The EU Green Deal and the ensuing policies and regulations to stimulate the transition toward a circular economy pose challenges to policymakers and authorities. Taking planetary boundaries into account is a nascent topic on all regulatory levels, and data-driven policymaking and its implementation require the collection and access to new types of data in all policy-making phases, from agenda-setting to policy formulation, implementation, and evaluation. Extant studies into data-driven policymaking have not yet addressed which information types are needed and how policymakers and enforcement agencies can gain access to data sources, whereas the urgency to prepare for this is high. We use the lens of the policy cycles to assess the required data. In three typical cases, we explore the data sources at different policy levels of monitoring to develop a conceptual framework of data attributes to inform policymakers. We position that the extant data used in the policy phases for the transition to the circular economy are different from the familiar data that public administrations use in their respective domains. Our conceptual framework provides an initial overview of new types of data and potential shared use among the policy phases to support policymakers and enforcement agencies to timely prepare for access to the relevant data and data sources. We recommend the creation of data ecosystems for public administrations, the adoption of new capabilities for CE literacy, exploring the added value of Digital Product Passports, and AI-based tools and mechanisms to handle large volumes of data to structure messy data.
Under the EU regulatory framework to implement the vision of the Green Deal [
2.1.
Nowadays, the CE is often seen as a means for achieving sustainability by preserving and maintaining
natural resources while mitigating the negative environmental impacts of existing ‘linear’ economic
models and enhancing territorial resilience by reducing dependence on virgin materials [
The EU's emphasis on circularity has also trickled down to its Member States. For instance, the
Netherlands has committed to establishing a fully circular economy by 2050, with an interim goal of
reducing raw abiotic material use by 50% by 2030 [
More recently, the CE has also gained traction as a strategic framework for urban development.
Recognizing the central role of cities in this transition, the EU’s CEAP [
Data-driven policymaking builds on the concept of evidence-based policymaking, where data is
instrumental to ground political judgments in scientific and empirical findings [
In the context of CE, Medaglia et al. found that digital government plays a key role in driving the
CE transition by establishing policies, regulations, and requirements for monitoring and control [
The cycle begins with agenda-setting, where data is essential for raising awareness about societal issues. In the policy formulation stage, data is used to ground potential solutions in empirical evidence. During decision-making, policies are selected based on a set of measurable criteria, ensuring that the most effective options are chosen. In the implementation phase, one can distinguish two aspects with respect to the use of data. One is that enforcement agencies can use data to monitor the compliance of businesses or citizens with existing regulations. Second, data can also be used to track whether the policy is being implemented as planned. As policymaking is not an exact science, data is needed after an initiative has been implemented to evaluate whether the intervention is successful or needs to be refined.
In the context of the CE, data-driven policymaking necessitates the availability and use of relevant data throughout each stage of the policy cycle. However, current research offers limited insight into the specific data requirements at different stages of CE-related policymaking, as well as how these data can be coordinated and aligned across stages. This article seeks to explore and conceptualize the types of data currently utilized by policymakers and enforcement authorities across the various phases of the policy cycle. We limit ourselves to four policy phases; further research can focus on adding the decision-making phase as well.
