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Data Mining For CO2 Emissions Prediction In Italy

Yıl 2021, Cilt: 3 Sayı: 1, 59 - 68, 29.04.2021
https://doi.org/10.46387/bjesr.862179

Öz

This study is a preliminary evaluation of the situation of CO2 emissions in Italy, reviewing the international and national literature, using global datasets, and using data mining techniques for analysis and prediction. The study used descriptive methods. It focuses on finding the main potential parameters that effect the concentration of CO2 emissions based on energy resources in Italy. SMOreg, Linear Regression, and Simple Linear Regression are used. Based on the analysis, the Liquid Fuel sector has had the highest rate of increase in CO2 emission 56.8%. R. Linear Regression algorithm gives us a better performance of the prediction for the CO2 emissions than the second algorithm Simple Linear Regression. These results are in line with the present condition in Italy due to the Italian National Program on Climate Change which focuses on reducing carbon dioxide emissions.

Kaynakça

  • Italian Report on Demonstrable Progress under Article 3.2 of The Kyoto Protocol (2018), United Nations Climate Change report, Feb. 2018. (61 pages) Access https://unfccc.int/resource/docs/dpr/ita1.pdf Proposal for a Council Decision concerning the approval, on behalf of the European Community (2002), of the Kyoto Protocol to the United Nations Framework Convention on Climate Change and the joint fulfilment of commitments thereunder /* COM/2001/0579 final - CNS 2001/0248 */, Official Journal 075 E , P. 0017 - 0032, March 2002 (10 pages).
  • Dervis K.l (2007), Devastating for the world's poor: climate change threatens the development gains already, UN Chroicle, Volume: 44 Issue: 2 Page: 27, June 2007 (4 pages).
  • Blois J., Zarnetske P, Fitzpatrick M. and Finnegan S. (2013), “Climate Change and the Past, Present and Future of Biotic Interactions”, Science 2ed issue, August 2013 (6 pages).
  • Massetti, E.; Simona P., Davide Z., (2007). “National through to local climate policy in Italy”. J. Integr. Environ. Sci., 4(3), 149–158. (11 pages)
  • Kwangbok J.; Taehoon H.; Jimin K.; Jaewook L., (2020). A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques. Renewable and Sustainable Energy Reviews, 110497–(29 pages).
  • Jeslet S. and S Jeevanandham. (2015), CLIMATE CHANGE ANALYSIS USING DATA MINING TECHNIQUES, IJARSE, Vol. No.4, Special Issue (03), March 2015 (8 pages).
  • Somu, N., Raman M R, G., & Ramamritham, K. (2021). A deep learning framework for building energy consumption forecast. Renewable and Sustainable Energy Reviews, 137, 110591 (21 pages).
  • Farhate CVV, Souza ZMd, SRdM O., RLM T., JLN C. (2018), Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field. PLoS ONE 13(3): e0193537 (18 pages).
  • Report of the WHO–China Joint Mission on Coronavirus Disease 2019 (COVID-19) (2020), (40 pages) https://www.who.int/publications/i/item/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19)
  • Friedlingstein, P., Matthew W. J., Michael O'S., Robbie M. A., et al. (2019), Global Carbon Budget 2019 (2019). Earth Syst. Sci. Data 11, 1783–1838 (15 pages).
  • Peters, G.P., Andrew, R.M., Canadell, J.G., Friedlingstein P., Jackson R. B., Korsbakken J. I., Quéré C. Le & Peregon A. (2020), Carbon dioxide emissions continue to grow amidst slowly emerging climate policies. Nat. Clim. Change 10, 3–6 (3 pages).
  • Le Quéré, C., Jackson, R., Jones, M., Smith, A., Abernethy, S., Andrew, R., De-Gol, A., Shan, Y., Canadell, J., Friedlingstein, P., Creutzig, F., & Peters, G. (2020). Supplementary data to: Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement (Version 1.2). Global Carbon Project (8 pages). https://doi.org/10.18160/RQDW-BTJU
  • Hassani, H., Huang X. and Silva E. (2019), Big Data and Climate Change, Big Data Cogn. Comput., 3, 12, (17 pages).
  • Deniz S., Gokcen H., and Nakhaeizadeh G. (2016), Application of Data Mining Methods For Analyzing of The Fuel Consumption And Emission Levels, International Journal Of Engineering Sciences & Research Technology (IJESRT), 5(10): October 2016 (13 pages).
  • Zhu, B.; Wei, Y. (2013). Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology. Omega, 41(3), 517–524 (8 pages)..
  • Dai, S., Niu, D., & Han, Y. (2018). Forecasting of energy-related CO2 emissions in China based on GM (1, 1) and least squares support vector machine optimized by modified shuffled frog leaping algorithm for sustainability. Sustainability, 10(4), 958 (17 pages).
  • Ye, F., Xie, X., Zhang, L., & Hu, X. (2018), “An improved grey model and scenario analysis for carbon intensity forecasting in the Pearl River delta region of china”, Energies, 11(1), 91 (17 pages).
  • Zhang, Z. and Li J. (2020), “Big Data mining For Climate Change”, Elsevier (344 pages).
  • Lakshmanan, V., Gilleland E., McGovern A., Tingley M. (2015), Machine Learning and Data Mining Approaches to Climate Science, Proceedings of the 4th International Workshop on Climate Informatics. Springer, (243 pages).
  • Brownlee J. (2016), "Machine Learning Mastery With Weka Analyze Data, Develop Models and Work Through Projects", 2016 Jason Brownlee (248 pages).
  • Abuzir Y., Abuzir S. (2020), Data Mining Techniques for Prediction of Concrete Compressive Strength (CCS), Palestinian Journal of Technology and Applied Sciences (PJTAS), No 3 (16 pages).
  • Agarwal S., Boulle B., Briand Y. et al. (2017), Brown to Green: The G20 Transıtıon to A Low-Carbon Economy, Italy Country Facts 2017, Climate Transparency 2017 (36 pages). http://www.climate-transparency.org/ g20-climate-performance/g20report2017
  • Brett S. (2015), How Clean is Your Country?, Italy: Environmental Issues, Policies and Clean Technology (azocleantech.com), Jun 11 2015 (4 pages). https://www.azocleantech.com/article.aspx?ArticleID=536
  • Montini, M., Italian Policies and Measures to Respond to Climate Change (February 2000). SSRN Electronic Journal (17 pages). Available at SSRN: https://ssrn.com/abstract=235084
  • Watts J., and Kommenda N. (2020), Coronavirus pandemic leading to huge drop in air pollution, The Guardian, march 2020 (4 pages). https://www.theguardian.com/environment/2020/mar/23/coronavirus-pandemic-leading-to-huge-drop-in-air-pollution.
  • Massetti E., Simona P., Zanoni D. (2007). National through to local climate policy in Italy. Environmental Sciences, 4(3), 149–158 (9 pages).
  • Italy’s Actıon Plan On CO2 Emıssıons Reductıon(2012) , Ente Nazionale L'Aviazone Civil (ENAC), June, 2012. (73 pages) Access https://www.icao.int/environmental-protection/Documents/ActionPlan/Italy_AP_En.pdf
  • Rugani B. and Caro D., (2020). Impact of COVID-19 outbreak measures of lockdown on the Italian Carbon Footprint. Sci. Total Environ, 737(), 1 October 2020, 139806 (14 pages).

