The Circular EEE SD Model was created to meet the following purpose: to represent long-term nationwide dynamics of EEE stocks and flows when introducing specific scenarios for Circular Economy strategies implementation.
The simulation model development followed the iterative process prescribed by Sterman (2000): (i.) Problem articulation, (ii.) Formulation of Dynamic Hypothesis, (iii.) Formulation of a Simulation Model, (iv.) Testing and (v.) Policy Design and Evaluation. The model was conceptualised following a dynamic Material Flow Analysis approach. It makes use of publicly available data of inflows and outflows of EEE products in countries to determine the stocks through time.
Among the critical endogenous sub-models are:
- A sub-model for technology adoption following a diffusion of innovation (DoI) structure that determines the EEE demand from adoption, replacing and additional purchases
- A sub-model for the obsolescence of EEE determining EEE average age through a co-flow structure in combination to the probability of obsolescence using the Weibull lifetime distribution
- A sub-model for the EEE and WEEE flows resulting from first-use, second-use, remanufacturing, recycling, and disposal connected to mechanisms to verify Circular Economy implementation
- A sub-model for the material supply determining the need for the extraction of material following a stock management structure
Vensim® DSS for Windows Version 8.0.7 Double Precision x64 was employed to develop the simulation model.
The figure below details the structure for data acquisition and model use.
The model is composed of two complementary portions: circularEE_v.1retrospective and circularEEE_v.1prospective.
The retrospective model makes use of a dataset containing country and EEE specific time series.
Following, the output of the retrospective model is used to calibrate the prospective model. Model calibration happens for three reasons: (1) enable the simulation to range from 1980 to 2050 with continuous behaviour, (2) adjust the behaviour of the adoption sub-model to the historical flow of EEE into a region, (3) fine-tune the behaviour of stocks and flows of EEE considering an initial structure in place.
Finally, different scenarios of Circular Economy strategies implementation can be verified.
Both models are available in the /models/ folder in the original version developed (.mdl) and in a Vensim Model Reader compatible format (.vpmx). A colour scheme identifies specific input and output variables to facilitate understanding and use:
blue: dataset input
pink: retrospective model output
green: prospective sub-model output
purple: input variables to test CE mechanisms
Reports for both models using the SDM-Doc (Martinez-Moyano, 2012) documentation tool are available on the /models/reports/ folder. In this document, model users can access an overview of model information, all the model views – which constitute the structure of critical sub-models –, and a detailed description of each variable used in the model. Unit warnings are detailed using the ‘†’ character.
Data for the adoption of Flat Panel Television (UNU 408) in the Netherlands (NL) was used to test the model extensively. The dataset assembled is available in the /datasets/ folder in the data_NL408.xlsx file.
Following, a set of guidelines for model users that intend to use the model to investigate the nationwide dynamics of EEE stocks and flows under CE strategies implementation:
First, the model user may choose a specific country and EEE.
He/she may obtain the values for the variables mentioned in the ‘Data preparation’ table for the specified range. The example below contains sources for the specific case of Flat Panel Television in the Netherlands. For other countries and EEE types, further research is necessary.
| Class | Variable | Range | Source |
|---|---|---|---|
| Country-specific | purchasing power parity per capta | 1980-2050 | International Monetary Fund (2019); PricewaterhouseCoopers (2017) |
| Country-specific | total households | 1980-2050 | Statistics Netherlands (n.d.) |
| Country-specific | population | 1980-2050 | Statistics Netherlands (n.d.) |
| EEE-specific | EEE average unit weight | 1980-2020 | van Straalen et al (2016) |
| EEE-specific | EEE unit price | 1980-2020 | Morrison (Morrison, 2017) |
| EEE-specific | Weibull scale | constant | Forti et al. (2018) |
| EEE-specific | Weibull shape | constant | Forti et al. (2018) |
| EEE-specific | recyclability | constant | Zeng and Li (2016) |
| Country_EEE-specific | historic EEE put on market | 1980-2020 | van Straalen et al (2016) |
| Country_EEE-specific | historic disposal of EEE | 1980-2020 | van Straalen et al (2016) |
| Country_EEE-specific | initial adoption fraction | 1980 | de Almeida and Rosenfeld (1987, p. 80) |
Now, in the retrospective model, the model user should import the dataset created. The file used for importing the data for Flat Panel Television in the Netherlands into the model is available in the /datasets/ folder: input_NL408.vdfx.
The ‘potential adoption fraction’ must be calibrated by using the variables (a) ‘normalised ratio EEE price per PPP’ and (b) ‘R EEE per household’. The pairs of values to be inserted into ‘potential adoption fraction’ are constituted by [ai, bi]. This relation means the adoption level follows the price of the good and the purchasing power of that population. When the price lowers, and purchasing power is high enough, a certain percentage of the population will access that technology.