To develop the conceptual framework, we draw on the author’s expertise and insights from ongoing and finalized research projects, and related project reports, other publications, and artefacts. The policy phases of agenda-setting and policy formulation are examined through two research projects focused on developing governmental CE monitoring systems in Flanders. Project A involves the creation of a regional CE monitor, developed collaboratively by researchers and policymakers to provide more direct feedback on the CE transition [23]. The development of this online monitor spanned five years, beginning in 2017, with its initial publication in 2021 and an update in 2024. The publicly accessible platform (https://cemonitor.be/) provides a comprehensive assessment of circularity through approximately 100 indicators. These indicators, sourced from statistical bodies and new research findings [24] follow a layered approach, measuring the CE transition at three levels: micro (product categories), meso (need satisfier systems such as food, mobility, and housing), and macro (overall circularity and its broader effects) [25]. Project B is a monitoring framework developed together with the public administration of a city government to measure and monitor the progress towards circularity in the city. In this case, indicators are linked to the action possibilities of different stakeholders in the urban ecosystem, emphasizing the city government’s role in steering the transition. A central focus of the monitoring system is to track and assess circular initiatives taking place in the city. The phases of policy implementation and evaluation were prominent in the finalized project C (DATAPIPE project), in which the goal was to explore how data available in (business) digital infrastructures and in future Digital Product Passports (DPPs) can be used for CE monitoring tasks. In this project, the concept of CE monitoring was explored with a team of experts with, on the one hand, a background in international trade and the use of data for border control (for the policy implementation phase) and, on the other hand, experts from policy and environmental analysis (for the policy evaluation phase). For each phase, we selected an illustrative case to show the different requirements for data-driven policymaking in the execution of monitoring tasks by policymakers and enforcement agencies.
In the following section, we elaborate on the four phases of CE monitoring to arrive at a conceptual framework with an information and data typology for CE monitoring. This framework shows the diversity of data requirements and the relevant data sources to support CE monitoring tasks on the micro, meso, and macro levels in four phases of the policy cycle. This framework is meant to inform and support policymakers to timely prepare for their CE monitoring tasks.
4.1.
The agenda-setting phase often relies on aggregated data from statistical bodies, presented as indicators within a monitoring system. This macro level information helps conceptualize the CE, providing policymakers with a structured framework to enhance their understanding and raise awareness of the focal areas of the CE transition to intervene. In this regard, these indicators enable policymakers to identify and focus on high-priority issues in discussions and debates.
For instance, the indicator mass of new cars on the market in the CE monitor remained stable until 2018 but exhibited a sharp upward trend from 2019 onward, with an average annual increase of approximately 60 kg between 2019 and 2023. This shift is likely attributable to the growing adoption of electric vehicles, which tend to be heavier than their conventional counterparts. As a result, the visualisation of this trend may spark debates among policymakers on the rebound effects of the electric transition, particularly regarding the need for additional materials. A similar trend in the mobility sector was the sudden rise of electric bicycles in the data, which serves as evidence for the modal shift. Setting the CE agenda at the municipal level requires city-specific data. Macro-level information, such as the average regional material footprint, offers limited insights into the specific contextual factors of individual cities. While in reality, such indicators are sometimes used as proxy indicators to quantify the broader landscape of the CE, they fail to address the heterogeneity for evidence-based policymaking at the city level. Capturing the diversity of demographic, spatial, and socio-economic features that characterize a city requires more granular data. Meso-level information that is available at the city level, such as waste collection rates, is sometimes used as a proxy to determine the resource efficiency in cities. This allows local governments to pinpoint areas where additional awareness campaigns or infrastructure improvements may be necessary to improve the collection rates, ensuring that policy discussions are tailored to the specific needs of each municipality. At the micro level, data on household characteristics and consumption patterns offer insights into the demographic and socio-economic factors driving (un)sustainable consumption behaviors. For example, data on household expenditure behavior on reuse activities reveal that younger people are more likely to repair their goods [26]. As a result, this type of micro-level information can raise awareness among policymakers to target other demographic groups, encouraging broader adoption of the reuse movement. So, in the agenda-setting phase data is mainly used as a conversation starter to ground and steer the policy debate by evidence. 4.2.
In the policy formulation stage, access to relevant data is essential for policymakers aiming to make evidence-based decisions for the CE transition. There are no formally established CE policies at the Flanders Region or city level. However, in recent years, policy discourse has increasingly focused on setting macro-level targets to operationalize the CE transition in Flanders [27]. Data is needed to ground these targets in scientific evidence. For instance, the Flemish government has set the target to reduce the material footprint by 30% by 2030. To operationalize this target, they rely on material flow accounts (MFA) analysis, using environmental regional input-output databases, to quantify resource consumption and flows in the economy [26]. Another example of how data informs the formulation of policy targets is seen in the 2024 update of the monitor, where material productivity indicators were given a more prominent role. This change was the result of dedicated research on CE targets in dialogue with stakeholders from the Flemish transition landscape As a result, the policy document from the Department of Economy, supporting the 2024 government agreement of the newly formed Flemish government, designated material productivity as a key performance indicator (KPI) for the Flemish economy [28]. Current efforts are therefore directed toward a more nuanced understanding and interpretation of material productivity metrics, with the aim of informing future discussions on setting concrete targets for this KPI.
