2.1 EUROGREEN model in a nutshell

This study extends the EUROGREEN model (for a comprehensive description, see D’Alessandro et al. Reference D’Alessandro, Cieplinski, Distefano and Dittmer2020), which is grounded in the tradition of Ecological Macroeconomics. The model integrates a Post-Keynesian macroeconomic framework with core principles of Ecological Economics, enabling the simulation of social, economic, and environmental outcomes under alternative techno-policy scenarios.

Figure 1 illustrates the extended structure of EUROGREEN used in this study, which incorporates three additional modules: raw materials, waste generation, and recycling. These extensions are described in detail in the subsections that follow and are summarised in Table A3.

Figure 1.

Macroview of the model structure. This figure presents the main components and feedback mechanisms of the extended EUROGREEN model (new modules in yellow). The extended model includes France’s raw material footprint, measured as domestic material consumption, into four material categories: biomass, metal ores, non-metal ores, and fossil fuels. Each material type is linked to specific economic sectors (e.g., biomass to agriculture), enabling the assessment of extraction and recycling flows by category. France’s residual waste management system is driven by technological innovations, with improvements in material efficiency influencing both waste generation and the availability of secondary materials. The dynamic substitution of primary raw materials with recycled inputs generates structural adjustments within the economy, captured through time-varying technical coefficients in the EEIO tables. Red circles highlight modules or parameters driven by exogenous assumptions, notably those pertaining to technological change and policy interventions (detailed in Section 3).

EUROGREEN is a demand-driven macroeconomic model built on the assumption of idle productive capacity. Investment decisions depend on the gap between actual and potential capacity utilisation, on the profit rate, the depreciation rate, and an autonomous investment component that does not expand productive capacity but stabilises the economy against Harrodian instability. The model links the financial and real sectors through a portfolio approach in which households are disaggregated into 13 socio-economic groups, defined by occupational status and skill level. They allocate their wealth across different financial assets. This formulation allows for a detailed evaluation of the distributional implications of public policies. Production is represented using Input—Output (IO) tables, disaggregated into ten sectors. Endogenous technical change is incorporated by allowing technical coefficients (the “A matrix”) to evolve over time. These coefficients quantify the share of inputs required from each sector to produce one unit of output and therefore encode both the production technology and the structural configuration of the economy. Through a system-dynamics approach, the model captures complex and potentially non-linear macroeconomic dynamics emerging from feedback loops. In line with recent macro-simulation studies of energy transition pathways (e.g., Nieto et al. Reference Nieto, Brockway, Sakai and Barrett2024; Di Felice et al. Reference Di Felice, Pérez-Sánchez, Manfroni and Giampietro2024), EUROGREEN endogenously generates key socio-economic variables. Targets for GDP growth, labour productivity, or other macroeconomic indicators are not imposed exogenously; instead, their trajectories emerge from the model’s internal feedback mechanisms and time-lagged structural responses. Table A2, in the Appendix, summarises the core assumptions and key feedback mechanisms linking all modules in the seminal EUROGREEN. The model is grounded on data collected from several databases (Eurostat, KLEMS, IEA, and WIOD) for France, facilitating the replication and calibration of the model to accurately reflect real-world dynamics. Data sources and main parameter settings are described in the Appendix.

In this context, the inclusion of raw material flows, waste generation, and recycling builds on the theoretical foundations of ecological macroeconomics, where the economy is modelled as an open subsystem of the biosphere with material throughput constraints; embedding these processes endogenously enables a systemic evaluation of CE pathways.

2.2 Raw Materials

We apply Material Flow Analysis to model the consumption-based material footprint of the French economy, commonly referred to as Domestic Material Consumption (DMC). Data are taken from EUROSTATFootnote ³ for estimating the material footprint of different economic activities. DMC is calculated using the following formula, with the material footprint of domestically produced output ( $M^d$ ), imports ( $M^i$ ), and exports ( $M^e$ ) measured in tons of raw material equivalents:

(1) \begin{equation} DMC = M^d + M^i - M^e \end{equation}

Since EUROSTAT does not provide disaggregated data by economic sector, we estimated the raw material intensity coefficient ( $\gamma _s^j$ , in tonnes/ $\unicode{x20AC}$ ) using data from the GLORIA database (Lenzen et al. Reference Lenzen, Geschke, West, Fry, Malik, Giljum, Milà i Canals, Piñero, Lutter, Wiedmann, Li, Sevenster, Potočnik, Teixeira, Van Voore, Nansai and Schandl2022), as reported in Table A6 in Appendix A. This coefficient represents the ratio of the total domestic extraction of raw materials ( $M^d_j$ ), for each category $j$ , to the total monetary output of the sector $s$ ( $x_s$ ). This can be expressed as:

(2) \begin{equation} \gamma _s^j = \frac {M^d_{s,j}}{x_s}, \end{equation}

where $j$ includes four categories: biomass, fossil fuels, metal ores, and non-metal ores. To ensure consistency in the units of measurement, monetary values are expressed in real terms, meaning the output is adjusted for inflation.

