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A Framework for Pairing Circular Economy and IoT

Creating value by developing business models for the interplay of circular economy with IoT

Published onJun 20, 2023
A Framework for Pairing Circular Economy and IoT
Chrysostomos Adamidis1, * Annette Craven1, Dariusz Dober 2, George Alexandris1
1École des Ponts Business School, France; 2 BlueSoft Sp. z o.o;
*[email protected]


Economic growth from increasingly scarce resources is becoming a key global challenge. The circular economy (CE) provides a promising alternative to the current economic system. It is conceived as a continuous positive development cycle, reforming the current economic model of “take-make-dispose” by preserving and enhancing natural capital, optimizing resource yields, and minimizing system risks by efficiently managing finite stocks and renewable flows (Morlet et al., 2016).

The internet of things (IoT) connects the digital and physical worlds by collecting, measuring, and analyzing data to predict and automate business processes. It enables the implementation of circular economy principles, including resource efficiency, asset lifecycle extension, and maximizing asset retained value and recovery.

This paper explores the business model for the interplay of circular economy with IoT. Specifically, pairing circular principles and integrating IoT architecture to develop Poland’s intelligent Demand Side Response (DSR) electricity system. The model proposes the use of intelligent technology assets to regulate energy demand according to energy supply and thereby reducing the costs for the consumers and improving asset utilization for the energy distributors while at the same time reducing overall energy consumption and carbon emissions and increasing the potential for the use of renewable energy sources.

The DSR model is developed by focusing on the external digital business ecosystem. Further research is required to identify the intra-organizational drivers and challenges organizations face in adopting digitally enabled circular business models.


CE-IoT framework, circular business models, data-driven business models, demand response, energy sector. 


The circular economy (CE) aims to decouple economic growth from resource use, eliminate waste by design and support climate neutrality by 2050 (European Commission, 2019). Transitioning to the CE is a viable path toward curbing greenhouse carbon emissions (GHG) and maintaining the temperature increase below 2 degrees (Circle Economy, 2021). Some estimates indicate that implementing the CE can reduce 39% of global GHG emissions and 28% of virgin resource use (Circle Economy, 2021).

Decoupling economic growth from resource constraints requires new circular business models and value generation methods (Rosa et al., 2020) across all value chains aiming to transition from the linear economy, reduce carbon emissions, and reach climate neutrality by 2050 (Khanna, Gusmerotti & Frey, 2022).

The internet of things (IoT) plays a significant role in adopting CE principles. It is estimated that 10 billion physical objects with embedded information technology exist today, and many additional smart and connected devices are being built (HBR, 2014). In this sense, IoT can positively impact the efficient exploitation of natural resources and the development of circular business models. Further clarity is required on IoT’s impact on enabling the CE and how it can support the design of circular business models to advance circularity (Rosa et al., 2020).

This research aims to develop an innovative framework of the interplay between CE and IoT (Figure 1) and explore novel ways in which this interaction can drastically change the nature of products, services, business models, and ecosystems. The research question is, How can value be created by developing business models for the interplay of circular economy with IoT?

The proposed CE-IoT framework can help develop novel CE business models and service supply chains to unlock CE-IoT synergies. These will generate direct value for customers/end-users and augment resource productivity across economies by forming new ecosystems that eliminate negative externalities and the need for considerable resources. Additionally, it will develop open, circular-by-design IoT architecture, where “smart” IoT objects (e.g., sensors, devices, systems, and components) are integrated into the IoT ecosystem through patterns with proven key circularity-enabling properties (scalable connectivity, end-to-end security/privacy, dependability, and interoperability) thereby maximizing IoT resources and data harvesting in a new breed of circular-by-design IoT ecosystems.

Figure 1: CE-IoT concept (Source: CE-IoT grant agreement 777855, part b, p. 11)

This paper explores a business model for the interplay of circular economy with IoT. Specifically, pairing circular principles and integrating IoT architecture to develop Poland’s intelligent Demand Side Response (DSR) electricity system. In this framework, stakeholders engage with intelligent meters that collect and communicate the electricity consumption, which is then processed and analyzed to predict future energy usage. As a result, the technology embedded in the system determines the essential and non-essential load consumptions, and based on the energy supply, determines the energy distribution within the system. The DSR solution is preliminary and will be further elaborated as a case study for Bluesoft, a software provider and IT solutions company in Poland, for IoT-enabled circular business models for the Polish energy sector.

