# Data Lab for Climate Change Adaptation: A Literature and Practice Review

Jean-Paul Albert Castro, Laurent Dupont, Didier Grimaldi, Grégory Hamez

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Jean-Paul Albert Castro, Laurent Dupont, Didier Grimaldi, Grégory Hamez. Data Lab for Climate Change Adaptation: A Literature and Practice Review. 2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), Jun 2025, Valencia, Spain. 10.1109/ICE/ITMC65658.2025.11106573. [hal-05128285](https://hal-univ-lorraine.fr/hal-05128285v1)

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Distributed under a Creative Commons Attribution 4.0 International License
Data Lab for Climate Change Adaptation: A
Literature and Practice Review

Jean Paul CASTRO

Université de Lorraine, ERPI, F-
54000

Nancy, France

alcastrov@unal.edu.co

Laurent DUPONT

Université de Lorraine, ERPI, F-
54000 Nancy, France

ORCID : 0000-0002-8279-9690

l.dupont@univ-lorraine.fr

Didier GRIMALDI

Universitat Politècnica de
Catalunya, Management

Department, Spain

didier.grimaldi@upc.edu

Grégory HAMEZ

Université de Lorraine, LOTERR,
F-57000 Metz, France

gregory.hamez@univ-lorraine.fr

Abstract— The rapid pace of urbanization and the intensifying effects of climate change require innovative adaptation strategies. Data Labs have emerged as promising tools for enhancing decision-making by integrating diverse data sources and engaging multiple stakeholders. However, their effectiveness in climate adaptation remains largely underexplored. This paper conducts a systematic review of the literature on Data Labs, examining their role within digital decision-support frameworks. Additionally, a benchmarking analysis of existing climate adaptation platforms is carried out to identify prevailing trends, challenges, and gaps in current approaches. Our findings highlight the need for improved data integration, inclusive stakeholder participation, and governance structures that enhance urban resilience. Based on these insights, this study proposes a refined Data Lab model, outlining its core functionalities, technological requirements, and participatory mechanisms. The research contributes to ongoing discussions on data-driven climate adaptation strategies and offers recommendations for strengthening urban adaptation policies through collaborative digital innovation.

**Keywords:** Climate Adaptation, Decision-Support Tools, Data Lab, Quadruple Helix Innovation, Urban Resilience.

I. INTRODUCTION

The increasing rate of urbanization and the accelerating impacts of climate change necessitate the development of effective adaptation and mitigation strategies for urban areas. In this context, data science offers valuable opportunities, as long as it enables the integration of both supranational environmental data and localized datasets. Addressing these complex challenges requires a rigorous scientific approach and broad stakeholder collaboration. Within this context, Data Labs have emerged as promising tools for enhancing decision-making by facilitating the integration of diverse data sources and fostering local stakeholder engagement. However, the specific ways in which these data laboratories contribute to climate adaptation remain insufficiently defined.

This study aims to explore and conceptualize a Data Lab for local climate adaptation. It addresses the following research questions: How can the benchmarking of existing platforms and a review of the literature on urban climate change adaptation and digital technologies contribute to the

development of an improved Data Lab model for climate
change adaptation at the local level? How do existing
platforms enable citizens to participate? Can improvements be
identified?

The paper addresses the critical need for data-driven approaches to climate adaptation in urban contexts. Our study examines existing practices, identifies key gaps, and proposes an optimized framework for integrating Data Labs into urban climate adaptation strategies. The following section presents a literature review (based on research in Web of Science and Scopus) aimed at understanding the current research landscape and identifying unresolved challenges. Section 3 outlines the methodological approach, including a benchmarking analysis of 22 platforms to evaluate prevailing practices. The findings of this analysis are detailed in Section 4, leading to a functional assessment of the expected capabilities of a Data Lab with the proposed ten-component Data Lab framework, as a conceptual model (see. Section IV.C). Finally, the concluding section summarizes the key insights and outlines future research directions.

II. LITERATURE REVIEW

Data Labs have become increasingly popular in astrophysics and medicine or healthcare over the last 10 years. In March 2025, few publications associate the Data Lab (DL) concept with decision-making (DM) or climate change (CC) or the theme of the city (C). For example, the database OpenAlex provides respectively (DL and DM, n=18) (DL and CC, n=6) (DL and C, n=21). The result is similar with Web of Science and Scopus. Neither of these databases contain a scientific paper that cross-references the four concepts (DL and DM and CC and C).