These ratios are incorporated into EUROGREEN. In the absence of specific policy scenarios that could affect them (which will be described in the following sections), they are assumed to remain constant in the baseline scenario, based on historical averages. This assumption is supported by empirical observations showing that despite some fluctuations, these ratios tend to be very stable over time, as evidenced by the very low standard deviations presented in Table A6 in Appendix A.

To calculate the footprint and the physical trade balance (expressed as $M^i \hbox{-}\hbox{-} M^e$ ), we need to determine the intensity coefficients related to monetary imports and exports, expressed in real terms. In this case, since a single sector is responsible for the near-total extraction of each raw material category (as shown in Table A5), we calculated these coefficients at the national level. Hence,

(3) \begin{align} \gamma _e^j & = \frac {M^e_j}{x_e}, \end{align}

(4) \begin{align} \gamma _i^j & = \frac {M^i_j}{x_i}, \end{align}

where $x_i$ and $x_e$ stand for total national imports and exports, respectively.

Monetary imports and exports fluctuate according to the endogenous dynamics of the economy. In EUROGREEN, imports are calculated as a fraction of domestic intermediate and final demand, both of which are subject to endogenous variations, while exports are tied to GDP growth. Consequently, under different scenarios, the physical value of trade may vary despite constant intensity coefficients. Each year, the model endogenously computes the domestic material consumption of the French economy, disaggregated by material type. The physical trade balance is then derived by subtracting the raw materials embedded in imports from that of exports.

The concept of dematerialisation is central to the mainstream narrative on CE. Material efficiency, in particular, is considered a cornerstone of green growth strategies. It is considered to serve as a critical pathway for decoupling GDP growth from environmental degradation (Del Rey and Lopez-Garcia, Reference Del Rey and Lopez-Garcia2017). We propose two different hypotheses regarding external technological advancements that reduce the raw material content embedded in final products. Under the cases “ $\Delta _\gamma ^{10}$ ” and “ $\Delta _\gamma ^{20}$ ,” we assume a gradual and linear reduction in the intensity coefficient ( $\gamma$ ) between 2024 and 2050, achieving total reductions of 10% and 20% by the end of the period, respectively. The first case constitutes an optimistic presupposition, considering that historical data has demonstrated a fluctuating pattern with no clear trends. Conversely, the latter scenario presupposes a rate of efficiency enhancement that is twice as rapid as this optimistic assumption. It enables us to evaluate the practical outcomes of very optimistic material efficiency assumptions.

2.3 Waste management

We first added waste generation ( $W$ ) to EUROGREEN’s EEIO tables. We compiled and harmonised Eurostat data on physical waste generation by sector ( $W_s$ ) to align with EUROGREEN’s classification (from Table A4). We also introduced a coefficient ( $\omega _h$ ) representing the municipal waste ( $W_h$ ) linked to household consumption, which contributes to a small fraction (less than 10%) of French waste generation. This allowed us to create a new vector of waste-to-output coefficients for industries and household consumption, effectively adding a row to EUROGREEN’s EEIO matrix (see Table A7 in the Appendix). This vector allows for an endogenous calculation of annual waste generation in France. Namely,

(5) \begin{align} W_s &= \omega _s \cdot x_s, \end{align}

(6) \begin{align} W_h &= \omega _h \cdot f, \end{align}

(7) \begin{align} W &= \sum _s W_s + W_h. \end{align}

where $\omega _s$ is the waste intensity coefficient, $W$ is the total amount of waste at the national level, and $f$ is the final demand. As per raw material extraction, the intensity coefficients ( $\omega$ ) are assumed to remain constant in the baseline scenario, as historical data indicates no significant change over time. However, for consistency reasons, we assume that waste generation follows the same reduction trajectory as raw material extraction under the “ $\Delta _\gamma ^{10}$ ” and “ $\Delta _\gamma ^{20}$ ” cases.

