# Green investment support measures: a multi-sectoral, macro-financial analysis for the European Union

Hugo Bailly

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Hugo Bailly. Green investment support measures: a multi-sectoral, macro-financial analysis for the European Union. 2026. hal-05522653

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Green investment support measures: a multi-sectoral,
macro-financial analysis for the European Union.

Hugo Bailly<sup>a,b</sup>

**Abstract**

The transition to a low-carbon economy requires substantial investment to replace the production technologies and infrastructure reliant on fossil fuels. In addition to regulation and carbon pricing, a range of financial policies has been proposed to accelerate green investment. This article evaluates the implications of three of them — direct green investment subsidies, green public guarantees, and capital market deepening — in terms of emission reduction, economic activity, and public debt. The analysis relies on a stock-flow consistent, input-output model of the EU economy, which explicitly incorporates industries' marginal abatement costs, intersectoral input-output linkages, and investment financing channels. Model simulations reveal that direct subsidies are the most effective tool for achieving significant emission reductions; however, they also result in substantial increases in the debt-to-GDP ratio. In contrast, public guarantees and equity market development tend to strengthen public finances and economic activity but yield only moderate emission cuts. The results further suggest that combining policies can effectively balance emission mitigation and economic activity without compromising public finance sustainability.

**Keywords:** Energy transition, Green financial policies, Ecological macroeconomics, Stock-flow consistent modelling, Input-output modelling

**JEL:** Q50, E61, E44

<footnote><sup>a</sup>CES - Centre d'économie de la Sorbonne, Maison des sciences économiques, 106-112 bd de l'Hôpital, 75647 Paris Cedex 13, France</footnote>

<footnote><sup>b</sup>Deloitte Economic Advisory, 6, place de la Pyramide, Paris La Défense, 92908, France</footnote>
# 1. Introduction

A landslide shift in production technologies is required in Europe if the European Union (EU) wants to reach its climate policy targets. Achieving net-zero emissions by 2050 requires a substantial reduction in the use of fossil fuels, not only in energy generation but also across energy-intensive sectors such as transportation and manufacturing (European Commission, 2023b). Since existing infrastructure and machinery powered by fossil fuels cannot be operated with alternative energy sources, energy substitution often requires large-scale capital replacement. As summarised by Pisani-Ferry and Tagliapietra (2024): ‘there is no green transition without green investment’.

The investment required for the transition is further increased by the fact that green technologies are often more capital-intensive and have higher upfront costs than their conventional counterparts (Egli et al., 2022). Overall, the European Commission estimates that nearly €620 billion in additional investments are needed each year to meet the European 2030 objectives (European Commission, 2023a). Recent investment trends fall short of these targets (Bizien et al., 2024), raising questions about the barriers to green investment.

Against this backdrop, funding for green investment has recently emerged as a critical economic policy issue. Several public reports highlight that high energy prices, rising emission costs, and international competition restrict the leeway firms have to fund necessary investments (Pisani-Ferry and Mahfouz, 2023; Letta, 2024; Draghi, 2024). They also point out that firms’ predominant reliance on bank loans, rather than bonds or equity, does not allow for the financing of economically risky and innovative technologies. Hence, both the availability and structure of EU firms’ financing are being questioned in the context of the transition.

Various policy options have been proposed to stimulate and support green investment financing. A first category focuses on monetary policies and banking regulations. It includes measures designed to increase the overall lending capacity of banks, for instance by making securitisation easier (Letta, 2024; Draghi, 2024). Other proposals focus on incentives for transition loans, such as green supporting factors (Dombrovskis, 2017) or adjustment to the central bank’s collateral framework (Dafermos et al., 2021). Direct money creation by central banks has also been put forward to fund non-profitable but essential environmental projects (Couppey-Soubeyran et al., 2024). A second strand of policy options relies on the deepening of the European equity market. Letta (2024) and Draghi (2024) argue for instance that the development of a capital market union could streamline financial flows across Europe and improve the channelling of households’ savings into productive investments. Lastly, the need for public support in facilitating the green transition is widely acknowledged. Es-
timates suggest that direct public investments could account for between 25% and 50% of total transition investment needs (Pisani-Ferry and Tagliapietra, 2024). Public sector financing can play a crucial role in supporting green investments that are unlikely to yield private returns (Belle-Larant et al., 2024). Furthermore, direct support could be complemented by de-risking mechanisms to stimulate private funding. Examples include contracts for difference as implemented in Germany (IEA, 2023a), as well as credit guarantee schemes introduced in Sweden (IEA, 2023b). While all the above-mentioned policies are potential instruments to facilitate green investments, their macroeconomic implications and the channels through which they operate may vary considerably. To my knowledge, a comprehensive analysis and systematic comparison of these policy instruments has not yet been conducted to date.

Indeed, the economic literature has predominantly explored the connection between transition and finance from the standpoint of risks to financial stability. Popularised by Carney (2015), the distinction between transition risk and physical risk has widely structured the analysis of climate-related economic impacts. Physical risks, associated with the economic impact of climate change, have been shown to affect financial stability. Significant climate damages may lead to reduced production and increased investment needs, driving firms' indebtedness up (Bovari et al., 2018) and impacting banks' economic health (Dafermos et al., 2018). On the other hand, abrupt changes in policies and behaviours aimed at mitigating climate change may pose risks to financial stability if implemented over too short a timeframe. This so-called transition risk, often modeled through a surge in carbon price, could result in unsustainable private debt levels (Bovari et al., 2018), as well as increased default risk, inflation, and asset price volatility (Daumas, 2023; Jackson and Jackson, 2025). Sudden and unexpected transition policies may also lead to asset stranding, with potential spillover effects on financial institutions (Daumas, 2024). Overall, the interaction between transition and physical risks leads to a 'double materiality' of climate risks (Gourdel et al., 2024). An increase in firm defaults driven by both physical and transition risks may negatively impact banks' lending conditions, thereby making green investments more difficult and further exacerbating climate-related risks. In summary, there is broad agreement that an anticipated and orderly transition would be less disruptive than either the physical damage resulting from a lack of climate action or the financial instability caused by a disorderly and delayed transition.

Policy options designed to support clean investment have only recently become a research focus (Sanders et al., 2022). Dafermos and Nikolaidi (2021) and Lamperti et al. (2021) highlight that macroprudential policies differentiating capital requirement for green or brown assets may mildly reduce global warming. Such policies,
like the ‘green supporting factor’, could however come at the cost of higher bank leverage and greater financial instability. In contrast, public guarantees can simultaneously encourage green investment and strengthen financial stability by mitigating the impact of non-performing loans on banks during economic downturns (Lamperti et al., 2021). Similarly, direct public support for green technologies can help reconcile decarbonisation with economic activity in the short run (Monasterolo and Raberto, 2018). However, both public guarantees and direct subsidies have a negative impact on the public debt, at least in the long run (Lamperti et al., 2021; Monasterolo and Raberto, 2018). Finally, Dafermos et al. (2018) demonstrate that green quantitative easing can lower climate-induced financial instability and modestly reduce greenhouse gas emissions by decreasing investment costs. Overall, the literature suggests the existence of potential trade-offs between green investment, financial stability and public debt.

This paper aims to further explore how financing policies could foster decarbonisation in the EU. It focuses on the following research questions:

* Which policy options could effectively support the financing of corporate green investment?

* Should such policy tools be combined?

Three specific policy tools are assessed: capital market development; green investment subsidies, representing direct public support; and public guarantees for green loans, which illustrate investment de-risking policies. Monetary policy and banking regulation, which fall primarily under the authority of the central bank rather than EU governments or the Commission, are not considered here.

Analysing transition policies, and even more so financing policies, requires suitable modeling of monetary and financial dynamics (Pollitt and Mercure, 2018; Sanders et al., 2022). Accordingly, the model developed in this paper is grounded in the stock-flow consistent (SFC) modelling literature, as popularised by Godley and Lavoie (2016), which emphasizes the explicit representation of the monetary and financial spheres of the economy. While most SFC models represent economic production through a single productive sector, accounting for industries’ heterogeneity is critical to capturing transition dynamics. Indeed, industries are unevenly exposed to climate damages, decarbonisation costs, and financial risks. Distinguishing between economic activities also enables the modeling of supply-chain effects, which significantly influence the impact of the transition on employment and economic structures (Perrier and Quirion, 2018; Bastidas and Mc Isaac, 2019; Pettena and Raberto, 2025).
This paper contributes to the emerging literature that integrates input-output multisectoral approach within the stock-flow consistent modelling framework (Berg et al., 2015; Sers, 2022; Cordova, 2023; Thomsen et al., 2025; Pettena and Raberto, 2025; Jackson and Jackson, 2025). It extends this literature by developing a seven-industry model calibrated to match European data. In each industry, firms' green and brown investments are determined endogenously, driven by market incentives and technology-specific net present values. The investment costs associated with clean technologies are explicitly modelled and calibrated to reflect sector-specific abatement costs. These investments are financed through three channels: retained profits, equity, and debt. Using the model, we evaluate the three financial policies — capital market development, investment subsidies, and public guarantees — within net-zero baseline scenarios that combine sector-specific carbon taxes and regulations.

The theoretical model is presented in Section 2 while Section 3 describes the calibration of the model and of the baseline scenarios. The results of the policy simulations are presented in Section 4 and discussed in Section 5. Section 6 concludes the paper.

## 2. A multi-industry stock-flow consistent model of the European Union

The model represents the EU economy as an input-output network consisting of seven distinct non-financial industries. These productive sectors interact with a representative household, the government, the central bank, and a private bank. Building on the stock-flow consistent literature (Lavoie and Godley, 2001; Godley and Lavoie, 2016; Dafermos et al., 2017; Monasterolo and Raberto, 2018), the model explicitly incorporates firms' investment financing through retained earnings, debt, and equity. Decarbonisation in each sector is achieved by replacing conventional capital with clean capital. Firms determine the share of clean technology in new investments based on the difference between the net present values (NPV) of clean and conventional ('dirty') investments.

The balance sheets and the transaction-flow matrix of the model can be found in Appendix A. In the following, uppercase letters denote nominal variables, while lowercase letters are used for real-term variables, parameters, rates, and prices. The exponent $c/d$ signals that a variable is defined separately for clean and conventional technologies.

### 2.1. *Households*

Households earn income from both employment and asset holdings. Each period, they consume a portion of their income and wealth, while allocating their remaining wealth among equity, government debt and bank deposits.
Households' disposable income (YD) consists of wages as well as revenues from deposits, government debt and equity holding:

$$YD = (1 - \tau^l)W^{tot} + r^d D_{-1}^h + r^g GD_{-1}^h + \Pi_{tot}^{fd} + \Pi^{bd}, \quad (1)$$

where $W^{tot}$ is the sum of wages earned across sectors, $\tau^l$ is the labour tax, $D^h$ the households' deposits, and $GD^h$ the government debt held by households. The rates $r^d$ and $r^g$ are the interest rates earned from deposits and government debt, respectively, while the variables $\Pi_{tot}^{fd}$ and $\Pi^{bd}$ denote dividends paid by non-financial firms and banks, respectively.

At each period, households consume shares $\alpha_1$ and $\alpha_2$ of their nominal revenues and wealth ($V^h$) respectively:

$$C = \alpha_1 YD_{-1} + \alpha_2 V_{-1}^h.$$

Total consumption, corrected for the value-added tax (VAT) and deflated using the consumer price index (CPI), is allocated across industries according to constant shares.