Section 2 briefly presents the theoretical framework and the literature in the field of CE and IoT. Section 3 outlines the proposed DSR framework for the energy sector in Poland. Section 4 presents a recommended way forward and a proposed methodology for an embedded case study at Bluesoft to establish the enabling factors and the barriers for driving organizational change in pairing CE-IoT and creating business value with the proposed solution. Section 5 presents the conclusion. Section 6 is the contribution to the conference.

Theoretical framework and literature review

Theoretical framework

The pairing of CE-IoT aims to define new value-creation mechanisms for the energy sector by utilizing the enabling power of IoT to implement circular business models, which result in efficient asset utilization, increasing use of renewable energy sources, and reduction of carbon emissions. We approach the research from an organizational change perspective since, ultimately, organizations need to implement organizational changes in structure, processes, and culture to transition from the “business as usual” linear models to circular business models and take advantage of new business opportunities and value creation mechanisms while enhancing resource efficiency (Khanna et al., 2022). The need for organizations to adapt their business models invites an organizational change perspective viewed as a “difference in form, quality, or state over time in an organizational entity” to examine the phenomenon of implementing a CE-IoT framework (Hanelt, 2021, p.2).

In this respect, the first step in our research is to identify the circular opportunity and develop the possible DSR solution for the energy sector in Poland, which could create for Bluesoft a first-mover competitive advantage and new value streams for all stakeholders while simultaneously promoting circularity by reducing energy demand and improving asset utilization. This addresses the research question. How can value be created by developing business models for the interplay of circular economy with IoT? It does not explore the challenges organizations face in practically implementing digitally enabled circular strategies and the barriers to adopting such technologies to promote circularity (Rejeb et al., 2022). Therefore, through an embedded case study, the recommended follow-up is to identify the enabling factors and barriers for driving Bluesoft’s organizational change in creating business value by adopting a CE-IoT circular business model. The two-step approach supports a holistic understanding of organizational change in the surrounding digital business ecosystem, including adopting IoT technology and CE principles and the intra-organizational drivers supporting the transition to circular business models (Hanelt, 2021).

Circular Economy and IoT

The CE concept is an amalgamation of many ideas (Winans et al., 2017) aiming “to slow depletion of scarce natural resources, reduce environmental damage from extraction and processing of virgin materials, and reduce pollution from the processing, use, and end-of-life of materials” (Ekins, 2019, p.17). The European Commission has developed the nine R strategies (refuse, rethink, reduce, re-use, repair, refurbish, remanufacture, repurpose, recycle) (European Commission, 2020), which support the implementation of the key identified circular business models (circular supply models, resource recovery models, product life extension models, sharing models, product service system models) and can lead organizations to competitive advantage and sustainable development (OECD, 2018).

Circular business models can support new value creation by strengthening the collaboration and coordination of all stakeholders and improving resource use and efficiency (Geissdoerfer et al., 2020). To this end, IoT and other digital technologies play a significant role by enabling the orchestration of the stakeholders’ activities and resources to pursue system-level goals and enable sustainable development (Blackburn et al., 2022).

IoT brings together devices, things, or objects and facilitates communication and cooperation with people using modern wireless communications such as sensors and radio frequency identification (RFID) (Rosa, 2020). The result is improved monitoring and control of processes, location tracking, and condition of assets, data gathering and analysis, and strengthened feedback loops facilitating real-time decision-making. As such, IoT is the key driver for integrating processes, facilitates predictive analytics, and supports data-driven solutions, thus enhancing the circular economy value drivers such as ecosystem collaboration and resource management (Chauhan et al., 2022). In addition, the possibility to monitor and track a product’s lifecycle and use, combined with technologies such as big data analytics and artificial intelligence, enables the adoption of user-oriented models, and improves customer experience. In this way, users can be guided on optimal product use, extending product lifetime, improving resource efficiency, and supporting maintenance and reusability of components (Han et al., 2023).