At the city level, meso-level information supports alignment with regional goals and justifies interventions. For instance, the Flemish government set a target to reduce residual waste to a maximum of 100kg per capita per municipality [29]. Local authorities use waste volume data to monitor progress, compare performance across cities, and design targeted measures such as increasing the cost of residual waste treatment to encourage better sorting and reduction.
In the context of higher-value material retention, the city is exploring innovative ways to recover waste streams and reintegrate them into the urban ecosystem. One key initiative is the Material Bank, which aims to recover over 1,000 tons of building materials annually by creating a secondhand market for building materials. It is considered a strategic project to help achieve the city’s climate and circular economy goals. Achieving this target requires micro-level data on both supply (e.g., demolition project locations) and demand (e.g., quality of recovered materials). This information is essential to refine policy targets and scaling up urban circular procurement initiatives. Overall, in both case studies, data in the policy formulation phase is primarily used to operationalize CE targets and ensure their scientific and practical foundation. 4.3.
CE monitoring in the policy implementation phase is mainly driven by the challenge to control and manage business compliance with the legislation. The implementation phase includes both businesses implementing the legislation to be compliant with the new requirements, as well as enforcement agencies checking the compliance. In the context of the transition toward a CE, new legislations such as Ecodesign for Sustainable Products regulation [30] and the Battery Regulation put CE requirements on products that are put on the EU market (e.g. EV batteries) [31]. These products are often produced abroad and imported into the EU. Related developments are the Critical Raw Materials (CRM) Act aimed at keeping CRMs in the EU [32] and waste-related legislation like the Waste Shipment Regulation [33]. In the context of these legislations, an important monitoring task for EU governmental executive institutions such as border authorities is to monitor the import and export of products and materials. The question is which business data for CE monitoring can be used in this policy implementation phase.
For border authorities, business data such as invoices containing information about goods and the value of the goods is potentially valuable for cross-validating import declarations for fiscal aspects. This is to check whether the declared value of the goods in the import declarations corresponds to the value of the goods as reported in business documents [34]. This is an example of business data that authorities use for their current operations. However, with CE-related legislation, other data elements may be needed to monitor for circularity and sustainability concerns.
From the point of view of the information scope (macro, meso, and micro), in the implementation phase, authorities are focused on controlling specific good flows (e.g. border authorities monitoring import and export of cars, waste, etc., and risk assessing individual shipments; or market surveillance authorities monitoring cars put on the market whether they comply with the legislative requirements). Due to the nature of these tasks, micro-level data about the individual products is most relevant. For instance, very specific data elements about the battery used in electric cars can be used for controlling individual cars as well as the EV batteries in those cars.
But, in the implementation phase, macro and meso level data can also be relevant to serve as a trigger for increased attention for monitoring specific areas. For example, a steep increase in export numbers of electric vehicles from Europe or a steep increase in the export of scrap from one year to another at the macro level can be a trigger for the border authorities to conduct more checks on individual cars (micro level) to identify what is going on and to ensure that regulations are properly enforced. Also, meso-level data (for example, data about drastic shifts of export flows of cars or scrap from one region- e.g., the Port of Rotterdam to another, e.g., the Port of Antwerp) can be insightful for authorities and trigger extra checks to identify irregularities. However, while the macro and the meso-level data may act as triggers to direct attention towards specific streams of goods, for the daily monitoring and risk assessment of the goods, the micro-level data (or additional data about the specific transaction or products) is more important. To illustrate, we zoom in on a specific example related to EV battery data.