Households' wealth evolves with disposable income, consumption, and capital gains or losses from fluctuations in non-financial firms' equity prices. It is allocated based on the returns associated with each asset, following a two-step 'Tobinesque' approach (Lavoie and Godley, 2001). In the first step, households decide the share of wealth allocated to aggregate equity, government debt, or held as bank deposits:

$$E^{tot} = \left( \lambda_{10} + \lambda_{11}r_{-1}^e + \lambda_{12}r^g + \lambda_{13}r^d + \lambda_{14}\frac{YD_{-1}}{V_{-1}^h} \right) V_{-1}^h, \quad (2)$$

$$GD^h = \left( \lambda_{20} + \lambda_{21}r_{-1}^e + \lambda_{22}r^g + \lambda_{23}r^d + \lambda_{24}\frac{YD_{-1}}{V_{-1}^h} \right) V_{-1}^h, \quad (3)$$

$$D^h = V^h - E^{tot} - GD^h, \quad (4)$$

where $E^{tot}$, $GD^h$, and $D^h$ correspond respectively to households' demand for non-financial firms' equity, government debt, and deposits, while $r^e$ denotes the aggregate return on equity. The parameters $\lambda_{x,y}$ are set to ensure that an increase in the return of a given asset results in higher demand for that asset, accompanied by a corresponding decrease in demand for alternative assets. The inclusion of the disposable income to wealth ratio $\frac{YD_{-1}}{V_{-1}^h}$ aims to account for the transactions demand
for money (Lavoie and Godley, 2001)<fnref n="1" />. In a second step, households follow a similar approach to allocate their total demand for equity investment across sectors according to industry-specific returns. To account for inertia in portfolio evolution, the actual demand for equity in each sector is subject to adjustment rigidities.

## 2.2. Non-financial firms

Non-financial firms in each sector produce goods and services using capital, intermediate inputs from all industries, and labour. Firms invest in either clean or conventional capital, depending on the net present value associated with each investment type. While clean investment enables decarbonisation — thereby reducing the carbon tax burden — clean technologies are relatively more capital-intensive than conventional alternatives. Firms’ investment — financed by retained profits, equity, or debt — may be constrained by credit rationing imposed by banks.

### 2.2.1. Sales, pricing and profits

Each sector fully meets its demand, which consists of final consumption by households and government, as well as investment and intermediate demand from the other industries. In real terms, sales are defined as follows:

$$s_i = c_i + gc_i + \Gamma_i inv^{tot} + \sum_j a_{ij}s_j, \quad (5)$$

where $c_i$ and $gc_i$ are, respectively, the real household and government consumption of goods and services from industry $i$, while $\Gamma_i$ is the fixed share of the economy-wide investment demand $inv^{tot}$ produced by sector $i$. Parameters of the matrix $(a_{ij})$ are input-output coefficients. Specifically, $a_{ij}$ corresponds to the quantity of intermediate goods or services from sector $i$ consumed by sector $j$ to produce one unit of real production. Hence, $\sum_j a_{ij}s_j$ is the total quantity of goods or services sold by sector $i$ as intermediates to other industries.

The unit production cost, $uc_i$, consists of the labor-productivity-adjusted wages and intermediate consumptions:

$$uc_i = \frac{w}{pr^l} + \sum_j a_{ji}p_j, \quad (6)$$

with $w$ the unit wage rate, $pr^l$ the labour productivity and $p_j$ sector $j$'s price.

<footnote n="1">¹While $\lambda_{14}$ and $\lambda_{24}$ are theoretically expected to be negative, their signs ultimately depend on the calibration data. In the baseline calibration, $\lambda_{14}$ is found to be positive.</footnote>
Each sector sets its desired price as a markup on unit costs. The price is further impacted by the capital utilisation rate — capturing demand-led inflationary effects — as well as by the direct carbon tax faced by the sector, partly passed on to customers through price increase (Daumas, 2023):

$$p_i = (1 + \nu_i) \times \nu_i^{ut} \times uc_i + \tau_i^{pass} \frac{tax_i^{carb}}{s_i}. \quad (7)$$

In Equation (7), $\frac{tax_i^{carb}}{s_i}$ is the unit carbon price and $\tau_i^{pass}$ the pass-on rate on carbon pricing. Parameter $\nu_i$ is a constant markup rate, while $\nu_i^{ut}$ is a variable markup rate coefficient defined as:

$$\nu_i^{ut} = p_0 + \frac{p_1}{1 + \mathrm{EXP}(p_2 - p_3 \times r_{i-1}^{ut})}, \quad (8)$$

where $p_0$, $p_1$, $p_2$ and $p_3$ are fixed coefficients and $r_i^{ut}$ is the capital utilisation rate. The variable $\nu_i^{ut}$, which revolves around 1, allows to capture demand-driven inflationary effects. As clean energy are associated to larger capital-to-output ratios, $\nu_i^{ut}$ also allows to capture the impact of transition on prices (Jacques et al., 2023; Attílio, 2025).

Firms' profit corresponds to the residual value-added ($VA_i$) remaining after the payment of wages ($W_i$), taxes on profits and emissions ($TAX_i$ and $TAX_i^{carb}$), interests on clean and conventional loans ($L_i^c$ and $L_i^d$), and adjustments for the share of non-performing loans ($npl_i$):

$$\Pi_i^f = VA_i - W_i - TAX_i - TAX_i^{carb} - r_i^{l,d} L_{i-1}^d (1 - npl_i) - r_i^{l,c} L_{i-1}^c (1 - npl_i). \quad (9)$$

A fixed share of these profits is distributed as dividends to households, who hold all of the firms' equity. The remaining undistributed profits are retained to help finance new investments.

### 2.2.2. Capital stocks and decarbonisation

The interplay between investment and production levels is reflected through two channels. First, rising demand increases the capital utilisation rate, thereby stimulating higher investment (see Equation 15). Second, an increased capital utilisation rate contributes to higher prices, which in turn reduces real demand and production (see Equation 8).

Each sector can invest in either clean capital ($K_i^c$) or conventional capital ($K_i^d$), both of which are built using the same intermediate inputs (see Equation 5) and depreciate at the same rate, $\delta^{depr}$:
$$K_i^{c/d} = K_{i,-1}^{c/d}(1 - \delta^{depr}) + INV_i^{c/d}. \quad (10)$$

Clean and conventional capital differ in their capital-to-output ratios and emission intensities. Decarbonisation primarily involves shifting from conventional to clean capital, the latter being less emission intensive. Specifically, the overall sector-specific emission intensity is modeled as a weighted average of the emission intensities of clean and conventional capital, while autonomous technological progress is captured by a fixed coefficient $\delta^{emi}$.

$$\gamma_i^{all} = \frac{k_{i,-1}^{all}}{k_i^{all}} \gamma_{i,-1}^{all} (1 - \delta^{emi}) + \frac{\gamma_i^c \Delta k_i^c + \gamma_i^d \Delta k_i^d}{k_i^{all}}, \quad (11)$$

where $k_i^{all}$ denotes the total real capital stock for sector i. Sector-specific emissions are a function of the real sales and the emission intensity of production:

$$emi_i = s_i \times \gamma_i^{all}, \quad (12)$$

and the carbon tax is paid based on previous period emissions:

$$TAX_i^{carb} = \tau_i^{carb}emi_{i,-1}. \quad (13)$$

### 2.2.3. Investment demand

Firms determine their total desired investment levels according to prevailing financial and demand conditions. The allocation between clean and conventional capital types is based on a comparative analysis of the net present values (NPV) associated with available clean and conventional technologies. More precisely, firms make investment decisions in two steps. In a first step, each sector sets its aggregate desired investment:

$$INV_i^{all,*} = g_{k,i} K_{i,-1}^{all}. \quad (14)$$

The variable $g_{k,i}$ is influenced by the industry's rate of retained profits ($r_i^{\pi,k}$), utilization rate ($r_i^{ut}$), and average interest rate on loans ($r_i^{l,all}$):

$$g_{k,i} = g_{k,i}^{00} + g_{k,i}^{01} \times r_{i,-1}^{\pi,k} + g_{k,i}^{02} \times r_{i,-1}^{ut} - g_{k,i}^{03} \times r_{i,-1}^{l,all}, \quad (15)$$

where parameters $g_{k,i}^{00}$ to $g_{k,i}^{03}$ are fixed coefficients.
In a second step, following Gourdel et al. (2024), each sector defines its desired share of clean investment, $\theta_i^c$, by comparing the net present values (NPVs) associated with clean and conventional technologies. For each period, the NPVs of clean (c) and conventional (d) investments are derived based on the initial investment costs, the cash flows associated with the investments, and the weighted average cost of capital for the project, $wacc^{npv,c/d}$. It is assumed that firms expect their selling prices, cost structures, and the prevailing carbon tax to remain constant throughout the entire lifespan of the capital. Firms also expect that the capital associated with new investments will be put to full use. Under these assumptions, the NPV per euro invested in both clean and conventional technologies can be expressed as follows:

$$ \left\{ \begin{aligned} NPV_i^c &= -(1 - gs_i) + \frac{1 + wacc_{i,-1}^{npv,c}}{wacc_{i,-1}^{npv,c} + \delta^{depr}} \times \frac{p_{i,-1} - uc_{i,-1} - \tau_i^{carb}\gamma_i^c}{p_{-1}^k \sigma_{i,-1}^{k,c,marg}}, \\ NPV_i^d &= -(1 + g_i^{norm}) + \frac{1 + wacc_{i,-1}^{npv,d}}{wacc_{i,-1}^{npv,d} + \delta^{depr}} \times \frac{p_{i,-1} - uc_{i,-1} - \tau_i^{carb}\gamma_i^d}{p_{-1}^k \sigma_i^{k,d,marg}}, \end{aligned} \right. \quad (16) $$

where parameter $gs_i$ denotes the proportion of investment subsidised by the government and $g_i^{norm}$ captures the penalising impact of climate regulations on the NPV of conventional capital. The variable $\sigma_i^{k,c/d,marg}$ represents the capital-to-output ratio of the newly installed technology. Here, the weighted average cost of capital is technology-specific and accounts for the differential in interest rates across investment types:

$$ wacc_i^{npv,c/d} = \frac{(L_i^c + L_i^d) \times (1 - \tau) \times r_{-1}^{c/d} + \Pi_i^{fd}}{L_i^c + L_i^d + E_i}, \quad (17) $$

where $\Pi_i^{fd}$ denotes the sector-specific dividends paid by non-financial firms. Finally, the share of clean investment, $\theta_i^c$, is a linear function of the gap between clean and conventional investment NPVs:

$$ \theta_i^c = \theta_i^{c,0} + \theta_i^{c,1} \times NPV_i^\Delta, \quad \theta_i^c \in [0, 1], \quad (18) $$

where $NPV_i^\Delta$ is the difference between clean and conventional investments, normalised to 1 at the first simulation period.

The higher upfront investment costs associated with low-emission technologies are captured by assuming different productivity levels for clean and conventional capitals. Two types of capital-to-output ratios are modeled. The marginal capital-to-output ratio reflects the productivity of the technologies available for investment at a given
time; it is used in firms’ net present value (NPV) calculations and influences decisions regarding clean investment. In contrast, the actual capital-to-output ratio reflects the productivity of existing production capacities, affecting both the utilisation rate of current capital and the overall need for new investment.

