# Building a hydrogen economy: Does France have the industrial capacity for a low-carbon transition?

Raphael Guionie, Lionel Lemiale, Rodica Loisel

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Raphael Guionie, Lionel Lemiale, Rodica Loisel. Building a hydrogen economy: Does France have the industrial capacity for a low-carbon transition?. Journal of Policy Modeling, In press, 10.1016/j.jpolmod.2025.11.001. hal-05461362

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# Building a hydrogen economy: Does France have the industrial capacity for a low-carbon transition?

Raphael Guionie *, Lionel Lemiale, Rodica Loisel

Nantes Université, LEMNA, Nantes F-44000, France

Received 12 March 2025; Received in revised form 24 September 2025; Accepted 9 October 2025

Available online xxxx

## Abstract

The energy transition towards low-carbon hydrogen ($H_2$) in France is expected to require deep industrial planning to develop electrolysis and $H_2$ production infrastructure. This study employs an input-output method to simulate a new sector of electrolysis-produced hydrogen (e-$H_2$) that supplies two-hydrogen intensive sectors, petroleum refining and ammonia. We construct two input-output models, a demand-driven model for e-$H_2$ sector development (the investment phase) and a mixed model for e-$H_2$ production (the operation phase). The results demonstrate that the e-$H_2$ sector depends on industries such as machinery, electrical equipment, construction and metal products manufacturing in the investment phase, with strong backward linkages to the power sector in the exploitation phase. The results reveal that the energy shock (350 kt of e-$H_2$ per year) generates significant growth (€1.3 Bn of gross domestic product) and jobs (3600), but strongly depends on industries' capability to expand and recruit. Recommendations advise public policy development to address the need to reinforce key industries to support e-$H_2$ production due to inter-industry dependence and the need for more attractive skilled and technician jobs in sectors that are already experiencing recruitment tensions. At much higher e-$H_2$ shocks in the steel sector (700 kt e-$H_2$) and other industries (415 kt e-$H_2$), even greater amounts of domestic resources would be required. Therefore, de-carbonising the entire $H_2$ sector require ambitious policy planning to support

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: raphael.guionie@univ-nantes.fr (R. Guionie).</footnote>
R. Guionie, L. Lemiale and R. Loisel

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industrial empowerment, research programmes and labour training so that H₂ becomes an enabling technology of the energy transition.

© 2025 The Author(s). Published by Elsevier Inc. on behalf of The Society for Policy Modeling. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

JEL Classification: L70; C67; O52

Keywords: Input-output; Linkages; Backward linkage index; Forward linkage index; Domestic hydrogen

# 1. Introduction

Achieving global carbon neutrality requires eliminating fossil fuel energy production by promoting the development of low-carbon energy sources and reducing energy consumption (IPCC, 2022). Macro-economic studies regarding energy point out the complex relationships that exist between energy and the economy, as all sectors implicitly produce or consume energy (Blackburn & Moreno-Cruz, 2021). This study examines low-carbon energy solutions by using input-output (IO) models to simulate inter-industrial relationships (Leontief, 1936). These linkages further contribute to industrial planning in France to substitute fossil-based hydrogen (H₂) with low-carbon H₂ produced domestically, i.e. no H₂ imports.

In recent years, growing worldwide interest has emerged in H₂ as a key source of fuel in the energy transition for the massive de-carbonisation of transport, industry, heat and power sectors (Ball & Weeda, 2015; Brandon & Kurban, 2017; Maggio et al., 2019). It is estimated that the global H₂ market attains 95 million tonnes (Mt) in 2022 with 3 % growth since 2020. The future H₂ market could reach 150 Mt by 2030 and 500 Mt by 2050 (IEA, 2021, 2023), while European Union (EU) targets are 10 Mt of domestic H₂ and 10 Mt of imports by 2030 (EC European Commission, 2020; European Commission, 2023). Currently, H₂ is primarily used as a raw resource in refining (40 Mt) and industry (45 Mt), predominantly in the steel sector and ammonia production. However, H₂ has a much wider range of potential applications as fuel in the mobility sector (light and heavy transport, aviation and shipping) and as an energy carrier in the power and heat sectors (energy storage). Traditional H₂ production methods such as steam methane reforming (SMR) and coal or natural gas processing are energy-intensive and generate significant carbon emissions. In contrast, electrolysis-produced hydrogen (e-H₂) and H₂ that is obtained from biomass and organic waste is considered one of the emerging, enabling technologies that can facilitate the transition away from hydrocarbons (EC European Commission, 2020). Nevertheless, H₂ currently represents a marginal share of the global market, due to physical and economic barriers to mass deployment (IEA, 2019).

