VALORE-EU

While the model is applicable to any EU Member State or even region or city/municipality, currently the only underlying data available for performing analyses is a hypothetic EU27 data set.

The model consists of two main modules: one module focuses on the EU municipal waste management only, while the second module focuses on the entire EU waste management. The modular model structure allows users to easily modify or update specific parameters as new data or information becomes available.

The model is operated on the software EASETECH that was designed by Technical University of Denmark/Kongens Lyngby, Department of Environmental and Resource Engineering in 2012. The model features both available and created data to assume the entire EU waste management system, from generation, collection, treatment to disposal. The modelling approach is based on a combination of linear mathematical equations and databases.

Each of the two modules consists of several sub-modules that are interconnected to represent the different stages of municipal waste management, including:

• Waste Generation Sub-Module: This module quantifies the amount of waste generated based on waste collection data.
• Waste Collection Sub-Module: This module simulates the collection of waste, considering the type of collection system in place (e.g. collection of multiple waste fractions at once, e.g. plastic together with metals) and expected impurities.
• Waste Treatment Sub-Module: This module represents the various treatment options for waste, including sorting, recycling, composting, energy recovery, and landfilling.
• Material and Substance Sub-Model: This module assesses the material, substance, and energy flows via full mass, substance and energy balance.
• Environmental Impact Sub-Module: This module assesses the environmental impacts of waste management, including greenhouse gas emissions, other pollution, health impacts, and resource depletion based on the Life Cycle Impact Assessment Model Environmental Footprint (EF) of the European Commission.
• Economic Sub-Module: This module evaluates the economic costs and benefits of different waste management strategies, including the costs of waste collection, treatment, and disposal, as well as the revenue generated by recyclable materials and energy recovery.

Policy interventions like mandatory waste separation and collection schemes, mandatory treatments, and increased recycling rates can be represented in the model. For example, for a mandated increased recycling rate for plastic packaging waste, irrespectively of the comparative economic competitiveness of different waste management options and irrespectively of the suitability of different charges of plastic waste for recycling, the necessary quantities of plastic to meet the new recycling target are channelled away from other treatment and disposal solutions, notably, from plastic waste exports, from domestic landfill and then from domestic incineration (in this order). As more recycling activity requires more labour input, and less activity in export, landfill and incineration goes hand in hand with a release in labour input, the direct net employment effect of more recycling in the waste management sector is indicated by the model.

The modelling approach is based on the following key assumptions:

• Linearity: The model assumes that the relationships between the different variables are linear, which may not always be the case. For example, if there is a higher recycling and material reuse rate in the industrial sector, this can have an impact on the prices of recycled substances impacting other sectors. Such feedback loops are not accounted for. The model assumes that secondary raw materials obtained can always be sold, even if the production costs/prices are usually higher, the quality, consistency of quality is usually lower for recycled materials than for virgin materials.
• Steady-state: The model assumes that the waste management system is in a steady-state, meaning that the inputs and outputs are constant over a selected timeframe.
• Average values and assumed values: In its current version, the model uses average EU values for parameters such as waste generation rates, collection efficiencies, and treatment capacities, which may not reflect the variability across the EU. For waste composition and the physico-chemical characterisation of waste, EU values are assumed based on a study conducted in 2012 that analysed the composition and characterisation of waste generated during a 2-week time-period by 100 single-house households in the Danish city of Abenraa (alternatively, the model has also the option to apply another physico-chemical characterisation based on a 2009 Danish sampling campaign by Riber et al., 2009).
• Waste composition: Currently, the model assumes that the composition of municipal waste is constant over time and across different EU countries, which may not be the case in reality. The projection of waste composition is mainly based on historic facts and trends (e.g. largest share of waste is demolition waste, decreasing paper and increasing electronic/electric/batteries waste), effects of ongoing EU economic restructuring (including the re-orientation of civil engineering to defence manufacturing) on resource use and availability, on waste generation and composition/quality have not been considered.
• Technological efficiencies: Currently, the model assumes technological efficiency levels irrespectively of the real physico-chemical characterisation of collected waste (which is unknown), and it assumes that the technological efficiencies of waste treatment options, such as recycling and energy recovery, are constant over time and across different EU countries.
• Economic parameters: Currently, the model assumes that economic parameters, such as costs and prices, are constant over time and across different EU countries, and economic parameters do not hamper the feasibility in the model of circular economy policy interventions (for example, even if energy recovery from waste might be economically attractive, the model would implement a higher material recovery/recycling target if policy requires so).

Limitations and Simplifications:

Key limitations of the model relate to the simulation of ‘market/pricing’ effects (disregarded), economic ‘macro’ analyses, the uncertain representation of EU waste and waste management reality (currently, the only underlying data available for performing analyses is a hypothetic EU27 data set), the information value of model output as regards strategic and critical raw materials/rare earths, and the partial approach to impact assessment, that, for example, neither accounts for employment quality in the waste management sector nor for emissions, resource depletion, and employment impacts of energy production shifted outside the waste management system due to mandated higher recycling rates.

• Simplification of complex systems: The model simplifies the complex systems and processes involved in municipal waste management. Uncertainties are dealt with Global Sensitivity Analysis, which is incorporated as a feature in the underlying EASETECH software and described in scientific publications (Bisinella et al., 2016; Clavreul et al., 2012). The input data applied in VALORE-EU come with their own uncertainty range (i.e. each parameter has a likely value and a distribution, which can be uniform, normal or triangular) allowing for sensitivity and perturbation analysis, Montecarlo propagation or traditional analytical uncertainty analysis.
• Non-consideration of social and behavioural factors: The model does not consider the social and behavioural factors that influence waste management, such as consumer behaviour, social norms, and cultural values.
• Lack of spatial and temporal resolution: In its current version, the model has a limited spatial and temporal resolution, which may not capture the variability and heterogeneity across the EU. Currently, the only underlying data available for performing analyses is a hypothetic EU27 data set.