The marginal capital-to-output ratio of clean and conventional capital are related through the following equation:

$$ \sigma_i^{k,c,marg}(k_{share,i}^{clean}) = \sigma_i^{k,d} \times (1 + \sigma_i^{k,\Delta,marg}(k_{share,i}^{clean})) \quad (19) $$

where $\sigma_i^{k,\Delta,marg}$, the gap between clean and conventional capital-to-output ratio, is defined as an increasing linear function of the share of clean capital in total capital ($k_{share,i}^{clean}$). Hence, unlike conventional technologies — whose capital productivity, $\sigma_i^{k,d}$, is constant — the marginal capital-to-output ratio of clean technologies increases as capital in the sector becomes greener.

In the absence of an explicit modeling of capital vintages, the actual capital-to-output ratio of installed capacities, $\sigma_i^k$, is defined as the average of the clean and conventional capital-to-output ratios:

$$ \sigma_i^k = \frac{\sigma_i^{k,c} k_i^c + \sigma_i^{k,d} k_i^d}{k_i^{all}} \quad (20) $$

where the clean capital-to-output ratio, $\sigma_i^{k,c}$, is also defined in relation to the gap from the constant conventional ratio, $\sigma_i^{k,d}$:

$$ \sigma_i^{k,c} = \sigma_i^{k,d} \times (1 + \sigma_i^{k,\Delta}), \quad (21) $$

while $\sigma_i^{k,\Delta}$ is a sigmoid function of the share of clean capital in total capital.

Overall, this modeling approach makes it possible to capture multiple implications associated with green investment throughout the transition process. Firstly, the influence of increasingly expensive abatement technologies on investment decisions is captured by changes in marginal capital productivity ($\sigma_i^{k,c,marg}$), which progressively diminishes the net present value (NPV) of clean investments as the transition advances. Furthermore, changes in the capital-to-output ratio of installed capacities ($\sigma_i^k$) reflect increased investment needs during the transition: an increase in this ratio drives up the capital utilisation rate and, in turn, raises investment demand (see Equation 15). Finally, a higher capital utilisation rate also contributes to increased variable markups, resulting in inflationary pressures (see Equation 7).
### 2.2.4. Investment financing

Following Lavoie and Godley (2001), investment is financed through retained earnings, equity, and debt. However, as in Dafermos et al. (2017), actual investment may be lower than desired due to credit rationing. A fixed share $\chi_i$ of the desired real investment is financed by an increase in sector's equity:

$$ \Delta e_i = \chi_i \frac{INV_i^{all,\star}}{p_i^e}. \qquad (22) $$

The equity market is assumed to be in equilibrium, whereby the equity price $p_i^e$ adjusts endogenously to equate households' equity demand with firms' supply. Industries turn to banks to finance investments that are not covered by retained profits ($\Pi_i^{fnd}$) and equity. Therefore, desired new clean (resp. conventional) loans write:

$$ \left\{ \begin{array}{l} NL_i^{c,\star} = \theta_s^c (INV_i^{all,\star} - [\Pi_i^{fnd} + p_i^e \Delta e_i]) + \text{rep } L_{i,-1}^c (1 - npl_i) - GS_i \\ NL_i^{d,\star} = (1 - \theta_s^c) (INV_i^{all,\star} - [\Pi_i^{fnd} + p_i^e \Delta e_i]) + \text{rep } L_{i,-1}^d (1 - npl_i), \end{array} \right. \qquad (23) $$

where rep is the rate of loans repayments, npl_i the rate of non-performing loans and GS_i the amount of government support to green investment. Not all loans are granted by banks: credit is increasingly rationed as the banks' capital adequacy ratio (car) approaches the regulatory minimum (car$^{min}$), or as the sector's debt-service ratio (dsr_i) rises. The proportion of new loans rationed by banks, cr_i, is given by$^2$:

$$ cr_i = \frac{cr^{max}}{1 + r_o \text{EXP}(r_{1,i} - r_2 dsr_{i,-1} + r_3 (car_{-1} - car^{min}))}, \qquad (24) $$

where cr$^{max}$ and the coefficients r$_0$ through r$_3$ are fixed. Furthermore, a portion of green new loans may be guaranteed by the government. These guarantees allow firms to benefit from lower interest rates and enable banks to classify these loans as risk-free assets when calculating their capital adequacy ratios. In the event of default, the government reimburses banks for the value of the guaranteed loans.

Finally, actual investments correspond to desired investments to the extent permitted by financing obtained through self-financing, equity increase, and bank credit. In other words, credit rationing can explain the gap between desired and actual investments.

<footnote>$^2$Credit rationing is normalised to zero in the initial year.</footnote>
## 2.3. Private banks

Private banks provide both clean and conventional loans to non-financial sectors and collect deposits from households. They hold government debt and are required to maintain a minimal level of reserves at the central bank. The monetary authority, in turn, provides on-demand loans to help balance private banks’ balance sheet.

Private banks’ revenues stem from interests on loans and government bonds. They pay (small) interests on households’ deposits and on loans granted to them by the central bank. Consequently, private banks’ profit writes:

$$ \Pi^b = \sum_i r_i^{l,d} L_{i,-1}^d (1 - npl_i) + \sum_i r_i^{l,c} L_{i,-1}^c (1 - npl_i) + r^g GD_{-1}^b - r^d D_{-1}^h - r^{CBL} CBL_{-1}, \quad (25) $$

where $GD^b$ denotes government debt held by private banks, $D^h$ represents deposits from households, and $CBL$ denotes loans granted by the central bank. The variables $r^g$, $r^D$, and $r^{CBL}$ are the interest rates on government bonds, deposits, and central bank loans, respectively.

Following Gourdel et al. (2024), targeted clean and conventional interest rates are set by banks according to the risk associated with each industry, as measured by the non-performing loans rate ($npl_i$) and the proportion of loans recovered by banks in the event of borrower default ($R$):

$$ \left\{ \begin{aligned} r_i^{l,c,*} &= \nu^c + \left(1 - \frac{L_i^{cgl}}{L_i^c}\right) \times npl_i \times (1 - R) \\ r_i^{l,d,*} &= \nu^d + npl_i \times (1 - R). \end{aligned} \right. \quad (26) $$

Risk-free rates, $\nu^{c/d}$, incorporate both the banks’ margin and a premium associated with clean lending, capturing the fact that clean loans are, overall, more expensive than conventional ones. Loans benefiting from a public guarantee, $L_i^{cgl}$, are considered by banks to be risk-free assets. When calculating the capital adequacy ratio, these loans are also assigned the same risk weight as government debt, $w^b$, which is lower than that of standard loans ($w^l$):

$$ car = \frac{K^b}{w^l \left( \sum_i (L_i^{all} - L_i^{cgl}) \right) + w^b \left( GD^b + \sum_i L_i^{cgl} \right)}. \quad (27) $$

Following Alogoskoufis et al. (2021), the sector-specific share of non-performing loans, $npl_i$, is assumed to increase with sector leverage and decrease with the profit-to-capital ratio. Finally, fixed shares of total bank profits are distributed as dividends to households and as taxes to the government. Banks are also required to hold
both a minimum (and actual) level of reserves at the central bank and are assumed to purchase government bonds, with both reserves and bond holdings defined as constant shares of household deposits.

## 2.4. Central bank

The central bank holds government debt ($GD^{cb}$), grants loans to private banks on demand ($CBL$), and keeps reserves from those banks. The central bank's profit is therefore given by:

$$ \Pi^{cb} = r^{CBL}CBL_{-1} + r^{g}GD_{-1}^{cb}, \qquad (28) $$

where $r^{CBL}$ corresponds to central bank loans to private banks. Following Dafermos et al. (2017), the central bank acts as a residual buyer in the government debt market, purchasing any government debt not acquired by households or private banks.

## 2.5. Government

The total revenues of the government comprise taxes levied on firms and banks' profits ($TAX_i$ and $\Pi^{bt}$ respectively), carbon emissions ($TAX_i^{carb}$), labor ($TAX^l$) and value-added ($VAT^{rev}$):

$$ TAX^{rev} = \sum_{i} TAX_{i} + \sum_{i} TAX_{i}^{carb} + TAX^{l} + \Pi^{bt} + VAT^{rev}. \qquad (29) $$

Government consumption, $GC$, is assumed to grow at the constant balanced growth nominal rate, $g$, while the total real government consumption is supplied by each sector in fixed proportions. The government therefore has a 'neutral' spending behaviour, unrelated to the economic or financial conditions. Although simplified, this assumption enables evaluation of policy effects on public debt without reliance on assumptions about highly political debt reduction strategies.

Discrepancies between revenues and expenses lead to fluctuations in the level of government debt:

$$ GD = GD_{-1} + GC + GS^{tot} + r^{g}GD_{-1} - TAX^{rev} + GDL, \qquad (30) $$

the government compensating banks for the non-performing portion of the green guaranteed loans ($GDL$). Finally, since the model represents the EU as a closed economy, the real GDP is defined as:

$$ gdp = c + gc + inv^{tot}, \qquad (31) $$

where $c$, $gc$ and $inv^r$ are the real households' consumption, government consumption and corporate investment respectively.
### 3. Model calibration and baseline scenarios

#### 3.1. Model simulation

The model consists of a dynamic system in which the variables depend on current and lagged values. It is solved using a fixed-point iteration method. In the absence of lag variables to solve for the first period, we assume that the model is on a balanced growth path at the beginning of the simulation. Additionally, the first period is characterised by the absence of credit rationing, a zero default rate and a utilisation rate index $r_i^{ut}$ of one. These assumptions enable the specification of a set of initial conditions that ensure the model begins on a balanced growth path, while remaining consistent with the empirical initialisation of certain variables.

#### 3.2. Calibration and scaling approach

The model is calibrated to reflect the key macroeconomic variables of the European economy in level terms. In addition to the financial sector, represented by private banks, seven non-financial industries are modelled: agriculture, energy and mining, standard industries, energy-intensive industries, construction, transport, and the rest of the sectors.

The parameters are calibrated using three distinct approaches. A first set of parameter values is derived directly from the existing literature and data. A second set of parameters is set to ensure that the model starts on a balanced growth path in the initial period. This approach is applied, for instance, to the constant parameters in the investment functions ($g_{k,i}^{00}$), which are calibrated so that sectoral investment dynamics match balanced growth in the base year. Finally, a third set of coefficients is adjusted such that the model replicates key observed variables or ratios. Table 1 summarises these parameters alongside the values used to initialise the model.

Special emphasis is placed on the calibration of green investment costs. Decarbonisation expenses are typically represented by increasing marginal abatement cost curves. These curves reflect the fact that, while the initial stages of decarbonisation can be achieved using the least costly solutions, achieving full decarbonisation of a sector requires the deployment of increasingly expensive technologies.