Similarly, organizations improve their decision-making ability by using smart sensing and data analysis to inform on the condition of the assets and create an industrial network for re-using/recycling assets to maximize product life and economic benefit (Hatzivasilis et al., 2019). The application of IoT technologies to achieve CE outcomes includes data collection using IoT sensors and actuators, data integration using relational database management systems and database handling systems, and data analysis using machine learning and big data analytics (Pagoropoulos et al., 2017). This process can occur from the extraction of resources until the end of the use cycle providing invaluable data to support a more efficient and circular value chain. Moreover, the accumulated interactions between a vast amount of connected smart objects and stakeholders and the potential for evaluating their actions and outcomes facilitate the development of institutional frameworks and processes to enable the implementation of CE (Han et al., 2020).

Leveraging IoT in the energy sector to advance circularity

Global energy consumption is steadily increasing and is projected to increase by another 50% by 2050 (Figure 2). Currently, the energy sector primarily uses fossil fuels to cover energy needs, with a significant negative impact on the environment and human health (Motlagh et al., 2020).

Figure 2: Global primary energy consumption by region 2010-2050, ( Source: Ahmed et al., 2022, p. 956)

Europe has introduced several policies to reach efficient use of energy and increase renewable sources in the energy mix to reduce carbon emissions and reach the 2050 net zero targets, such as the Energy Performance of Buildings Directive (2010), the Energy Efficiency Directive (2012) and the European Green Deal (2019). This implies an enormous increase in electricity generation from renewable sources with the consequent intermittency and variability in energy production, necessitating a rethink of the energy system to maintain reliability, security, and comfort levels (Deconinck and Thoelen, 2019). The increasing need to use renewable sources coupled with the increasing energy demand provides additional reasons for concern on top of the existing difficulties in limiting energy wastage, security, stability, and reliability of the energy system (Alhasnawi et al., 2022). A holistic and system-wide approach is needed to address these issues to increase resource use efficiency and minimize the negative environmental impacts (Motlagh et al., 2020).

The use of IoT technologies can transform the conventional energy grid into a smart grid and act as an enabler of circularity by inducing demand reduction and shifting of demand to valley periods to maximize resource efficiency and limit the need to increase peak power and therefore, grid capacity (Deconinck and Thoelen, 2019). The real-time data collection, integration, and analysis of data supports the operational optimization of the smart grid, making possible improved management of variability and uncertainty (Asian Development Bank, 2017) and the introduction of new circular business models enabled by the new digital technologies (da Silva and Sehnem, 2022).

In this sense, a critical element in achieving the net zero target and transitioning to an efficient European energy system is establishing Demand Response programmes (DSR) where the consumers understand the added value and engage with other energy system stakeholders to maximize energy efficiency (de Fátima Castro et al., 2018). The programme works by incentivizing consumers to reduce their electricity loads on request and charging higher costs for using electricity during peak hours (Sardar et al., 2022). This means the need for expensive spot market operations to trade electricity and balance supply and demand can be limited, thus incentivizing electricity retailers to adopt DSR. For further applications of IoT in the energy sector, please find the table below, as developed by Motlagh et al. (2020).

Table 1: Applications of IoT in the energy sector
(Source: Motlagh et al., 2020, p.12)

Proposed DSR framework

Poland’s energy sector

Poland’s strong economic growth over the last several years (World Bank, 2021) exacerbated concerns about the increasing energy consumption, use of non-renewable resources, and pollution (Han et al., 2020), thus increasing the need to seek alternative solutions. Transforming Poland’s energy sector, which relies on fossil fuels, is crucial for implementing the circular economy and mitigating the negative environmental consequences (Siuta-Tokarska, Thier & Hornicki, 2022). The share of coal in electricity generation in 2021 increased to over 72%, while the share of renewables fell to about 17% despite record production from these sources (Figure 3). According to the assumptions in the recently adopted “The Energy Policy until 2040” (PEP2040) (Ministry of Climate and the Environment, n.d), Poland will gradually reduce its use of coal to 56% and increase its share of renewable sources to 23%. Given the country’s policy targets, work on several fronts must be coordinated (International Trade Administration, 2021). In this respect, DSR offers a potential solution to reduce electricity consumption (Han 2020) and integrate additional renewable sources into the energy mix.

