The SDGs provide a global framework with a narrative to be implemented at the local level. Despite the inclusion of input from representatives of local governments during the formulation process, the SDGs remains a global agenda with an implicit focus on the national level. Consisting of a policy framework endorsed by all 193 member states of the United Nations (UN) and are set to be achieved by 2030. Currently, there is an ongoing process of localization facilitated by subnational actors, i.e., regional, and local entities (Greene and Meixell, 2017; Oosterhof, 2018).

The SDGs are associated with a large set of 248 indicators, which are intended to provide a comprehensive overview of progress while offering insights for future action. However, the reporting of these indicators is predominantly done at the national level, creating a challenge for subnational governments in identifying meaningful data sources to effectively measure the progress (Greene and Meixell, 2017; Oosterhof, 2018).

There is a critical need for high-quality, accessible, timely, reliable, and disaggregated data to facilitate the measurement of progress towards the SDGs (United Nations, 2020). Traditional data sources, such as official statistics, are proving inadequate for SDG measurement, necessitating innovative approaches for data collection (United Nations, 2016). At the same time, a “data revolution” is unfolding, driven by technological advances that have exponentially increased the volume and variety of available data (IEAG, 2014; Lämmerhirt et al., 2016; Fritz et al., 2019). The digital revolution, characterized by the widespread accessibility of broadband internet, mobile tools (e.g., dedicated apps and smart devices), and big data analytics, has transformed the landscape of information sharing, collection, and processing.

In addition, the rise of social media facilitates the promotion and encourages public participation in science projects at a range of scales, from local to global (De Rijck et al., 2020; van den Homberg and Susha, 2018; West and Pateman, 2017). This phenomenon opens unprecedented opportunities for societal transformation and environmental protection (Mahajan et al., 2022). Data generated directly by citizens to monitor, address, and instigate change on issues directly affecting them (referred to as citizen-generated data) holds the potential to offer a more precise and robust representation of progress. Often produced in real or near real-time, this data is firmly rooted in local contexts, amplifying citizen voices and perspectives on SDG progress, arguably including those typically marginalized and hard-to-reach populations. The production and utilization of citizen-generated data also fosters the direct, active and invested participation of individuals in advancing the SDGs (Higgins and Cornforth, 2015; Hecker et al., 2019; Moczek et al., 2021). While certain initiatives - such as eBird for conservation planning, and the European bird index for biodiversity and agicultural planning - have been successful in supporting environmental action, at both EU and Member State levels, the evidence points to a gap between the policy relevance and policy uptake (De Rijck et al., 2020).

In this research, we aim to offer nuanced insights to local governments regarding the barriers to the integration of citizen-generated data into monitoring initiatives like the SDGs and to suggest ways for optimization. To achieve this objective, we will construct a comprehensive framework delineating the data characteristics essential for suitable local SDG monitoring. Subsequently, we will translate these characteristics into requisite design requirements that must be considered to obtain these characteristics. The overarching goal is to formulate a set of generalized design principles that transcend diverse contexts. Citizen science projects can then refine and tailor these principles during the actual application phase.

The initial stage of this research involves desk research to identify pertinent data characteristics and their associated design requirements. Subsequently, the framework will undergo validation via a qualitative approach involving 10 semi-structured expert interviews (Van Audenhove and Donders, 2019). These interviews, conducted within the domain of citizen-generated data, will seek expert perspectives on the identified characteristics and design requirements.

The article’s structure follows a logical sequence, commencing with an overview of current literature on citizen-generated data for local SDG monitoring. Subsequent sections detail the research methodologies employed and the data derived from the interviews. The article concludes with a summary and points for discussion, aiming to contribute to the discourse on citizen-generated data for local SDG monitoring.