In the DATAPIPE project, we focused on how authorities may potentially benefit in the future from accessing business data that resides in business digital infrastructures and DPPs to enhance their compliance monitoring tasks for the CE. The Ecodesign for Sustainable Products Regulation (ESPR) [32] stipulates that products from specific product groups that are placed on the European market will need a DPP. The first DPPs for batteries that fall under the Battery Regulation are mandatory from 2027, with the expectation that other product groups will be gradually introduced under the ESPR and other legislations to include textiles, electronics, tires, toys, etc. These DPP developments are in an early phase: the allocation of roles and responsibilities of the authorities for enforcing these legislations are still being shaped. Hence, we used a potential imaginary scenario, building on insights from the Battery Regulation to assess the potential benefits of using DPP data for compliance monitoring. Next to the Battery Regulation, we took as inspiration the EU policies on limiting the export of waste to non-EU countries and on the enhancement of resource resilience by keeping CRMs in the EU. Our perspective was based on these (forthcoming) regulations and the potential monitoring role of EU border authorities, and we focused on the export of Electric Vehicles (EVs) that contain EV batteries. This example was further inspired by the cases where authorities make use of micro-level business data (as the example about the fiscal risk assessment given before) for their risk assessment processes. EV batteries contain CRMs which the EU wants to keep within their own borders when they become waste, to become less dependent on the import (of virgin CRMs) from non-EU countries. Current macro data indicates that second-hand vehicles that approach their end-of-life are exported to Africa. This raises issues with dispatching some of the responsibilities for (sustainable) end-of-life treatment to countries outside of the EU. This displacement may also have environmental consequences in the destination country.
In this scenario, we explored the potential role of data in Battery Passports for CE compliance monitoring tasks. In particular, the dynamic data related to the battery's state of health can be of interest to the border authorities as one of the indicators of whether a car that is declared for export approaches its end-of-life. When available, this data element can be valuable for customs to decide whether a car is approaching end-of-life. If so, they can be considered as waste and banned from export and the car would need to be dismantled following EU regulations. This will also allow the CRMs to be retained for reuse in the EU. In this example, similarly to the invoice example for fiscal risk assessment, transaction level (micro-level) DPP data is potentially valuable to border authorities as additional data for their risk assessment for future monitoring of the CE and for sustainability concerns. 4.4.
As the EU aims to achieve climate neutrality, establish a CE, and ensure strategic autonomy, monitoring the state of play regarding material flows, stocks, and (embodied) environmental impacts on a macro-level (national and/or EU level) is vital. In the policy evaluation phase, this allows for the evaluation of the progress towards meeting the sector, national or continental commitments and obligations set in various regulations. The European Green Deal lays down targets for the emission reduction of greenhouse gases [35], and the Critical Raw Materials (CRM) Act [33] sets targets on the mining, processing, and recycling capacity of CRMs within the EU. To keep track of the intended policy pathway and ensure transparency and accountability, regular monitoring of the state of play is required. Particularly, as the transition is intended to happen within a determined time frame, there is a high pace of change in the economy, and monitoring of sector, national, or continental commitments should be on top of the progress. This requires macro-level data.
The need extends beyond monitoring the progress towards the commitments: authorities need to be able to steer and enforce as well. The EU implemented a series of regulations for the transition towards a sustainable economy. Monitoring might identify undesired effects, such as carbon leakage or shifts in material use. Monitoring the state of play allows us to identify whether the legislation effectively contributes to the transitions and whether it is sufficient to meet the targets. If legislation proves inadequate, public decision-makers need to provide evidence of undesired effects to justify the possibility of adapting legislation throughout the transition. Some sustainability regulations, such as the Ecodesign regulation [30] and the Battery Waste regulation [36], lay down sustainability requirements based on the realistic technical and scientific situation. To make sure that the requirements are feasible and challenging at the same time, continuous refinement is required to align with the market situation.