At the country level, some national roadmaps include the development of low-carbon H₂ (in Germany, the United Kingdom, Russia, Australia, Korea, the United States etc.). In France, the government has enacted a Hydrogen Plan as part of its strategy for achieving carbon neutrality by 2050 (Ministry of Ecological Transition, 2020; NHS National Hydrogen Strategy, 2020). The nation aims to replace the H₂ currently produced with fossil fuels (grey H₂) for existing industrial applications with low-carbon H₂ produced through water electrolysis<fnref n="1" /> (green H₂) and to further substitute fossil fuels for new usages.

<footnote n="1">¹ Note that France's power generation is highly de-carbonised, with 94 % of the total national power based on nuclear power and renewable energies.</footnote>
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Although e-H₂ has not yet been implemented on a large scale, it has developed within niches at the micro-level (Alkemade & Suurs, 2012). At this stage of development, e-H₂ is an emerging technology, yet due to its large potential to significantly contribute to sustainability, it can become an enabling technology of the energy transition (IEA, 2023). In policy strategies, an enabling technology is described as a complementary asset that alone or in combination with associated technologies, can generate deep industrial transformation, promoting innovation and productivity gains (Teece, 2018). When new technology is adopted by several sectors at meso- and macro-economic levels, the innovation might become a general purpose technology (GPT), which is defined as a set of technologies with a wide range of applications across all sectors, affecting an entire economy (Bresnahan & Trajtenberg, 1995).

Unlike radical breakthroughs associated with other enabling or general purpose technologies or GPTs such as the digital economy for instance, H₂ production builds on a well-known process that has been available since the nineteenth century. To become a GPT in the context of the global ecological transition, innovations in the field of e-H₂ generation are essential, with the development of alkaline-based electrolysis and polymer-electrolyte membrane (PEM) technologies and less mature approaches such as solid oxide or biomass pyrolysis electrolysis (IRENA, 2020). On the H₂ consumption side, more innovation is needed for H₂ to be used more widely, particularly in the transportation field (e.g. fuel cell vehicles for light and heavy mobility, aircraft and shipping) and industry (steel production with H₂-based direct reduced iron technology). The range of innovative applications is broad (semi-conductors, internal combustion engine, steam engine, chemical processes such as the Haber process, electricity etc.; Lipsey et al., 2006), and their variety and expansion are key elements for building a GPT frame (Bresnahan, 2010). A specific terminology is used referring to general purpose energy source or GPES (i.e. oil, electricity and H₂) wherein the energy source or carrier is isolated from the equipment/machinery that actually consumes the energy, with internal combustion engines, semi-conductors, steam engines and related forms being traditional GPT (Noce, 2015).

Low-carbon H₂ is at an early stage of deployment, which makes it difficult to predict whether it will become a GPT (Goldfarb et al., 2023; Petralia, 2020). Nevertheless, it is essential to anticipate the possible emergence of low-carbon H₂ as an enabling energy source or GPES as it will largely modify the economic system and all production infrastructure (Bresnahan, 2010). To prepare for this transition, industrial policies and State intervention remain essential (Van der Spek et al., 2022) at this stage of investment planning for de-carbonisation due to the competition with other energy carriers such as natural gas and biofuels (Alkemade & Suurs, 2012).

As the market alone cannot trigger enabling technologies, policymakers are key for identifying and promoting promising technologies as engines of the energy transition (Waßenhoven et al., 2023). Among supporting policy options, funding schemes and research programmes can contribute to the development and the application of enabling technologies. France has already identified low-carbon H₂ as a solution for de-carbonising industry, predicting a considerable proportion (35 H₂) of e-H₂ in the nation's energy mix by 2050 (RTE 2 2022). The French H₂ strategy first relies on e-H₂ production for industry to develop a mature H₂ sector (350 kt per year).<fnref n="2" />

The French government strategy on e-H₂ at the first stages of H₂ deployment elaborated in this study is illustrated in Fig. 1. From left to right, the traditional process of grey H₂ is replaced with e-H₂ in an emerging stage of the electrolysis process. In the next stage, additional funding and research

<footnote n="2">² French industrial H₂ production amounts to 900 kt, among which 60 % is by-products of industrial processes, and the remaining 40 % (~350 kt of H₂) is primarily produced using a steam methane reforming process (RTE [3], 2020) that can be replaced by carbon-free production processes.</footnote>
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<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Overview of public policy steps in the e-H₂ deployment.</figcaption></figure>

policies stimulate deployment and generate learning by doing and scale effects, diffusing e-H₂ across many other sectors such as power and transport industries. In the last stage, e-H₂ may become an enabling technology if it becomes cost-effective and efficient enough to replace fossil fuels, with expected outcomes in terms of energy security, independence and economic growth. Note that the process of e-H₂ development from emerging to enabling or general purpose phases is a long-term process due to necessary societal changes and technology regime replacement (Teece, 2018).