The model contains two types of inputs: the first type are parametrised input values that are used to represent the foreground system, i.e. the waste management activities and processes (their energy and material consumption, their costs, their emissions, their employment needs). As NACE classification includes waste management sector in a very aggregated manner (one sector including all activities as well as water, wastewater, and sludge management), the cost data (OPEX, CAPEX, tax, labour) for the different activities involved in waste management are taken from sectoral studies, plant- and company-specific data as well as scientific literature. The model in its current version contains default parameter values representing hypothetic EU27 conditions, which (if data was available) can be further adjusted and tailored to the specific case study. These parameters are provided via an Excel spreadsheet. The second type of inputs are the datasets to describe background processes (such as energy, ancillary material used by the foreground system), which are provided in ecospold compatible format. Importing Environmental Footprint compliant datasets is possible by using a dedicated mapping file, which is available as part of the model package.

The scope of the model encompasses all the waste streams included in the Eurostat waste generation statistics (waste_gen dataset), which report data by activity of origin (all NACE codes with limited exceptions). On top, the model has an ad hoc module dedicated to Municipal Waste (MW, ca. 10% of total waste) that allows to perform in-depth environmental and economic analysis specifically on this waste stream generated mainly at household’s level. The waste generation and collection data for MW (plastic, paper, biowaste, glass, etc.) are based on the Eurostat statistics for MW (env_wasmun) and on European Environment Agency data on waste collection rates retrieved in the context of the EU27 Early Warning Reports (this is thoroughly described in Albizzati et al.; 2024). The Eurostat data provide the information on the material composition at an aggregated level (e.g. total amount of plastic, paper, biowaste, glass, etc.). However, a further breakdown of this aggregated composition is then done in VALORE-EU using more specific waste composition datasets published in (Edjabou et al., 2015). For example, plastic is composed of many sub-types of packaging, non-packaging materials, bottles, films. Finally, the physico-chemical characterisation (amount of carbon, nitrogen, metals, energy content, etc.) of the waste material fractions is also done in the model based on laboratory analyses of waste samples (Götze et al., 2016; Riber et al., 2009).

If recent and reliable data was available, the user could provide data on:

• Waste composition by material fraction and amount (e.g. breakdown of plastic, paper, biowaste, glass into their sub-fractions)
• Physico-chemical composition of the material fractions (carbon, nitrogen, energy, etc.)
• Collection, sorting, recycling, landfilling, incineration rates
• Energy recovery efficiency for incineration or other waste-to-energy
• Energy recovery efficiency for anaerobic digestion gas engines
• Energy recovery efficiency for landfill gas engines
• Quality factors (for substitution of virgin materials by recovered materials)
• Ancillary materials and energy used by treatment facilities and processes
• Amortised CAPEX, OPEX, transfers, employment factors for facilities and processes
• External costs associated to environmental emissions
• Life cycle inventory datasets to describe background processes (such as energy, ancillary material)

Additional information on input and parametrization, such as formats and sources, could be provided as well.

Importantly, the model allows to input ranges and distributions to perform perturbation and uncertainty analyses using both stochastic (Montecarlo) and analytical (Taylor) methods of propagation. Such methods and their practical implementation in the software EASETECH (on which the VALORE_EU model is operated) have been described in the scientific literature and are considered state-of-the-art methods for propagating uncertainty in waste management assessment by the scientific community (Bisinella et al., 2016; Clavreul et al., 2012). However, no recent uncertainty/sensitivity analysis has been done on VALORE-EU, the uncertainty with which the assumed EU waste management system can be considered representative is not quantified.

Environmental indicators:

• Greenhouse gas emissions (GHG)
• Resource depletion (e.g., water use, land use, biotic and abiotic resource depletion)
• Pollution levels (e.g., air, water, soil)

Economic indicators:

• Private and External Costs of waste management activities (e.g., collection, transportation, treatment, disposal)
• Revenues generated from recycling and energy recovery
• Net economic benefits of different waste management scenarios
• Direct employment in the waste management sector

Waste & Resource recovery indicators:

• Landfill, incineration, recycling, recovery rates
• Quantity of materials recovered from waste (e.g., paper, plastic, glass, metal)
• Quality of materials recovered from waste (e.g., purity, contamination levels)
• Substance flows and recovery (carbon, nitrogen, phosphorous, metals, etc.)
• Energy recovery from waste

Scenario comparison outputs:

While the model does not offer a graphic or numerical comparison of scenarios endogenously, this is typically easily done by the users in the after-math to obtain the following:

• Comparison of environmental and socio-economic impacts of alternative waste management scenarios (e.g. where each scenario looks at the management of a specific waste stream)
• Identification of the most effective and sustainable waste management strategies (where a strategy is normally a combination of various scenarios addressing various streams)

• Evaluation of the potential for waste management to contribute to a circular economy (e.g. how much GHGs could we reduce by a certain year? How much employees would be needed in the sector?)

Based on data availability, the model can help with policymaking in several ways. It can be used to assess the potential impact of new policies, monitor how well they're working, and evaluate their effectiveness over time.

The model was developed to support the European Commission's Circular Economy Action Plan, the Packaging and Packaging Waste Regulation, the Circular Economy Act. It's also been used to inform policies on waste lubricant oil, plastic recycling, textile waste, and other areas.

The model is a useful tool for policymakers because it can help them:

• Understand the potential effects of new policies
• Track progress and identify areas for improvement
• Evaluate the success of policies and make adjustments as needed

Overall, the model can help policymakers make informed decisions and develop effective waste management strategies.