The systematic literature review was based on Web of
Science and Scopus, combining keywords around the
concepts Decision-Making, Climate Change, Urban Context
(urban OR city OR cities), and Data-Driven Technologies
("data-driven" OR "machine learning" OR "deep learning"
OR AI OR "artificial intelligence" OR hub OR "digital
shadow" OR "decision support system" OR "digital twin" OR
"digital technology" OR "decision support tool"). The review
identified articles containing the keywords in the title,
abstract, or keyword fields, up to the end of February 2025.
After deleting duplicates, a corpus of 367 articles was
analyzed using VOSviewer to map the connections between
terms and identify research trends.

The following subsections present our results.

<footnote>The authors thank the ORION program of Université de Lorraine for its
contribution to research funding. This work benefited from a government grant
managed by the Agence Nationale de la Recherche under the Programme
d'Investissements d'Avenir, reference ANR-20-SFRI-0009.</footnote>
A. *Definition of key concepts*

Climate change adaptation and mitigation: Adjustment process to current or expected climate conditions, integrating risk mitigation and territorial resilience strategies [1]. This is a complex subject that requires a multidisciplinary approach.

Data Lab: A collaborative platform to facilitate the exploitation and analysis of data. These laboratories enable experimentation and the pooling of data to meet the data science challenges, particularly in astrophysics [2]. Data labs are integrated collaborative environments for processing, exploiting and analyzing mass data [3], [4], [5].

Concerning the term “Data Lab”, preliminary findings have been identified, showing its use in various contexts such as platform [2], [6]; unique integrated collaborative environments for data processing, exploitation, and analysis [3]; big data workflow management systems [4]; big data analytics platforms [5] and research groups focused on digital tools [7].

Data Lab is also described as an online tool which turns academic research findings into actionable outputs [7]. For the moment, the literature does not mention any relevant applications in response to climate and urban challenges, and the term has not yet been addressed in relation to the broader concepts of adaptation or resilience to climate change in urban environments.

Decision Support Systems (DSS): Applications enabling decision-makers to evaluate different adaptation options and optimize urban policies [8]. This technology is already being used for urban planning and climate risks.

Quadruple and Quintuple Helix Innovation System and Living Labs: knowledge is distributed throughout society, and the mobilization of as many people as possible is necessary to democratically solve complex problems, especially those relating to ecology [9]. Living labs are tried-and-tested means of facilitating collaboration between actors in different fields [10], [11].

B. *Systematic Literature Review*

Data-driven technologies and their role in assisting decision-makers to prepare for climate change are still under development. A systematic review of the literature based on concepts from data science, current applications of Data Labs and quadruple helix, identifies three dimensions to be considered: data integration, stakeholder involvement and the limitations of technological approaches.

a) *Climate change needs data, accessible information and decision-support systems*

The rise of digital technologies and open databases has improved the collection and management of environmental data [12]. Using big data and artificial intelligence (AI) in urban planning can improve climate risk prediction and infrastructure adaptation [13]. However, the challenges of data interoperability and real time updating still limit their impact on urban governance and the generalization of the process.

Decision Support Systems (DSS) have been increasingly applied to aid climate change adaptation, enhancing decision-making by providing structured methodologies to evaluate and implement adaptation strategies. As mentioned in the work of [8, p. 463], with the increase in the number of papers on decision support platforms for climate change adaptation.

Some published papers discussing DSS and climate change adaptation, such as [8] exploring decision support platforms for climate change adaptation, or [14] providing an overview of Decision Support Tool (DST) in relation to urban heat islands (UHI). Also, DSS has been used in a range of topics, such as climate change risks, extreme heat events, heat resilience adaptation and mitigation measures, air quality, flooding, rainwater management, overall management of water resources (collection, supply, quality, etc.), urban agriculture, transport, tourism, forestry, soil, energy, road planning, low-carbon urban development and health.

A double bibliometric analysis of the 367 articles is possible using author keywords and extended keywords from the publications databases. Two results are available. The first includes all extended keywords with a minimum number of occurrences of 6 (detailed results available online: https://tinyurl.com/246svzfm - we will not provide details here). There are 188 items in 5 clusters. The second includes only author keywords with a minimum number of occurrences of 4 (detailed results available online: https://tinyurl.com/29cq643w). There are 49 items in 7 clusters.

According to VOSviewer, keywords are clusters. The first cluster relates to deep learning and smart city; the second refers to climate change associated sustainability and decision-making; the third cluster focuses to machine learning related with ecosystem services and random forest; the fourth cluster refers to uncertainty, multicriteria decision analysis, vulnerability and climate change adaptation; the fifth cluster relates to a decision support system, resilience and urban planning; the sixth cluster refers to decision support, remote sensing and Geographic Information System (GIS); finally, the seventh cluster focuses on artificial intelligence and sustainable development.