To examine the potential trajectory of the emerging phase, this study investigates the resources needed to establish a new H₂ sector. Methodologically, we construct a framework for investigating the industrial needs for H₂ deployment, applying an IO model to the French economy. We build the IO model in two stages. First, a demand-driven model is built to simulate the construction of a new e-H₂ infrastructure (electrolysers). Second, a mixed model is built to simulate the e-H₂ sector’s operation and e-H₂ production. Results assessing industry output and employment allow us to estimate the resources required by the new H₂ sector such as labour and intermediate consumption and their impacts on the other sectors. The study concludes that e-H₂ sector deployment primarily builds on few industrial sectors and construction, questioning these sectors’ ability to recruit and expand without public policy intervention. The difficulty of developing a massive electrolyser infrastructure should be considered in a more general context of France’s overall de-industrialisation, which began in the 1970s (with a low industry share in the gross domestic product (GDP) of 15 % in 2023) and requires substantial industrial planning to reverse this trend (DGE, 2024).

The remainder of this paper is organised as follows. Section 2 provides a literature review of IO models applied in environmental research, and the H₂ energy sector in particular. Section 3 describes the econometric formulation of the problem and proposes a model in which data from the French economy are cross tabulated with data on energy flow. Section 4 discusses the results on which the policy recommendation section is based (in Section 5). Section 6 concludes and presents future research perspectives.

## 2. Literature review

IO models were originally developed as linear models used for characterising interactions across all sectors in an economy (Miller & Blair, 2022). First developed by Leontief (1936, 1941), such models are still widely used to analyse the impact of economic policies and demand and price variation shocks. The IO method has been extended to the environmental field as well.
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In the water sector, Velázquez (2006) examines water management and identifies the sectors that consume the most water in Andalusia. Llop (2013) constructs an IO model to assess how water resources are allocated following changes in final demand under the constraint of technical water needs in Catalonia. Integrated IO models are used to evaluate water use in northeast China, with implications in terms of risk management policy (Zhang et al., 2020).

In the agriculture sector, pioneers such as Heady and Schnittker (1957) apply a basic model to describe the relationships between agriculture and the other sectors of the economy. Johnson and Kulshreshtha (1982) use IO to perform an analysis of the Canadian province of Saskatchewan, and Heringa et al. (2013) assess the impact of multi-functional agriculture in a Dutch region. Loizou et al. (2019) use an IO analysis to examine the linkages between agriculture and other sectors and demonstrate the importance of agriculture in promoting integrated development in regional rural economies.

Concerning greenhouse gas (GHG) emissions, Alcántara and Padilla (2009) develop an IO model to examine the carbon dioxide (CO2) emissions of Spain’s service sector. Lixon et al. (2008) assess the economic impacts of reducing GHG emissions by decreasing industrial output to a level consistent with the target set out in the Kyoto protocol. At a global level, Hertwich and Wood (2018) conduct an IO analysis to investigate the indirect carbon dioxide emissions of the economy. Liu et al. (2018) perform an environmentally extended IO simulation to study industrial GHG mitigation policies and determine the most effective reduction pathway. For China, Wei et al. (2022) examine households’ footprint using a multi-regional IO model covering 1995–2019, and Zhang et al. (2019) explore the impact of a carbon tax on sectoral competitiveness when the tax is either production- or consumption-based.

In the energy field, Hans et al. (2004) perform an IO analysis to investigate the role of the power sector in the Korean economy. Liang et al. (2010) build an energy IO model to study the energy metabolic system in China. Cellura et al. (2013) assess the effects of national strategies for sustainable production and consumption using an IO model applied to Italy. Llop (2020) takes a different approach by evaluating the impacts of energy imports on costs using an IO price model. Wu et al. (2013) use an oil price shock on the Chinese economy to build an IO model assessing follow-up inflation.

Few studies use IO models applied to H₂. The majority of H₂ research uses computable general equilibrium models to assess the viability of investment in H₂ or the potential role of H₂ in achieving national objectives of net-zero carbon emissions. For example, see Lee (2014) for Japan, Lee (2012) and Lee et al. (2009) for Taiwan, Jokisch and Mennel (2009) for the EU, Bae and Cho (2010) for Korea, Silva et al. (2014) for Portugal and Espegren et al. (2021) for Norway.

