Relations between fossil fuel returns and climate change variables using canonical correlation analysis

ABSTRACT

This study identifies the relations between fossil fuel (FF) prices and climate change (CC) by canonical correlation (C_j) analysis of the layer data. The variable set Y includes the oil, natural gas, and coal price returns, whereas variable set (X) comprises green energy index, carbon dioxide (CO₂), temperature, and precipitation. This article discusses the application and principle of C_j analysis in the context of the study variables. In particular, the paper examines the relationship between the CC-related variables, or the “greening of the world” and their influence on the pricings of oil, gas, and coal during the same periods. The significant relations (p < 0.01) were the C_j between variable sets X and Y and one set of canonical variates. FFs return contributed most heavily to the relationship, whereas the CC variables played a lesser role.

KEYWORDS:

• Climate change
• canonical correlation
• fossil fuel
• loading
• redundancy

Notes

¹ Covers geothermal, wind, and solar energies, biodiesels, biofuels, ethanol-enabling enzymes, and solar photovoltaics

² Includes automated meter reading, smart grids, superconductors, conservators, power controls, and energy management systems

³ Includes silicon-based materials and substitutes, nanotechnology, bioplastics, and advanced membranes that enable clean energy implementations and/or decrease the requirements for petroleum-based materials

⁴ Covers hybrid drivetrains, advanced batteries, fuel cells for portable, stationary, and transport applications, and hydrogen