The ten most frequently occurring terms are, in order: climate change (69), machine learning (43), decision support system (22), deep learning (21), smart city (18), artificial intelligence (17), decision support (17), sustainability (17), decision making (15) and resilience (14).

Fig 1 shows the VOSviewer’s overlay visualization, i.e., the time distribution of the most popular keywords from authors (49 items). The size of the bubbles corresponds to the occurrence of each word. The figure also illustrates the links between keywords. The colors highlight their appearance in the literature over time. Keywords referring to AI are very recent in the literature (2023). The key technological issues for the research community are machine learning, deep learning, artificial intelligence, digital twins. Similarly, recently, the term “urban resilience” has also come to be associated with climate change and, more surprisingly, with the circular economy. While the issues of decision-making, technology and risk (in particular concerning water) are much older (2019). On the other hand, the word citizen does not appear, the only term that emerges is “participatory approach”, with items dating back from 2018.

According to bibliometrics, the existing literature seems to report is little, if any, consideration or mobilization of citizens, yet there are several reasons for taking them into account.

b) *Citizens and climate mitigation*

When it comes to climate change, citizen participation is fundamental. For example, as it points out [15], flood risk management requires combined assessment of various
<figure><img src="image_2.png" /><figcaption>Fig 1. VOSviewer's "overlay visualization" showing the time distribution of most popular Authors' keywords (367 papers – 49 items – 7 clusters).</figcaption></figure>

sources of data by different stakeholders. Through a wide-ranging review of the literature, [16] has shown that there is a lack of shared understanding of citizen participation in climate adaptation across all disciplines. Improving the articulation of public participation and civic engagement processes requires a transformation of governance. In this study citizens are particularly considered and involved by social media platforms. We have identified at least three levels of citizen involvement in the data to be mobilized for adaptation to climate change: collection, processing and use.

First, citizen involvement can improve data acquisition by multiplying the data collected and the amount of information available. Crowdsourced user-generated content became an important source of data for organizations, particularly for natural science research [17]. The concern arises of data quality, but today's methods are increasingly reliable thanks to tried and tested thanks to participatory science campaigns (citizen science) [17], [18]. These approaches “could also contribute to localizing, defining, and achieving the SDGs” [19]. Innovation labs could also play a role to support collaborative process involving citizen for smart and sustainable cities, helping stakeholders to select relevant data and acceptable technological solutions [20].

Second, citizens' knowledge can be integrated into data processing and analysis to refine models or gain a better understanding of local phenomena. The combination of AI and citizen science means that more localized data can be collected and analyzed, leading to more accurate climate models that are better adapted to the context [21]. In addition, this approach makes it possible to take into account what is already in place, such as the information available and decision-making processes, and to help users formulate their needs; sometimes the users don't know what they want, or don't know it in great detail [22].

Third, the complexity of climate data can be a significant barrier to its effective use. User-friendly interfaces and visualization tools that transform raw data into easily understandable formats can give citizens access to information and empower them by increasing the awareness of current phenomena. Interactive dashboards and maps are particularly effective in communicating complex information to non-experts, empowering individuals to understand climate change impacts and contribute to mitigation efforts [23]. Simple tools, such as carbon footprint calculators, provide personalized feedback, encouraging them to make more sustainable choices [24].

According to [16], citizen participation in adaptation strategies remains limited to consultative phases, with no real decision-making power. However, initiatives such as the Living Labs [25] illustrate how codesign between experts, local authorities and citizens can lead to more effective, locally accepted solutions and generate quadruple / quintuple helix innovation system.

### c) Limits and Challenges of Data Labs

Despite their potential, Data Labs have their drawbacks and face several obstacles. The study by [26] highlights the risks associated with algorithmic governance, where urban decisions become dependent on predictive models without human validation. In addition, the digital divide and unequal access to technologies remain major challenges for ensuring equitable adoption of these tools [27], considering that their democratic use depends on it. Furthermore, disciplines need to be able to cross-fertilize their work and provide empirical research [16].

In summary, Data Labs represent an opportunity to improve cities' adaptation to climate change. However, their effectiveness will depend on their ability to integrate reliable
<figure><img src="image_3.png" /><figcaption>Fig 2. Classification of benchmark items by color code and type. Symbology based on [18]</figcaption></figure>

data, active citizen participation and balance governance between experts and local players.

### C. Research Question

The literature review shows that there is no established process or standard for involving citizens in technological systems, collating, processing and disseminating data associated with climate change. Nevertheless, this initial review of the literature leads to the following questions.