Most H₂ IO models focus on transport sectors. Wietschel and Seydel (2007) identify shifts in employment from H₂ development in the EU, particularly in the field of fuel cell vehicles. Chun et al. (2014) conduct an IO analysis on H₂ deployment in Korea. Only a few studies focus specifically on electrolysis infrastructure. Lee et al. (2011) and Lee and Chiu (2012) show that investment in biohydrogen projects should be prioritised over investment in H₂ infrastructure. O'Connor (2021) conducts an economic assessment of the H₂ production process (SMR and electrolysis) using an IO table (IOT) for Australia. Gupta et al. (2023) analyse the impact of the new green H₂ sector in Switzerland, employing a two-step IO model strategy similar to the method developed in this study. Nevertheless, previous research focuses on new H₂ demand (transportation) using demand-driven models, while our study focuses on current H₂ use and further analyses the associated industrial linkages using a mixed model. To the best of our knowledge, this study is the first to attempt an evaluation of the resources needed to develop a domestic H₂ industry in France and its potential impacts.
I-O Demand-driven model: emergence of a new H2 sector

<figure><img src="image_5.png" /></figure>

I-O Mixed model: operation of an established H2 sector

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Methodology flow chart.</figcaption></figure>

# 3. Methodology

This study constructs a two-stage IO model for producing H₂ based on water electrolysis with low-carbon electricity,<fnref n="3" /> for which electrolysis capacity must be built. In the first (investment) phase, a demand-driven IO model simulates the construction of a domestic H₂ sector of electrolysers and components. Once the infrastructure is operational, in the second (exploitation) phase, we construct a mixed model to mimic H₂ production. A standard demand-driven (Leontief) model determines the direct and indirect output effects on an economy referencing the exogenous final demand. A mixed model generally combines exogenous production in some sectors and endogenous production in other sectors (Miller & Blair, 2022). This method follows the gradual procedure described in Fig. 2.

## 3.1. Data and assumptions

This study uses a symmetric IOT with 64 products (lines) and 64 sectors (columns) for France that is based on Eurostat data for the year 2018, and a database on employment using the population census conducted by the French National Institute of Statistics and Economic Studies in 2018 (INSEE) to calculate employment effects.

Grey H₂ is currently predominantly used by two sectors, the oil refining and the ammonia sector, which are respectively classified as Coke and refined petroleum products (CRP) and Chemicals and chemical products (CC) in the Eurostat IOT (Eurostat, 2008). In 2019, these sectors consumed 350 kt of H₂ (130 H₂ by the CRP industry and 220 H₂ by the CC industry)<fnref n="4" /> (RTE 2 2022). In line with the French National Low-Carbon Strategy (Ministry of Ecological Transition, 2020), these sectors must substitute grey H₂ with e-H₂ by 2035. In our model, the two sectors will consume domestic e-H₂ to replace the grey H₂ produced using imported natural gas.

<footnote n="3">³ Fossil fuel-based power generation represents less than 7 % of the power mix.</footnote>

<footnote n="4">⁴ We refer to net H₂ consumption, excluding co-production.</footnote>
## 3.2. Demand-driven input-output model (H₂ investment phase)

Hydrogen production through water electrolysis requires electricity and electrolysers. In our model, investments in the H₂ infrastructure include electrolysers (including stacks) and technologies for H₂ processing, cooling and water circulation. Based on recent scenarios of the French system operator, power generation in 2035 will be sufficient to produce the H₂ required in the model shock (RTE, 2023). Therefore, e-H₂ deployment in this study is assumed to have no effect on power plant construction. Note that H₂ storage and compression facilities (if any) belong to industries that already consume grey H₂ and are also used for the low-carbon H₂; therefore, they are not included in the new H₂ sector.

**Step 1. New H₂ infrastructure data.** Numerically, producing 350 kt of e-H₂ requires 16.5 TWh<sub>e</sub>, representing an electrolyser capacity of 2.1 GW with 70 % efficiency and 93 % capacity factor (RTE 3 2020). Assuming infrastructure costs of €850 k/MW (IEA, 2019; IRENA, 2020; Saba et al., 2018), shifting grey H₂ to e-H₂ requires a €1.79 Bn investment. Fig. 3 presents a breakdown of H₂ infrastructure expenditures (IRENA, 2020; Lee, 2014; Wietschel & Seydel, 2007). The investment shock is next spread into the economy using sectoral assumptions and computed with a demand-driven IO model (step 2). In our scenario, we assume that all electrolysers are built in France, which means that no import is required for their construction.