Figure 3: Poland’s energy mix (Source: International Trade Administration, 2021)

Poland’s electricity sector uses a supply-side response (SSR) (Figure 4) system where power generation follows the demand (International Trade Administration, 2021).

The National Power System (NPS) includes four main subs-systems:

  1. Power Generators: Responsible for generating power through owning and operating power plants.

  2. Transmission Service Operator (TSO): Responsible for owning and maintaining the transmission network infrastructure at the national level (high-voltage cables). There is only one TSO in Poland, a wholly state-owned enterprise, PSE SA.

  3. Distribution Service Operators (DSO): Responsible for owning and maintaining the transmission network infrastructure at the regional level (typically medium and low-voltage cables). DSOs are also responsible for electricity transmission from national grids to end customers.

  4. Retailers: Responsible for providing electricity services to end customers (households and industrial users). The end-to-end business process of electricity provided to customers.

Figure 4: Electricity value chain in Poland (Source: CE-IoT work package 2, D2.2, p.22)

The difficulty in storing electricity and the need to maintain the balance of electricity production and consumption constantly require continuous adjustments through market operations in the day-ahead market (one day prior to transmission), intra-day market (one hour prior to transmission), and balancing market (fifteen minutes prior to transmission) (TGE, 2020). The short-term markets are increasing in trading volume, with the intraday market volume growing 12 times the volume of 2019. The spot market accounted for 14.3% of the total volume in 2020 (TGE, 2020). This indicates the opportunity for value creation for Bluesoft and other stakeholders by designing a data-driven circular DSR service, integrating it into the DSO’s IT infrastructure, and offering it to retailers as an alternative to short-term market operations.

Bluesoft is a private company in Warsaw, Poland providing IT services, including IT system integration and cloud services, for over 15 years. BlueSoft is well-placed to develop such a solution. It possesses many technical skills required to build the service and strong relationships with key players in the energy sector, including the DSOs. The service can be a viable alternative to the spot market if the DSR service is priced competitively, taking into account the inclusion of a monetary incentive to entice consumer participation.

A DSR program encourages changes in consumer electricity usage through multi-tier pricing or reward-based incentives, aiming to lower peak demand, optimize generator asset utilization, and reduce CO2 emissions. This way, it improves the efficiency of the electricity system, reduces costs, and creates value for all stakeholders. As a result of reduced power consumption during peak hours, the peak load decreases, lowering the necessary production capacity. Utilizing the saved power during off-peak hours (Valley Filling) allows generators to operate optimally, improving asset utilization. This approach leads to load shifting, flattens the demand curve, and stabilizes production (Figure 5). In the long run, this method could also contribute to energy conservation, as consumers might not consume the saved power during off-peak hours (Azka Sardar, Saad Ullah Khan, Muhammad Azhar Hassan, Ijaz Mansoor Qureshi, 2022).

Figure 5: Electricity load profile changes with DSR (Source: CE-IoT work package 2, D2.2, p.23)

Establishing the DSR service can support accomplishing the following targets:

  1. Reduction of net energy consumed and consequently fewer carbon emissions.

  2. Improved management of renewable energy output variability by tailoring demand to electricity supply. This also reduces the risk of failing to meet electricity demand and cutting supply.

  3. Power Generators can improve their asset utilization since they will face reduced peak power consumption and few fluctuations in demand.

  4. DSOs can reduce spending on electrical transmission infrastructure and create new revenue streams.

  5. Retailers will reduce their dependence on costly short-market solutions.

  6. End customers can receive monetary incentives and lower energy bills.

CE-IoT framework for Demand Side Response model

The proposed DSR solution involves implementing IoT to automate and streamline the electricity provision process. DSOs would control the DSR by adjusting consumption to match supply, for example, switching off a boiler or other non-essential appliances to reduce the system load. This requires the installation of IoT load control devices. The intelligent asset protocol will determine the essential vs. non-essential load consumptions, communicate with other intelligent assets to determine energy shortfall/ surplus and take action based on the supply and demand. In Figure 6 below, the power supply is less than the demand. The action based on the data is to balance supply and demand by curtailing the power supply of one commercial and one household participant in the system.