How can current practices of platforms regarding urban climate change adaptation support the development of an improved Data Lab model for local-level climate change adaptation?

How has citizen involvement been addressed in the existing platforms for climate change adaptation?

Given the current state of knowledge, what recommendations will help decision-makers and developers to design technological systems capable of integrating the diversity of the data from global to local scale to support the process of adapting local areas to climate change? Which system can induce or support a collaborative dynamic aligned with the quintuple helix? What would be the functionalities of such systems?

Consequently, this study aims to propose an improved Data Lab model based on current practices for collecting, storing and processing data for adapting to climate change at a local level. The previous sections highlighted some insights from the existing literature, that should be considered in the proposed framework for Data Labs (see. Section IV) such as stakeholder involvement, data integration, limitations of technological approaches (digital divide, unequal access, algorithmic governance) and the challenges with the data (interoperability, reliable data, combined with various sources of data by different stakeholders, complexity of climate data).

A benchmarking of the tools developed provides the state of art of the practices and technologies implemented by decision-makers and operators involved in climate change adaptation.

## III. RESEARCH DESIGN

The methodology consists of a systematic bibliometric analysis of the key concepts associated with Data Lab, combined with a benchmarking of existing platforms whose objective is to have a better adaptation to climate change. The bibliometric analysis was presented in the previous section.

A benchmarking analysis conducted initiated through a literature review and targeted online searches, and expanded using a “snowball methodology,” where each discovery — such as websites, decision support systems (DSS), GIS tools, and others (hereafter referred to as items)— led to the discovery of additional items. We based our work on previous studies, such as [8], [14], [28], [29]. We identified 22 platforms and digital tools dedicated to climate adaptation (fig. 2). These platforms were evaluated based on 20 criteria derived from the literature, categorized into four primary dimensions:

1. **Technological Aspects:** Includes the type of technology used, territorial scale, availability of data, integration of geospatial visualization tools, and access to open databases.

2. **User Interaction:** Assesses features such as subscription to newsletters, submission of user feedback, the ability to propose solutions, interface customization, and opportunities for collaboration with other stakeholders.

3. **Objectives and Functionalities:** Evaluates the extent to which platforms facilitate information dissemination, project sharing, stakeholder engagement, and decision-support capabilities.

4. **Additional Criteria:** Considers the availability of analytical reports, scenario modeling, target audience
<table><caption>TABLE I. CLASSIFICATION BASED ON THE REVIEW AND ANALYSIS OF ONLINE TOOLS AND PLATFORMS</caption><thead><tr><th>Criteria / Item</th><th>AV</th><th>AC</th><th>CA</th><th>CT</th><th>C40</th><th>RD</th><th>CO</th><th>ES</th><th>TA</th><th>CU</th><th>CR</th><th>UA</th><th>MC</th><th>EI</th><th>DH</th><th>SDI</th><th>CR2</th><th>R4RE</th><th>ZV</th><th>BC</th><th>TV</th><th>GP</th></tr></thead><tbody><tr><td>A</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>B</td><td></td><td>1</td><td>1</td><td></td><td></td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td>1</td><td>1</td><td>1</td><td></td><td>1</td></tr><tr><td>C</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>NF</td><td>1</td></tr><tr><td>D</td><td>1</td><td>1</td><td>1</td><td>+/-</td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td>1</td><td></td><td>1</td></tr><tr><td>E</td><td></td><td>1</td><td>1</td><td></td><td>1</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>+/-</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>F</td><td>1</td><td>1</td><td></td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>1</td><td>+/-</td><td></td><td>1</td><td>1</td><td></td><td></td><td>1</td><td>1</td><td>1</td></tr><tr><td>G</td><td>1</td><td>1</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>+/-</td><td></td><td></td><td></td><td></td><td></td><td></td><td>+/-</td></tr><tr><td>H</td><td></td><td></td><td>1</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td>1</td><td>1</td><td></td><td></td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>I</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>+/-</td><td>+/-</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>J</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td></tr><tr><td>K</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>L</td><td></td><td>1</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>+/-</td></tr><tr><td>M</td><td></td><td>+/-</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>+/-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>N</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td>+/-</td><td>+/-</td><td>+/-</td><td>1</td><td>+/-</td><td></td><td></td><td></td><td></td><td>+/-</td><td>+/-</td><td>+/-</td><td></td><td>1</td><td>+/-</td><td>+/-</td><td>1</td><td>1</td></tr><tr><td>O</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td>1</td><td>1</td><td>1</td></tr><tr><td>P</td><td></td><td>1</td><td>1</td><td></td><td>1</td><td>1</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td>1</td><td>1</td><td>+/-</td><td>1</td><td>1</td><td></td></tr><tr><td>Total</td><td>9</td><td>15</td><td>12</td><td>7</td><td>12</td><td>9</td><td>11</td><td>7</td><td>3</td><td>2</td><td>2</td><td>10</td><td>9</td><td>7</td><td>5</td><td>8</td><td>9</td><td>7</td><td>8</td><td>10</td><td>4</td><td>14</td></tr></tbody></table>