**Step 2. Leontief input-output driven-demand model.** The IO framework is based on the following balance equation for each economic sector (i = 1..n):

$$x_i = \sum_{j=1}^{n} x_{ij} + f_i = \sum_{j=1}^{n} a_{ij}x_j + f_i \quad (1)$$

where x_i is the value of the production of sector i, x_{ij} is the value of intermediate consumption of product i consumed by sector j (j = 1..n), a_{ij} (a_{ij} = \frac{z_{ij}}{x_j}) is the technical coefficient indicating the proportion of input i consumed by sector j and f_i represents the final demand of product i.

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Sectoral demand to construct H₂ sector infrastructure. Figure Reading: The fact that Electrical equipment represents 14 % of infrastructure cost implies that for €1 invested in the H₂ infrastructure, the final demand (or investment) in an electrical equipment product is €0.14.</figcaption></figure>
In matrix form, we rewrite Eq. (1) as follows:

$$x = Ax + f \qquad (2)$$

where x and f are column vectors (n,1) of $x_i$ and $f_i$, respectively, and A is the matrix composed of the technical coefficients of dimension (n, n), with n = 64.

Solving Eq. (2) obtains the following:

$$x = (I - A)^{-1}f = Lf \qquad (3)$$

where I is the unit matrix of dimension (n, n), and $(I - A)^{-1}$ is the Leontief matrix (L) that measures the effect of a €1 change in final demand on the output. From Eq. (3), any effect of a final demand variation on the global output is computed as follows:

$$\Delta x = L\Delta f \qquad (4)$$

**Assessment indicators.** We next construct traditional impact assessment indicators for GDP,<fnref n="5" /> sectoral output and employment. Impact on sectoral employment ($\Delta e$) is defined as follows:

$$\Delta e = \hat{E} \cdot L \cdot \Delta f \qquad (5)$$

where $\hat{E}$ is the diagonal matrix referring to the sectoral employment coefficient, which has the following form:

$$\hat{E} = \begin{bmatrix} \frac{e_1}{x_1} & \dots & 0 \\ \vdots & \ddots & \vdots \\ 0 & \dots & \frac{e_n}{x_n} \end{bmatrix}$$

where $e_j$ represents the employment in sector j.

Finally, we construct a new IOT1 table incorporating the impact of the new H₂ sector infrastructure deployment, which represents the starting point of Phase 2.

### 3.3. Input-output mixed model with 64 + 1H₂ sectors (H₂ exploitation phase)

The methodology constructed above is specific to the new H₂ infrastructure investment shock but did not include e-H₂ production. At this stage, the H₂ sector becomes operational and begins producing e-H₂ to substitute grey H₂. As the need for H₂ exogenously establishes the H₂ sector's output level, we develop a mixed model to assess the impact of the exploitation phase. Beforehand, we introduce the new sector into the previous IOT (IOT1), which requires adding a column (for the H₂ sector's input) and a row (for H₂ distribution), resulting in (2·n+1) technical coefficients.

The new IO table (IOT2) now has a set of 65 sectors comprising the 64 initial sectors and the new H₂ sector. A new technical coefficient matrix (65 × 65), denoted as A*, is obtained as well as a new (65 × 65) Leontief matrix $(L^* = (I - A^*)^{-1})$.

#### Step 1. H₂ sector input structure (column vector).

The methodology used to compute technical coefficients is highly dependent on the availability of data. One solution is to reference an existing sector to approximate them, as in

<footnote n="5">⁵ GDP is calculated as the sum of added values, plus taxes and minus subsidies.</footnote>
<table><caption>**Table 1**
OPEX cost breakdown by input type (for 350 H₂).
Sources for assumptions: IEA (2019), Lee et al. (2021).</caption><thead><tr><th>Type of input</th><th>Assumptions</th><th>Input cost (€M)</th><th>Technical coefficient</th><th>Supplying sector in input-output table</th></tr></thead><tbody><tr><td>Electricity</td><td>16,5 TWh<br/>€60/MWh</td><td>990</td><td>0.7645</td><td>Electricity, gas, steam and air conditioning</td></tr><tr><td>Operation &amp; Maintenance</td><td>3.5 % of CAPEX·yr</td><td>62</td><td>0.0482</td><td>Repair and installation services for machinery and equipment</td></tr><tr><td>Water consumption</td><td>0.0013€/L-9L/kgH₂</td><td>6</td><td>0.0045</td><td>Natural water, water treatment and supply services</td></tr><tr><td>Labour</td><td>1.3 % of CAPEX·yr</td><td>17</td><td>0.0130</td><td>Added value</td></tr><tr><td>Return on capital</td><td>6 %</td><td>220</td><td>0.1698</td><td></td></tr><tr><td><strong>Total</strong></td><td>-</td><td><strong>1295</strong></td><td><strong>1</strong></td><td></td></tr></tbody></table>

