The design of incentives for generating and giving access to data is essential for the availability of good quality data. This is particularly true for data on the environmental aspects of investments, corporate operations and products, for example to support alignment of finance with net-zero goals. It is also a key factor in the successful development of the EU Green Deal Dataspace and digital product passports that can help shift the economy towards sustainability.
As part of the Environmental Diplomacy in G7/G20 project, Life Itself Labs was commissioned to launch discussions on how to best design the incentives for increasing access to this environment-related data.
We engaged stakeholders from a number of sectors on themes of:
- existing blocks to giving access to data;
- their needs from incentive models; and
- their views on the suitability of different incentive models.
Our work saw us provide expert inputs to workshops with relevant groups of members of the World Business Council for Sustainable Development (WBCSD), the Consumer Goods Forum (CGF) and key figures within the product life cycle analysis (LCA) community. We undertook further exploratory conversations with members of the Global Battery Alliance (GBA). Stakeholders in each case were selected based on their status as leading figures in sectoral efforts around data sharing for environmental action. More detail on the specific groups can be found below.
This summary provides strategic insights from those workshops and our own expertise in incentive design for data access. It aims to inform decisions on the value and direction of the development of incentive models for these goals.
2. The 5 most interesting practical outcomes
1. Improved sharing of environment-related data is an essential part of achieving climate and environment related goals
- The quality of decisions to align investments, corporate operations and product choice with net-zero and environmental sustainability goals, depends on the availability and quality of the data used in those decisions.
- The most serious environmental impacts often take place in value chains, or at the end-of-life of products. Good quality data therefore needs to be accessed from actors all along the value chains of economic activities, however long and complex these are.
- As a starting point to improve the access to this data, various co-operative international initiatives have been formed to develop the systems (or ‘data infrastructure’) which can facilitate access to the required data.
2. Initiatives on environmental data access have a gap relating to incentive structures
- Current initiatives focus the majority of development work on the technological solutions and content standards to facilitate data access, with relatively little (or no) exploration of the incentive structures which would optimise data generation and access.
- This approach is analogous to constructing roads and specifying what is to be transported along them, without designing the rules and rewards which encourage people to transport the goods, and remove risks which might dissuade them.
3. There is an existing body of expertise and practice and research in the optimal design of incentives for data generation and access
- There are a range of important considerations, alternative possible models and sets of past-experience, which can inform future design if incentives for environment-related data.
- Key considerations are summarised in Section 5 below, and in more detail in Section 7 of the full report.
4. There is demand for practical ways forward, to engage existing expertise in the bottom-up design of incentives for environment data access
- This project engaged different stakeholder initiatives around simple examples of alternative incentive models. This allowed us to explore how to practically move forward to design appropriate incentives structures. The outcomes of this engagement were as follows:
- Stakeholders in existing data access initiatives understood the importance of designing incentive systems for data access, and are looking to engage further.
- They requested further, deeper engagement processes, with support from technical expertise - as they do not have the expert knowledge, experience or resources to move forward alone.
5. Small, parallel design pilots with a range of initiatives would be the most practical way forward
- As the expressed needs of the different initiatives are relatively similar, a process of deeper stakeholder engagement on incentive structures could be efficiently replicated with appropriate adjustment, for a selection of the most relevant stakeholder initiatives.
- Based on past process experience for data access design, this would most usefully be based on 3 steps:
- Engagement of representatives from all along supply chains, to the needs of all the diverse providers or users of data can be discovered.
- Hosting of a participatory design process to exchange and reconcile this wide set of user needs.
- Piloting of prototypes and iteration to test designs.
- By running parallel processes with different initiatives, information can be gathered on which incentive structures an integrated/interoperable system for access to data across multiple value-chains/sectors would be appropriate - one which works for the range of actors who need to provide access to data for investor, corporate or policy needs.
3. Ten points to guide incentive model design
There were 10 particularly important points expressed in our discussions with stakeholders, which can guide future incentive design processes in this area:
- The generation and access of high-quality - granular, specific, timely, accurate, reliable - data, in the forms needed to be accessed by automated processes will involve a wide range of different actors along international value chains - including multinational companies, small farmers in developing countries, consumers and civil society and research organisations.
- There will be a key role for organisations which check the quality, relevance and accuracy of data, and prepare, maintain and amalgamate it into data products which are easily understandable for users.
- The value of the data for users depends on its quality, as much as its availability.