The model user should try to make ‘adoption purchases’ mimic the first peak of ‘R EEE commissioning’. Once the calibration of the ‘potential adoption fraction’ is done, the output dataset from the retrospective model runs the prospective model. The file obtained from the calibration of the retrospective model is available in the datasets folder: prerun_NL408.vdfx.
After that, the model user should examine the fit among the curves of ‘historic EEE put on market’ (dataset input), ‘R EEE in use’ (retrospective model output), and ‘historic disposal of EEE’ (dataset input) to the calculated values of ‘EEE commissioning’, ‘Total EEE in use’ and ‘disposal of EEE as WEEE’. The calibration graphs are available in the view’ 5. Calibration and tests’.
The model user may also the Theil’s inequality statistics structure available in the same view to verify the sources of error among simulated and imported time series: bias (Um), unequal variation (Us), and unequal covariation (Uc).
Following the fit curves obtained considering a Baseline scenario, applying the infrastructure levels of CE mechanisms for the Netherlands obtained from secondary research.
Once the curves are adequately calibrated, the model user can start using the model to examine Circular Economy strategies implementation.
The following variables influence the implementation of Circular Economy mechanisms in the model:
- lifetime ratio
- second-use infrastructure level
- repairing infrastructure level
- remanufacturing infrastructure level
- recycling infrastructure level
Towards a Circular Economy, organisations and governments aim to decrease the inflow of materials and products in the system while keeping them in high use. Thus, we recommend the model user to check which CE mechanisms lead to less need for material extraction, EEE commissioning and WEEE disposal in the system.
The table below outlines the testing activities performed in the model. Learning outcomes and model changes are described for each activity. Transparency in testing activities and learning outcomes help determine the suitability of the model for the defined purpose.
| Test type | Test description | Learning outcomes and model changes |
|---|---|---|
| Boundary adequacy | Model boundary chart development | Facilitates to identify relevant structures to model. Reliable data is available for most of the exogenous variables. |
| Boundary adequacy | Model expansion to include the Adoption sub-model | Enables prospective simulation from historical data. |
| Structure assessment | Subsystem diagram development | Represents the main concepts and feedback structures in the model. |
| Structure assessment | Model conceptualisation following the dynamic material flow analysis (MFA) concept | Conservation of matter is embedded in the model structure. Non-recycled WEEE stock is the sink of the system. |
| Structure assessment | Application of the mass-balance check (Dangerfield, 2014; Schwaninger & Groesser, 2016) to products and materials | Enables examining gains or losses of materials and products in the model. Total gains should be zero. |
| Structure assessment | Development and testing relevant sub-model parts in isolation | Enables building confidence in plausible behaviour of sub-models before aggregate analysis. |
| Structure assessment | Experimentation with stocks in varied conditions to identify negative values | Identification of variables to constrain, e.g., limiting the flows into second use, remanufacturing and recycling (in that order of preference) to prevent negative values of Available use-EEE. |
| Dimensional adequacy | Use of parameters with real-world meaning when modelling | Name and description clearly identify variables’ meaning. There are a few ad hoc variables as ‘effect of PPP on the average number of EEE per household’. |
| Parameter assessment | Elaboration of a step-by-step guide to calibrating relevant partial models | Enables calibration of the behaviour of EEE adoption from retrospective data from 1980–2015 to obtain the behaviour from 1980–2050. |
| Extreme conditions | Bounding the use of the model by levels of CE mechanisms implementation | The 0 to 4 infrastructure level indexes bound the model. |
| Extreme conditions | Bounding auxiliaries and flows with potential extreme behaviour | Combination of MIN and one-minus function to prevent negative values. E.g., “fraction of decommissioned EEE introduced to WEEE”. |
| Extreme conditions | Shocks and extreme condition testing | It is possible to examine the inflow of a shipment of used products as “Available used-EEE” with a given average age in a moment in time, mimicking what happens in countries under development. Mass balance is maintained. |
| Integration error | Testing the model using RG4 of 1 year to 0.125-year time steps | Results are not sensitive to these choices in the time step. Nevertheless, the supply chain delays are proportional to the time step. |
| Behaviour Reproduction | Setting up a dashboard to fit the prospective model to external data and data calculated in the prospective model | Helps to calibrate the model through behaviour reproduction of the following variables: ‘EEE commissioning’ and ‘historic annual EEE put on market’; ‘Total EEE in use’ and ‘R EEE in use’; ‘disposal of EEE as WEEE’ and ‘historic disposal of EEE’ |
| Behaviour Reproduction | Application of descriptive statistics tools to calibrate the model – Theil inequality statistics (Oliva, 1995; Sterman, 2000) | A structure to verify bias, unequal variation, and unequal covariation of simulated and input variables enables verifying the fit among two variables. |
| Family member test | Using Netherlands (NL) and Estonia (EST) data for Fridges (UNU 108) | Calibration is possible from retrospective data in both cases (NL108 and EST108). EST data closer to 1980 is harder to obtain. Although NL and EST are from different stratum in van Straalen et al. (2016), they are both mature markets for fridges. Comparing EEE dynamics in both cases does not provide much insight. |
| Family member test | Using (Netherlands) NL data for Fridges (UNU 108) and Flat Display Panel TVs (UNU408) | Calibration is possible from retrospective data in both cases (108NL and 408NL). The adoption curve for Flat Display Panel TVs enables more general discussion of CE strategies implementation as it shows the full adoption of a technology. |
The table below contains the model limitations and learning outcomes from potential tests. These tests are out of scope for this modelling effort, but they indicate future endeavours to enhance confidence to the model, or reframe the model purpose. Model limitations present an opportunity for other model users, researchers and decision-makers to enhance the Circular EEE SD Model.