O'Connor (2021), where electricity generation sector mimics the H₂ input structure. Another solution is to use coefficients from the H₂ industry, as in Gupta et al. (2023). Referencing existing literature, we compute the technical coefficients of the new sector ($a^{*}_{i,h2}$) by breaking down the operational expenditures (OPEX) into the main input categories for H₂ production, which include electricity, labour, maintenance and capital return, the data for which are obtained from secondary data (Table 1). The e-H₂ sector is assumed to replace 350 kt of grey H₂ used in the two highest purchasing sectors (CRP and CC). Assuming a unit cost of H₂ of €3.7/kg by 2030, referencing the prediction for France from RTE 3 (2020), producing 350 kt of H₂ will cost €1295 M. Table 1 presents a breakdown of the total cost of e-H₂ into component costs (Input cost column) and further computes technical coefficients (Technical coefficient column). Each OPEX cost type is finally associated with its relevant supplying sector in the IO table.

**Step 2. H₂ use and changes in technical coefficients in consuming industries (row vector).**

As noted previously, two industries replace grey H₂ with e-H₂, CRP (130 kt) and CC (220 kt). Therefore, the row distribution structure of H₂, composed of 65 elements ($a^{*}_{h2,j}$), contains two positive coefficients, $a^{*}_{h2,CRP}$ and $a^{*}_{h2,CC}$, which introduce additional technical coefficients into the cost structure of these sectors. To balance the IOT, we assume that the grey H₂ used is self-produced by CC and CRP with imported gas. We balance the table by lowering these industries' natural gas imports (Use of imported product). With a 70 % efficiency rate for the steam reforming process (Ishaq et al., 2022; Nikolaidis & Poullikkas, 2017; Staffell et al., 2018), we calibrate two import coefficients ($a^{*}_{m,CRP}$ and $a^{*}_{m,CC}$) that balance the table IOT2.<fnref n="6" />

**Step 3. Assessment indicators.**

In addition to traditional indicators such as total output and GDP, we assess the impact of the new sector on the economy using output-to-output (OTO) multipliers that link the exogenous H₂ output to the other sectors (Miller & Blair, 2022). The OTO multiplier ($oto_{ij}$) measures the direct and indirect cumulative impacts on sector i production following an output change in sector j. The algebraic expression of the OTO multiplier for sector j is as follows:

<footnote n="6">⁶ These coefficients also allow us to compute the threshold price of imported gas that is necessary to substitute grey H₂ with e-H₂ (i.e. 78 €/MWh).</footnote>
$$o_{i,j} = \frac{l_{i,j}^*}{l_{j,j}^*}$$

where $l_{i,j}^*$ and $l_{j,i}^*$ are elements of Leontief matrix ($L^*$). We measure the impact on the total output change in production of product j as follows:

$$oto_j = \sum_{i=1}^{n} o_{i,j}$$

In the IO model framework, production sectors are doubly linked to one another, wherein increasing activity in a given sector expands its input purchases and boosts the activity of upstream sectors. The BLI measures the effects of the activity change in a specific sector on the other sectors in the economy. We use a normalised version of backward linkages to compare the e-H₂ sector with the other sectors j as follows:

$$BLI_j = \frac{\sum_{i=1}^{n} o_{i,j}}{\left(\frac{1}{n}\right) \sum_{i=1}^{n} \sum_{j=1}^{n} o_{i,j}}$$

Sectors with above-average BLI are greater than 1 and sectors with weaker than average BLI are below 1. In contrast, increasing sector j output raises input availability for the downstream sector. The FLI quantifies the degree of other sectors’ use of sector j products as inputs. Referencing Miller and Blair (2022), FLI is computed using the Ghosh model (Ghosh, 1958) as follows:

$$FLI_j = \frac{\sum_{i=1}^{n} g_{ij}}{\left(\frac{1}{n}\right) \sum_{i=1}^{n} \sum_{j=1}^{n} g_{ij}}$$

where $g_{ij}$ denotes elements of Ghosh inverse matrix $G$, which is defined as follows:

$$G = (I - B)^{-1}$$

where $B$ is the matrix of output coefficients, composed of $b_{i,j}$ elements as the share of production j that is supplied to sector i to obtain product i as follows:

$$b_{i,j} = \frac{z_{i,j}}{x_i}$$

Similar to the BLI, sectors with above-average FLI are greater than 1, and sectors with weaker than average FLI are below 1.