This assumption reflects the idea that, if fewer amounts of raw materials were embedded in the output, a corresponding reduction in waste generation would occur. It is important to note, however, that we do not account for any specific waste management plans or changes in product lifespan. In particular, product lifetime extension would affect the durability of goods and the composition of the final demand. However, as the level of sectoral aggregation in EUROGREEN limits the precise evaluation of these effects, such considerations fall beyond the scope of this paper.

In a second step, we calculated waste treatment ( $W^\ast$ ) — with $W^\ast = k_w \cdot W$ , and $k_w$ constant based on historical data (see Table A7 in the Appendix) — by compiling data by waste category, hazardousness, and waste management operation from Eurostat. It enables us to calculate the recycling rate ( $\rho$ ) in France, which remains stable and is assumed to stay constant in the baseline. The volume of recycled waste ( $W_\rho$ ) refers to the amount of "Materials recovery," excluding backfilling. Namely,

(8) \begin{equation} \rho = \frac {W_\rho }{W^\ast }. \end{equation}

Acknowledging that the categorisation of waste generation and treatment differs from that of raw materials, we estimated the portion of $W_\rho$ attributable to the four raw material types $j$ . This was done by applying the CMU or $\phi$ , with $0 \leq \phi \leq 1$ , which is available only at the EU level.Footnote ⁴ This estimation of the Domestic recovered materials ( $R$ ) is based on the Circular Material Use ( $1phi$ ) general formula, omitting the specification per material category $j$ for simplicity, as

(9) \begin{align} \phi & = \frac {R - W_i + W_e}{DMC + R - W_i + W_e},\,\, then \end{align}

(10) \begin{align} R & = \frac {(1 - \phi )(W_i - W_e) - \phi \cdot DMC}{(1 - \phi )}. \end{align}

Note that EUROSTAT provides data by material category at an aggregated scale for the domestic production, exports, and imports of waste, $W_e$ and $W_i$ respectively. Hence, we recover the recovered materials, by raw material category ( $R_j$ ), indirectly, and we use it to calculate the shares of recycled waste to be imputed to each raw material category asFootnote ⁵

(11) \begin{align} \rho _j &= \frac {R_j}{W_\rho }, \end{align}

(12) \begin{align} \sum _j \rho _j &= 1. \end{align}

This step is essential for calculating the quantity of secondary materials that, within a CCE framework, ought to replace primary raw materials, subsequently affecting resource extraction.

2.4 Closing Supply Chains (CSC)

Closing Supply Chain (CSC) is often described as a key mechanism of the CE framework, as it aims to maximise the reuse of waste in the form of new inputs in the production process. Replacing new inputs with secondary materials can limit raw material extraction and reduce the environmental pressures inherently tied to economic activities (Beylot et al. Reference Beylot, Vaxelaire and Villeneuve2016; Merciai and Schmidt, Reference Merciai and Schmidt2018; Wiebe et al. Reference Wiebe, Harsdorff, Montt, Simas and Wood2019), including GHG emissions, loss of biodiversity, and changes in land-use(Nakamura, Reference Nakamura2023; Donati et al. Reference Donati, Aguilar-Hernandez, Sigüenza-Sánchez, de Koning, Rodrigues and Tukker2020; Aguilar-Hernandez et al. Reference Aguilar-Hernandez, Sigüenza-Sanchez, Donati, Rodrigues and Tukker2018).

The Circular Material Use (CMU) rate is an indicator representing the quantity of recycled materials ( $R$ ) used relative to the total material use in the economy( $\overline {M} = DMC + R$ ). We can derive from it the amount of newly extracted raw materials and secondary materials processed in the economy. To represent the substitution effect between primary and secondary recycled materials, we have adjusted the technical coefficients of extractive sectors accordingly (Wiebe et al. Reference Wiebe, Harsdorff, Montt, Simas and Wood2019). Considering that the technical coefficient ( $a_{n,s}$ ) represents the proportion of intermediate goods, expressed in monetary terms ( $Z_{n,s}$ ), that a given sector $s$ is buying from another sector $n$ to produce its own output — i.e. $a_{n,s} = Z_{n,s} / x_s$ — then if the proportion of secondary raw materials increases, each sector will demand less primary materials from the extracting sectors. To endogenise this mechanism, we assume that the technical coefficients of the extractive sectors are updated annually based on the proportion of primary raw materials to total material use ( $\psi = 1 - \phi$ ) relative to the initial period ( $\psi _0 = DMC_0/M_0$ ). Namely

