An empirical analysis of the relationship among price, demand and CO2 emissions in the Spanish electricity market

Abstract

CO2 emissions play a crucial role in international politics. Countries enter into agreements to reduce the amount of pollution emitted into the atmosphere. Energy generation is one of the main contributors to pollution and is generally considered the main cause of climate change. Despite the interest in reducing CO2 emissions, few studies have focused on investigating energy pricing technologies. This article analyzes the technologies used to meet the demand for electricity from 2016 to 2021. The analysis is based on data provided by the Spanish Electricity System regulator, using statistical and clustering techniques. The objective is to establish the relationship between the level of pollution of electricity generation technologies and the hourly price and demand. Overall, the results suggest that there are two distinct periods with respect to the technologies used in the studied years, with a trend toward the use of cleaner technologies and a decrease in power generation using fossil fuels. It is also surprising that in the years 2016 to 2018, the most polluting technologies offered the cheapest prices.

Keywords

• Energy generation
• CO2 emissions
• Clustering
• Machine learning