fossil fuels for energy, deforestation, and agriculture, all contribute to GHG emissions Earth’s temperature due to the accumulation of greenhouse gases (GHGs) in the atmosphere, and resulting in rising sea levels, frequency and intensity of heat waves, droughts, floods and in excess weather events. India has seen a sharp rise in greenhouse gas emissions in recent decades and was the third largest emitter of greenhouse gases in the world according to the Global Carbon Project In contrast, the United Kingdom has worked aggressively to reduce greenhouse gas emissions in recent years. The objective of this study, is to provide a composite time series approach, to estimate GHG emissions for various industries producing greenhouse gases contributing to climate change. We used estimated greenhouse gas emission data available for the UK, which occurred in 1990. We subsequently conducted an exploratory data analysis to study the contributions to the total GHG emissions observed suggests that in the energy supply, it accounts for about 40% of total emissions. Furthermore, the study shows that greenhouse gas emissions decreased significantly in the years following the implementation of policies to increase energy efficiency Compared to emissions in the case of the UK to those in India, we gained a better understanding of emission norms in India and identified areas that require improvement.

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Time Series Data Analysis And Prediction Of CO2 Emissions

Cited by 7 publications

References 8 publications

Predicting CO2 Emission Footprint Using AI through Machine Learning

Predicting CO2 Emission Footprint Using AI through Machine Learning

The assessment and forecasting of carbon emission for Gansu-Qinghai-Shaanxi of China

Forecasting the Emission of Greenhouse Gases in the UK using SARIMA Model

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