Info: The "+/-" is considered as 1; NF: Not Found.

specifications, and the presence of institutional or private-sector partnerships.

This structured evaluation framework provides a comparative basis for assessing the effectiveness and limitations of existing climate adaptation platforms.

Based on the paper of [14], the symbology has been adapted to reflect our numbers of criteria. We create a code classification that considers 16 of the 20 criteria presented above, applying a binary coding, where 1 indicated that the criterion was met and “empty cell” indicated that it was not met. Sometimes we put +/- when the tool seems to satisfy the criterion without being able to validate it at 100% (Table 1). Four criteria are excluded because they are systematically met: type of technology, territorial scale, target audience, partnerships.

Despite the challenges associated with evaluating the nature of user interaction, the assessment was informed by the researcher's direct experience navigating these platforms, as well as publicly available information, particularly from the “About Us” section. This approach enabled the identification of key evaluation and characterization criteria. Additionally,

several platforms, such as Adaptaville, the C40 Knowledge Hub, and the Database - Case Studies section of Climate ADAPT, highlight user engagement by enabling experience-sharing on projects and fostering connections among various stakeholders.

The analysis also examined the stakeholders and partnerships involved in the development and operation of climate adaptation platforms. Various entities contribute to the design and provision of data, including international organizations, government ministries, local authorities, universities, research centers, associations, private companies, and startups. The selected initiatives receive support from institutions at multiple levels: globally, from UN-Habitat, the World Bank, and the C40 Cities Climate Leadership Group; at the European level, from the European Environment Agency and the European Commission; and at the national level in France, from organizations such as ONERC, ADEME, CEREMA, and MeteoFrance.

In terms of stakeholder inclusion and engagement, current practices predominantly employ participatory methods, as highlighted in previous research [30] “include small group discussions, co-mapping workshops, citizen assemblies,

<footnote>I. **Criteria**: A: There are data available; B: Include a Viewer?; C: Open Data; D: Does it give access to other databases?; E: Subscribe to a newsletter; F: Fill a form or submit feedback. Contact the services (ask, request or report data), G: Share experiences (methods, ways, results founded and tips) or propose a solution, H: Tailored options (Create an account, modify the model, User-defined functions, user model specified, user corner, user consultation, among others), I: Other (create work teams, interact with the community, participate, etc.), J: Facilitate the access and spreads information, data and knowledge ("usable and meaningful," K: Share the different projects carried out and provide usable and meaningful information about it (directory of solutions of CCAdaptation, evidence and info to further the replicate), L: Facilitate the communication between the different actors (or with one in specific) or call for projects, M: Help the decision-making process and access to information about it or adapt the territory (implicit), N: Communicate and facilitate the access to tools, O: Share reports, webinars or other activities, P: Show predictions (scenarios)</footnote>

<footnote>II. **Items**: AV: Adaptaville; AC: AdapteCCa; CA: Climate-ADAPT; CT: CACTUS (Climat Adaptation Changements Territoires Usages); C40: C40 Knowledge Hub; RD: Risk Data Hub; CO: Copernicus; ES: Earth System Data Lab (ESDL); TA: TACCT (Trajectoires d'Adaptation au Changement Climatique des Territoires) -Diagnosticsquer les impacts; CU: CURB (Climate Action for Urban Sustainability); CR: City Resilience Profiling Tool (CRPT) - ONU Habitat; UA: Urban Adaptation Support Tool (UAST); MC: Mission Climat; EI: Eionet (European Environment Information and Observation Network); DH: Data Hub EEA; SDF: EEA SDI - Geospatial Data Catalogue; CR: Centre de ressources pour l'adaptation au changement climatique; R4RE: Resilience for Real State; ZV: Zurich virtual; BC: Brno City Digital Platform; TV: TerraVision Lab; GP: Global Platform for Sustainable Cities.</footnote>
youth forums, participatory budgets, one-on-one interviews, and creative methods involving arts and culture.”