Different monitoring practices have already been put into place by the EU and its Member States to keep track of material flows, stocks, and related environmental impacts. Most implemented practices apply a top-down assessment approach, which is based on collected statistical transaction data and makes use of supplementary information regarding composition and/or input-output flows. Supplementary information is applied when the collected data does not suffice for the desired information. The representativeness of the supplementary information used is a key challenge for the monitoring’s accuracy following a top-down approach. The analysis’ accuracy is particularly affected when there is a large uncertainty or variability in the supplementary information. Furthermore, the information is often not up to date as it is based on historical situations [37].
The deployment of data in business digital infrastructures for gathering micro-level data from economic operators can enhance macro-level monitoring. Collecting more detailed sustainabilityrelated information concerning products, processes, facilities, or transport of economic operators allows the retrieval of previously inaccessible information, reducing the need for supplementary information and increasing the credibility of public interventions. Adding business data to macro-level monitoring can help overcome challenges related to data representativeness and timeliness.
This study demonstrates that effective CE monitoring within public administration requires a multilevel information approach, integrating diverse data types and aligning them with different phases of the policy cycle. As shown in Figure 2, each phase has distinct information needs and objectives, which determine how information is computed at the macro, meso, and micro levels. Figure 2 places these policy phases horizontally and vertically distinguish the three information levels: macro (supernational, national, regional), meso (industry sector, municipalities), and micro (business, product, households). At the macro level, authorities receive aggregated statistics and proxies that capture broad trends in resource consumption, employment in circular sectors, and material footprints. These data points support high-level agenda setting and help policymakers evaluate whether overarching policy goals, such as reducing total material use, are being met. However, macro-level data can mask local variations and requires complementary insights from the meso and micro levels.
At the meso level, municipal and city-specific data refines broad aggregated metrics. Indicators like waste collection rates, building demolition numbers, and localized product trends enable policymakers to design interventions that reflect the needs of distinct urban or sectorial contexts. By highlighting local conditions, this approach bridges the gap between strategic policy objectives and practical implementation. Meso-level data is used in policy formulation to identify specific areas requiring attention and in policy implementation to support ongoing monitoring of local performance.
At the micro level, attention shifts to granular information, often based on transactional and process data that businesses record in their digital infrastructures, including DPPs. These systems offer detailed insights into product characteristics, materials used, and manufacturing or operational processes, all of which are essential for real-time risk analysis and compliance checks. For instance, product-level data can help border authorities determine if a vehicle’s battery is near end-of-life, an issue with potential ramifications for illegal exports or improper waste handling. This level of detail allows for immediate corrective actions when legislative requirements are not met.
Figure 2 also categorizes data by characteristics, amount or volume, process, exchange, monetary, and location data, such as material origin or the presence of substances of concern, illustrating how each type can be aggregated or analyzed in different policy phases. The data can serve as direct input for policymaking in the format of micro-level information or form the basis for the analytics that generate meso and macro information. Ownership and control of data sources is a key factor in the design of effective CE monitoring systems. At the micro level, private businesses frequently hold data, which means that authorities often need (legal) incentives and consent mechanisms to gain access. Certificates and assurances that verify regulatory compliance may be shared through direct digital transmission or via intermediaries such as certification agencies. In contrast, data stored in government systems, such as centralized DPP registries, is under public control and can be accessed by authorities with the proper permissions. This dual ownership model requires transparent and secure data-sharing arrangements to maintain trust between businesses and public administrations.
There are several limitations to our research that merit discussion. First, our focus on existing digital infrastructures and data typologies may not fully capture the rapid technological changes that influence how data is generated, stored, and analyzed. As new tools and platforms emerge, the data categories we have identified could shift or expand, indicating a need for ongoing refinement of the typology. Second, we used the policy cycles as a framework to simplify reality by distinguishing different stages. However, in practice, these stages often overlap and interact. This was illustrated in the city monitoring case: data to inform the policy formulation on waste treatment was later used to report waste fractions and to evaluate the effectiveness of the intervention. By combining information at the macro, meso, and micro levels, policymakers can develop an adaptive, evidencebased approach to guiding the transition to a circular economy. Aggregated statistics and proxies at the macro level inform national targets and long-term strategies, more granular data at the meso level shapes localized interventions, and detailed exchange data at the micro level facilitates realtime compliance checks. Although our framework provides a solid foundation for data-driven multilevel CE monitoring, the evolving nature of digital infrastructures and the potential for AI-based technologies to enhance data analysis and predictive capabilities suggest that further refinement is necessary. Future research is needed to investigate innovative technological solutions, including AIbased analytics, to strengthen public administrations’ ability to monitor and enforce CE initiatives in an increasingly dynamic digital landscape. Third, our study is predominantly grounded in a European context and may not translate seamlessly to regions with different regulatory frameworks or technological capabilities. Further research is needed to ensure that the proposed monitoring framework remains relevant and adaptable to diverse contexts.
