Concerns about the potential effects of tidal turbines and wave energy devices on the marine environment continue to slow siting and consenting/permitting (hereafter “consenting”) of single devices and arrays worldwide. While research studies and early results from post-installation monitoring over the past decade have informed interactions between marine renewable energy (MRE) devices, marine animals, and habitats, regulators still demonstrate considerable reluctance to accelerate the consenting process for devices and arrays. Furthermore, the MRE industry is struggling with the high costs of baseline assessments and post-installation monitoring, as well as long timelines for obtaining licenses, which leads to uncertainty and risk related to project financing. Regulators require assessment and monitoring information to allow them to carry out the necessary analyses to describe, consent, and manage the environmental risks associated with new MRE technologies and new uses of ocean space. One way to reduce risks to the industry and the environment and to allow for acceleration of the consenting process could be to transfer research, analyses, and datasets from one country to another, among projects, and across jurisdictional boundaries. However, data are collected and analyzed around early-stage MRE devices using many different measures, instruments, and methods. If similar parameters and accessible methods of data collection were used for baseline assessments and post-installation monitoring around all early-stage devices and MRE developments, the results would be more readily comparable. This comparability would lead to a decrease in scientific uncertainty and support a common understanding of the risk of MRE devices to the marine environment. This in turn would facilitate more efficient and shorter consenting processes, which would decrease the financial risk for MRE project development.
As a means of addressing the concept of transferring data (information, learning, analyses, and datasets) among projects and collecting data consistently, OES-Environmental (formerly Annex IV) has developed a data transferability process that has been socialized with the MRE community including regulators, industry, developers, consultants, and researchers. The data transferability process consists of four components:
- The data transferability framework brings together datasets in an organized fashion, compares the applicability of each dataset for use on other projects, and guides the process of data transfer.
- The data collection consistency table provides preferred measurement methods or processes, reporting units, and the most common methods of analysis or interpretation and use of data.
- The monitoring datasets discoverability matrix allows a practitioner to discover datasets based on the approach presented in the framework.
- The best management practices (BMPs) include four BMPs related to data transferability and collection consistency.
This report documents the background and development of the data transferability process and associated components and summarizes the next steps needed to successfully implement and apply the data transferability process. The successful implementation of the data transferability process within the MRE community will accomplish the following:
- Ensure that regulators have access to datasets and processes for transferring data from already consented projects to future projects.
- Assist regulators in understanding the applicability of these processes through an active outreach and engagement process.
- Provide technical assistance to help regulators implement the data transferability process using OES-Environmental and Tethys to facilitate the exchange of relevant data and information.
- Ensure developers and their consultants are active participants in OES-Environmental’s outreach and engagement efforts to ensure their familiarity with and acceptance of the data transferability process.
- Provide added value to the data transferability process through engagement activities and the consistent collection of data around MRE devices.
- Support the risk retirement process.