İtalya’daki CO2 Emisyon Tahmini için Veri Madenciliği

Yıl 2021, Cilt: 3 Sayı: 1, 59 - 68, 29.04.2021
https://doi.org/10.46387/bjesr.862179

Öz

Bu çalışmada uılusal ve uluslararası literatür çalışmaları gözden geçirilerek ve
global veri kümeleri ve veri madenciliği teknikleri kullanılarak İtalya’daki
CO2 emisyon durum tahmini ve değerlendirmesi yapılmıştır. Çalışmada
tanımlayıcı teknikleri kullanılmış ve İtalya'daki enerji kaynaklarına dayalı
olarak CO2 emisyonlarının konsantrasyonunu etkileyen ana potansiyel
parametreleri bulmaya odaklanılmıştır. Sıralı Minimal Optimizasyon regresyonu (SMOreg), Doğrusal Regresyon ve Basit Doğrusal Regresyon kullanılmıştır. Analize göre, Sıvı Yakıt sektörü CO2 emisyonunda en yüksek artış oranı% 56,8 olmaktadır. R. Doğrusal Regresyon algoritması, CO2 emisyonları tahmininde ikinci algoritma Basit Doğrusal Regresyondan daha iyi bir performans sağlamaktadır. Elde edilen bu sonuçlar, karbondioksit emisyonlarını azaltmaya odaklanan İtalyan İklim Değişikliği Ulusal Programı nedeniyle İtalya'daki mevcut
urumla uyumludur.