Figure 6: Proposed IoT solution (Source: CE-IoT work package 2, D2.2, p.24)Diagram Description automatically generated

This power distribution will be determined by the technology embedded in the system.

Three demonstrative IoT Load Control categories could be:

Circuit control: Night circuits can be retrofitted with IoT smart meters to remotely turn off and on power to all devices on the circuit.

Appliance control: Smart appliances can have embedded IoT chips for remote control, or non-IoT devices can be controlled through a socket adapter.

Micro-grid control: Micro-grids can be disconnected from the electrical grid and powered by a short-term battery supply or generator, allowing for remote management of various loads.

Having control of a wide range of loads (Figure 7) allows the operator of the DSR system enough flexibility to disconnect a single residential boiler or an industrial facility, depending on the need. It would be preferable, however, to concentrate the efforts on controlling non-essential devices such as boilers that, even if turned off for a short period, will not cause any inconvenience; otherwise, the public may perceive the DSR negatively and object to remotely turning off their electricity.

Figure 7: Possible load profiles for IoT load control (Source: CE-IoT work package 2, D2.2, p.25)

DSR architecture

The solution would require a new architecture for the DSR system which would transform the current model to monitor electricity consumption (Figure 8) to a complex event processor (Figure 9) that allows for real-time analysis of electricity consumption and issues load control commands to IoT devices.

Figure 8: Current architecture of the electricity system (Source: CE-IoT work package 2, D2.2, p.25)

Figure 9: Proposed DSR architecture – complex event processor (Source: CE-IoT work package 2, D2.2, p.26)Diagram Description automatically generated

Architecture operational flow model:

  1. Smart meter communicates usage information.

  2. Information is processed by a complex event processor (CEP), and data is sent to meter data management (MDM) for storage and to reporting and analytics engines.

  3. Retailers use analytics to compare and predict energy usage.

  4. Requests are made to reduce load via a portal that communicates with CEP.

  5. CEP communicates with IoT devices based on customer information from MDM and Customer Relationship Management (CRM).

Circularity and IoT patterns

The IoT load control is a critical factor for the operation of the DSR system and is responsible for adapting the energy consumption to the energy supply. To facilitate the smooth load control operation, the circular economy design patterns (location, condition, availability, or LCA) and the IoT architectural patterns (connectivity, security, privacy, dependability, interoperability, or CSPDI) need to be satisfied.

The real-time and overtime data collection on asset locations, conditions, availability quality, and performance, made possible with IoT, transforms physical assets into intelligent ones. The accumulated knowledge enables companies and consumers to identify new value-creation methods throughout an asset’s lifecycle. In this regard, the application of the LCA perspectives enhances the circularity of business models as it facilitates efficient resource use, re-use, resource recovery, and recycling at the end of life, preemptive maintenance and prolongation of asset life based on the recorded condition of the asset, and practical logistics that minimize transportation costs. The overall effect is reducing waste, more efficient use of resources, and mitigating the environmental impact.

The IoT technical properties of scalable connectivity, end-to-end security, privacy and dependability, and IoT (CSPDI) interoperability define the overall design goal of “circularity by design” architectural approach for developing IoT technologies. This is beyond the scope of this paper and will suffice to note that the CE-IoT project aims to improve on the state-of-the-art in many technical areas that comprise circularity, including scalable IoT connectivity through software-defined networking (SDN) and network function virtualization (NFV) to develop efficient wireless interconnectivity of smart objects. In addition, CE-IoT’s CSPDI patterns extend existing work on patterns (e.g., SSO patterns) by covering in an integrated manner not only security but also connectivity, dependability, privacy, and interoperability properties covering the entire landscape of circularity at the IoT system level and extending patterns like the SSO ones, and focusing on IoT systems.

