Modelling latent carbon emission prices: Theory and practice
Climate change and global warming are significantly affected by carbon emissions that arise from the burning of fossil fuels, specifically coal, oil and gas. Accurate prices are essential for purposes of measuring, capturing, storing and trading in carbon emissions at the regional, national, and international levels, especially as carbon emissions can be taxed appropriately when the price is known and widely accepted. The paper uses a novel KLEM production function approach to calculate the latent carbon emission prices, where carbon emission is the output and capital (K), labour (L), energy (E) (or electricity), and materials (M), are the inputs into the production process. The variables K, L and M are essentially fixed on a daily or monthly basis, whereas E can be changed more frequently, such as daily or monthly, so that changes in carbon emissions depend on changes in E. If prices are assumed to depend on average cost pricing, the prices of carbon emissions and energy may be approximated by an energy production model with a constant factor of proportionality, so that carbon emission prices will be a function of energy prices. Using this novel modelling approach, the paper estimates carbon emission prices for Japan using seasonally adjusted and unadjusted monthly data on the volumes of carbon emissions and energy, as well as energy prices, from December 2008 to April 2018. The econometric models show that, as sources of electricity, the logarithms of coal and oil, though not LNG, are statistically significant in explaining the logarithm of carbon emissions, with oil being more significant than coal.
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|23rd International Congress on Modelling and Simulation - Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019|
|Organisation||Department of Econometrics|
Chang, C-L, & McAleer, M.J. (2019). Modelling latent carbon emission prices: Theory and practice. In 23rd International Congress on Modelling and Simulation - Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019 (pp. 547–553). Retrieved from http://hdl.handle.net/1765/127929