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The challenge

With the impacts of climate change intensifying globally, urgent action is needed now more than ever.

Data is critical to addressing these threats. Beyond the typical climate data – weather patterns, tide levels, and agricultural conditions – datasets on infrastructure, transportation, energy, and mobility could also inform mitigation and adaptation efforts and provide vital information for disaster prevention and response. 

Much of this data already exists. Yet, it is often locked away, housed by private companies, governments, and academic institutions. The result is that crucial, climate-relevant data is often inaccessible to the frontline governments and communities that need it the most.

To unlock this data and make it more readily available for climate action, new governance and financial models are needed. To address this challenge, the Digital Impact Alliance convened the Climate Data Joint Learning Network (JLN). Comprised of 21 leading data and climate organizations, the JLN identified the top challenges and recommendations to unlocking data for climate action. This flagship report – Beyond the tipping point: How climate data can decide our future – is the culmination of our findings.

The opportunity

Data-driven insights could – and should – be at the heart of climate mitigation and response. As cutting-edge technologies like artificial intelligence evolve, data will be more critical than ever to our ability to harness technology for the fight against climate change. By strengthening the infrastructure for trusted data sharing, more people, communities, and institutions can have access to the data they need to find, build, and share climate solutions.

Our research

The Climate Data Joint Learning Network conducted research to surface innovative data models that could enable more governments and communities around the world to harness data for climate action. This research was grounded in discussions with frontline municipalities and focused on data relevant to the challenges faced by these communities – from water scarcity to coastal erosion, from extreme weather to emissions tracking. Through a series of roundtable discussions and an in-depth analysis of key use cases and examples, our research has uncovered critical insights.

Read the expert comment.

The appropriate data sharing model for a given use case depends on factors such as the type of data, data owners’ privacy requirements, and the intended data users. Where data requires more careful governance and privacy, centralized models like data trusts and data spaces are likely more appropriate. Where widespread participation is essential, and privacy concerns are lower, decentralized models like open transaction networks and open data models offer greater flexibility and access

The barriers to data sharing are not primarily technical. Greater time and effort are required to establish governance models that are fit-for-purpose, foster trust, and enable data sharing and use across borders and sectors.   

The entities driving these data sharing innovations are struggling to attract the financing they require, particularly as they rely on hybrid financing models that do not neatly fall into typical categories of private, public, and not-for-profit. The Data Empowerment Fund demonstrated demand for catalytic capital to scale data sharing innovations.

For frontline communities to benefit from the data unlocked through these different data sharing innovations, they require support from intermediaries (entrepreneurs and others) who turn data into actionable and accessible information, thereby minimizing the need for high levels of technical or human resource capacity

Our recommendations

Based on our learnings, we have identified four key stakeholder groups that, with targeted actions, can accelerate the broader use of data for climate action and decision-making.

Hover on the images below to reveal our recommendations for each stakeholder group.

Challenges to data-sharing

As a framework for the research, we bucketed the obstacles to data-sharing into four categories: data, financial, trust, and capacity.

Click through the image below to explore these challenges further and understand how the key features of these models can overcome or address each of these challenges.

The data models

The multi-disciplinary network identified multiple promising models to explore in the context of unlocking data for climate action. Our research examined and produced separate spotlight reports on four emerging models for data sharing and governance: data trusts, open transaction networks, data spaces, and open data.

Data Trusts

A data trust is a legal entity designed to responsibly manage and govern data assets on behalf of its members. Data trusts provide independent, fiduciary stewardship of data. 

Download the spotlight to learn more.

Open Transaction Networks

An open transaction network refers to a network system where the nodes—computers, individuals, or organizations—are free to join and interact without a centralized controlling authority. This decentralized and horizontal approach allows for a dynamic exchange of information and resources and facilitates unrestricted and non-hierarchical interactions among nodes.

Download the spotlight to learn more.

Data Spaces

Data spaces are a model of data sharing that enables the reliable exchange of data while retaining sovereignty and ensuring trust and security under a set of mutually agreed rules.

Download the spotlight to learn more.

Open Data

Open data is a subset of digital public goods in which data, methodologies, and often the code underlying the data platform itself are freely accessible for use and adaptation.

Download the spotlight to learn more.

Data models in action

For each data model, we researched real-world examples of these data governance models being used in the context of climate data or climate use cases. The examples below – along with others – are all featured in detail in the spotlight reports.

 

 

 

Model type: Data trust

Type of data: High-resolution aerial and street imagery, geospatial

Climate use cases: Urban planning, disaster risk, transportation, ecological

Financial model: Member contributions and data license fees, grants

 

Model type: Data trust

Type of data: Open data on carbon markets

Climate use cases: Transparency on climate credits

Financial model: Grant funded

 

 

Model type: Open transaction network

Type of data: Weather, soil quality, agriculture markets, etc.

Climate use cases: Good agricultural practices, climate-smart technologies, regenerative agriculture

Geographies served: Kenya

 

Model type: Open transaction network

Type of data: Energy transaction data, consumption data, pricing data, location data, energy generation and storage data

Climate use cases: Electric vehicle charging, battery swapping, renewable energy management

Geographies served: India

 

Model type: Data space

Type of data: Mobility data from transportation, OEMs, insurance companies

Climate use cases: Green city, traffic optimization, electric vehicle planning, etc.

Geographies served: Europe

Model type: Data space

Sectoral focus: Climate, Agriculture

Climate use cases: AgData for farmers

Geographies served: Ivory Coast, Senegal

Model type: Open data

Type(s) of data: Geospatial, infrastructure, socioeconomic, resources, environment 

Climate use cases: Energy planning, clean energy, energy market access, household assessments, climate compatible agriculture

Financial model: Grants and partnerships

 

 

Model type: Open data

Type(s) of data: Geolocated user generated reports

Climate use cases: Documenting real-time climate impacts, mapping vulnerabilities, supporting planning

Financial model: Grants and partnerships

Model type: Open data

Type(s) of data: Geolocated air quality data, metadata 

Climate use cases: Monitoring air pollution trends, supporting public health interventions, and validating satellite data

Financial model: Corporate sponsors, grants, and partnerships

The Climate Data Joint Learning Network

The Joint Learning Network (JLN) is composed of 21 partner organizations across the climate and data ecosystems. Led by the Digital Impact Alliance, the JLN brings together experts in climate change, data exchange, and digital public infrastructure to explore the data infrastructure needed to accelerate climate action. We are grateful for their participation, expertise, and support.

The catalyst

Much of this work was inspired by the multistakeholder Green Digital Action (GDA) initiative, convened by the International Telecommunications Union with participation from 40+ organizations.

Following our engagement on the GDA track at COP28, we launched the Joint Learning Network on Unlocking Data for Climate Action. The year-long research effort has culminated in the publication of this final report at COP29.

Learn more about Green Digital Action.