Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 339–348 | Cite as

Causal dynamics of CO2 source emissions and population in India using Bayesian approach

Original Article
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Abstract

The present study was undertaken to model causal relationship between several sources contributing to CO2 emission and population growth in India using a Bayesian approach. The data were collected from an International database. The Bayesian model was developed and evaluated using evaluation metrics, and the results were found to be promising. Based on probabilistic learning, it was found that with the increase in population, there is a higher probability that gas fuel and liquid fuel-based emissions will go higher, followed by solid fuel-based emission in order of decreasing probability. The sensitivity analysis confirmed strong influence of population growth on liquid fuel-based emissions. The study reveals the potential of a probabilistic study in identifying the sources that can be given due consideration for the purpose of managing CO2 emissions.

Keywords

Global warming Fossil fuels Bayesian network k-means clustering Incremental association algorithm Sensitivity analysis 

References

  1. Apergis N, Payne JE, Kojo M, Wolde-Rufael Y (2010) On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecol Econ 69:2255–2260CrossRefGoogle Scholar
  2. Boden TA, Marland G, Andres RJ (2011) Global, regional, and national fossil-fuel CO2 emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tenn., USA. http://cdiac.ess-dive.lbl.gov/trends/emis/overview_2008.html. Accessed 01 Jan 2017
  3. Chen SH, Pollino CA (2012) Good practice in Bayesian networks modeling. Environ Modell Softw 37:134–145CrossRefGoogle Scholar
  4. Chontanawat J, Hunt LC, Pierce R (2008) Does Energy consumption cause economic growth? Evidence from systematic study of over 100 countries. J Policy Model 30:209–220CrossRefGoogle Scholar
  5. Cinar D, Kayakutlu G (2010) Scenario analysis using Bayesian network: a case study in energy sector. Knowl Based Syst 23:267–276CrossRefGoogle Scholar
  6. Coondoo D, Dinda S (2002) Causality between income and emissions: a country group-specific econometric analysis. Ecol Econ 40:351–367CrossRefGoogle Scholar
  7. Daly R, Shen Q, Aitken S (2011) Learning Bayesian networks: approaches and issues. Knowl Eng Rev 26:99–157CrossRefGoogle Scholar
  8. Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc (Ser B) 39(1):1–38Google Scholar
  9. Dlamini WM (2011a) Application of Bayesian networks for fire risk mapping using GIS and remote sensing data. GeoJournal 76(3):283–296CrossRefGoogle Scholar
  10. Dlamini WM (2011b) A data mining approach to predictive vegetation mapping using probabilistic graphical models. Ecolo Inf 6(2):111–124CrossRefGoogle Scholar
  11. Dlamini WM (2016) Analysis of deforestation patterns and drivers in Swaziland using efficient Bayesian multivariate classifiers. Model Earth Syst Environ 2:173CrossRefGoogle Scholar
  12. Ganesan K, Vishnu R (2014) Energy Access in India-Today and Tomorrow. CEEW (Council on Energy, Environment & Water) Working paper 2014/10, New DelhiGoogle Scholar
  13. Jensen FV (2001) Bayesian networks and decision graphs. Springer, New YorkCrossRefGoogle Scholar
  14. Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. Wiley, HobokenCrossRefGoogle Scholar
  15. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. The MIT Press, LondonGoogle Scholar
  16. Lee SR, Yoo S-H (2016) Energy consumption, CO2 emissions, and economic growth in Korea: a causality analysis. Energy Source Part B 11(5):412–417CrossRefGoogle Scholar
  17. Menyah K, Wolde-Rufael Y (2010) CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energ Policy 38:2911–2915CrossRefGoogle Scholar
  18. MOSPI (Ministry of Statistics and Programme Implementation) (2015) Statistics related to climate change-India. Social Statistics Division, New Delhi. http://www.mospi.gov.in. Accessed 23 Jan 2017
  19. Mustafa YT, Tolpekin V, Stein A (2011) Application of the EM-algorithm for Bayesian Network modelling to improve forest growth estimates. Procedia Environ Sci 7:74–79CrossRefGoogle Scholar
  20. Myllymaki P, Silander T, Tirri H, Uronen P (2002) B-course: a web-based tool for Bayesian and causal data analysis. Int J Artif Intell Tool 11(3):369–387CrossRefGoogle Scholar
  21. Nadkarni S, Shenoy PP (2001) A Bayesian network approach to making inferences in causal maps. Eur J Oper Res 128:479–498CrossRefGoogle Scholar
  22. Pollino CA, Woodberry O, Nicholson A, Korb K, Hart BT (2007) Parametrization and evaluation of a Bayesian network for use in ecological risk assessment. Environ Modell Softw 22:1140–1152CrossRefGoogle Scholar
  23. Quadrelli R, Peterson S (2007) The energy-climate challenge: recent trends in CO2 emissions from fuel combustion. Energy Policy 35:5938–5952CrossRefGoogle Scholar
  24. Raghuvanshi SP, Chandra A, Raghav AK (2006) Carbon dioxide emissions from coal based power generation in India. Energy Convers Manag 47:427–441CrossRefGoogle Scholar
  25. Salami ES, Ehteshami M (2016) Application of neural networks modeling to environmentally global climate change at San Joaquin Old River Station. Model Earth Syst Environ 2:38CrossRefGoogle Scholar
  26. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modeling. Ecol Modell 203:312–318CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of EngineeringGD Goenka UniversityGurgaonIndia
  2. 2.Department of Civil EngineeringThapar Institute of Engineering and TechnologyPatialaIndia

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