Policymakers and public authorities have an important role in steering the CE transition. Whereas governments rely on specific data sources for their tasks (e.g., policymakers on macro-economic statistical data and border authorities on customs and other declaration data), new CE monitoring tasks require them to look beyond these familiar data sources. This creates uncertainties on how to get access to relevant data and requires preparations with a sense of urgency in view of the timelines of the EU regulatory framework for the transition to a CE. For their (potential) CE monitoring tasks, data is essential through all stages of the policy process: agenda setting, policy formation, implementation, and evaluation, on all levels. By zooming in on the different needs and roles governments have during these stages of the policy cycle, we identified a variety of information types and data sources that governments (can) deploy. By analyzing three typical cases of CE monitoring in each policy phase, we created a conceptual framework to inform and support policymakers and public authorities on different levels of policymaking and enforcement to prepare for gaining access to relevant data.
In the early policy phases, combining data sources can enhance CE monitoring by improving data quality and granularity. Currently, macro-indicators such as material footprints or material flows are derived from top-down regional input-output models, which provide a general idea of resource consumption in Flanders. However, these indicators overlook sectoral and municipal differences, which limits their usefulness for targeted policy interventions. The integration of micro- and mesolevel data, such as LCA data or household budget surveys, can identify key drivers (e.g. specific product categories, household characteristics or sectoral impacts) and highlight hotspots for agenda setting and provide more actionable insights for policy formulation
With the advancement of digital infrastructures on the business side (either due to business drivers or because of policy interventions, as is the case with DPPs), increasingly more data will become available. This data can be used for different purposes. For example, in the policy implementation phase, micro-level business data from DPPs can be used to cross-validate legally required transactional data for the purpose of compliance control by customs authorities. And in the policy evaluation phase, aggregated micro level data can yield additional input to the macro-economic data that is used for policy insights. Hence, data quality and availability can be enhanced for CE monitoring tasks.
Beyond showing the information and data typology in our conceptual framework, we offer two ways forward for policymakers and enforcement agencies. First, the preparations for gaining access to the relevant data require investments in IT capabilities and technical skills. If public organizations have similar interests, they can collaborate to share their capabilities and jointly create incentives for businesses to voluntarily share data beyond the mandatory data. Second, even if the same data (source) is of interest to several public organizations, a tailored approach is required as their roles in CE monitoring are either on different levels or in different policy phases. Therefore, we recommend future research into the creation of data ecosystems for public administrations to exchange knowledge and capabilities for CE literacy, to explore the added value of DPPs, and of AI-based tools and mechanisms to handle large volumes of data to structure messy data.
This paper has been partially funded by the DATAPIPE project, which has received funding from the European Union’s Technical Support Instrument (TSI) programme under grant agreement No 101094495. Ideas and opinions expressed by the authors do not necessarily represent those of all partners. L. Alaerts acknowledge the financial support received from the Flemish administration via CE Center (Steunpunt Circulaire Economie). R. H. Reich is grateful for the internal C2-funding of KU Leuven (Grant No: 3E210013). M. Pauwels is grateful for KU Leuven's funding provided by VITO (Grant No: 3E230665). This publication contains the opinions of the authors, not those of the Flemish administration. The Flemish administration will not carry any liability with respect to the use that can be made of the produced data or conclusions.
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