An expanding body of literature highlights the significance of localizing the SDGs. Although the SDGs were developed by and designed for national governments, the idea that there would be a crucial role for regional and local governments in the success of the goals was already present during the establishment of the Agenda 2030 (Reddy, 2016). Although the Agenda 2030 explicitly recognizes the key role of cities and municipalities by dedicating a specific SDG to Sustainable Cities and Communities (SDG 11), the importance of the local level within the SDGs goes further than SDG 11. The reasoning is that efforts to achieve certain subgoals will mainly have to be done in the context of local governments (cities and municipalities), which is the closest level of governance to citizens and is a necessary level to stimulate other actors to work towards these sustainability goals (UNDG, 2014). Various sources have estimated that around two-thirds or 65 per cent of the 169 SDG Targets will only be reached with a clear mandate and role for local (urban) actors in the implementation process (Cities Alliance, 2015; Lafortune et al., 2019; OECD, 2020).

These targets are linked to 62 per cent of all the Official Global Indicators (Ciambra et al., 2020), which brings us to the topic of local monitoring of the SDGs. To operationalise the SDGs, a form of progress measurement towards the goals will be necessary. However, the existing efforts done on the global (international) and national scale will not be sufficient at the local level because of various reasons. For example, national averages of scores on indicators can misinterpret realities on the ground and mask large regional or local disparities (SDSN & IEEP, 2019).

Local SDG monitoring not only necessitates a substantial volume of data but also demands data of high quality, extensive coverage, frequent availability, and spatial disaggregation (Fritz et al., 2019). Presently, SDG monitoring heavily relies on conventional and official data predominantly sourced from national statistical offices, like household surveys or administrative registers, often referred to as traditional data sources. Although essential and valuable, these traditional data sources exhibit several limitations (Ballerini and Bergh, 2021). Primarily are technical constraints, involving costly and time-consuming collection processes, along with limited spatial variation and coverage which results in infrequent data collection cycles, substantial data gaps, and unrepresentative samples that systematically exclude marginalized populations, such as vulnerable minorities, and sensitive issues (Lämmerhirt et al., 2016). Other often-mentioned constraints are related to the risk of manipulation by public officials to suppress contentious information, downplay their public institutions’ challenges or artificially enhance their performances, and inadequacy of traditional data sources for capturing contextual information and local knowledge (Ballerini and Bergh, 2021).

Given the inherent limitations in traditional data sources, there is a growing consensus for SDG monitoring to complement them with unofficial and alternative data sources, often referred to as non-traditional data sources (Fritz et al., 2019; San Llorente Capdevila et al., 2020; Ballerini and Bergh, 2021). There are various examples of non-traditional data sources. Fritz et al. (2019) divided them into 5 main categories: Earth Observations, spatial data infrastructure, official sensor networks, commercial data and citizen-generated data. This last category consitutes data voluntarily generated and gathered by individuals, herein referred to as “citizens,” with the purpose to monitor, advocate for, or instigate change in matters directly affecting them (Lämmerhirt et al., 2016; Ballerini and Bergh, 2021). It includes a wide variety of approaches and methods, with citizens utilizing a diverse array of technologies and participatory methodologies such as community-based monitoring, crowdsourcing on online platforms, or digital sensors (Lämmerhirt et al., 2016; Ballerini and Bergh, 2021). Typically, the production of citizen-generated data is initiated by citizens or civil society organizations and overseen by various intermediary entities, including non-governmental organizations (NGOs), academic researchers, private companies, and government agencies (Lämmerhirt et al., 2016).

It is worth noting that the existing literature lacks a universally accepted definition of citizen-generated data. Depending on the context, the term is frequently used interchangeably with concepts like citizen science data, community-driven data or participatory data (Lämmerhirt et al., 2016; Fritz et al., 2019; Ballerini and Bergh, 2021). Nonetheless, aligning with the perspective of Fritz et al. (2019), we position citizen science data as an integral part of the broader concept of citizen-generated data, where individuals actively contribute data as part of a scientific research process (McKinley et al., 2017; Serrano et al., 2018; Phillips et al., 2021). Presently, the prevailing focus within the literature predominantly centres on elucidating the significance of citizen science in contributing to the SDGs, as well as the utility of citizen-science data for monitoring SDG progress, as there are many examples of citizen science projects covering a diversity of domains that can contribute to the SDGs (Fritz et al., 2019; Phillips et al., 2021). We will thus use citizen science and citizen science data as umbrella terms that cover all these terms and diverse activities.

