Research

This paper investigates the effectiveness of emission trading systems and emission taxes on firms’ investments in green technologies. A numerical simulation of a model with heterogeneous firms producing commodities causing greenhouse gas emissions is used to study the policies’ impact. The model considers firms paying taxes or purchasing certificates to incentivize investment in abatement technologies. The paper focuses firms’ investment decisions under different market settings which are trained via Q-learning (reinforcement learning) algorithms. The results suggest that firms strategically delay investments, in particular if such investments cause positive spillover effects for competitors who imitate investment decisions at a later time step. Delayed investments might be particularly prominent if emission taxes are applied. Furthermore, I compare these results to markets where firms formulate their investment decisions on rather simple heuristics rather than more sophisticated algorithms. 

Keywords: Abatement, Carbon Tax, Climate Policy, Emission Trading, Green investments, Q-learning
JEL codes: Q58, Q52, H23, O33

Draft available on request

Many countries have introduced leniency programs into their Antitrust law to prohibit illegal cartelization. In our paper, we investigate two common approaches for leniency programs: A non-discriminatory leniency program and ringleader discrimination, where the main instigator (Ringleader) is excluded from leniency. We find those discriminating policies being less effective to incentivize whistle-blowing but at the same time more effective to prohibit the formation of cartels. Furthermore, we show that whether one or the other policy is more effective in total depends on the market concentration but can be adjusted for other market factors. We test our theoretical results in a laboratory experiment. We expect non-discriminatory leniency programs to be more efficient in small markets (three firms), while ringleader discrimination policies are more effective in large markets (four firms).

Keywords: antitrust, collusion, leniency programs, ringleader discrimination
JEL codes: C92, K21, L41

Draft available on request

Work in progress