Emission discharge permits with regulatory tiering: Numerical simulations with a computer-assisted smart market

Abstract

Permit trading for a cap and trade policy where the property right traded is an emission discharge permit (EDP) and with ‘hot spots’ can be addressed in a cost-effective manner in a framework combining regulatory tiering and a ‘computer-assisted’ smart market model. A smart market model is an optimisation-based framework used to compute permit trading solutions and the actual calculation of permit trades is based on a linear programming model. The model objective function is defined as the aggregate net benefit function for the market traders subject to a well-defined constraint set. The key characteristic of the smart market model is that all EDP trades are with a common pool and no bilateral trades are allowed to occur. The ‘hot spot’ problem is addressed by adding a set of regional pollutant constraints to the smart market model constraint set. In the paper, we design and implement a smart market model with regulatory tiering. The property right traded is an EDP and the regulatory tiering component is introduced into the smart market model constraint set as a set of regional pollutant constraints. Using a linear programming model, a set of experiments are extracted from numerical simulations to demonstrate the smart market usefulness for dealing with the specific environmental problems from the policy perspective.

Keywords:

• environmental pollution polices
• air pollution
• permit trading
• regulatory tiering
• smart market models

JEL codes:

• Q50
• Q53
• Q58

Acknowledgements

This paper was prepared with the support of the research project ‘Increasing the quality of doctoral studies and the support of the international research at the FNE, University of Economics in Bratislava’, OP Education, ITMS 26140230005, activity 1.1, Modern education for knowledge society; project is co-financed by the European Union.

Notes

1. Our analytical results in this research are easily generalised to include multiple pollutants with multiple receptor locations.

2. The data used to construct the individual firm inverse EDP demand functions shown in Table 1 are taken from Ando and Ramirez-Harrington (2006). We have chosen to use these formulations, since they have suitable mathematical properties for our exercise.