An examination of the potential role of citizen science in defining, monitoring and implementing the SDGs was initially presented in a discussion brief published by the Stockholm Environment Institute (West and Pateman, 2017; Müller et al., 2023) and since then the discourse on the impact of citizen science has evolved unto a growing research domain with growing output (Müller et al., 2023). Current research predominantly operates on the premise that citizen science can significantly contribute to the SDGs by facilitating the envisioned environmental and social transformations outlined in the SDG agenda, driven by its democratic principles (Sauermann et al., 2020; Alarcon Ferrari et al., 2021). These principles not only align with the SDG objective of “Leaving No One Behind” but also aim to enhance scientific productivity by involving society in the research process. Building upon these theoretical foundations, diverse stands of scientific literature have emerged. For instance, researchers have investigated how citizen science project coordinators evaluate alignment with the SDGs and the challenges encountered in contributing to them (Moczek et al., 2021). Moreover, studies have documented the actual and potential scope of various forms of contribution at project, local, national, and global levels (Fritz et al., 2019; Fraisl et al., 2020; Schleicher and Schmidt, 2020).

Despite acknowledging the potential of citizen science data for monitoring SDGs, the literature also highlights challenges and the limited integration of such data into monitoring and policymaking processes (De Rijck et al., 2020; Alarcon Ferrari et al., 2021). This aligns with broader challenges like data ownership (MacFeely, 2019), and the tradeoffs and negative impacts associated with big data and smart technology solutions for SDGs (Sharifi et al., 2024). For instance, financial limitations in implementing these technologies in cities can hinder progress towards specific goals like poverty reduction (SDG 1), zero hunger (SDG 2), and affordable and clean energy (SDG 7). Additionally Sharifi et al. (2024), identify trade-offs related to privacy, cybersecurity, infrastructure costs, biased decision-making that can exacerbate social inequalities (SDG 10) and limit accessibility due to the digital divide (SDG 9).

However, it is crucial to acknowledge that smart technologies also offer opportunities for achieving SDGs Sharifi et al. (2024). Highlight successful cases where responsible data governance practices and community-driven data collection (potentially linked to citizen science) helped mitigate challenges and contributed to positive outcomes. For example, initiatives that prioritize data privacy and security while empowering communities to collect and own their data can address concerns about data ownership and promote inclusive participation (SDG 10). Therefore, a nuanced approach that carefully considers both the potential benefits and drawbacks of smart technologies is necessary to ensure they contribute to achieving SDGs in a responsible and equitable manner.

This article extends the focus to understand how citizen science initiatives can be more effectively incorporated into local environmental SDG monitoring. Therefore, we delve into the conditions for enhancing the uptake of citizen science data into local governmental SDG monitoring mechanisms.

The focus centers on SDGs with a primary environmental emphasis, including clean water and sanitation (SDG 6), sustainable cities and communities (SDG 11), responsible consumption and production (SDG 12), climate action (SDG 13), life below water (SDG 14), and life on land (SDG 15) (UNEP, 2021). Citizen science activities span various environmental policy areas related to SDGs, making existing initiatives particularly relevant for providing valuable data to address these goals (McKinley et al., 2017; Nascimento et al., 2018; Serrano et al., 2018; Phillips et al., 2021).

The exploration begins with the scholarly work of Fritz et al. (2019), which introduces a conceptual model with five dimensions—spatial, temporal, thematic, process, and data management—each featuring distinct attributes highlighting the intrinsic value of citizen science data for SDG monitoring (Fritz et al., 2019). It provides a structured way to analyze various aspects of citizen science data across key dimensions, helping to understand its strengths and limitations for specific SDG indicators.