(13) \begin{equation} a_{n,s}^j(t+1) = a_{n,s}^j(t) \frac {\psi ^j(t)}{\psi _0^j}. \end{equation}

At the beginning of the period, the economy requires a given quantity of primary raw materials ( $\psi _0$ ) to satisfy its production needs, from which a corresponding flow of waste is generated. A fraction of this waste stream is subjected to treatment and recycling processes, yielding secondary (recycled) materials. In the subsequent period, the extraction of primary raw materials declines, as part of the material demand is now met by these recycled inputs. In the following period, the total quantity of waste generated diminishes, and consequently, the volume of recyclable material also decreases. Under a steady-state economic regime, this dynamic of material substitution between primary and secondary resources is expected to give rise to an oscillatory pattern over time. By contrast, in a context of sustained economic growth, the overall demand for raw materials may increase even in the presence of rising recycling rates. It is noteworthy that alterations in technical coefficients, as represented by the A matrix within the Leontief framework, vary according to material type $j$ . For simplicity, we assume that changes in the technical coefficients affect only one sector for each type of raw material. This assumption is strongly supported by data presented in Table A5 in Appendix A.

On the other hand, an increase in the volume of recycled materials ( $R$ ) over time necessitates more activities within the corresponding NACE sector, specifically "Water supply, sewerage, waste management and remediation activities" ( $s_\omega$ ).Footnote ⁶ Demand for this sector increases along with changes in waste generation. Consequently, $s_\omega$ experiences a rise in both demand and output to accommodate and process the greater volume of waste materials. To effectively capture the consequences of fluctuations in recycling, a recycling evolution coefficient is employed ( $\zeta = \frac {R(t)}{R_0}$ ). This coefficient dynamically connects recycling levels with the technical coefficients of the public sector (which includes waste treatment activities in EUROGREEN). It thereby influences the structure of the output and the final demand level within an Input—Output analytical context. Namely,

(14) \begin{equation} a_{s_\omega , n}(t+1) = a_{s_\omega , n}(t) \cdot \zeta . \end{equation}

Figure 2.

Closing supply chain scheme. This figure presents a simple depiction of the principal variables and linkages incorporated into the EUROGREEN model to encompass residual waste management and closing supply chains. For simplicity, the division by raw material categories is not depicted. The circular material use rate ( $\phi = 1 - \psi$ ) influences the technical coefficients (A matrix), which in turn impact total production, subsequently affecting international trade and the physical trade balance. Additionally, $\gamma$ , $\omega$ , and $\rho$ influence extraction, waste treatment, and recycling, generating delayed feedback effects (arrow with double vertical lines) on the economy.

Figure 2 depicts the main variables, parameters, and relations described in the current Section. The structure of the economy is shaped by the A matrix and the final demand, which together determine the level of output of each sector. Lagged feedback effects on the demand through the labour market are included in the model. Raw material intensity coefficients ( $\gamma$ ) (net of the impact of international trade) determine the volume of French Domestic Material Consumption. Waste intensity coefficients ( $\omega$ ) and the recycling rate ( $\rho$ ) determine the volume of materials recycling. The Circular Material Use rate ( $\phi$ ) and its complementary variable, the rate of raw material use ( $\psi$ ), alter the demand for inputs (A). These, in turn, feed back into economic activities through adjustments in the technical coefficients that determine output levels in the following periods, ultimately spurring changes in raw material extraction. In summary, we represented the feedback loops that connect raw material use with economic activities.

Table 1.

Summary of the main assumptions for every scenario

Note: $\gamma$ and $\omega$ denote the efficiency of resource utilisation and waste minimisation, respectively. $\Delta ^{10}_\gamma$ and $\Delta ^{10}_\omega$ represent an external enhancement in material efficiency by 10% and 20% by the year 2050, while $\lt \rho ^{max}\gt$ indicates the projected elevation of recycling rates to 95% by 2050. Technological advancement corresponds to improvements in energy efficiency and labour productivity, as detailed in the foundational work EUROGREEN. WTR, JG, and DG signify the reduction in working hours, the assurance of employment, and post-growth, respectively. The term Wealth tax refers to a Piketty-type tax imposed on financial wealth. The values of all the parameters, by sector, are reported in Appendix A5, A6 and A7.