## IV. FINDINGS

### A. *Practices revealed by the Benchmark*

The benchmarking analysis reveals that 17 of the identified platforms meet at least 7 of the 16 evaluation criteria (Fig. 2). Notably, two platforms — AdapteCCA, Spain's national climate adaptation website, and the Global Platform for Sustainable Cities — it corresponds to the largest set of criteria. On the other hand, the City Resilience Profiling Tool (CRPT) and the Climate Action for Urban Sustainability (CURB) platform address only one or two criteria, mainly focusing on decision-support functions and providing access to relevant adaptation information.

The identified platforms operate at various geographical scales, with six at a global scale, seven focused on Europe, and five designed for national-level. Additionally, three platforms specifically address urban and metropolitan areas, while one is dedicated to natural parks.

Regarding data availability, 82% of the platforms provide access to climate-related datasets, 91% integrate open data sources, and 64% either host or redirect users to external databases. However, 14% only provide indirect access to data, and 23% do not facilitate access to additional databases.

The analysis underscores the importance of integrating citizen participation throughout multiple stages of the adaptation process rather than limiting it to isolated engagement phases. Existing climate adaptation platforms have begun incorporating participatory elements. For example, the CACTUS (Climate Adaptation, Changements, Territoires, Usages) platform was co-developed by stakeholders in the Gulf of Morbihan, including the regional natural park and the AMURE research laboratory at the University of Western Brittany. This initiative supports local actors in identifying and evaluating potential climate adaptation strategies [31].

In terms of decision-support tools, several resources are either available or under development. Some examples include the Regional Adaptation Support Tool (RAST) and the Urban Adaptation Support Tool, the latter developed within the framework of the Covenant of Mayors for Climate and Energy and accessible via Climate ADAPT.

Furthermore, the benchmarking analysis highlights the role of certain platforms as centralized repositories for climate adaptation resources. These platforms, as outlined [32], institutional contacts, training materials, local initiatives, and research projects (e.g., Climate Change Adaptation Resource Center, the C40 Knowledge Hub, Adaptaville, and Climate ADAPT).

Finally, the literature review and benchmarking findings indicate that the concept of a Data Lab is primarily associated with technological functions, such as data hubs and digital databases. However, these platforms often neglect the critical role of user contributions. Current implementations tend to treat citizens as passive users rather than active participants, limiting their role to providing feedback for website improvements rather than engaging them meaningfully in decision-making processes.

### B. *The direct benefits of benchmarking: features to be incorporated into a Data Lab*

A Data Lab designed for local climate adaptation has the potential to enhance urban resilience and sustainability. Furthermore, it can serve as a key driver of territorial competitiveness and attractiveness, as mentioned in previous research [33, p. 3].

To be effective, a Data Lab must support multi-scale, multidimensional, and multi-stakeholder analysis. According to [28], climate change adaptation support tools (CCASTs) should facilitate a broad range of adaptation measures, including the evaluation of their effectiveness. A Data Lab could incorporate a structured framework to catalogue adaptation options, assessing their impact, and identifying key stakeholders and beneficiaries, similar to the approach used by Adaptaville. Additionally, it is essential to include a section dedicated to financial estimation and the prioritization of adaptation strategies to aid decision-making.

To foster-stakeholder engagement, the Data Lab could support participatory scenario planning, an approach that enables collective reimagining of urban futures [34, p. 173]. Such a platform would facilitate dialogue, collaborative reflection, and a focus on co-production models for urban services [34, p. xxviii].

Existing initiatives such as CRACC, the C40 Knowledge Hub, the Global Platform for Sustainable Cities, and the climate adaptation programs listed in the "Adaptation Portals and Platforms" section of Climate ADAPT [35] already play a role in improving information accessibility and creating opportunities for participatory decision-making. These tools address the growing need to connect key actors in climate action efforts [16], making this a critical feature for an optimized Data Lab.

Finally, leveraging past research and existing projects is essential. In France, the Etalab program and its Artificial Intelligence Laboratory (https://github.com/etalab-ia) [36] provide valuable resources that can inform the development of data-driven adaptation tools. At the European level, the Sustainable Urban Development Planner for Climate Change Adaptation (SUDPLAN) project (2010–2012) led to the creation of a web-based planning, forecasting, and training tools aimed at supporting long-term urban planning and climate adaptation efforts [37], [38], [39]. Integrating insights from such initiatives will be crucial in designing a Data Lab that effectively contributes to climate adaptation strategies.

### C. *Data Lab for climate change adaptation: initial functions and usage scenarios*

This analysis facilitates the conceptualization of design and implementation scenarios for a Data Lab to support collective decision-making in climate change adaptation at the local level.