# 4. Results

## 4.1. H₂ investment phase (demand-driven IO model)

The results concerning the investment shock in terms of aggregated direct effects (Table 2) reveal that investing €1.79 Bn to build H₂ sector infrastructure generates €1.3 Bn of added value in the economy, €2.9 Bn of output and 14,622 jobs. However, the investment phase is longer than one statistical year (around four years), and the GDP, output and jobs are obtained over the entire investment period. Therefore, assuming identical distribution and a constant structure of expenditure over the four years, annual employment is then between 3600–4000 jobs.
<table><caption>**Table 2**
Aggregated results of the hydrogen investment shock.</caption><thead><tr><td>Final demand shock (€Bn)</td><td>GDP (€Bn)</td><td>Output (€Bn)</td><td>Yearly employment (1000)</td></tr></thead><tbody><tr><td>1.79</td><td>1.3</td><td>2.98</td><td>3.6</td></tr></tbody></table>

Considering the industry-intensive nature of the shock, constructing domestic electrolysers primarily impacts industry sectors such as the machinery and equipment sector, followed (far behind) by construction, electrical equipment and fabricated metal product sectors (Table 3). The ranking of the 20 sectors that predominantly contribute to the overall output during the H₂ investment phase is similar to the ranking of the sectors that supply the H₂ sector (Fig. 4). The impact on total output (Fig. 3) can be divided in three different effects: first, sectors affected by the shock increase output (initial effect; Table 4) and stimulate their suppliers (direct effect) as well as their suppliers’ suppliers and so on (indirect and spillover effects).

Fig. 4 indicates that the sectors that are most involved in e-H₂ sector development such as Machinery and equipment, record high initial effects but low direct and indirect effects.

Employment impacts depend on activity effects and sectors’ labour intensity. As expected, job creation is highest in industries that are directly involved in constructing e-H₂ infrastructure. Nevertheless, about half of the employment variation is attributable to induced effects (Fig. 5).

## 4.2. e-H₂ exploitation phase (IO mixed model)

The investment shock simulated using a demand-driven model analysed in Section 4.1. enabled the construction of e-H₂ infrastructure. We next apply a production shock to simulate the annual production of the new e-H₂ sector.

Producing €1295 M of e-H₂ brings an additional €969 M to the GDP and increases total output by about €3.41 Bn or a supplementary output of €2.14 Bn net of the initial shock of the e-H₂ production effect (Table 4). While results in Phase 1 are obtained over the four years necessary to construct electrolysers, the outcomes in this phase are calculated annually for the functioning of the refining and ammonia sectors, assuming that no other change in the economy

<table><caption>**Table 3**
Distribution of investment expenditure across sectors (€M).</caption><thead><tr><td>Sector</td><td>Investment</td></tr></thead><tbody><tr><td>Machinery and equipment n.e.c.</td><td>589</td></tr><tr><td>Electrical equipment</td><td>250</td></tr><tr><td>Construction and construction work</td><td>214</td></tr><tr><td>Other personal services</td><td>214</td></tr><tr><td>Fabricated metal products, excl. machinery and equipment</td><td>143</td></tr><tr><td>Chemicals and chemical products</td><td>89</td></tr><tr><td>Rubber and plastic products</td><td>89</td></tr><tr><td>Wholesale trade services, excl. motor vehicles and motorcycles</td><td>71</td></tr><tr><td>Computer, electronic and optical products</td><td>54</td></tr><tr><td>Financial services, excl. insurance and pension funding</td><td>36</td></tr><tr><td>Real estate services excl. imputed rents</td><td>36</td></tr><tr><td>Total</td><td>1790</td></tr></tbody></table>
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<figure><img src="image_8.png" /><figcaption>Fig. 4. Output generated by sector, cumulative initial, direct and indirect effects (€M).</figcaption></figure>

<table><caption>**Table 4**
Sector impacts and aggregated results.</caption><thead><tr><td>Production shock (€M)</td><td>GDP (€M)</td><td>Output (€M)</td><td>Gas import (TWh)</td></tr></thead><tbody><tr><td>1295</td><td>969</td><td>3441</td><td>-16.5</td></tr></tbody></table>

occurs. Beyond figures, the outcome generated by the e-H₂ sector enhances positive externalities such as establishing a domestic business environment with local rather than relocatable assets, as well as domestic maintenance jobs, reduced energy dependence on gas and an improved trade balance.