In the model, the cost of decarbonisation is captured by the higher investment required for clean capital to achieve the same level of output as conventional capital. Specifically, both the capital-to-output ratio of new investments ($\sigma^{k,c,marg}$) and that of installed capacities ($\sigma^{k,c}$) increase with the deployment of clean capital. Concretely, the gap between the marginal capital-to-output ratios of clean and conventional capital — which influences the net present value calculation for firms' investment — is assumed to be a linear function of the share of clean capital ($k_{share_i}^{clean}$).
This gap can be expressed as a function of the sector's maximum marginal abatement cost, $mac_i^{max}$, as well as sector-specific parameters:

$$ \sigma_i^{k,\Delta,marg}(k_{share,i}^{clean}) = \frac{\gamma_i^d - \gamma_i^c}{p^k \sigma_i^{k,d,marg}} \times mac_i^{max} \times k_{share,i}^{clean}. \quad (32) $$

The gap between the productivity of installed clean and conventional capital is modeled as a sigmoid function of the share of clean capital. To ensure consistency between the capital productivity of new investments and that of installed capacities, the gap in the installed capital-to-output ratio ($\sigma_i^{k,\Delta}$) is set equal to the average value of the marginal gap ($\sigma_i^{k,\Delta,marg}$) as the share of clean capital approaches one:

$$ \sigma_i^{k,\Delta}(k_{share}^{clean} = 1) = \int_{0}^{(k_{share}^{clean} = 1)} \sigma_i^{k,\Delta,marg}(x) dx \\ = \frac{\gamma_i^d - \gamma_i^c}{p^k \sigma_i^{k,d}} \frac{mac_i^{max}}{2}. \quad (33) $$

This approach amounts to assuming that, at the end of the transition process, the productivity of installed capital will correspond to the average productivity of all investments made up to that point. Table 1 presents the selected values for $mac_i^{max}$, which, whenever possible, correspond to the abatement costs of specific technologies relevant to each sector.

Finally, the share of clean investment is calibrated such that, when the carbon tax reaches the maximum marginal abatement cost ($mac_i^{max}$), the sector allocates 100% of its investments to clean technologies. In other words, firms exclusively invest in clean capital once the carbon tax exceeds the cost of the most expensive decarbonisation technology. Further details regarding the specifications of the decarbonisation function are provided in Appendix B.1.

### 3.3. Baseline scenarios

Financial policies alone are insufficient to achieve decarbonisation (Lamperti et al., 2021; Dafermos and Nikolaidi, 2021). These measures are primarily intended to reinforce and complement 'core' policies — namely, price signals or regulations – which drive the transition. In this paper, green investment subsidies, capital market development, and green public guarantees are assessed against several baseline scenarios in which the EU meets its climate goals by 2050 through two types of 'core' policy measures. The first is a carbon price ($\tau^{carb}$), representing all price-based instruments, including cap-and-trade systems and carbon taxes. The second consists
<table><caption>Table 1: Parameter calibration references, variable initialisation values and maximal abatement costs hypotheses</caption><thead><tr><th>Variable</th><th>Value</th><th>Source</th><th>Parameter calibrated</th></tr></thead><tbody><tr><td colspan="4"><strong>Empirical targeted values and calibrated parameters</strong></td></tr><tr><td>Capital-adequacy ratio</td><td>0.145</td><td>ECB, CET1 ratio - 2019 average</td><td>Banks' reserve rate (ρ<sub>res</sub>)</td></tr><tr><td>Government consumption</td><td>2 917 bln€</td><td>Eurostat</td><td>Labo tax (τ<sup>f</sup>)</td></tr><tr><td>Value-added-to-wages ratio</td><td>[1.6, 4.7]</td><td>Eurostat</td><td>Sectoral markup rate (ν<sub>s</sub>)</td></tr><tr><td>Observed sect. emissions</td><td>[59, 975] mtCO<sub>2</sub>-e</td><td>Eurostat</td><td>Emission intensity of conventional capital (γ<sup>d</sup>)</td></tr><tr><td>Utilisation rate</td><td>1</td><td>Assumption</td><td>Conventional capital-to-output ratio (σ<sub>s</sub><sup>k,d</sup>)</td></tr><tr><td>Variable markup</td><td>1</td><td>Assumption</td><td>Variable markup utilisation rate (p<sub>2</sub>)</td></tr><tr><td>Observed conventional/clean interest rate</td><td>0.032 / 0.037</td><td>ECB, Cost of borrowing for corporations ; a 0.005 premium for clean interest rate is assumed</td><td>Interest rate constant (ν<sub>d/c</sub>)</td></tr><tr><td colspan="4"><strong>Variables initialisation</strong></td></tr><tr><td>Households' consumption</td><td>7 536 bln€</td><td>Eurostat</td><td></td></tr><tr><td>Total govt debt</td><td>12 495 bln€</td><td>Eurostat</td><td></td></tr><tr><td>Govt debt held by households</td><td>274 bln€</td><td>Eurostat and Pavot and Valenta (2021)</td><td></td></tr><tr><td>Central bank loans to private banks</td><td>2 213 bln€</td><td>Eurostat and Andersen and Sánchez Serrano (2024)</td><td></td></tr><tr><td>Sectoral investment</td><td>[32, 1 360] bln€</td><td>Eurostat</td><td></td></tr><tr><td>Share of clean capital</td><td>10%</td><td>Assumption</td><td></td></tr><tr><td>Share of equity in firms' total liabilities</td><td>[0.24, 0.42]</td><td>BACH database (Eccbso)</td><td></td></tr><tr><td colspan="4"><strong>Maximal abatement cost assumption</strong></td></tr><tr><td colspan="4"><strong>Sector</strong></td></tr><tr><td>Agriculture</td><td>400 €/tCO<sub>2</sub>-e</td><td>Lanigan et Al. (2023) (Irlande)</td><td>Drainage of wet mineral soils</td></tr><tr><td>Energie & mining</td><td>559 €/tCO<sub>2</sub>-e</td><td>cCarbon (value for 2040)</td><td>Storage - Battery storage - Commercial</td></tr><tr><td>Industry - standard</td><td>250 €/tCO<sub>2</sub>-e</td><td>Assumption (reasonable value)</td><td></td></tr><tr><td>Industry - ener. intensive</td><td>1070 €/tCO<sub>2</sub>-e</td><td>cCarbon (value for 2040)</td><td>Low-carbon Steelmaking - Alternative Ironmaking techniques</td></tr><tr><td>Building</td><td>500 €/tCO<sub>2</sub>-e</td><td>Perrissin Fabert, 2016 (France)</td><td>Wood boiler</td></tr><tr><td>Transport</td><td>1000 €/tCO<sub>2</sub>-e</td><td>cCarbon (value for 2040)</td><td>Biofuel / SAF</td></tr><tr><td>Others</td><td>250 €/tCO<sub>2</sub>-e</td><td>Assumption (reasonable value)</td><td></td></tr></tbody></table>
of regulatory standards ('norms'), which are modelled by exogenously penalising the net present value of conventional investments via the parameter $g_i^{norm}$ (see Equation 16). Within this framework, regulatory norms encourage firms to transition towards clean capital and to bear the associated investment costs. Unlike carbon pricing mechanisms, regulatory norms do not generate direct economic flows such as tax or auction revenues.

Given the uncertainty regarding the share of decarbonisation achieved through regulation versus price signals, three baseline scenarios are considered. Each of them is calibrated to ensure that sector-specific emissions are broadly consistent with the European Commission's projected trajectories. In the 'full-tax baseline' scenario, industries' transition is fully driven by the sector-specific carbon tax ($\tau^{carb}$). In the 'full-norm baseline' scenario, industries meet their emission targets solely through increases in the parameters $g_i^{norm}$, which compel them to invest in green technologies, while the carbon tax remains unchanged at its 2019 level. Finally, the 'mixed scenario' combines the carbon cost and norms to reach net zero. Although this scenario is the most realistic, it is also the most challenging to calibrate, since the respective contributions of price signals and regulatory norms to the transition are difficult to disentangle. In the absence of precise estimates for each contribution, we assume that the increase in the carbon tax between 2019 and 2050 accounts for 40% of its growth in the full-tax scenario, while the 'norm' intensity parameter, $g_i^{norm}$, is set at 60% of its value in the full-norm scenario.

To assess whether our results depend on the 'core' transition policy selected, financing policies are evaluated across all baseline scenarios in the following analysis. While only 'mix scenario' baseline results are presented in the main article, results for the 'full-tax' and 'full-norm' baselines are provided in the Appendix D.1. A detailed discussion of the construction and analysis of the baseline pathways is also available in Appendix B.2.

## 4. Impact of financial policies supporting the transition

We use the model to evaluate three distinct financial policy instruments designed to support the transition: direct public subsidies for green investments, public guarantees on green loans and measures to promote capital market development. Policies are simulated in combination with the carbon tax and regulatory pathways set out in the baseline scenarios. Table 2 summarises the key features and modelling approaches for each policy instrument. Subsections 4.1 to 4.3 separately analyse the implications of each policy instrument for arbitrary levels of policy intensity. A systematic comparison of the policies is presented in subsection 4.4.
<table><caption>Table 2: Overview of the financial policies assessed</caption><thead><tr><td>Policy</td><td>Description</td><td>Modelling</td></tr></thead><tbody><tr><td>Green investment subsidies</td><td>A portion of green investment is financed by government subsidies. Two settings are considered: general subsidy or targeted subsidy (energy-mining, energy-intensive industries, transportation).</td><td>A share gs of green investment is directly financed through government spending instead of private debt. Firms take the subsidy into account when calculating the NPV of green investments.</td></tr><tr><td>Green guaranteed loans</td><td>The government provides guarantees on a share of green loans, which benefit from a lower interest rate.</td><td>A proportion pg of new green loans is guaranteed and benefits from:<ul><li>A risk-free interest rate,</li><li>A zero risk weight in banks' capital adequacy ratio (CAR),</li><li>Government compensation to banks in the event of borrower default.</li></ul></td></tr><tr><td>Capital market development</td><td>The demand for equity by households increases (e.g. thanks to a better channelling of the savings).</td><td>Exogenous increase in the constant coefficient of equity demand in households' portfolio (λ<sub>10</sub>).</td></tr></tbody></table>

## 4.1. *Green investment subsidies*

The impact of a public subsidies covering up to 10% of clean investment<fnref n="3" /> is displayed in Figure 1. The support is either provided to all economic activities (orange lines) or targeted at three specific sectors (blue lines): energy production and mining, energy-intensive industries, and transportation.

Public subsidies—whether targeted or not —tend to boost GDP (Panel a). By lowering investment costs, government support raises profits and drives firms to invest more. The resulting increase in demand for capital goods from other sectors, along with the lower interest rates due to reduced leverage and default rates, leads to higher capacity utilisation and further amplifies the positive impact on investment (Panel b). Since the public support specifically targets green investments, it also leads to faster growth in the share of clean investment than in the baseline. Although this effect is barely visible for the targeted support, it applies to both types of subsidies (Panel e). Hence, despite the greater economic activity, emissions largely decrease in both the targeted and the generalised subsidy scenario, compared to the baseline. Simulations indicate that, for similar levels of subsidies, targeting does not result in greater emission reductions.

The simultaneous increase in GDP and reduction in emissions comes at the expense of a significant rise in public debt, which exceeds 130% of nominal GDP by

<footnote n="3">³The subsidy share gs follows a sigmoid function, reaching approximately 10% by 2040.</footnote>
2050 in the generalised subsidy scenario (Panel g). As public investment support accelerates decarbonisation, it also reduces the tax base for environmental taxes — a phenomenon highlighted by France's Directorate General of the Treasury (DGT, 2025). Simultaneously, the deleveraging of non-financial firms induced by investment support lowers bank profits and, consequently, bank profit tax revenues. Appendix C provides a detailed analysis of the efficiency of subsidy targeting and of the impact of subsidies on the public budget.

