Modelling and forecasting of carbon-dioxide emissions in South Africa by using ARIMA model

Kour, Manjit

doi:10.1007/s13762-022-04609-7

Cited by 25 publications

(6 citation statements)

References 24 publications

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“…ARIMA Model TCE data are a non-stationary series, also known as weak stationarity, characterized by dependence, i.e., the value of a specific time in the future depends on its past information. The ARIMA model is a time series and prediction method [30]. Its basic principle is first to use the d-order difference to stabilize the non-stationary time series and then use Autoregressive AR(p), Moving Average MA(q), Autocorrelation Function (ACF), and Partial Correlation Coefficient (PCF) to identify the model for the stabilized time series.…”

Section: Methodology and Datamentioning

confidence: 99%

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

Sun,

Yang,

Liu

et al. 2023

Sustainability

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The transportation sector is a major contributor to carbon emissions, and managing its carbon peak is essential for China to reach the 2030 carbon peak target. This paper uses the autoregressive integrated moving average model (ARIMA) to design baseline scenarios and “double carbon” scenarios (carbon peak and carbon neutrality) based on the accounting of transportation carbon emissions in 30 provinces and cities in China to facilitate regional differentiation and forecast the development trend of transportation carbon emissions. Using the fuzzy comprehensive evaluation method, a comprehensive transportation carbon emission research and judgment system has been developed based on the forecast results. The research indicates a substantial increase in carbon dioxide (CO2) emissions from transport in China over the past 15 years, with an average growth rate of 5.9%, from 387.42 mt in 2005 to 917.00 mt in 2019. In the scenario prediction analysis, the overall carbon emission of the “two-carbon” scenario exhibits varying levels of reduction compared with the baseline scenario. According to the comprehensive research and judgment system, when the comprehensive evaluation index corresponding to the turning point year of transportation carbon emissions is greater than 0.85, and the index remains above 0.85 after the turning point, it can be judged that a region has achieved the peak of transportation carbon dioxide emissions under 95% possibility. It shows that China’s policies and strategies for carbon and emission reduction have played a significant role in transportation, but the low-carbon transformation and development still face great challenges.

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Section: Methodology and Datamentioning

confidence: 99%

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

Sun,

Yang,

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…Our analysis includes key goodness-of-fit measures such as Rsquared (R2), Akaike Information Criterion (AIC), Schwarz Criterion, F-statistic, and likelihood of F-statistic. However, to have an effective model, SIGMASQ need to be small, Adjusted R-squared must be higher, AIC needs to be small, and there should be more significant coefficients [45,46]. As can be observed from Table 3, ARIMA (0,2,1) has lowest SIGMASQ, highest Adjusted R-squared, small AIC and the coefficient of MA is significant.…”

Section: Testing Of the Modelmentioning

confidence: 99%

Forecasting CO2 Emissions in Malaysia Through ARIMA Modelling: Implications for Environmental Policy

Segar,

Akhir,

Sanusi

2024

IJDNE

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“…The authors of [25,135] provide relevant examples of the use of ARIMA models for projecting time series data. In [25], the authors employed ARIMA models for forecasting energy consumption and GHG emissions from pig iron manufacturing in India.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Enhancing Transparency of Climate Efforts: MITICA’s Integrated Approach to Greenhouse Gas Mitigation

Martín-Ortega,

Chornet,

Sebos

et al. 2024

Sustainability

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Under the Paris Agreement, countries must articulate their most ambitious mitigation targets in their Nationally Determined Contributions (NDCs) every five years and regularly submit interconnected information on greenhouse gas (GHG) aspects, including national GHG inventories, NDC progress tracking, mitigation policies and measures (PAMs), and GHG projections in various mitigation scenarios. Research highlights significant gaps in the definition of mitigation targets and the reporting on GHG-related elements, such as inconsistencies between national GHG inventories, projections, and mitigation targets, a disconnect between PAMs and mitigation scenarios, as well as varied methodological approaches across sectors. To address these challenges, the Mitigation-Inventory Tool for Integrated Climate Action (MITICA) provides a methodological framework that links national GHG inventories, PAMs and GHG projections, applying a hybrid decomposition approach that integrates machine learning regression techniques with classical forecasting methods for developing GHG emission projections. MITICA enables mitigation scenario generation until 2050, incorporating over 60 PAMs across Intergovernmental Panel on Climate Change (IPCC) sectors. It is the first modelling approach that ensures consistency between reporting elements, aligning NDC progress tracking and target setting with IPCC best practices while linking climate change with sustainable economic development. MITICA’s results include projections that align with observed trends, validated through cross-validation against test data, and employ robust methods for evaluating PAMs, thereby establishing its reliability.

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Modelling and forecasting of carbon-dioxide emissions in South Africa by using ARIMA model

Cited by 25 publications

References 24 publications

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

Forecasting CO2 Emissions in Malaysia Through ARIMA Modelling: Implications for Environmental Policy

Enhancing Transparency of Climate Efforts: MITICA’s Integrated Approach to Greenhouse Gas Mitigation

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