Kaynakça

  • Italian Report on Demonstrable Progress under Article 3.2 of The Kyoto Protocol (2018), United Nations Climate Change report, Feb. 2018. (61 pages) Access https://unfccc.int/resource/docs/dpr/ita1.pdf Proposal for a Council Decision concerning the approval, on behalf of the European Community (2002), of the Kyoto Protocol to the United Nations Framework Convention on Climate Change and the joint fulfilment of commitments thereunder /* COM/2001/0579 final - CNS 2001/0248 */, Official Journal 075 E , P. 0017 - 0032, March 2002 (10 pages).
  • Dervis K.l (2007), Devastating for the world's poor: climate change threatens the development gains already, UN Chroicle, Volume: 44 Issue: 2 Page: 27, June 2007 (4 pages).
  • Blois J., Zarnetske P, Fitzpatrick M. and Finnegan S. (2013), “Climate Change and the Past, Present and Future of Biotic Interactions”, Science 2ed issue, August 2013 (6 pages).
  • Massetti, E.; Simona P., Davide Z., (2007). “National through to local climate policy in Italy”. J. Integr. Environ. Sci., 4(3), 149–158. (11 pages)
  • Kwangbok J.; Taehoon H.; Jimin K.; Jaewook L., (2020). A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques. Renewable and Sustainable Energy Reviews, 110497–(29 pages).
  • Jeslet S. and S Jeevanandham. (2015), CLIMATE CHANGE ANALYSIS USING DATA MINING TECHNIQUES, IJARSE, Vol. No.4, Special Issue (03), March 2015 (8 pages).
  • Somu, N., Raman M R, G., & Ramamritham, K. (2021). A deep learning framework for building energy consumption forecast. Renewable and Sustainable Energy Reviews, 137, 110591 (21 pages).
  • Farhate CVV, Souza ZMd, SRdM O., RLM T., JLN C. (2018), Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field. PLoS ONE 13(3): e0193537 (18 pages).
  • Report of the WHO–China Joint Mission on Coronavirus Disease 2019 (COVID-19) (2020), (40 pages) https://www.who.int/publications/i/item/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19)
  • Friedlingstein, P., Matthew W. J., Michael O'S., Robbie M. A., et al. (2019), Global Carbon Budget 2019 (2019). Earth Syst. Sci. Data 11, 1783–1838 (15 pages).
  • Peters, G.P., Andrew, R.M., Canadell, J.G., Friedlingstein P., Jackson R. B., Korsbakken J. I., Quéré C. Le & Peregon A. (2020), Carbon dioxide emissions continue to grow amidst slowly emerging climate policies. Nat. Clim. Change 10, 3–6 (3 pages).
  • Le Quéré, C., Jackson, R., Jones, M., Smith, A., Abernethy, S., Andrew, R., De-Gol, A., Shan, Y., Canadell, J., Friedlingstein, P., Creutzig, F., & Peters, G. (2020). Supplementary data to: Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement (Version 1.2). Global Carbon Project (8 pages). https://doi.org/10.18160/RQDW-BTJU
  • Hassani, H., Huang X. and Silva E. (2019), Big Data and Climate Change, Big Data Cogn. Comput., 3, 12, (17 pages).
  • Deniz S., Gokcen H., and Nakhaeizadeh G. (2016), Application of Data Mining Methods For Analyzing of The Fuel Consumption And Emission Levels, International Journal Of Engineering Sciences & Research Technology (IJESRT), 5(10): October 2016 (13 pages).
  • Zhu, B.; Wei, Y. (2013). Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology. Omega, 41(3), 517–524 (8 pages)..
  • Dai, S., Niu, D., & Han, Y. (2018). Forecasting of energy-related CO2 emissions in China based on GM (1, 1) and least squares support vector machine optimized by modified shuffled frog leaping algorithm for sustainability. Sustainability, 10(4), 958 (17 pages).
  • Ye, F., Xie, X., Zhang, L., & Hu, X. (2018), “An improved grey model and scenario analysis for carbon intensity forecasting in the Pearl River delta region of china”, Energies, 11(1), 91 (17 pages).
  • Zhang, Z. and Li J. (2020), “Big Data mining For Climate Change”, Elsevier (344 pages).
  • Lakshmanan, V., Gilleland E., McGovern A., Tingley M. (2015), Machine Learning and Data Mining Approaches to Climate Science, Proceedings of the 4th International Workshop on Climate Informatics. Springer, (243 pages).
  • Brownlee J. (2016), "Machine Learning Mastery With Weka Analyze Data, Develop Models and Work Through Projects", 2016 Jason Brownlee (248 pages).
  • Abuzir Y., Abuzir S. (2020), Data Mining Techniques for Prediction of Concrete Compressive Strength (CCS), Palestinian Journal of Technology and Applied Sciences (PJTAS), No 3 (16 pages).
  • Agarwal S., Boulle B., Briand Y. et al. (2017), Brown to Green: The G20 Transıtıon to A Low-Carbon Economy, Italy Country Facts 2017, Climate Transparency 2017 (36 pages). http://www.climate-transparency.org/ g20-climate-performance/g20report2017
  • Brett S. (2015), How Clean is Your Country?, Italy: Environmental Issues, Policies and Clean Technology (azocleantech.com), Jun 11 2015 (4 pages). https://www.azocleantech.com/article.aspx?ArticleID=536
  • Montini, M., Italian Policies and Measures to Respond to Climate Change (February 2000). SSRN Electronic Journal (17 pages). Available at SSRN: https://ssrn.com/abstract=235084
  • Watts J., and Kommenda N. (2020), Coronavirus pandemic leading to huge drop in air pollution, The Guardian, march 2020 (4 pages). https://www.theguardian.com/environment/2020/mar/23/coronavirus-pandemic-leading-to-huge-drop-in-air-pollution.
  • Massetti E., Simona P., Zanoni D. (2007). National through to local climate policy in Italy. Environmental Sciences, 4(3), 149–158 (9 pages).
  • Italy’s Actıon Plan On CO2 Emıssıons Reductıon(2012) , Ente Nazionale L'Aviazone Civil (ENAC), June, 2012. (73 pages) Access https://www.icao.int/environmental-protection/Documents/ActionPlan/Italy_AP_En.pdf
  • Rugani B. and Caro D., (2020). Impact of COVID-19 outbreak measures of lockdown on the Italian Carbon Footprint. Sci. Total Environ, 737(), 1 October 2020, 139806 (14 pages).
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Enerji Sistemleri Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Saleh Abuzir 0000-0002-9825-3473