<figure><img src="image_2.png" /><figcaption>Figure 1: Impact of clean investment subsidies (10% by 2040) on all industries (orange lines) or targeted sectors (blue lines). Black lines indicate the mixed baseline scenario in investment shares and public-debt-to-GDP ratio figures.</figcaption></figure>

## 4.2. *Green guaranteed loans*

Figure 2 illustrates the impact of public guarantees covering 50% of new green loans issued from 2022 on. The policy has a modest positive impact on GDP (Panel a), primarily driven by an increase in real investment (Panel b). Banks consider
guaranteed loans less risky, allowing these loans to benefit from lower interest rates. This reduction in borrowing costs stimulates investment demand from firms. At the same time, green guarantees tend to increase private banks' capital-to-output ratio (CAR) through two channels. First, guaranteed loans are assigned a zero weight in the calculation of risk-weighted assets. Second, guarantees protect banks' capital from losses on non-performing green loans. Together, the improvement in banks' CAR and in firms' debt-service ratios leads to reduced credit rationing and increased investment. However, the additional activity is largely financed by new loans, resulting in higher leverage for firms (Panel h). From around 2037 onward, the debt-service ratio exceeds its baseline value, eroding profits (Panel f) and dampening the positive effect on investment (Panel b).

From an environmental perspective, public guarantees have a limited impact. By lowering the interest rate on green loans, they encourage the adoption of green technologies and increase the share of clean investment (Panel e). However, this positive effect is offset by the rise in overall economic activity. As a result, total emissions are higher than in the baseline scenario by the end of the simulation (Panel d).

The policy improves public finances (Panel g). The debt-to-GDP ratio declines not only due to higher GDP, but also because public debt rises more slowly. The limited increase in non-performing loans means that government payments to banks for guaranteed loans remain contained. In addition, tax revenues tend to rise — driven by higher profit tax receipts until the 2040s, followed by increased VAT, and bank profit tax revenues thereafter.

### 4.3. Capital market development

The impact of increased household demand for equity is presented in Figure 3. The shock is modelled by gradually raising the exogenous equity demand coefficient, $\lambda_{10}$, by up to 10%$^4$. As shown in Panel a, increased demand for equity has a positive effect on GDP. Unlike in the case of public subsidies and guarantees, this effect is largely driven by higher household consumption (Panel b). By substituting deposits for equity, households benefit from greater capital gains, reinforced by raising equity prices (Panel i). The resulting increase in consumption boosts corporate profits (Panel f) and encourages firms to invest more (Panel b). From a financial perspective, higher profits allow for greater self-financing and reduce firms' leverage (Panel h).

By reducing the cost of capital for both clean and conventional technologies, the equity demand shock drives up the net present value of investments across both

<footnote>$^4$The percentage increase follows a sigmoid function, reaching approximately 10% by 2040.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Impact of public guarantees of 50% of clean investment from 2022 (green lines). Black lines indicate the mixed baseline scenario in investment shares and public-debt-to-GDP ratio figures.</figcaption></figure>

sectors. This effect is slightly more pronounced for clean investments, since green loans are characterised by higher interest rates<fnref n="5" />. As a result, the share of clean investment is slightly higher than in the baseline scenario (Panel e), leading to a mild reduction in emissions (Panel d).

Finally, the increase in consumption and corporate profits resulting from the policy tends to raise government revenues. Higher tax receipts, together with an increase in GDP, contribute to a reduction in the debt-to-GDP ratio (Panel g).

<footnote n="5">⁵An increase in equity financing reduces the relative importance of the interest rate in the weighted average cost of capital (WACC). Because the interest rate on clean loans is assumed to be higher, this adjustment yields a greater positive impact on the NPV of clean investments.</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 3: Impact of an increase in the households’ demand for equity of around 10% by 2040 (red lines). Black lines indicate the mixed baseline scenario in investment shares and public-debt-to-GDP ratio figures.</figcaption></figure>

4.4. *Comparing and combining policy instruments*

The four policy instruments are compared across a range of parameter values: up to 50% for increased equity demand, 80% for public guarantees, and 20% and 30% for generalised and targeted subsidies, respectively (Figure 4). The policy tools are evaluated along three key dimensions—total emissions, real GDP, and public deficit—using the following indicators:
$$
\left\{
\begin{aligned}
\text{Indic}^{\text{Emissions}} &= \frac{1}{n} \sum_{2019}^{2050} \frac{\text{Total emissions}_t^{\text{Baseline}}}{\text{Total emissions}_t^{\text{Policy}}}, \\
\text{Indic}^{\text{Real GDP}} &= \frac{1}{n} \sum_{2019}^{2050} \frac{\text{Real GDP}_t^{\text{Policy}}}{\text{Real GDP}_t^{\text{Baseline}}}, \\
\text{Indic}^{\text{Deficit}} &= \frac{1}{n} \sum_{2019}^{2050} \frac{\text{Public deficit}_t^{\text{Baseline}}}{\text{Public deficit}_t^{\text{Policy}}}
\end{aligned}
\right.
\qquad (34)
$$

where *n* denotes the duration of the simulation. Defined in this way, an increase in the value of the indicators implies that, on average over the simulation period, the policy improves outcomes relative to the baseline scenario.

The systematic comparison of the policies confirms the findings previously identified for specific parameter values. The three policy instruments lead to an increase in economic activity without inducing higher emissions (Panel a). Nevertheless, they exhibit clear differences in their economic and climate effects. First, even substantial public guarantees have a relatively limited impact on all evaluated dimensions. Second, while both subsidies and equity market development can generate substantial impacts on GDP, they diverge considerably in terms of their effects on climate outcomes and public finances (Panel b). Public subsidies — whether targeted or not — are the only policy instruments that produce a significant reduction in emissions. However, this environmental benefit is accompanied by a systematic increase in the public deficit as government support expands. In contrast, the development of the equity market improves the public finance situation but has a comparatively limited effect on emissions. Mostly similar conclusions can be drawn when policies are evaluated under alternative baseline scenarios. The only notable difference relates to public guarantees, whose impact on emissions is larger when the transition is primarily driven by a carbon tax (see Appendix D.1).

Overall, our results suggest that all three policies constitute legitimate policy tools for the transition, since they stimulate economic activity without compromising emission targets. However, their impacts clearly differ. Public subsidies, on the one hand, directly incentivise green investment and emission reductions, albeit at the expense of increased public expenditure. In contrast, promoting equity market demand primarily stimulates aggregate economic activity and investment. The resulting emission reductions remain limited and are contingent upon complementary measures, such as the carbon tax and the regulatory standards. As for the green loan guarantees, the results suggest that they should be regarded primarily as microeconomic instruments aimed at supporting specific technologies, rather than as
macroeconomic policy tools.

<figure><img src="image_5.png" /><figcaption>Figure 4: Comparison of the policies according to real GDP, emissions and public deficit indicators. The indicators increase when the policy improves the climate, economic or public finance situation relative to the baseline, on average over the period 2019–2050. The figures correspond to the magnitudes of the policy shocks (share of green subsidies, share of guaranteed loans, and percentage increase in equity demand).</figcaption></figure>

To analyse the joint effect of the policies, we simulate all combinations of instruments across the range of plausible magnitudes<fnref n="6" />: equity demand increase is restricted to 25% and public guarantees to a maximum of 30% coverage of green loans. Identifying a single ‘optimal’ policy mix would require assigning weights to the various outcome dimensions, a process that inherently reflects political preferences and is therefore beyond the scope of this article. Instead, we focus on policy combinations that achieve substantial emission reductions and enhance real output without wors-

<footnote n="6">⁶Targeted and generalised subsidies are not simulated simultaneously.</footnote>
ening the public-debt-to-GDP ratio<fnref n="7" />. Based on these criteria, we identify a policy mix consisting in a 25% increase in equity demand, a 20% targeted subsidy for clean investments, and public guarantees covering 30% of clean loans. This combination is compared to the corresponding individual policies, as well as to a 20% untar- geted subsidy in Figure 5. The combined policy outperforms both targeted subsidies and equity market deepening in terms of economic activity and emission reductions (Panel a and b). It achieves these outcomes while maintaining fiscal sustainability, in contrast to broad-based subsidies (Panel c). In other words, combining the financing policies makes it possible to foster green investments and economic activity, while avoiding excessive increases in public debt.

The findings regarding the policies and their combination remain robust across baseline specifications and variations in key parameter values. Since international trade is not modelled, a scenario is also considered where some investments are im- ported instead of being produced domestically. This analysis confirms the relevance of a combined policy in an open-economy context. Comprehensive sensitivity anal- yses and robustness checks results are provided in Appendix D.

**5. Discussion**

This paper contributes to the literature from both policy and methodological perspectives. From an economic policy perspective, it provides valuable insights into green investment support measures, disentangling their impacts on economic activity, climate, and public finance. Although public subsidies, public guarantees, and capital market development policies are sometimes presented as alternatives in the economic debate, this paper demonstrates that none can be unequivocally dismissed. More precisely, we show that each policy can help to achieve different goals, and that combining them can effectively balance emission mitigation and economic activity without compromising public finance sustainability.

From a methodological perspective, this paper contributes to the emerging lit-
erature on input-output stock-flow consistent modelling. The multi-sectoral struc-
ture of the model allows us to capture sector-specific decarbonisation trajectories,
input-output linkages, and firms’ investment capabilities. To the best of my knowl-
edge, the paper introduces a novel approach to modelling green investment costs
within this framework. This approach bridges the gap between industry-specific
marginal abatement costs, investment decisions, and additional investment needs

<footnote n="7">⁷Only simulations in which the public-debt-to-GDP ratio in 2050 is less than or equal to its 2019 level are considered.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 5: Impacts of the combined policy (red) compared with single policies: generalised subsidies (orange), targeted subsidies (blue), guaranteed loans (green), and increased equity demand (brown). Figures are expressed as percentage deviations from the baseline, except for the public-debt-to-GDP ratio, for which the black dashed line represents the baseline scenario.</figcaption></figure>

along transition pathways. As a result, decarbonisation costs are not treated as purely theoretical expenditures disconnected from economic flows, but are instead consistently represented as additional investment outlays.

Given our focus on investment policies, the transition is analysed primarily through the process of capital replacement rather than direct fuel substitution. This perspective underlies the sectoral aggregation in our model, in which all energy types are combined. In this respect, our analysis complements that of Jackson and Jackson (2025), who examine the pure economic impacts of an exogenous change in fuel type, independent of the underlying economic policy driving this transformation. In contrast, our approach evaluates the effects of public policies that endogenously influence firms’ decisions to invest in clean technologies.
However, several limitations should be acknowledged. First, policy measures have been compared without accounting for their operational implementation. Specifically, equity market deepening is analysed as though policymakers had direct control over households’ equity holdings. In reality, they can only introduce measures to incentivise households to invest, and the actual impact on the portfolios remains uncertain.

Furthermore, the macroeconomic focus of our analysis may overlook important microeconomic implications associated with the policies under study. For instance, while our simulations indicate a limited macroeconomic impact of public guarantees, such policies could still be relevant for promoting specific technologies at the microeconomic level. Likewise, the social implications of the transition are assessed solely through changes in GDP, which prevent an evaluation of the potential distributive effects of the policies.

The theoretical model also has several limitations that could be addressed in future extensions. For instance, the financial dimension of the model remains simplified, and changes in firms’ financial structures are only partially captured in our simulations. This may influence the effect of capital market development policies, whose impact is mainly associated with households’ increased demand in our analysis.

