“It is to the interest of the public, and to the majority of riparian proprietors, to protect the purity of our rivers, whereas it is generally to the convenience of sanitary authorities and of manufacturers to pollute them”. “A Treatise on the Law Relating to the Pollution & Obstruction of Watercourses: together with a Brief Summary of the Various Sources of River Pollution”. Preface. Clement Higgins, Stevens and Haynes, London 1877
Water quality trading is a flexible approach that provides a polluting entity the choice of installing onsite technology or implementing onsite pollution reduction practices, or working with other sources offsite to generate equal or greater pollutant reductions. Source: Willamette Partnership
Water quality trading is a flexible approach that provides a polluting entity the choice of installing onsite technology or implementing onsite pollution reduction practices, or working with other sources offsite to generate equal or greater pollutant reductions. Source: Willamette Partnership
The general name for trading water pollution credits in the US is “Water Quality Trading” (WQT). The market allocation of rights per se to water resources operates in Australia. Practically and theoretically, the water quality trading is most developed in the USA. The regulatory framework is the 2003 WQT Policy (EPA’s 2003 WQT Policy) and the 2007 Toolkit (2007 WQT Toolkit for Permit Writers), providing guidance for creating market systems for compliance with the Clean Water Act, discharge limits, based on water quality based effluent limitation (WQBEL) using the National Pollutant Discharge Elimination System (NPDES).
Basic principles virtually coincide with public blockchain inherent principles: transparent, real (measurable and verified), accountable (transaction tracking and reporting), defensible (use of dynamic models for monitoring water quality, certification of generated credits, development of scientifically based trading ratios, i.e. ratio of the number of credits to the number of compensated pollution, for example 4: 1), enforceable (responsibility for non-compliance with the established limits).
Sources of discharges are divided into “point” (for example, industrial enterprises, utilities) and “nonpoint” (for example, farmland, livestock,etc.).
The simplest form of trading is between point sources.
If the objects of the program are both, then the direct trade of credits between point and nonpoint sources, and the exchange platform would be effective.
The basis for establishing the scope for the trading system is the integrity of the water environment with respect to various pollutants.
Pollutants suitable for inclusion in the trading system include those that cumulatively affect water quality in relatively large areas, in contrast to specific pollutants that are acute in relatively small areas of distribution.
Pollutants suitable for trading are divided into non-conventional: nutrients, biogenic, nitrogen-phosphorus-containing pollution (agricultural discharges, utilities); and conventional: sediment loads, thermal loads (industrial discharges).
Cross-trade in offset reductions (that is, reductions of various pollutants) is possible in the case of a calculated (quantified) and approximately equal impact of the loads mass of the pollutants.
The basis of the trading system is the limits, permissions set by the regulator for pollution sources. A source incapable of staying within the limits is a buyer of credits.
The process of generating credit credits almost completely coincides with the generation of carbon credits: from project documentation to determination (establishing a baseline level of discharges), establishing the total number of project units, the scope and duration of the project, to verification by an independent organization and a monitoring and reporting system.
All these fundamental features of the architectonics of the discharge market can best be implemented in public blockchain using the already working DAO IPCI modules. The use of IoT devices (sensors, meters, drones) significantly enhances the autonomy and efficiency of the system. Thus, water drones can perform the task of monitoring for conventional and non-conventional pollution to develop dynamic models and comparing monitoring results with water quality targets and results of mitigation projects.
The development and testing of this model began on the DAO IPCI platform in cooperation with Airalab.