Putting a price on water quality

One way to clean-up water pollution is to award `credits` to facilities that reduce their emissions. These credits can then be traded with other facilities to compensate for pollution outputs. This controls overall pollutions levels, while creating an economic incentive to reduce emissions. Research using models that simulate water quality has been used to establish the value of these credits and analyse how the price of credits might change under various conditions, such as the seasonal changes in a river’s ability to deal with pollutants.

Water pollution arises from fertilisers, industrial chemicals, animal wastes, human sewage and pesticides. Population growth and economic development place increasing demands on the environment and make it necessary to develop programmes to maintain water quality standards. The total maximum daily load (TMDL) is one approach used to control water pollution. This approach takes into account the entire watershed, the region from which water drains into streams, rivers, lakes, and reservoirs and is often referred to as a drainage basin. TMDL accounts for `point source` (PS) pollution, such as sewage pipes, as well as more difficult to measure `non-point source` (NPS) pollution such as pollution caused by soil erosion and pesticide runoff. The TMDL is the sum of PS, NPS and background pollution. It also includes an added margin of safety.

Under TMDL programmes, facilities are given discharge permits, which regulate the amount of pollution that ends up in water. It is then possible to organise trading schemes where facilities are awarded credits for reducing pollution below their permitted allowance. These credits can be traded with other facilities. This helps prevent pollution levels from rising and creates an economic incentive to reduce pollution.

Potential trading schemes between point source polluters were evaluated using the Kao-Ping river basin in Taiwan as a case-study. Values, or `shadow prices`, were assigned using the QUAL2E1 water quality simulation model. This model incorporates two water quality indicators, biological oxygen demand measurements and ammonia-nitrate levels. The researchers developed a pricing framework for trading schemes and showed that prices for credits should change in response to the river`s changing ability to accommodate pollutants, since factors such as the river`s flow-rate during the wet and dry seasons affect the ability of the river to accommodate pollution.

This study provides a pricing framework for decision-makers that will help in the development of strategies for water quality management. However, trading schemes can be complex to set up with factors such as the number of facilities involved in the scheme and the variety of the pollutants discharged influencing the complexity. Success depends upon achieving a balance between the environmental benefits of improving water quality and administration costs.

In Europe the drive to improve water quality under the EU Water Framework Directive (WFD)2 encourages European citizens to get involved in improving water quality. Following recommendations from the IPCC3, the WFD aims to prevent and control emissions into water. Implementation of trading schemes could contribute to these goals.