#### Proposed Data Lab Scenarios

1. **National-Level Data Lab:** This model operates at the national scale while allowing users to create accounts for submitting and processing data relevant to specific local territories. In this case, the Data Lab could serve as an extension or enhancement of existing platforms, such as CRACC.

2. **Territory-Specific Data Lab:** This approach focuses on a specific region or locality and could leverage existing
national initiatives, such as beta.gouv in France, which develops digital services for local public administrations [36]. In this scenario, the Data Lab would integrate with pre-existing territorial platforms to enhance data accessibility and stakeholder engagement.

Regardless of the scenario, the Data Lab should not rely exclusively on digital infrastructure (e.g., Digital Twins, hubs, or adaptation platforms). Instead, it should also include a physical space to facilitate interactions among quadruple-helix stakeholders (government, academia, industry, and civil society). This space would host co-design workshops and participatory initiatives, involving municipalities, local startups, associations, citizens, and companies at both local and national levels. Such an approach would help bridge the digital divide and increase accessibility to immersive technologies [40]. The convergence of Data Labs with innovation spaces aligns with the objective of promoting responsible innovation in climate adaptation [41].

FabLabs and other innovation labs can play a pivotal role in driving climate adaptation initiatives, particularly through citizen science projects and climate data monitoring. For instance, in the CapCI project [42], sensors are developed within third places and distributed to residents to measure air quality and temperature. This approach enables finer spatial coverage of environmental data, particularly in detecting urban heat islands. Moreover, by engaging local populations, this strategy transforms residents into ambassadors of climate action and public awareness campaigns.

To align with the quadruple-helix model for smart and city [43], a Data Lab must integrate multiple functionalities to address the needs of diverse stakeholders (Fig. 3). The functional analysis identifies ten key components:

1) *Mapping Climate Adaptation Initiatives:* Providing access to adaptation projects and national funding opportunities.

2) *Citizen Engagement in Adaptation Processes:* Enabling public participation in climate awareness and environmental monitoring initiatives.

3) *Localization of Global Climate Data:* Adapting international climate regulations and datasets to local contexts.

4) *Facilitating Participatory Dialogue and Co-Design:* Creating collaborative spaces for stakeholder engagement that promote the role of citizens, their involvement and even their empowerment?

5) *Connecting National Initiatives with Local Actors:* Enhancing accessibility to government-led adaptation programs and fostering communication among stakeholders.

6) *Industry-Government Collaboration:* Encouraging public-private partnerships for adaptation actions (e.g., repurposing brownfield sites, promoting renewable energy, and enhancing sustainable mobility).

7) *Knowledge Sharing and Best Practices Exchange:* Supporting interregional knowledge transfer through research collaborations and enterprise-driven doctoral programs.

8) *Climate Impact Assessment Tools:* Providing methodologies for industrial emissions reporting, carbon footprint analysis, and other environmental documentation.

<figure><img src="image_4.png" /><figcaption>Fig 3. Functional analysis of a Data Lab in a quintuple helix innovation system facilitating adaptation to climate change</figcaption></figure>

Additionally, facilitating access to non-confidential corporate data to support industrial symbiosis, circular economy initiatives, and regional attractiveness.

9) *Decision-Support and Data Accessibility:* Offering interactive tools such as directories, chatbots, and code-based interfaces (e.g., Google Earth Engine for data integration). The platform should also provide impact classifications for adaptation measures, along with stakeholder engagement insights.

10) *Ambitious, Independent Research:* guaranteeing academics the permanent deposit and use of reliable, open data to ensure open and collaborative science. Academia can also play an intermediary role by providing missing expertise and facilitating collaboration between stakeholders.

The identified functionalities reflect both proposed and observed user needs, derived from the literature review and benchmarking analysis. While the Data Lab should incorporate data hub capabilities (data collection, integration, sharing, governance, and security), these elements have already been addressed through existing national and regional data management initiatives (e.g., Data France, Data Gouv, and Data Grand Est). Instead, the emphasis should be on bridging the gap between data availability and stakeholder engagement to maximize the impact of climate adaptation efforts.

The proposed model addresses several gaps identified during the benchmarking and literature review. In addition, the model considers the three dimensions mentioned in Section II B (data integration, stakeholder involvement and the limitations of technological approaches) as key components.

Nevertheless, one of the current limitations identified is the lack of means to really involve all stakeholders, especially citizens, beyond a simple feedback or contact form.