Finally, the model’s calibration entails certain limitations. Aligning real-world data with a simplified stock-flow consistent model inevitably requires some distortion of actual balance sheets<fnref n="8" />, such as the omission of households’ dwellings. The assumption of a balanced growth path in the initial period also constrains certain parameter values, which may influence the model’s dynamics. For example, government tax revenues in the model do not fully capture the complexity of real-world public budgets. Furthermore, the absence of precise estimates for certain parameters can constrain the accuracy of the model’s calibration. This is particularly true for for sector-specific abatement costs, for which further research is required to develop robust sectoral abatement cost functions.

In this context, our quantitative results should be interpreted with caution. However, the consistency of the key mechanisms highlighted — confirmed through sensitivity analyses — indicates that these limitations do not compromise our main conclusions.

<footnote n="8">⁸Perfect alignment between real-world data and a simplified model is unattainable, resulting in some variables only partially reflecting their empirical counterparts. Where such trade-offs are necessary, we prioritised the accuracy of industry-related variables, as the investment-finance nexus of firms is central to our research question.</footnote>
## 6. Conclusion

This paper analyses the impact of financial policies designed to support transition investments. An input-output, stock-flow consistent model is developed and calibrated using EU data. In the model, decarbonisation is explicitly captured through a shift from conventional to green capital. Each industry allocates investment between these two types of capital by comparing their respective net present values, a process influenced by prevailing capital costs, financing conditions, and public policies. The upfront investment costs associated with transition technologies are incorporated via endogenous capital productivity parameters, which are calibrated to replicate sector-specific abatement cost profiles. To track the financial implications of these green investments, the model explicitly represents the financing channels available to industries—including retained earnings, equity issuance, and debt financing.

The model is employed to evaluate three types of financing policies: green investment subsidies, capital market deepening, and green loan public guarantees. Among these, direct subsidies are the only policy that both significantly improves economic activity and reduces emissions. However, this positive climate impact is associated with a deterioration of the public debt position. In contrast, capital market deepening and green public guarantees improve the debt-to-output ratio but have only a limited impact on emissions. Our results also indicate that combining the three policy instruments yields positive outcomes: we identify a specific policy mix that accelerates decarbonisation and enhances economic activity without worsening public finances. Rather than a one-size-fits-all solution to support the transition, our findings therefore advocate for a multi-instrument financing policy mix. Although illustrative given the model's simplicity, the results are robust to various baseline scenarios, key parameter values, and to a sensitivity analysis on the model's trade closure.

The work carried out in this article focuses on three investment support measures among many others. The analysis of alternative policies — such as modifications to private banking regulations or direct investment financing via central bank money creation — is left for future research. The model developed herein offers a suitable framework for such extensions.

## 7. Acknowledgment

I am grateful to Edith Darin for her insightful feedback on the manuscript. I would also like to thank Olivier Sautel, Emmanuel Bovari and Maxime Bouter for their advice and for the stimulating discussions we shared. All errors are my own.
Appendix A. Stock-flow accounting tables: balance sheet and transaction-flow matrices

<table><caption>Table A.3: Balance sheet matrix of the model</caption><thead><tr><th>Balance sheet</th><th>Households</th><th>Firms sector i</th><th>Govt</th><th>Banks</th><th>Central bank</th><th>Sum</th></tr></thead><tbody><tr><td>Deposits</td><td>+D<sup>h</sup></td><td></td><td></td><td>-D<sup>h</sup></td><td></td><td>0</td></tr><tr><td>Clean capital</td><td></td><td>+K<sub>i</sub><sup>c</sup></td><td></td><td></td><td></td><td>+&Sigma;K<sub>i</sub><sup>c</sup></td></tr><tr><td>Conv. capital</td><td></td><td>+K<sub>i</sub><sup>d</sup></td><td></td><td></td><td></td><td>+&Sigma;K<sub>i</sub><sup>d</sup></td></tr><tr><td>Clean loans</td><td></td><td>-L<sub>i</sub><sup>c</sup></td><td></td><td>+&Sigma;L<sub>i</sub><sup>c</sup></td><td></td><td>0</td></tr><tr><td>Conv. loans</td><td></td><td>-L<sub>i</sub><sup>d</sup></td><td></td><td>+&Sigma;L<sub>i</sub><sup>d</sup></td><td></td><td>0</td></tr><tr><td>Govt debt</td><td>+GD<sup>h</sup></td><td></td><td>-GD</td><td>+GD<sup>b</sup></td><td>+GD<sup>cb</sup></td><td>0</td></tr><tr><td>Banks' reserves</td><td></td><td></td><td></td><td>+RESB</td><td>-RESB</td><td>0</td></tr><tr><td>CB loans</td><td></td><td></td><td></td><td>-CBL</td><td>+CBL</td><td>0</td></tr><tr><td>Firms' equity</td><td>+E<sup>tot</sup></td><td>-e<sub>i</sub>P<sub>i</sub><sup>e</sup></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Tot. (net worth)</td><td>V<sup>h</sup></td><td>V<sub>i</sub><sup>f</sup></td><td>-GD</td><td>K<sup>b</sup></td><td>+V<sup>cb</sup></td><td>K</td></tr></tbody></table>
<table><caption>Table A.4: Transaction-flow matrix of the model. The variables CINT<sub>i</sub> and PINT<sub>i</sub> correspond to intermediate consumption and intermediate production of sector i, respectively.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Households</th><th colspan="2">Firms sector i</th><th rowspan="2">Govt<br>Current<br>Capital</th><th rowspan="2">Banks<br>Current<br>Capital</th><th rowspan="2">Central banks<br>Current<br>Capital</th><th rowspan="2">Sum</th></tr><tr><th>Current</th><th>Capital</th></tr></thead><tbody><tr><td>HH consumption</td><td>-C</td><td>+C<sub>i</sub> - V<sub>i</sub>AT<sup>rev</sup><br>+G<sub>i</sub><br>+T<sub>i</sub>JN<sup>tot</sup></td><td>-INV<sup>all</sup><br>-CINT<sub>i</sub> + PINT<sub>i</sub><br>-W<sub>i</sub></td><td>+VAT<sup>rev</sup><br>-∑<sub>i</sub> G<sub>i</sub></td><td></td><td></td><td>0</td></tr><tr><td>Govt consumption</td><td></td><td>-TAX<sub>i</sub> - TAX<sup>carb</sup></td><td>-TAX<sub>i</sub> - TAX<sup>carb</sup></td><td>+∑<sub>i</sub> (TAX<sub>i</sub> + TAX<sup>carb</sup>)<br>+TAX<sup>l</sup></td><td></td><td></td><td>0</td></tr><tr><td>Investments</td><td>+W<sup>tot</sup></td><td>-Π<sub>i</sub><sup>f</sup></td><td>+Π<sub>i</sub><sup>fd</sup></td><td>-GS<sup>tot</sup></td><td>-Π<sup>b</sup></td><td>-Π<sup>cd</sup></td><td>0</td></tr><tr><td>Intermediate consumption</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Wages</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Taxes on firms</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Taxes on households</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Firms' profit</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Govt. inv. supp.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Banks' profit</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>CB profit</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Int. on deposits</td><td>+r<sup>d</sup>D<sup>h</sup><sub>-1</sub></td><td>-r<sup>l</sup>LC<sup>c</sup><sub>d-1</sub>(1 - npl<sub>i</sub>)</td><td>-r<sup>l</sup>LC<sup>d</sup><sub>d-1</sub>(1 - npl<sub>i</sub>)</td><td>-r<sup>d</sup>D<sup>h</sup><sub>-1</sub></td><td>+r<sup>l</sup>LC<sup>c</sup><sub>d-1</sub>(1 - npl<sub>i</sub>)</td><td>+r<sup>l</sup>GD<sup>h</sup><sub>-1</sub></td><td>0</td></tr><tr><td>Int. on clean loans</td><td></td><td></td><td></td><td></td><td>+∑<sub>i</sub> r<sup>l</sup>LC<sup>d</sup><sub>d-1</sub>(1 - npl<sub>i</sub>)</td><td>+r<sup>l</sup>GD<sup>h</sup><sub>-1</sub></td><td>0</td></tr><tr><td>Int. on conv. loans</td><td>+r<sup>g</sup>GD<sup>h</sup><sub>-1</sub></td><td></td><td></td><td></td><td>-r<sup>g</sup>GD<sup>-1</sup></td><td>-r<sup>g</sup>GD<sup>h</sup><sub>-1</sub></td><td>0</td></tr><tr><td>Int. on govt debt</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Int. on CB loans</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Δ Deposits</td><td>-ΔD<sup>h</sup></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Δ Clean loans</td><td></td><td>+ΔL<sup>c</sup><sub>i</sub></td><td>+ΔL<sup>d</sup><sub>i</sub></td><td>-ΔD<sup>h</sup></td><td>-∑<sub>i</sub> ΔL<sup>c</sup><sub>i</sub></td><td>-ΔGD<sup>h</sup></td><td>0</td></tr><tr><td>Δ Conv. loans</td><td>-∑<sub>i</sub> p<sub>i</sub>Δe<sub>i</sub></td><td>+p<sub>i</sub>Δe<sub>i</sub></td><td></td><td></td><td>-∑<sub>i</sub> ΔL<sup>d</sup><sub>i</sub></td><td>-ΔGD<sup>h</sup></td><td>0</td></tr><tr><td>Δ Equity</td><td>-ΔGD<sup>h</sup></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Δ GD</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Δ RESB</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Δ CBL</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Defaulted loans</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Guaranteed defaulted loans</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>Total</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
# Appendix B. Details on the calibration process and on baseline simulations

## Appendix B.1. Calibration of the sector-specific decarbonisation process

In the model, the transition is driven by the shift from conventional to clean capital. The additional costs associated with clean investments are modelled through the gaps in capital productivity ($\sigma^{k,\Delta}$ and $\sigma^{k,\Delta,marg}$). In other words, the cost of decarbonisation is reflected in the fact that clean capital requires higher investment to achieve the same level of output as conventional capital.

The variable $\sigma_i^{k,\Delta,marg}$ represents the gap in capital productivity between available clean and conventional investment technologies, affecting firms' NPV calculations. This variable can be directly related to sector-specific marginal abatement costs (MAC), which reflect the cost of reducing one tonne of CO$_2$:

$$mac_i(k_{share,i}^{clean}) = \frac{Capital\ cost_i^c - Capital\ cost_i^d}{emiss_i^d - emiss_i^c} = \frac{p^k \sigma_i^{k,d,marg}}{\gamma_i^d - \gamma_i^c} \times \sigma_i^{k,\Delta,marg} (k_{share,i}^{clean}), \quad (B.1)$$

where the marginal abatement cost ($mac_i$) — usually defined as a function of total emissions abated — is instead expressed as a function of the share of clean capital in total capital ($k_{share,i}^{clean} = \frac{k_i^c}{k_i^{all}}$). The underlying mechanism remains the same: decarbonisation initially relies on the most cost-effective technologies, but achieving full decarbonisation of a sector requires the adoption of progressively more expensive technologies. Further assuming that the MAC is a linear function, the marginal capital-to-output ratio gap writes:

$$\sigma_i^{k,\Delta,marg} (k_{share,i}^{clean}) = \frac{\gamma_i^d - \gamma_i^c}{p^k \sigma_i^{k,d,marg}} \times mac_i^{max} \times k_{share,i}^{clean}, \quad (B.2)$$

where $mac_i^{max}$ represents the maximum marginal abatement cost for a given sector. Table 1 presents the selected values for $mac_i^{max}$, which, whenever possible, correspond to the abatement costs of specific technologies relevant to each sector.