Yousef Abuzir 0000-0002-1220-1411

Yayımlanma Tarihi 29 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 3 Sayı: 1

Kaynak Göster

APA Abuzir, S., & Abuzir, Y. (2021). Data Mining For CO2 Emissions Prediction In Italy. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 3(1), 59-68. https://doi.org/10.46387/bjesr.862179
AMA Abuzir S, Abuzir Y. Data Mining For CO2 Emissions Prediction In Italy. Müh.Bil.ve Araş.Dergisi. Nisan 2021;3(1):59-68. doi:10.46387/bjesr.862179
Chicago Abuzir, Saleh, ve Yousef Abuzir. “Data Mining For CO2 Emissions Prediction In Italy”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 3, sy. 1 (Nisan 2021): 59-68. https://doi.org/10.46387/bjesr.862179.
EndNote Abuzir S, Abuzir Y (01 Nisan 2021) Data Mining For CO2 Emissions Prediction In Italy. Mühendislik Bilimleri ve Araştırmaları Dergisi 3 1 59–68.
IEEE S. Abuzir ve Y. Abuzir, “Data Mining For CO2 Emissions Prediction In Italy”, Müh.Bil.ve Araş.Dergisi, c. 3, sy. 1, ss. 59–68, 2021, doi: 10.46387/bjesr.862179.
ISNAD Abuzir, Saleh - Abuzir, Yousef. “Data Mining For CO2 Emissions Prediction In Italy”. Mühendislik Bilimleri ve Araştırmaları Dergisi 3/1 (Nisan 2021), 59-68. https://doi.org/10.46387/bjesr.862179.
JAMA Abuzir S, Abuzir Y. Data Mining For CO2 Emissions Prediction In Italy. Müh.Bil.ve Araş.Dergisi. 2021;3:59–68.
MLA Abuzir, Saleh ve Yousef Abuzir. “Data Mining For CO2 Emissions Prediction In Italy”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, c. 3, sy. 1, 2021, ss. 59-68, doi:10.46387/bjesr.862179.
Vancouver Abuzir S, Abuzir Y. Data Mining For CO2 Emissions Prediction In Italy. Müh.Bil.ve Araş.Dergisi. 2021;3(1):59-68.