Implementing FAIR Data Principles for Sustainable Development

📢 Fresh off the press! Excited to share my new Policy Brief:
“Seeing Sustainability Differently: New Metrics and Ethical Data Governance for a Just Transition”, authored by yours truly for the SPES Sustainability Performances, Evidence & Scenarios, funded by #HorizonEurope. We argue that just transitions require more than technical fixes – they demand rethinking how we see, measure, and govern sustainability. This brief outlines actionable steps for ethical, inclusive, and effective data generation and use in transition policymaking. 🚩 Key Policy Recommendations: 1 Establish EU-wide ethical guidelines for the use of novel data in transition monitoring, ensuring privacy, fairness, and cross-border comparability. 2 Fund and promote open, transparent datasets and mandate methodological transparency in all EU-funded sustainability programs. 3 Enable GDPR-compliant access to platform data for public interest research, while safeguarding vulnerable communities. 4 Recognize citizen-generated data as legitimate and embed public participation in environmental governance processes. 5 Boost public sector data literacy and create inclusive, participatory processes for data governance at all levels. 📘 Download the full brief here: https://lnkd.in/eNgNh3aR 
 Let’s work toward transitions that are not just smart – but just. #JustTransition #EthicalData #SustainabilityMetrics #OpenData #DataJustice #SPES #HorizonEurope #PolicyBrief #EnvironmentalGovernance #CitizenScience

Data is fundamental for decision-making, especially in sustainability, where it underpins efforts from measuring project impacts to evaluating policy effectiveness. However, in our rush to gather data, we often overlook a crucial question: is the information we need already available? Many times, organizations jump to new data collection projects without first examining existing resources, leading to unnecessary costs, wasted effort, and environmental impact. Building large analytics teams, purchasing third-party data, and conducting extensive surveys are standard practices, but failing to leverage existing datasets can contradict the core principles of sustainability. In my 15 years of experience working in the sustainability field, I have observed that many organizations don’t make the best use of available data. Too often, the first response to a data need is to collect fresh data, even when high-quality datasets already exist. This results in redundant data collection efforts, with multiple surveys and analyses producing similar findings. For example, in one project, a detailed city transportation survey conducted by another team provided data on vehicle composition and age. Through an analysis of existing data sources, we achieved nearly identical results, showing that sustainable data use is achievable. This experience inspired me to look more critically at how data can be used effectively. In my recent analysis, I estimated Vehicle Kilometers Traveled (VKT) per day by car in different Indian cities using available car sales data and existing datasets. This approach allowed me to produce results that were comparable to findings from previous primary surveys, which typically involve extensive fieldwork and resource investments. Additionally, using existing data enabled me to go further by obtaining detailed breakdowns by car type, engine type, transmission type, and providing estimates across a larger number of cities than would have been possible with a single primary survey. The chart below visualizes the VKT estimates across different cities, illustrating how leveraging existing data can yield reliable results that align closely with other studies. This example underscores that sustainable data practices aren’t just about reducing costs; they’re also about minimizing environmental impact and making efficient use of existing resources. By strategically using what is already available, we conserve time, money, and energy. Effective data use in sustainability starts with clear objectives and a careful evaluation of existing resources. Before new data collection, we should ask: Why do we need this data? What level of uncertainty is acceptable? Can available data meet our needs? Sustainable data practices help save costs, reduce environmental impact, and improve efficiency by repurposing existing datasets instead of conducting costly and redundant surveys.

Developing and emerging nations in the Global South must enhance the transparency and accessibility of climate change and other interconnected data. This is crucial because it enables them to make informed decisions and take appropriate action to address the challenges posed by these variables. Currently, adaptations are granted at face value. The funding is awarded to those with the most appealing policies that resonate with the sponsor. Typically, vulnerability, risk, and cost projection datasets are used to determine project feasibility. The lack of such data makes it challenging to understand the cost-effectiveness fully and attribute the impacts of projects. Developing nations should enhance data availability to improve the translation of paper money into concrete actions. This is possible by continuously gathering and storing datasets on climate scenarios, future predictions, and climate investments on an open-source platform, which will also increase accessibility. Access to data is a significant issue in Africa, where obtaining free datasets can be time-consuming, even at climate centres. Accessibility to data should not only be available to foreign investors alone but also to local people and private organisations. Providing access to data to local people will encourage the development of interventions informed by concrete and research-based datasets. Locally led initiatives are often developed without such a backing. Improving the accessibility and transparency of data for the private sector will increase their buy-in to invest, as risks can be better informed through conventional approaches. This will lead to a better use of resources, as gaps in the data will be identified after investing. Additionally, states should encourage the cross-sector and cross-organisation sharing of datasets to increase efficacy by merging resources to address common issues and avoid overlapping roles and responsibilities that the same dataset in multiple organisations can address.

UNESCO, has successfully developed a significant report, "Open Data for AI: What Now?". The report underscores that Open Data isn't an option, but a vital necessity for tracking and realizing sustainable development objectives. Given the extensive scope of these objectives, it's crucial for governments not just to unlock data but to construct a landscape that invites AI engagement. This would enable us to transform open data into new knowledge for informed decision-making. Governments will discover a comprehensive guide to devise an action plan and establish a policy centered on open data for sustainable development. Here are some actionable, yet strategic takeaways for leveraging the power of AI: 🔹 Create a data management and sharing policy 🔹 Collect and secure high-quality data 🔹 Nurture open data competencies 🔹 Prepare your data for AI implementation 🔹 Choose the datasets to be disclosed 🔹 Open your datasets in compliance with legal parameters 🔹 Facilitate technical access to your datasets 🔹 Cultivate an open-data-centric culture 🔹 Encourage citizen involvement 🔹 Advocate for international cooperation 🔹 Promote positive AI engagement 🔹 Sustain the maintenance of high-quality data In conclusion, strive to make your data FAIR - Findable, Accessible, Interoperable, and Reusable, along with making it AI-compatible. This way, we ensure that it can be efficiently processed and examined to further societal wellbeing by anyone.