Additionally, one of the model's functionalities is to propose a list of actions classified according to their “success” or impact, as well as the benefits and stakeholders involved in climate change adaptation projects within the territories. More than a simple repertoire of solutions, it is to provide a first level of information for decision making, with respect to the possible expected impact and the actors to be involved in the project.
## D. Limits and perspectives

Despite the advances highlighted in this study, several limitations deserve particular attention, to pave the way for further research and technological and organizational developments.

1) *An English-driven literature review:* The choice of the Scopus and WoS databases resulted in considering only references in English. But in human and social sciences, implied in the research on participation, other national languages are frequently used and should be considered.

2) *Lack of empirical validation:* The study relies primarily on a literature review and benchmarking of existing platforms. It is necessary to develop concrete case studies on the implementation of Data Labs for climate adaptation, based on user needs and local context to assess their effectiveness in real-world situations.

3) *Limited citizen participation:* Although citizen involvement in data collection and analysis is a promising avenue, governance mechanisms that enable genuine collective decision-making remain under-explored. Further research is needed to understand how to institutionalize these contributions.

4) *Integration of advanced technologies:* Emerging technologies such as Digital Twins and Artificial Intelligence offer considerable opportunities for urban adaptation. However, their effective implementation, interoperability, and accessibility for a wide range of stakeholders remain challenges.

5) *Consideration of Global South territories:* The study primarily relies on European and North American initiatives. It is essential to adapt these approaches to the realities of developing countries, where climate adaptation needs are often more pressing and technological infrastructure more limited.

6) *Cybersecurity and data protection:* The increasing use of open data raises crucial questions about cybersecurity and the protection of sensitive information. Establishing robust regulations and security protocols will be a major challenge.

7) *Funding and sustainability challenges:* The development of Data Labs requires initial investments and ongoing financial support. It is necessary to define sustainable economic models, including public and private funding as well as collaborative models.

8) *Developing greener technologies:* The development of Data Labs must not generate or amplify the climate phenomena that we are trying to limit.

Ultimately, realizing the full potential of Data Labs for climate adaptation will require systemic and inclusive approaches involving researchers, policymakers, businesses, and civil society.

Previous studies emphasize the need for sustained funding to support the development of climate services. Future European research and innovation (R&I) programs should prioritize investments that drive scalable and impactful solutions [22, p. 3]. However, evolving geopolitical landscapes and emerging political resistance to climate research could lead to significant barriers, requiring strategic adaptation in funding policies and research initiatives.

## V. CONCLUSION

The acceleration of climate change and the increasing complexity of urban challenges necessitate a profound transformation of adaptation strategies. In this context, Data Labs emerge as innovative tools capable of addressing the needs of information management and stakeholder mobilization for urban resilience. This study has identified the strengths and limitations of existing platforms and proposed an improved model based on key criteria such as data integration, citizen participation, and collaborative governance.

The results highlight several development areas:

* Optimization of data integration. The ability to merge diverse data sources (open data, participatory data, real-time data) remains a crucial issue.

* Development of inclusive interfaces. Making Data Labs accessible to all audiences, including non-experts, is key to maximizing their impact.

* Fostering a multi-stakeholder approach. A governance model integrating researchers, policymakers, businesses, and citizens enhance the relevance and sustainability of initiatives.

* Exploration of new technologies: Further integration of Artificial Intelligence, Digital Twins, and immersive platforms should be explored.

* Establishing a sustainable funding strategy. Involvement of public and private partners is necessary to ensure the continuity of Data Labs.

From a broader perspective, networking different Data Labs at national and international levels could enhance their effectiveness by facilitating knowledge sharing and good practices.

In conclusion, while Data Labs offer considerable opportunities for climate adaptation, their implementation and effectiveness depend on overcoming challenges related to interoperability, citizen participation, and data governance. Future research and experimentation will be necessary to define best practices and ensure the effective adoption of these tools in urban and environmental policies.

## VI. MATCH & CONTRIBUTION

As the IEEE TEMS bridges gaps between academia, industry, and government, Data Labs for climate change adaptation offer the opportunity to strengthen open innovation, open technologies and open communities (including civil society) as enablers of socio-economic and environmental transition [44].

The paper investigates the transfer of the Data Lab concept as tools for improving climate change adaptation in urban environments by integrating diverse data sources and engaging multiple stakeholders. As such, this contribution aligns well with the ICE IEEE 2025 conference theme on "Open Innovation and Collaboration: Living Labs and User-Driven Innovation". It is also a first work in designing a Data Lab as a strategy for "Innovation Ecosystems and Accountability: Public-Private Collaboration".

## ACKNOWLEDGEMENTS

The authors thank Prof. Claudine Guidat for her inputs.
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