CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

An Approach for Predicting CO2 Emissions using Data Mining Techniques

by Douglas Kunda, Hazael Phiri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 3
Year of Publication: 2017
Authors: Douglas Kunda, Hazael Phiri
10.5120/ijca2017915098

Douglas Kunda, Hazael Phiri . An Approach for Predicting CO2 Emissions using Data Mining Techniques. International Journal of Computer Applications. 172, 3 ( Aug 2017), 7-10. DOI=10.5120/ijca2017915098

@article{ 10.5120/ijca2017915098,
author = { Douglas Kunda, Hazael Phiri },
title = { An Approach for Predicting CO2 Emissions using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 3 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number3/28229-2017915098/ },
doi = { 10.5120/ijca2017915098 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:19.429376+05:30
%A Douglas Kunda
%A Hazael Phiri
%T An Approach for Predicting CO2 Emissions using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 3
%P 7-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The effects of climate change such as drought, storms and extreme weather are affecting the earth. Greenhouse gases are the main cause of climate change and carbon dioxide (CO2) takes the larger proportion of the total greenhouse gases emitted. CO2 is projected to continuously increase based on simulation and data mining tools relying on historical data. Globally, 80% of CO2 emission is from combustion of fossil fuel mainly in manufacturing industry or transportation industry. Governments in both developed and developing countries have formulated policies to manage CO2 emission by orienting them towards consumers or manufacturers. In Zambia, CO2 emission control has mostly focused on transportation industry where carbon emission tax is charged on motor vehicles depending on combustion capacity. Transportation is an industry that has had a high increase of total CO2 emission currently standing at 35% but with average of 31.7 % from 1971 to 2014 in Zambia. The manufacturing sector, though being the highest emitter of CO2 has seen no policies formulated to regulate emissions. Predicated emission values for both transportation and manufacturing sector show a continuous domination of these two sectors regarding carbon emission. The task is then left with policy makers to introduce policies that will regulate emissions as the current carbon tax policy does not seem to be effective in reducing emissions. This paper brings out the trends in CO2 emission from fossil fuels in Zambia from 1964 to 2016. The paper also highlights the main industries contributing to CO2 emission, the policies implemented to control CO2 emission levels globally and in Zambia, provides a forecast for CO2 emission levels till 2021 and suggests future research directions based on the findings.

References
  1. Nhekairo, W. M. "The Taxation System in Zambia, Jesuit Centre for Theological Reflections, Final Report".
  2. Han, J. 1997. "OLAP Mining: An Integration of OLAP with Data Mining,".
  3. Kriegel, H.-P. and Borgwardt, K. M. 2007. "Future Trends in Data Mining," Data Mining Knowledge Discovery, Springer.
  4. Kargupta, H., Gama, J,. and Fan, W. 2010. "The Next Generation of Transportation Systems,," ACM.
  5. Vance, C., and Mehlin, M. 2009. "Tax Policy and CO2 Emissions – An Econometric," Ruhr economic paper.
  6. Faghmous, J. H., and Kumar, V. 2014. "A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science," Mary Ann Liberty, Inc,, vol. 3, p. 2.
  7. Chen, V., Han, J., and Yu, P. S. 1996. IEEE, Trandactions on Knowledge and data Engineering,, vol. 8, no. 8,.
  8. Savage, L. 2006. "An Oveview of Climate Change and Possible Consequences for Gisbone District”.
  9. Stringer, L. C., Dougill, A. J., Mkwambisi, D. D., Dyer, J. C., and Kalaba, F. K. 2012. "Challenges and opportunities for carbon management in," Carbon Management, pp. 159-173.
  10. Rogan, F., Dennehy, D., Daly, H., Howley, M., and Gallachóir, B. P. Ó. 2011. "Impacts of an emission based private car taxation policy – First year," Transportation Research Part A, Elsevier.
  11. Morik, K., Bhaduri, K and Kargupta, H. 2012. "Introduction to data mining for sustainability," Data Mining and Knowledge Discovery, Springer,.
  12. Huo, H., Wang, M., Johnson, L., and He, D. "Projection of Chinese Motor Vehicle".
  13. Fu, Y. 1998. "Data Mining: Tasks, Techniques and Applications”.
  14. Ganguly, A. R. and Steinhaeuser, K. 2008. "Data Mining for Climate Change and Impacts," in IEEE International Conference on Data Mining Workshops.
  15. Schmalensee, R., Stoker, T. M., and Judson, R. A. 1997. "World Carbon Dioxide Emissions 1950-2050”.
  16. Kriegel, H.-P and Borgwardt, K. M. 2007. "Future Trends in Data Mining," Data Mining Knowledge Discovery, Springer.
  17. Witten, I. H., Eibe, F., Hall, M., Holmes, G., Kirkby, R and Pfahringe, B. "WEKA A Machine Learning Workbench for Data Mining," Hamilton.
  18. Witten, I. H., Eibe, F., Hall, M., Holmes, G., Reutemann, P and B. Pfahringe, "The WEKA Data Mining Software: An Update," SIGKDD Explorations, vol.11, no. 2
  19. Munir, A. Q and Winarko, E. 2015. "Classification Model Disease Risk Areas Endemicity Dengue Fever Outbreak based Prediction of Patients, Death, IR and CFR using Forecasting Techniques," International Journal of Computer Applications, vol. 114, no. 2, pp. 20-25.
Index Terms

Computer Science
Information Sciences

Keywords

Climate Policy Emission Data Mining WEKA SMOreg Carbon Tax