| Limitation | Potential learning outcomes |
|---|---|
| Limited explanatory ability of the adoption mechanism | Further understanding the types of adoption patterns and their influence in the effects of CE strategies implementation. |
| Limited explanatory ability of the types of purchasing | Further understanding of how first purchases, recurrent purchases to replace, and additional purchases influence and are influenced by CE strategies implementation. |
| Limited explanatory ability of the types of obsolescence | Increase understanding of the influence of psychological and functional dimensions of obsolesce. |
| Limited implementation of non-transaction based CE strategies | Increase understanding of the impacts of sharing and servitisation strategies |
| Limited explanatory ability of CE mechanisms leverages | Increase understanding of the structures and incentives for CE mechanisms implementation. |
When referring to 'The Circular EEE SD Model', please use:
Guzzo, D., Rodrigues, V. P., & Mascarenhas, J. (2020). The Circular EEE SD Model. Retrieved from: https://github.com/danguzzo/circularEEE_Sdmodel.
Also, please check results and insghts from model use in the publication:
Guzzo, D., Rodrigues, V. P., & Mascarenhas, J. (2021). A systems representation of the Circular Economy: Transition scenarios in the Electrical and Electronic Equipment (EEE) industry. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120414
de Almeida, A., & Rosenfeld, A. H. (1987). Demand-Side Management and Electricity End-Use Efficiency (Vol. 149; A. T. Almeida & A. H. Rosenfeld, Eds.). https://doi.org/10.1620/tjem.162.269
Dangerfield, B. (2014). Systems thinking and system dynamics: A primer. Discrete-Event Simulation and System Dynamics for Management Decision Making, 9781118349(2007), 26–51. https://doi.org/10.1002/9781118762745.ch03
Forti, V., Baldé, C. P., & Kuehr, R. (2018). E-Waste Statistics.
International Monetary Fund. (2019). World Economic Outlook. Retrieved from https://www.imf.org/external/datamapper/datasets/WEO
Martinez-Moyano, I. J. (2012). Documentation for model transparency. System Dynamics Review, 28(2), 199–208. https://doi.org/10.1002/sdr.1471
Morrison, G. (2017). Are TVs really cheaper than ever? We go back a few decades to see. Retrieved from https://www.cnet.com/news/are-tvs-really-cheaper-than-ever-we-go-back-a-few-decades-to-see/
Oliva, R. (1995). A Vensim® Module to Calculate Summary Statistics for Historical Fit. Dynamica, 10, 51–56.
PricewaterhouseCoopers. (2017). The World in 2050. Retrieved from https://www.pwc.com/gx/en/issues/economy/the-world-in-2050.html
Schwaninger, M., & Groesser, S. (2016). System Dynamics Modeling: Validation for Quality Assurance. Encyclopedia of Complexity and Systems Science, 1–20. https://doi.org/10.1007/978-3-642-27737-5
Statistics Netherlands. (n.d.). CBS Open data StatLine. Retrieved from https://opendata.cbs.nl/statline/portal.html?_la=en&_catalog=CBS
Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. In Management. https://doi.org/10.1057/palgrave.jors.2601336
van Straalen, V. M., Roskam, A. J., & Baldé, C. P. (2016). Waste over Time. Retrieved from https://github.com/Statistics-Netherlands/ewaste
Zeng, X., & Li, J. (2016). Measuring the recyclability of e-waste: An innovative method and its implications. Journal of Cleaner Production, 131, 156–162. https://doi.org/10.1016/j.jclepro.2016.05.055