Challenges for pairing CE-IoT for the energy sector

Despite IoT's promising potential for advancing CE, many challenges include technical issues such as scalability, lack of interoperability and adaptability of services, privacy and security concerns, and increasing complexity. This questions IoT devices' safe and secure connection and end-to-end connectivity (Rejeb et al., 2022). The complexity is evident if one considers the load needs of smart residential appliances. Their load profile depends on the use patterns of consumers and the surrounding environment, which increases the difficulty in developing a general predictive strategy for the load consumption of each device and consumer (Panda et al., 2022). In this case, it is challenging to consider a manageable model-based approach; therefore, scalability of the solution becomes a serious challenge. IoT enables a data-driven approach that uses the data collected to learn the flexibility of the devices and provide a more adaptable and scalable solution (Deconinck and Thoelen, 2019). Another challenge is the unpredictable generating capacity of renewable energy sources and the need for advanced optimization strategies to minimize tariffs and optimize energy consumption to maintain an economic system and consumer satisfaction (Panda et al., 2022). In this respect, there is a need to understand how stakeholders perceive the technology and CE, the attitudes towards CBMs, and their expectations according to their interests to improve trust and facilitate acceptance of circular IoT-enabled solutions (Rejeb et al., 2022).

Proposed methodology for further research

Embedded single-case study: Bluesoft

In the previous sections, we outlined a proposed framework that addresses the research question, ‘How can value be created by developing business models for the interplay of circular economy with IoT?’ Specifically, we explored the pairing of CE-IoT and proposed a data-driven DSR model for the energy sector of Poland. This approach focuses on the opportunities available regarding the surrounding digital business ecosystem, specifically the opportunities in pairing CE-IoT. This section outlines a methodological approach for a follow-up embedded case study that addresses the intra-organizational drivers supporting organizational change and the transition to circular business models (Hanelt, 2021). The complementary study promotes a holistic understanding of organizational change and supports the practical implementation of circular business models and enabling technologies (Rejeb et al., 2022).

This qualitative embedded case would explore how Bluesoft business executives and DSOs (the potential consumer of Bluesoft products) describe the enabling factors and barriers for driving organizational change in creating business value with the interplay of CE with IoT. At this stage in the research, the organizational change to circular business models will generally be defined as the adoption of business models that enable systems that are regenerative by nature; they seek to maintain resource value at its maximum for as long as feasible, and eliminating or reducing resource leakage, by closing, slowing, or narrowing resource flows (Salvador et al., 2020). The primary participant and analysis unit is Bluesoft; the subunits are Bluesoft’s executives, departmental units, and DSOs in the Polish energy sector. Bluesoft is a partner in the CE-IoT project and is actively contributing with other partners in developing the technical tools and the CE-IoT value drivers. Therefore, access to the company for data collection should not be particularly challenging. Bluesoft has an excellent relationship with the stakeholders of the energy sector and could therefore gain access to the DSOs for research purposes.

The advantage of the embedded case study approach is that it can provide a detailed and in-depth description of the enabling factors and the barriers to the change processes and the complex interactions between managers and employees, and the consumers to analyze emerging themes focusing on the intra-organizational drivers to change. To achieve this goal, semi-structured interviews can be conducted, allowing for good time management, consistency across the data, and flexibility for participants to contribute new themes. Additional material can be sourced from internal documentation, including annual reports, project reports, meeting minutes, financing decisions, etc.). Using multiple sources and triangulation by comparing and contrasting the data from different sources will support the credibility and validity of the findings and reduce potential bias. The data can be analyzed using narrative and thematic analysis to capture the essence of participants’ perceptions and lived experiences and reveal common themes to gain insights into the meaning and significance of the experiences and how they relate to the broader social context. To ensure the dependability of the findings, an audit trail mechanism can be established for all research activities. Given the importance of advancing CE, every effort can be made to provide the research context and theoretical and practical implications derived to maximize the possibility of transferability and applicability of the research from Bluesoft to other study groups.

Instrument: Semi-structured interviews

The interview protocol can be based on the work of Stouten et al. (2018), who integrated management practice with scholarly literature on organizational change and identified ten evidence-based steps in managing planned organizational change. We outline sample interview questions based on the ten steps.

