A Novel Bio-Inspired Framework for CO 2 Emission Forecast in India

Sangeetha, A.; Amudha, T.

doi:10.1016/j.procs.2017.12.048

Cited by 28 publications

(8 citation statements)

References 11 publications

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“…Hence, this could explain the difference in Mexico's carbon emissions curves under both scenarios. No specific amount was allocated to support the Indonesian renewable energy sector in the 2020 COVID-19 recovery package, but it included support for fossil fuel consumers (Sanchez 2021). The Turkish government also has not committed to a COVID-19 green recovery.…”

Section: E7 Countriesmentioning

confidence: 99%

Post-COVID-19 pandemic and the Paris agreement: a socioeconomic analysis and carbon emissions forecasting in developed and developing countries

Hoy

Leong

Woon

2023

Clean Techn Environ Policy

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The COVID-19 pandemic caused profound impacts on the global economy, resulting in a sharp drop in carbon emissions as energy demand fell. The emissions reduction due to past extreme events often follows with a rebound after the economy recovers, but the pandemic’s impacts on the long-term carbon emissions trend remain unknown. This study forecasts the carbon emissions of Group of Seven (G7) as developed countries and Emerging Seven (E7) as developing countries using socioeconomic indicators and artificial intelligence-powered predictive analytics to assess the pandemic’s impacts on the long-term carbon trajectory curve and their progress toward achieving the Paris Agreement goals. Most E7’s carbon emissions have strong positive correlations (> 0.8) with the socioeconomic indicators, whereas most G7’s correlate negatively (> 0.6) due to their decoupled economic growth from carbon emissions. The forecasts show higher growth rates in the E7’s carbon emissions after the rebound in the pandemic scenario compared to the pandemic-free scenario, while the impact on the G7’s carbon emissions is negligible. The overall impact of the pandemic outbreak on long-term carbon emissions is small. Still, its short-term positive impact on the environment should not be misunderstood, and stringent emissions reduction policies must be implemented urgently to ensure the achievement of Paris Agreement goals. Graphical Abstract Research methodology for assessing the pandemic's impacts on the G7 and E7 countries' long-term carbon trajectory curve. Supplementary Information The online version contains supplementary material available at 10.1007/s10098-023-02508-0.

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Section: E7 Countriesmentioning

confidence: 99%

Post-COVID-19 pandemic and the Paris agreement: a socioeconomic analysis and carbon emissions forecasting in developed and developing countries

Hoy

Leong

Woon

2023

Clean Techn Environ Policy

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“…Sangeetha and Amudha, Heydari et al and Lin et al selected historical data of energy sources such as oil, coal, natural gas, and primary energy sources to predict carbon dioxide emissions, and concluded that various energy consumptions play a crucial role in carbon dioxide emissions. (Sangeetha and Amudha 2018;Heydari et al 2019;Lin et al 2021). Song et al investigated the characteristics of carbon intensity in various regions of China, and energy intensity was the main factor affecting the spatial differences and temporal dynamics (Song et al 2019).…”

Section: Carbon Dioxide Emissions Forecastmentioning

confidence: 99%

“…In this paper, China's carbon dioxide emissions from 1965 to 2020 are used as the research object. According to previous studies (Sangeetha and Amudha 2018;Heydari et al 2019;Lin et al 2021), energy consumption is a major factor in carbon dioxide emissions, so in this paper, five important influencing factors of coal, oil, natural gas, hydroelectricity, and primary energy are selected to forecast carbon dioxide emissions (Table 3). Since historical carbon dioxide emissions affect carbon dioxide emissions, historical carbon dioxide emissions in China for the first 3 years are also used as input variables for prediction to improve the accuracy of prediction.…”

Section: Data Descriptionmentioning

confidence: 99%

China’s carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression

Dong

Zhang

Dong

et al. 2022

Environ Sci Pollut Res

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Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

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“…Sera gazı emisyonlarının büyük bölümünün fosil yakıtların kullanılması sonucu meydana gelmesi, küresel ısınma etkilerini azaltmak-önlemek için etkili stratejiler benimsemede CO2 emisyonlarının öngörülmesi, çevre üzerindeki artan etkilerin araştırılmasını önemli hale getirmiştir. Son yıllarda, CO2 emisyonunun değerinin tahmin edilmesi konusunda birçok araştırma yapılmıştır (Assareh & Nedaei, 2018;Cui vd., 2011;Liao, Lu, & Tseng, 2011;Lu, Lewis, & Lin, 2009;Sangeetha & Amudha, 2018;Yılmaz & Yılmaz, 2013). Araştırmacılar atmosferdeki sera gazı salınımının gün geçtikçe yükseldiğini bilimsel çalışmalarla göstermişlerdir (Dam, 2014).…”

Section: çKa Tabanlı Literatür çAlışmalarıunclassified

Prediction with Multi-layer Perceptrons of Greenhouse Gas Emission Belonging to Different Sectors in Turkey

Oral¹,

Uğuz²

2020

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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ÖzKüresel ısınmaya neden olan karbondioksit (CO 2 ), Nitröz oksit (N 2 O) ve Metan (CH 4 ) çeşitli sektörler tarafından oluşturulan sera gazlarıdır. Birleşmiş milletler iklim değişikliği çerçeve sözleşmesince (UNFCCC) Türkiye'nin de içinde olduğu ülkelerin çeşitli sektörleri tarafından oluşturulan sera gazı emisyon değerleri kayıt altına alınarak takip edilmektedir. Ülkelerin oluşturdukları sera gazı emisyon değerleri zaman içinde birçok etkene göre farklılık oluşturabilir. Bu değerin tahmin edilmesi ülkeler açısından önemlidir. Çalışmada 1990-2014 yılları arasındaki farklı sektörlerdeki sera gazı emisyon değerleri ile yapay sinir ağı modeli ve topluluk öğrenme yöntemleri kullanılarak en iyi tahmin modelini oluşturmak için çeşitli deneyler gerçekleştirilmiştir. Çok katmanlı algılayıcı (ÇKA) modeli ile en yüksek R 2 performans değeri üretim, enerji endüstrisi, yerleşim ve ulaşım sektörleri için sırasıyla 0.86, 0.93, 0.91 ve 0.95 olarak bulunmuştur. Topluluk öğrenme yöntemlerine dayanan algoritmalar da ise ortalama R 2 değerleri bakımından en iyi performansı Extra Tree regresyon ile kurulan model sağlamıştır. Kullanılan tahmin modellerinde en iyi performans Çok Katmanlı Algılayıcı tabanlı model ile elde edilmiştir. Çalışmada yer alan sektörlerden 2020 yılında doğaya salınacağı öngörülen sera gazı emisyon değerleri açısından geçmiş dönemi kapsayan salınım değerlerinin ortalamaları ile önemli farklılıklar olduğu görülmektedir. Ayrıca geçmiş döneme oranla üretim sektörü alanında ortalama %36, enerji endüstrisi sektöründe %64 ve ulaşım sektöründe %63 oranında bir artış öngörülmektedir.

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A Novel Bio-Inspired Framework for CO 2 Emission Forecast in India

Cited by 28 publications

References 11 publications

Post-COVID-19 pandemic and the Paris agreement: a socioeconomic analysis and carbon emissions forecasting in developed and developing countries

Post-COVID-19 pandemic and the Paris agreement: a socioeconomic analysis and carbon emissions forecasting in developed and developing countries

China’s carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression

Prediction with Multi-layer Perceptrons of Greenhouse Gas Emission Belonging to Different Sectors in Turkey

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