Analysis of the Robustness of Canada Economy and Energy Supply/Demand Fluctuations
Abstract
It is well-known that energy has an important role in social and
economic improvements. Understanding the relationships between
energy-related issues and the economic growth is crucial for the devel-
opment of reliable and appropriate energy policies and for handling
the possible economic local or regional impacts. Considering Canada
as a case study, this article investigates the relationships among gross
domestic product (GDP), energy consumption, energy consumption
in the industry, and the elasticity of oil prices. Results showed that
the GDP and energy consumption (total, industrial) are inelastic with
respect to the oil price and GDP, respectively. Moreover, Extra Trees
approach is utilized for modeling the primary energy consumption
and CO2 emissions. It was found that the proposed tree-based models
provide excellent predictions.
Downloads
References
U.S. Energy Information Administration (2013). International energy outlook
Washington, D.C.
BP (2016). Statistical review of world energy.
Shift Project Data Portal. Countries with highest primary energy production.
http://www.tsp-data-portal.org/TOP-20-Producer#tspQvChart, accessed 21
November 2017.
Government of Canada. Natural resources Canada. http://www.nrcan.gc.ca/
energy/oil-sands/18086, accessed 21 November 2017.
Government of Canada. Natural resources Canada. http://www.nrcan.gc.ca/
earth-sciences/geography/atlas-canada/selected-thematic-maps/16872, ac-
cessed 21 November 2017.
International Energy Agency (2016). Energy policies of IEA countries, Canada,
Review.
Natural Resources Canada (2006, January). “Legal and policy frameworks—
Canada.” North America: The Energy Picture, accessed 16 August 2008.
Natural Research Council Canada (2015). National energy code of Canada for
buildings. http://www.nrc-cnrc.gc.ca/eng/publications/codes_centre/2015_
national_energy_code_buildings.html, accessed 21 November 2017.
Gardner, D. (1993). Industrial energy use in Ontario from 1962 to 1984. Energy
Economics, pages 25–32.
Nanduri, M., Nyboer, J. and Jaccard, M. (2002). Aggregating physical intensity
indicators—results of applying the composite indicator approach to the Cana-
dian industrial sector. Energy Policy, pages 151–163.
Palmer, J. (2003). Decomposition of changes in energy use: the comparison of
two approaches from a Canadian perspective. Proceedings of the 2003 ACEEE
Summer Study on Energy Efficiency in Industry, 6, pages 150–158.
Steenhoff, P., Weber, Chris, J. (2011). An assessment of factors impacting Cana-
da’s electricity sector’s GHG emissions. Energy Policy, 39, 4,089–4,095.
Ang, B. and Xu, X. (2013). “Tracking industrial energy efficiency trends using
index decomposition analysis.” Energy Economics, 40, pages 1,014–1,021.
Enrica, D., Schymura, M., Verdolini, E. and Voigt, S. (2013). Energy intensity
developments in 40 major economies: structural change or technology improve-
ment? Paper No. 13-052. Centre for European Economic Research.
Siang, W., Chang, Y. and Chia, W. (2013). “Energy consumption, energy R&D
Strategic Planning for Energy and the Environment
and real GDP in OECD countries with and without oil reserves.” Energy Eco-
nomics, 40, pages 51–60.
Al-mulali, U. (2014) “Investigating the impact of nuclear energy consumption
on GDP growth and CO2 emission: a panel data analysis.” Progress in Nuclear
Energy, 73, pages 172-178.
Rodríguez-Caballero, C. and Ventosa-Santaulària, D. (2016). “Energy-growth
long-term relationship under structural breaks. Evidence from Canada, 17 Latin
American economies and the U.S.” Energy Economics, 61, pages 121–134.
Torrie, R., Stone, C. and Layzell, D. (2016). Understanding energy systems
change in Canada: 1. Decomposition of total energy intensity. Energy Economics,
, pages 101–106.
Fremeth, A., Holburn, G., Ventatesh, P. and Wilkie, A. (2014). “Developing
global market access for Canada’s oil and gas industry.” Ivey Energy Policy
and Management Centre.
World Bank. http://data.worldbank.org/indicator/NY.GDP.MKTP.
CD?locations=CA, accessed 21 November 2017.
International Energy Agency. Statistics. http://www.iea.org/countries/mem-
bercountries/canada/statistics, accessed 21 November 2017.
Geurts, P., Ernst, D. and Wehenkel, L. (2006). “Extremely randomized trees.”
Machine Learning, 63, pages 3-43.
Cholwich N., Nirattaya K., Luckhana L. and Pisanu, T. (2017). “A novel predic-
tion approach for antimalarial activities of Trimethoprim, Pyrimethamine, and
Cycloguanil analogues using extremely randomized trees.” Journal of Molecular
Graphics and Modelling, 71, pages 13-27
https://aeeeuropeenergy.com/