Variable $\sigma_i^{k,\Delta}$ represents the productivity gap between clean and conventional capital stocks already installed and available for production. It influences the utilisation rates of the sectors and, consequently, their total investment demand. Since the model does not account for capital vintages, we implicitly assume that $\sigma_i^{k,\Delta}$ reflects the average marginal capital-to-output ratio of all technologies installed to date. To ensure consistency in calibrating $\sigma^{k,\Delta}$ and $\sigma^{k,\Delta,marg}$, we impose that the maximum value of $\sigma^{k,\Delta}$ matches the average value of $\sigma^{k,\Delta,marg}$ when the share of clean capital approaches one, that is:
$$
\begin{align}
\sigma_i^{k,\Delta} (k_{share}^{clean} = 1) &= \int_0^{(k_{share}^{clean}=1)} \sigma_i^{k,\Delta,marg}(x) dx \nonumber \\
&= \frac{\gamma_i^d - \gamma_i^c}{p^k \sigma_i^{k,d}} \frac{mac_i^{max}}{2}. \tag{B.3}
\end{align}
$$

This equality is achieved by calibrating the parameters of the $\sigma_i^{k,\Delta}$ sigmoid function and relies on the assumption that the capital price remains unchanged as the share of clean capital increases<fnref n="9" />. It ensures that, when the transition is fully achieved, the calibrated gap in productivity of the installed capital is consistent with the marginal abatement cost assumed in the investment decision.

Finally, the share of clean investment is calibrated so that, when the carbon tax reaches the maximum marginal abatement cost ($mac_i^{max}$), the sector allocates 100% of its investments to clean technologies. Specifically, the coefficient $\theta_i^{c,1}$ is adjusted to ensure that:

$$ \theta_i^c (NPV_i^{\Delta,full}) = 1 = \theta_i^{c,0} + \theta_i^{c,1} \times NPV_i^{\Delta,full}. \quad (B.4) $$

Here, $NPV_i^{\Delta,full}$ denotes the difference between the net present values (NPV) of clean and conventional technologies when the carbon tax reaches the same value as the maximum marginal abatement cost, while the marginal gap between clean and conventional capital is at its highest. This setting represents the ‘end-of-transition’ phase, in which the carbon tax is sufficiently high to incentivise the adoption of all transition technologies, including the most expensive ones.

## Appendix B.2. Baseline scenarios construction and analysis

Section Appendix B.2.1 describes the construction of the baseline scenarios, while Section Appendix B.2.2 discusses the associated baseline pathways.

### Appendix B.2.1. Baseline scenarios construction

All baseline scenarios are calibrated to ensure that sector-specific emissions follow trends consistent with the prospective trajectories of the European Commission. The baseline scenarios differ in the manner by which these emission targets are achieved — whether through carbon pricing or the implementation of regulatory norms.

<footnote n="9">⁹This approximation is made only in the calibration process.</footnote>
Emission pathways are collected from the 's2' scenario of the European Commission's Impact Assessment Report for the 2040 climate target (European Commission, 2024). Since negative emissions are not represented in the model, only gross GHG emissions are considered. Each sector in the model — such as agriculture and energy-intensive industry — is mapped to emitting activities defined in the European Commission's projections (e.g., power generation, district heating). Since sector classifications differ between the dataset and the model, sectors are mapped to the emitting activities that best match their industrial profiles (multiple sectors may correspond to a single emitting activity). Sector-specific decarbonisation trajectories are then derived by applying the emission-reduction trends of the matched activities to each sector's initial emission level.

The emission accounts published by the European Commission encompass both firm-level and household activities. For each period, the share of household emissions — calculated as the difference between total base-year sector emissions and the Commission's aggregate for the same year — is excluded from the analysis. This approach assumes that the proportion of household emissions remains constant over time. Finally, we assume a linear decarbonisation trend between 2015 and 2030 to estimate 2019 emission levels consistent with Commission data.

Although our approach is based on approximations, it yields emission trajectories that are broadly aligned with the EU's Fit-for-55 and net-zero targets, while providing sector-specific pathways. Table B.5 summarises the sector-to-activity mapping and the resulting emission trends.

<table><caption>Table B.5: GHG Emission targets by sector, estimated from European Commission projections (2019 = 100%)</caption><thead><tr><th>Model sectors</th><th>Data sectors</th><th>2019</th><th>2030</th><th>2040</th><th>2050</th></tr></thead><tbody><tr><td>Total Gross GHG emissions</td><td>–</td><td>100%</td><td>66%</td><td>27%</td><td>12%</td></tr><tr><td>Agriculture</td><td>Agriculture</td><td>100%</td><td>95%</td><td>80%</td><td>66%</td></tr><tr><td>Energy and mining</td><td>Power and heat district; Other Energy sectors</td><td>100%</td><td>45%</td><td>10%</td><td>6%</td></tr><tr><td>Industry (standard)</td><td>Industry (Energy); Industry (Non-Energy)</td><td>100%</td><td>72%</td><td>34%</td><td>2%</td></tr><tr><td>Industry (ener. intensive)</td><td>Industry (Energy); Industry (Non-Energy)</td><td>100%</td><td>72%</td><td>34%</td><td>2%</td></tr><tr><td>Building</td><td>Residential and Services</td><td>100%</td><td>51%</td><td>21%</td><td>4%</td></tr><tr><td>Transport</td><td>Domestic transport; International transport (target scope)</td><td>100%</td><td>84%</td><td>23%</td><td>2%</td></tr><tr><td>Others</td><td>Other non-energy sectors</td><td>100%</td><td>51%</td><td>24%</td><td>20%</td></tr></tbody></table>

Since the share of the decarbonisation effort attributable to each 'core' policy type — regulation and price signals — remains unclear, three baseline scenarios are
proposed:

*Full-tax baseline (NZ tax).* The full-tax baseline scenario is defined by calibrating sector-specific carbon taxes to ensure that each sector is as close as possible to its emission targets. The initial sector-specific carbon tax level is derived as the observed ratio of environmental tax revenues to sectoral emissions.

*Full-norm baseline (NZ norm).* The parameters $g_i^{norm}$, which influence firms' net present value calculations and 'force' firms to invest in green technologies, are calibrated such that sectors reach their emission targets. The carbon tax level remains constant at its 2019 value.

*Mixed baseline (NZ mix).* Both tax and norm constraints are simultaneously employed to achieve emission targets. Although this scenario is arguably the most realistic, it is also the most challenging to calibrate, as the respective contributions of price signals and regulatory norms to the transition are difficult to disentangle. For example, while the EU ETS alone could cover approximately 75% of EU emissions from 2027 (European Commission, 2025), sectors subject to these price instruments may also be simultaneously regulated. In the absence of precise estimates for each contribution, we assume that the increase in the carbon tax between 2019 and 2050 accounts for 40% of its growth in the full-tax scenario, while the 'norm' intensity parameter, $g_i^{norm}$, is set at 60% of its value in the full-norm scenario.

Figures B.6 a and b display sector-specific carbon taxes and norms for each baseline scenario, while Figures B.7 and B.8 present the baseline emissions and investment trajectories, respectively.

## Appendix B.2.2. *Baseline scenarios analysis*

Figure B.9 presents the baseline scenarios for the main aggregates. All three scenarios are broadly consistent with EU climate targets: emissions decline by approximately 45% by 2030 and 90% by 2050, relative to 2008 levels (Panel a)<fnref n="10" />. The carbon price required to achieve the transition varies significantly across baseline scenarios, as illustrated in Panel b. In the full-norm scenario, the carbon cost remains steady at its 2019 level of approximately 50€/tCO₂-e. By contrast, it increases to over 500€/tCO₂-e in the full-tax baseline, aligning well with the 'value for climate action' estimated for France in the latest Quinet report (France Stratégie, 2025) (black points). The 'core' policy instrument driving the transition has significant

<footnote n="10">¹⁰The minor discrepancy with the EU emission projections arises because neither negative nor household emissions are included in the sector-specific emission target proxies used to calibrate the model. Furthermore, approximations were required in estimating these sector-specific emission trajectories.</footnote>
implications on GDP and investment trajectories (see Panel c and d). Specifically, the ‘full-norm’ scenario results in real GDP being nearly 2.5% higher than under the ‘no-policy’ baseline by 2050, a difference primarily attributable to increased total investment. Conversely, the carbon price implemented to incentivise firms to invest in clean technologies has a contractionary effect on the economic activity. In the mixed policy and the full-tax baseline scenarios, emission reduction targets are achieved through both a decrease in emission intensity of production and a reduced output level.
<figure><img src="image_7.png" /><figcaption>(a) Sector-specific carbon tax level in baseline scenarios</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) Sector-specific norm coefficients in baseline scenarios</figcaption></figure>

Figure B.6: Baseline scenarios - sector-specific carbon tax and norm coefficients
<figure><img src="image_9.png" /><figcaption>Figure B.7: Baseline scenarios - sectoral emissions. Black dots represent the sectoral emission targets derived from European Commission projections. The 'obs' series correspond to historical values.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure B.8: Baseline scenarios - sectoral total investment. The 'obs' series correspond to historical values.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure B.9: Emissions, effective carbon tax, GDP, and investment trajectories for baseline scenarios (NZ) and no policy scenario (No policy). Greenhouse gas (GHG) emissions and effective carbon tax are presented in absolute levels, while GDP and investment are shown as deviations from a no-policy scenario. In the carbon tax Panel, black dots indicate the estimated shadow carbon price for France as reported in the Quinet report (France Stratégie, 2025).</figcaption></figure>
## Appendix C. Complementary analysis of subsidies

To compare targeted and broad-based subsidies on a like-for-like basis, Figure C.10 presents the multipliers for emissions, real GDP, and the public deficit across a range of government support levels and for each type of baseline. For each variable $X$, the multiplier is calculated as follows:

$$X^{\text{multiplier}} = \frac{\sum_t \Delta X_t}{\sum_t \Delta \text{Government support}_t} \quad (C.1)$$

where $\Delta$ represents the gap in level with the baseline.

Regardless of whether subsidies are targeted, their efficiency declines as their size increases: larger subsidies are associated with lower emission reductions per euro granted (first row). For comparable levels of total investment subsidies, broad-based subsidies tend to achieve greater emission reductions per euro invested. The second row, however, indicates that beyond a certain amount of subsidy, targeted support results in a higher GDP increase per euro invested for the mixed and the full-tax baseline. Surprisingly, both targeted and untargeted public supports are associated with deficit multipliers greater than one, suggesting that a €1 subsidy leads to an increase in the deficit of more than €1 (third row).

Figures C.11 decomposes the fiscal revenues associated to the simulations presented in Figure 1. The increase in economic activity driven by public subsidies results in higher receipts from corporate profit tax, value-added tax, and labor tax. However, these additional tax revenues are offset by a relative decline in environmental taxes and bank profit taxes. As public investment support accelerates decarbonisation, it also reduces the tax base for environmental taxes—a phenomenon highlighted by France's Directorate General of the Treasury (DGT, 2025). Simultaneously, the deleveraging of non-financial firms induced by investment support lowers bank profits and, consequently, bank profit tax revenues. Overall, the net effect on aggregate tax revenues is negative under both the mixed-policy scenario and the full-norm baseline. It is more ambiguous when a full-tax baseline is assumed, exhibiting alternating negative and positive impacts throughout the simulation period.
<figure><img src="image_12.png" /><figcaption>Figure C.10: Emissions, real GDP and government multipliers for various levels of subsidies and for each baseline (mixed, full-norm and full-tax). Red dots correspond to broad-based subsidies and blue dots to targeted subsidies.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure C.11: Evolution of total and tax-specific government revenues in response to targeted and untargeted subsidies. Revenues are measured as deviations from the baseline (in euros). Lines correspond to shock types (broad-based or targeted), while columns correspond to baselines (mixed, full-norm, and full-tax).</figcaption></figure>
# Appendix D. Sensitivity analysis and robustness checks

## Appendix D.1. Sensitivity to the baseline scenario

Figure D.12 provides the same indicators as those in Section 4.4, under the full-tax and full-norm baseline settings. Note that the full-tax baseline leads to temporarily negative public deficits at the beginning of the simulation, which makes the deficit indicator unreliable and explains the extreme values appearing in the ‘Tax’ row of the figure. To ensure that the results are not misleading, we also report, in the second row, the values of the same indicators in 2050, when public deficits are back to positive levels.