- Generating and sharing data, and especially high-quality data, has costs. As does the curation, verification, quality control, and updating of data sets for user access.
- Many stakeholders expressed current disincentives to give access to data - fears of harming their competitive position, or breaching legal compliance of data rules.
- As a result, although there exists a huge amount of relevant data held in companies, public bodies or available through remote monitoring, this data is not accessible. Problems with data standards, searchability and interoperability also play a role.
- The success of creating access to quality data, with high value for investors and decision-makers therefore depends on: *Providing adequate positive incentives for generation and access to data *Removing, or compensating for, disincentives to give access to data
- There are several potential models for incentive design. These can be adapted or combined to optimise incentives for environment-related data in different situations. The models, described in more depth in Section 7.1 of the main report, are:
- User-specific licensing - custom access licences negotiated on a case by case basis, creating incentives through fees
- Subscription based preemptive licensing - paid subscriptions which carry predetermined access and use conditions, creating incentives through fees
- Mandating/Regulation - incentivising data sharing by making withholding costly, either through regulation or making data sharing a condition of trade
- Public grants and subsidies - upfront unconditional payments which incentivise data sharing by “covering” the the associated costs
- Prizes- data sharers are incentivised by financial remuneration for achieving some specific outcome, such as devising improved estimates of the environmental stage of a given area
- Remuneration rights - data sharers are incentivised by gaining entitled to a share of a central funding pool, which is allocated based on a set of outcome criteria
- Data commons models - data sharers contribute to a central pool of data which functions as a public good, and without receiving “hard” value in return
- Complementary goods/services models - data sharers are incentivised to voluntarily share data as this enables them to sell goods or services which are complementary to the data
- There are some important complicating factors to take into account in optimising data incentives design, so that rewards are able to sufficiently incentivise all the actors (e.g. all the way along value chains) to deliver adequate quality of data. These include:
- Designing incentive systems where data is re-used (e.g. where data first provided by one actor is used combined with other data to create additional value in a data product.)
- Providing adequate incentives for accurate data provision where data is unfavourable to the data-generator (e.g. where it points to below-average environmental performance)
- Avoiding market power (e.g in the reuse of data) reducing incentives for data generation and access, or use, in the wider chain of data providers or users
- Successful incentive design needs to be bottom-up, based on participation of future data providers and users, informed by past-practices and taking into account the future overarching goal.
A summary of the user group specific learnings is in Section 2 of the full report.
4. Additional key considerations
- The right model, or combination of models, must be selected to suit user group needs. A list of models can be found in Section 3 above. These are explored in more detail in the full report.
- Effective models must address the differing incentives across actors. At the most basic level these actors can be divided into data generators, data curators and data users. Each of these roles will have differing incentives around data sharing. Dealing with differing roles in the ecosystem using one model, or integrated combination of models, will be key to ensure value is realised for all.
- Effective models must be able to deal with reuse and the flow of value. Potential options include:
- Injunctive relief - data cannot be reused without consent, and custom agreements govern reuse
- Fixed share- data generators are entitled to a predetermined, fixed share of revenue from data reuse
- Value based revenue share - revenue shares are calculated based on the value added through reuse. This can be automated, and made subject to dispute as required
- Model design must address value measurement and create a mechanism for ensuring data quality. Data quality can either be independently certified as a condition of participation in the incentive system, akin to food safety certification, or factored into value calculations. Options for value measurement include:
- Expert judgement - the opinions of field experts determine value based on set criteria likely including quality
- Revealed preferences - value and quality are assumed to be tracked by demand.
- Multivariate value functions - value is calculated based on a weighted set of factors, which can include quality
- Custom value proxies - context specific measures such as willingness to pay based on stakeholder surveys. These can be incorporated into multivariate functions
- How revenue will be raised should be considered at the outset of the design process. At the most general level revenue can be raised from public bodies, data users or private philanthropic contributions. It is likely that an effective model will draw on a combination of revenue raising mechanisms
5. Summary recommendations
- Given the expressed needs and potentials, we recommend the best way forward is to run a small number of parallel pilot programmes to trial incentive model design across different user groups.
- Appropriate stakeholder groups/use cases include:
- Consumer goods
- Plastics over their life cycle
- Products relating to forests/deforestation
- Battery Passports
- We recommend a three phase approach to the pilot programmes:
- Phase 1: Cross- supply chain stakeholder engagement and needs analysis
- Phase 2: Participatory design process
- Phase 3: Piloting and iteration