It breaks down the evaluation of citizen science data into different dimensions, this comprehensive approach ensures a thorough assessment of diverse factors influencing data suitability for SDG monitoring. Each dimension highlights positive aspects (attributes) of citizen science data, such as denser spatial coverage, more frequent updates and diversity of domains covered. This emphasizes the potential contribution of citizen science beyond traditional data sources. The framework provides specific values or descriptions for each feature, offering a concise understanding of how citizen science data performs in each aspect. Table 1 further explains the different dimensions and attributes.

As the framework serves as a theoretical model, this research objective is to customize it towards policymakers and citizen science practitioners. The primary aim is to enhance its practical utility, thereby amplifying its impact and fostering broader integration of citizen science for SDG monitoring. Beyond discerning the inherent value of citizen science, we seek to illuminate the requisites for extracting actionable insights from the data. We are particularly keen on identifying potential challenges and critical considerations that could hinder realizing the full potential of citizen science for SDG monitoring.

This study adopts the framework outlined by (Fritz et al., 2019) to structure the insights gathered from our expert interviews. However for improved clarity, we introduce two overarching layers that consolidate the original set of five dimensions. The Data scope layer encompasses the spatial, temporal, and thematic aspects of citizen science data. It aligns with the prevalent understanding of data scope as the breadth and depth of information captured within a dataset (Abraham et al., 2019). Conversely, data governance focusses on the management, processing, and overall governance structure of the citizen-science projects. It incorporates the formalization of data policies, standards, and procedures (Abraham et al., 2019), encompassing the data management and process dimensions from the original framework. This way it emphasizes the interconnected nature of data collection, processing and sharing. Additionally, we integrate data quality into the framework as a separate dimension under data governance, recognizing its importance as a barrier to citizen-generated data utilization in monitoring efforts (Fritz et al., 2019). This dual-layered approach provides a comprehensive structure for organizing and analyzing expert insights. It enhances the clarity and coherence of our analysis, facilitating a holistic understanding of the various facets associated with citizen science data within data scope and data governance. Table 3 below outlines the key elements of the adapted framework, including layers, dimensions, and attributes.

The research methodology employed in this article presents certain constraints that warrant consideration. The geographic focus is primarily on the BCR, and caution should be exercised when generalizing the findings to other regions. The intentional choice to closely examine an individual locality underscores the research emphasis on advocating for a bottom-up refinement of SDGs, aligning with existing research recommendations.

While the presented methodology offers a valuable framework, its application in diverse contexts necessitates careful consideration of site-specific factors such as the number of relevant citizen science initiatives providing data and the degree of local administrative support. These factors significantly influence its potential contribution to SDG monitoring.

A limitation arises from the sample set used for interviews, which primarily comprised of data experts and which was limited to 10 interviews. This selection choice imposes certain restrictions on the breadth of perspectives considered.

Furthermore, while the study engaged with data experts and subject specialists, it did not involve stakeholders from the local governments themselves, especially at the political level. Future research endeavors may benefit from exploring how insights from this perspective align with of diverge from those gathered, thereby contributing to a more comprehensive understanding. It is acknowledged that local (political) preferences regarding data-driven governance exist, and their inclusion in future investigations would offer valuable insights into these nuanced dynamics.

This article emphasizes the growing recognition of the crucial role of local governments in achieving the SDGs and thus the need for monitoring them at the local level. Traditional data sources, while essential, have limitations, leading to a call for supplementing them with non-traditional sources, like citizen science data. Existing literature explores the significance of citizen science data in contributing to and monitoring SDG progress, acknowledging challenges and limited integration into policymaking. It extends this discussion by focusing on how citizen science data can be effectively incorporated into local environmental SDG monitoring, investigating the barriers for uptake of citizen science data into local SDG monitoring efforts, with a focus on environmentally relevant SDGs. The exploration builds upon a conceptual model introduced by Fritz et al. (2019), which outlines dimensions highlighting the intrinsic value of citizen science data for SDG monitoring.

