• Data trusts can speed up data sharing by overcoming trust challenges and challenges related to data quality. Through a combination of features, they build trust, improve access, and overcome traditional delays related to bureaucracy and uncertainty. By shortening the distance between data sharing and innovation/use, they can play a critical role in addressing urgent climate-related needs, such as managing extreme heat or mitigating the impact of floods. 
• However, while data trusts hold great potential, they are not a one-size-fits-all solution. Their success hinges on factors such as demand for data, well-defined use cases, and mature legal frameworks, which may vary across contexts and geographies. Moreover, data trusts require upfront investment in terms of time, resources, and expertise to establish and maintain, which may challenge widespread adoption.
• If data is available elsewhere, a data trust may not be relevant. As such, they are not applicable to data that should be widely available as a public good. In the context of climate, they are therefore less relevant to datasets such as historical climate data essential for predictive modeling and data measuring performance against global metrics like the Paris Agreement commitments. Historical climate data, for example, is critical for understanding long-term trends and making accurate climate predictions, and thus should be openly accessible to researchers, policymakers, and the public. 

• Governance structure: A data trust should have a clear governance structure defining roles, responsibilities, and decision-making processes.
• Legal framework: A data trust should operate within a robust legal framework that defines the rights, obligations, and liabilities of stakeholders involved in data sharing and management.
• Ethical guidelines: A data trust should adhere to ethical guidelines that prioritize principles such as transparency, accountability, fairness, and respect for individual privacy and rights.
• Data management policies: A data trust should have policies and procedures for managing and safeguarding data. These include defining data quality standards, security measures, access controls, and retention policies to ensure data integrity, confidentiality, and availability. 
• Technical infrastructure: A data trust should have the technical infrastructure and capabilities to securely manage and share data. (i.e. interoperability requirements, encryption technologies, access controls, and/or analytics tools.)

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• Actors at the frontlines of climate change – namely, local governments and community organizations – require data from across traditional sectors such as water, energy, transportation, and agriculture They need data sharing options which are unencumbered by traditional organizational, national, and sectoral boundaries. 
• Data spaces can play this role in a few ways:
• • With proper governance mechanisms and standards in place, data spaces can facilitate decentralized data transfer, where data is shared through APIs that comply with set standards, without any central platform or data repository. This allows for any one data space to link to others that are focused on different sectors or geographic locations. This is critical within the context of climate, where the nature of the climate crisis means that relevant data is held by actors across sectors and borders. 
  • Similarly, in different markets, different actors hold data, which changes the necessary incentives for unlocking that data. For example, in the U.S., large corporations tend to hold most of the data. In Europe, on the other hand, there are many public and private sector actors that hold smaller amounts of data, and they are often encouraged through regulation to share it. In many countries in Africa, including Ivory Coast and Senegal, there are also many data holders across the public, private, and development sectors, each with their own siloed platforms. In each of these different contexts, data sharing requires a different mix of incentives, ranging from regulation to monetization to community improvement of data shared within the data space.
• While this model requires extensive up-front work to agree upon governance and technical standards, the result is an approach that overcomes trust and financing issues by maintaining data sovereignty and creating a marketplace for data exchange. By putting in the effort now to establish trust and determine common standards and practices, the actors driving data spaces are putting into place the necessary infrastructure for cross-border data sharing that can drive climate action.

• Sovereign data control: Data owners maintain control of their data and the conditions for data sharing – establishing rules about who can see their data, and for how long. This feature differentiates data spaces from data trusts – where data is pooled and held by the central governance body.
• Monetization: Each member can set their own monetization policies and fees (or choose to offer data for free), thereby creating a market for incentivized data sharing. 
• Centralized governance: Ideally, there is a neutral and trusted actor who enforces rules regarding membership, data quality, and community interaction.
• Decentralized data transfer: Data is shared through APIs that comply with set standards. There is no one central platform or data repository. This allows for federation, and presents the possibility of allowing one data space to link to others that are focused on different sectors or geographic locations.

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