A big data study on emitting companies' performance in the first two phases of the European Union Emission Trading Scheme

As CO2 emissions are quantified by allowances and traded in markets, wise trading strategies will bring emitting companies higher profits or lower costs. Based on the big data of Community Independent Transaction Log (CITL), this article hereby presents a micro study on the emitting companies' efforts in increasing profits and saving costs during the allowances trading in the first two phases of the European Union Emission Trading Scheme (EU ETS). The efforts are measured by an after-action factor of trading performance, which is built on a series of behaviour and monetary variables. By comparison, demanders of the emitting companies are more inclined to reach a higher trading performance, while that inclination is heterogeneous among the suppliers. In addition, emitting companies with lower emission levels had a better trading performance. With a higher proportion of low-emitting companies, the manufacturing sector had a better trading performance than the energy sector. The effect of the trading requirement on trading performance are investigated via a quantile regression mode. Results suggest that: (1) the selling requirement of suppliers has a positive effect on their trading performance, while the effect becomes weaker when the selling requirement increases; (2) the buying requirement has a positive effect on the demanders' trading performance only when the requirement is high, and the effect becomes stronger as the requirement increases; and (3) when the buying requirement is at a lower level, demanders' trading performance becomes worse as the requirement grows. The conclusion is that the emission level, industrial sector and trading requirement do have influences on the trading performance of emitting companies in emission trading.