Table 2: Potential interview questions for the purposes of the embedded case study

Summary of change steps (Stouten et al., 2018)

Potential interview questions

Assess the opportunity or problem motivating the change

1. Describe what led to the realization that a change is necessary.

2. How can IoT-enabled CE practices benefit your company?

Select and support a guiding change coalition

1. Who is driving the change?

2. How did you select the individuals or teams responsible for driving change?

Formulate a clear, compelling vision

1. What is your company’s vision for implementing IoT and circular economy practices?

2. How is this vision integrated into the company‘s mission and values?

Communicate the vision

1. How are you addressing potential concerns or resistance to change?

2. How is the company vision communicated to employees?

Mobilize energy got change

1. How are you building momentum for this change throughout the company

2. How are you encouraging collaboration and cross-functional teamwork to support this change?

Empower others to act

1. How are you empowering employees to take ownership of implementing IoT and circular economy practices?

2. How are you providing training and development opportunities to support this change?

Develop and promote change-related knowledge and ability

1. Do the employees have the necessary knowledge and skills to support the implementation of IoT and circular economy practices?

2. How are you promoting a culture of continuous learning and improvement?

Identify short-term wins and use them as reinforcement of change progress

1. Describe some examples of how you are measuring progress and sharing results with employees and stakeholders.

2. How would you describe the impact of short-term wins?

Monitor and strengthen the change process

1. How would you describe a monitoring mechanism to assess the effectiveness of implementing IoT and circular economy practices?

2. How would you address potential barriers?

Institutionalize change in company culture, practices, and management succession

1. How are circular economy principles and adopting new technologies integrated into the company’s culture, practices, and management succession plans?

2. How are you incorporating IoT and circular economy principles into the company’s recruitment policy and talent management?

(Modified from source: Stouten et al., 2018, p. 756)


The interplay of CE and IoT presents exciting opportunities for exploring innovative circular business models that introduce novel value-creation ways that maximize resource efficiency and asset utilization and support achieving the climate neutrality target by 2050.

This paper presents the CE-IoT framework for developing a DSR system for the Polish energy sector. The paper explores the pairing of CE-IoT and addresses the problem of reducing fossil fuel use and, consequently, carbon emissions and increasing renewable energy sources, aiming to identify solutions that support the energy targets Poland sets. The DSR solution can improve the Polish electricity system’s efficiency, reduce cost, net energy consumption, and carbon emissions, and improve the stakeholders’ asset utilization. The research aligns with previous findings in recognizing the potential for value creation, innovation, and promotion of circularity by adopting IoT-enabled business models. Ultimately it supports the identification of CE-IoT solutions that can advance the decoupling of economic growth and resource use and facilitate the transition from a linear to a circular economy.

Contribution to the conference

This paper is relevant for the track 2.1 Data-driven Business Models for Sustainability, Resilience and Digital Transformation in Emerging Fields.

Adopting IoT technology to facilitate a CE approach is crucial to implementing this form of economic and business model. In this respect, our research has an invaluable impact on the introduction, implementation, and impact of novel circular business models. Moreover, it is critical to further our understanding of how data-driven technologies can drive circularity and sustainability.


This work has received funding from the European Union Horizon’s 2020 Marie Skodowska-Curie Actions(MSCA) Research and Innovation Staff Exchange (RISE), H2020-MSCA-RISE-2017, under grant agreement No.777855 (CE-IoT).


This paper is based on the deliverable “Business models for interplay of circular economy with IoT” of the EU-funded project “A Framework for Pairing Circular Economy and IoT: IoT as an enabler of the Circular Economy & circularity-by-design as an enabler for IoT” (CE-IOT) grant agreement 777855. The contents of this paper are the sole responsibility of the author(s) and do not necessarily reflect the views of the European Union, the CE-IOT project, or its partner organizations. While every effort has been made to ensure the accuracy and reliability of the information presented in this paper, any errors or omissions are solely the responsibility of the author(s). The deliverable “Business models for interplay of circular economy with IoT” was co-authored by partners University of Cambridge, Cablenet Communication Systems, Bluesoft Spolka Z Graniczona Odpowiedzialnoscia, and Deloitte Consulting & Advisory within the framework of the CE-IOT project. The contributions made by these partners have been invaluable to the development of this paper. However, the analysis, interpretations, and conclusions presented in this paper are the sole responsibility of the author(s) and do not necessarily represent the official position or policy of the partners. Readers are advised to consult the original deliverable “Business models for interplay of circular economy with IoT” for a comprehensive understanding of the research findings and context, as well as to verify any information presented in this paper. The author(s) disclaim any liability arising from the use or application of the information contained herein.


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