Similar patterns to those in Figure 4 emerge, confirming that the results are robust across baseline scenarios. However, one point should be noted. The impact of public guarantees tends to increase when the carbon price contributes more to the transition (in the mix and full-tax scenarios). This suggests that public guarantees exert a relatively stronger effect when carbon emission taxation constrains firms’ borrowing and debt repayment.

Figure D.13 compares the combined policy identified in Section 4.4 with the corresponding individual policies in the full-tax and full-norm baseline settings. The results, which are consistent with those highlighted for the mixed baseline case in Figure 5, confirm that combining financial policies can yield better outcomes than single-policy interventions.
<figure><img src="image_14.png" /><figcaption>Figure D.12: Comparison of policies according to indicators of real GDP, emissions and the public deficit. The indicators are shown for both the full-tax baseline (‘Tax’ and ‘Tax (2050)’ lines) and the full-norm baseline (‘Norm’ line). Indicators in the ‘Tax’ and ‘Norm’ lines are defined as averages over the simulation period, whereas they are displayed for the year 2050 specifically in the ‘Tax (2050)’ line.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure D.13: Impacts of the combined policy (red) compared with single policies, for the full-tax ('Tax') and full-norm ('Norm') baselines. Figures are expressed as percentage deviations from the baselines, except for the public-debt-to-GDP ratio, for which the black dashed line represents the baseline scenarios.</figcaption></figure>
Appendix D.2. Sensitivity to capital good imports

The model in this study treats the EU as a closed economy. While most intra-EU trade occurs within the Union (Eurostat, 2024), this assumption is a significant simplification, particularly during the transition period when key technologies may be unavailable or costlier within the EU. Additionally, as the model is primarily demand-driven, simulated pathways can be sensitive to spillover effects from increased investment needs during the transition.

While a comprehensive analysis of the trade implications of the transition is left for future research, we assess the sensitivity of our conclusions to the closed economy assumption. Specifically, we examine a scenario in which an increasing share, $imp_t^{rate}$, of total investment is not produced by EU sectors. The real sectoral sales become:

$$s_i = c_i + gc_i + \Gamma_i \times inv^{tot} \times (1 - imp_t^{rate}) + \sum_j a_{ij}s_j. \quad (D.1)$$

The rest of the world is not explicitly modelled in this analysis; rather, it is represented by a single flow of imported capital. Consequently, and in contrast to the remainder of the paper, these simulations are not fully stock-flow consistent.

Figure D.14 illustrates the effects of increasing capital goods imports to 20% by 2050 (Panel a). The impact on real GDP and the public-debt-to-GDP ratio is evaluated under both the baseline scenario (black lines) and the combined policy scenario (red lines). By 2050, capital imports substantially reduce real GDP under both the baseline and policy scenarios. The magnitude of this negative effect exceeds the positive impact of the policy when imports are not considered. A comparable pattern is observed for the public-debt-to-GDP ratio (Panel c): the decline in economic activity results in lower government revenues and, consequently, higher debt-to-GDP ratios. Despite the simplicity of the modelling, the significant magnitude of the impacts highlights the critical importance of the location of clean technologies production. From a policy standpoint, this also indicates that financial and industrial challenges in the transition cannot be addressed separately.

Figure D.15 shows the impact of the combined policy on output and public deficit for various levels of capital imports, ranging from 0 to 50%. For each specified import rate, $imp_t^{rate}$, the impact of the combined policy is assessed based on the real GDP and the deficit indicators already used in section 4.4 (see equations 34). Note that both the policy scenario and the baseline are affected by the exogenous trade shock. Since emissions from the rest of the world are not modelled, their evolution is not studied here.

Both indices tend to increase as capital imports rise. This indicates that the policy's impact becomes slightly stronger with higher import levels, suggesting that
<figure><img src="image_16.png" /><figcaption>(a) Share of K. import (%)</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(c) Public-debt-to-GDP ratio</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(b) Real GDP (vs baseline)</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Figure D.14: Impact of the capital import for baseline (black) and combined (red) policy scenarios</figcaption></figure>

the policy partially offsets the negative effects of offshoring. In level terms, the financing policy mix remains beneficial for GDP regardless of the degree of capital imports, as shown by the output index consistently exceeding one. Regarding debt, the combined policy was designed to ensure that the debt-to-output ratio does not exceed its 2019 level, which is slightly above the projected 2050 baseline<fnref n="11" />. As the share of imports increases, the index approaches (or exceeds) one, indicating that the debt-to-GDP ratio stays close to the baseline scenario. Comparable findings emerge regardless the baseline.

This sensitivity analysis does not fully capture feedback effects of trade on transition pathways. Nevertheless, it shows that the benefits associated with the financial policy mix are not compromised by the introduction of capital imports in the model.

<footnote n="11">¹¹The debt-to-output declines in the baseline scenario, which explains why the debt index associated with a zero import share is below one.</footnote>
<figure><img src="image_20.png" /><figcaption>Figure D.15: Impact of capital imports on the benefits of a combined financial policy, for all baselines (average real GDP and public-debt-to-GDP ratios versus baseline). Figures correspond to the exogenous share of imported capital associated to each point.</figcaption></figure>
Appendix D.3. Sensitivity to parameters’ values

The robustness of our results is evaluated across a range of key parameter values. Since several parameters are initialised as functions of other parameter values during the first period of the simulation process, sensitivity analyses on a single parameter may influence the values of other parameters. Furthermore, the simulations employ the same tax and regulatory policy trajectories as those in the baseline policy mix scenario, while the parameter $\theta_i^c$ remains fixed and is not recalibrated for each sensitivity analysis. As a result, unlike the core simulations, these sensitivity analyses are not necessarily aligned with EU emission targets. In this context, the sensitivity analysis does not constitute a strict ‘ceteris paribus’ assessment. Instead, its purpose is to ensure that our qualitative findings concerning the relative impacts of the policies remain robust.

Sensitivity parameter values are chosen as either alternative plausible parameterisations or, where such values are unavailable, as extreme values intended to test the robustness of the results. Table D.6 presents sensibility values, while Figures D.16 to D.19 display the associated single-policy and mixed-policy assessments. Outlier values are observed in the sensitivity analyses for $r^g$ and $r^{l,c}$: equity demand increases greater or equal to 45% lead to unrealistically high or low public deficit gaps. These values, which are attributable to numerical issues, are excluded from the subsequent figures.

The magnitude of the impact varies according to parameter choices; however, the main qualitative conclusions remain robust. Direct public support yields greater environmental benefits compared to equity market deepening and green guarantees, albeit at the expense of a substantial increase in public deficits. Furthermore, a policy mix can achieve a positive economic impact comparable to that of untargeted public subsidies, while delivering significant emission reductions and without exacerbating the public finance situation.
<table><caption>Table D.6: Parameter sensibilities</caption><thead><tr><td>Parameter</td><td>Description</td><td>Initial value</td><td>Sensibility value</td><td>Comment</td></tr></thead><tbody><tr><td>χ<sub>i</sub></td><td>Share of equity-financed real investment</td><td>0.025</td><td>0.1</td><td>Reasonable upper bound</td></tr><tr><td>g<sup>k</sup></td><td>Balanced growth path capital growth rate</td><td>0.113</td><td>0.12</td><td>Equivalent to a 2% GDP growth at BGP.</td></tr><tr><td>g<sup>02</sup><sub>k,i</sub></td><td>Invest. - Sensib to capital utilization</td><td>0.15</td><td>0.075</td><td>Baseline -50%</td></tr><tr><td>g<sup>03</sup><sub>k,i</sub></td><td>Invest. - Sensib to interest rate</td><td>0.13</td><td>0.065</td><td>Baseline -50%</td></tr><tr><td>mac<sup>max</sup><sub>i</sub></td><td>Maximal marginal abatement cost</td><td>[250, 1070]</td><td>[200, 856]</td><td>-20% compared to original values</td></tr><tr><td>τ<sup>pass</sup><sub>i</sub></td><td>Pass-on rate of carbon cost</td><td>[0.46, 0.885]</td><td>0.2</td><td>Based on (Daumas, 2023)</td></tr><tr><td>r<sup>g</sup></td><td>Int. rate on govt bonds</td><td>0.01</td><td>0.028</td><td>Average of 10-y maturity bond yield, Euro Area, 2004-2019 (Source: ECB)</td></tr><tr><td>r<sup>l,c</sup></td><td>Initial int. rate clean corporate loans</td><td>0.0373</td><td>0.0273</td><td>Negative premium of -0.005 compared to initial int. rate of conventional loans</td></tr></tbody></table>
<figure><img src="image_21.png" /><figcaption>Figure D.16: Sensibility of individual policy comparisons to parameter values. Emissions, GDP and public deficit indices are similar as in figure 4. (1/2)</figcaption></figure>
Max abatement cost: $mac_i^{max} = -20\%$

<figure><img src="image_22.png" /></figure>

Carbon tax pass-through rate: $\tau_i^{pass} = 0.2$

<figure><img src="image_23.png" /></figure>

Govt. int. rate: $r^g = 0.028$

<figure><img src="image_24.png" /></figure>

Initial clean int. rate: $r^{l,c} = r^{l,d} - 0.005$

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

Figure D.17: Sensibility of individual policy comparisons to parameter values. Emissions, GDP and public deficit indices are similar as in figure 4. (2/2)
Central simulation

<figure><img src="image_27.png" /></figure>

Share of equity financing: $\chi_i = 0.1$

<figure><img src="image_28.png" /></figure>

Capital BGP growth: $g^k = 0.12$

<figure><img src="image_29.png" /></figure>

Invest. sensibility: $g_{k,i}^{02} = 0.075$

<figure><img src="image_30.png" /></figure>

Invest. sensibility: $g_{k,i}^{03} = 0.065$

<figure><img src="image_31.png" /><figcaption>Figure D.18: Sensitivity of real GDP, emissions, and debt-to-GDP ratio to key parameters in both individual and combined policy scenarios. (1/2)</figcaption></figure>
Max abatement cost: $mac_i^{max} = -20\%$

<figure><img src="image_32.png" /></figure>

Carbon tax pass-through rate: $\tau_i^{pass} = 0.2$

<figure><img src="image_33.png" /></figure>

Govt. int. rate: $r^g = 0.028$

<figure><img src="image_34.png" /></figure>

Initial clean int. rate: $r^{l,c} = r^{l,d} - 0.005$

<figure><img src="image_35.png" /></figure>

Figure D.19: Sensibility of individual policy comparisons to parameter values. Emissions, GDP and public deficit indices are similar as in Figure 4. (2/2)
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