Fixing long-term price paths for fossil energy - The optimal incentive for limiting global warming

Autor: Schulmeister, Stephan
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Popis: Neither a gradually rising carbon tax nor emission trading schemes can ensure that the costs of emitting greenhouse gases, in particular CO2, will steadily rise faster than the general price level. If, e.g., global fossil energy prices decline faster than a carbon tax or the emission permit price rises, then the final good and its use become cheaper. Since the prices of fossil energy as well as CO2 emission permit prices belong to the most unstable prices in the global economy, carbon taxes and trading schemes cannot anchor the long-term expectation that the effective emission costs for firms and households will rise continuously. Such an expectation, however, is a prerequisite for steadily growing investment in energy efficiency and/or renewable energy because the profits from such investments consist of the saved fossil energy costs ("opportunity profits"). This paper presents an alternative approach: the EU sets a path of steadily rising prices of crude oil, coal and natural gas by skimming off the difference between the EU target price and the respective world market price through a monthly adjusted quantity tax. Instead of the prices of fossil raw materials, the (implicit) quantity tax should fluctuate. In this way, the uncertainty about future price developments of crude oil, coal and natural gas and, hence, of the effective emission costs would be eliminated. Firms and households could calculate the profitability of investments in avoiding carbon emissions. At the same time, such a tax would ensure a uniform European carbon price in all sectors, provided the initial level of the price paths of crude oil, coal and natural gas account for the different CO2 intensities of these types of fossil energy. Given the size of the EU import bill for fossil energy, the amount of potential receipts of such an implicit and flexible CO2 tax would be (very) huge.
Databáze: OpenAIRE