The study focusses on the BCR and involves qualitative data collection through 10 in-depth semi-structured interviews with citizen science data experts from various backgrounds. We started restructuring the original five dimensions of the conceptual model into two overarching categories—data scope and organizational data governance—which provides a clearer understanding of the complexities involved. Our examination of the conceptual framework unveils profound insights into the dimensions of data scope and data governance.

In the data scope layer, three key dimensions are highlighted. The first one is the extension of the boundaries of traditional data, surpassing them in terms of coverage, resolution, and granularity. It offers a nuanced understanding of issues such as air pollution and traffic flows. However, while expanding these data boundaries is essential, there is a need to ensure data aligns with monitoring objectives. Which brings us to our second dimension, the need for the data to be fit for purpose. Challenges include increased data processing demands and the importance of understanding local government’s data needs. Alignment with the SDGs is crucial, but projects often lack awareness and struggle with the complex nature of the SDG Framework. A third dimension revolves around the representativeness of the data being collected. Inclusivity is crucial to avoid biases and align with the SDG principle of “Leave No One Behind.” Challenges arise in achieving diversity, with current trends involving economically privileged individuals. The integration of underrepresented groups significantly enhances the richness of citizen science data, contributing to the democratization of knowledge crucial for informed policy formulation. Collaborations with intermediaries and educational institutions are suggested to enhance representativeness.

Regarding the data governance layer, there are two main components: the data process and data management. Within the data process, our analysis highlights the challenges of sustaining citizen science data projects beyond one-off initiatives. Often financial constraints lead to the temporal nature of data collection, presenting hurdles that require careful planning and resource allocation. Although there are examples of projects that successfully transitioned into sustainable service providers for local governments. The importance of citizen engagement is underscored, acknowledging the challenge of managing engagement fatigue. Furthermore, ethical and legal considerations are highlighted as crucial, emphasizing transparency, informed consent and clear guidelines for data ownership in citizen science data projects.

In the realm of data management, adherence to FAIR principles is identified as key to facilitating collaboration and ensuring the quality and usability of generated data. However, challenges persist in standardizing data practices, publication in open formats, achieving interoperability, and data integration with other existing data sources. Regarding data quality, our experts emphasize that not only the actual quality of citizen science data is a recurring concern, but they underscore the need for expertise in data interpretation, with higher governments in a supporting role.

Based on qualitative research, we enhanced the conceptual model for the BCR with data scope characteristics, including data extending traditional boundaries, the fitness for a specific objective and ensuring the representativeness of the collected data. The necessary design requirements are situated within the domain of data governance, including considerations related to the sustainability of data collection processes, ethical and legal dimensions, standardized data publication, data accessibility, data integration and data quality assurance.

Our results provide local stakeholders and in particular policymakers practical insights into how the barriers for uptake of citizen-generated data into local SDG monitoring of environmental indicators can be overcome. As such, we want to improve the uptake of citizen-generated data projects into Environmental SDG monitoring in the BCR.

The findings underscore the multifaceted nature of integrating citizen science data into policy, highlighting the need for a holistic and adaptive approach. Here, “holistic” refers to the inclusive consideration of diverse dimensions within the framework, while “adaptive” signifies the recognition that contextual factors play a crucial role, thereby acknowledging the limited generalizability. Effectively integrating citizen science data into SDG monitoring at the local level necessitates addressing challenges related to data scope, data governance and context-specific considerations.

In future research endeavors, this framework can be employed to substantiate the practical impact and iteratively refine the underlying framework. Additionally, its applicability may extend to other geographical territories or be adapted for application to citizen-generated data sources directed towards non environmental SDG indicators.