We first determine the new structure of the economy using OTO multipliers (Table 5) to identify the top 20 sectors with the highest OTO multipliers. The OTO multipliers illustrate the variation in total output produced in the economy in response to a €1 increase of production in sector *j*, reflecting a contribution of a sector's activity to the economy. In this new economy, the e-H₂ sector ranks first, with an output multiplier of 2.658. This means that a €1 increase in the e-H₂ production generates €2.658 of output value in the whole economy, composed of €1 of e-H₂ output (initial effect) and €1.658 from other sectors (direct and indirect effects). The e-H₂ sector's top position in the ranking is because 80 % of its inputs are goods or services<fnref n="7" /> that are domestically supplied. It then stimulates local sectors and increases their multipliers as well.

Table 6 decomposes H₂ OTO multipliers for the top seven sectors, revealing that when the output of the e-H₂ sector increases by €1, the Repair and installation services of machinery and equipment sector increases production by €0.056. The most significant OTO multiplier for the e-H₂ industry (1.309) is the Electricity sector due to the considerable electricity intensity of e-H₂ production. The linkage value is high, despite the fact that this sector has a low OTO multiplier of 1.2 (ranking 62 out of 65). The Electricity sector alone represents more than 80 %

<footnote n="7">⁷ Added value inputs account for only 18 %, which has no impact on activity in a closed IO framework.</footnote>
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<figure><img src="image_9.png" /><figcaption>Fig. 5. Job creation per sector.</figcaption></figure>

<table><caption>**Table 5**
Top 20 output-to-output multipliers.</caption><thead><tr><td>Sector</td><td>Output multiplier</td><td>Rank</td></tr></thead><tbody><tr><td>e-H<sub>2</sub></td><td>2.658</td><td>1</td></tr><tr><td>Water transport services</td><td>2.182</td><td>2</td></tr><tr><td>Insurance, reinsurance, pension funding services, etc.</td><td>2.043</td><td>3</td></tr><tr><td>Wood and of products of wood and cork, excl. furniture</td><td>1.774</td><td>4</td></tr><tr><td>Food, beverages and tobacco products</td><td>1.772</td><td>5</td></tr><tr><td>Basic metals</td><td>1.742</td><td>6</td></tr><tr><td>Other non-metallic mineral products</td><td>1.729</td><td>7</td></tr><tr><td>Mining and quarrying</td><td>1.725</td><td>8</td></tr><tr><td>Paper and paper products</td><td>1.706</td><td>9</td></tr><tr><td>Accommodation and food services</td><td>1.697</td><td>10</td></tr><tr><td>Machinery and equipment n.e.c.</td><td>1.691</td><td>11</td></tr><tr><td>Motion picture, video and television programme services</td><td>1.690</td><td>12</td></tr><tr><td>Fish and other fishing products; aquaculture products; etc.</td><td>1.688</td><td>13</td></tr><tr><td>Motor vehicles, trailers and semi-trailers</td><td>1.681</td><td>14</td></tr><tr><td>Wholesale trade services, excl. motor vehicles</td><td>1.668</td><td>15</td></tr><tr><td>Natural water; water treatment and supply services</td><td>1.665</td><td>16</td></tr><tr><td>Services auxiliary to financial services and insurance services</td><td>1.655</td><td>17</td></tr><tr><td>Advertising and market research services</td><td>1.628</td><td>18</td></tr><tr><td>Other professional, scientific, technical services, veterinary</td><td>1.625</td><td>19</td></tr><tr><td>Printing and recording services</td><td>1.622</td><td>20</td></tr></tbody></table>

of the output induced effect of e-H₂ production<fnref n="8" /> and appears to be a key supplier to the e-H₂ industry. Therefore, any production drop in this sector could strongly impact the e-H₂ sector itself, with a domino effect on all the sectors that depend on it (direct and indirect industries

<footnote n="8">⁸ Considering direct and indirect effects only.</footnote>
<table><caption>**Table 6**
Direct and indirect effects of the e-H₂ sector's output.</caption><thead><tr><th>Sector</th><th>Total effect</th><th>Rank</th></tr></thead><tbody><tr><td>Electricity, gas, steam and air conditioning</td><td>1.309</td><td>1</td></tr><tr><td>Repair and installation services of machinery and equipment</td><td>0.056</td><td>2</td></tr><tr><td>Legal, accounting services; services of head offices; etc.</td><td>0.035</td><td>3</td></tr><tr><td>Wholesale trade services, except of motor vehicles</td><td>0.026</td><td>4</td></tr><tr><td>Security and investigation services; services to buildings etc.</td><td>0.017</td><td>5</td></tr><tr><td>Constructions and construction work</td><td>0.017</td><td>6</td></tr><tr><td>Chemicals and chemical products</td><td>0.017</td><td>7</td></tr><tr><td>...</td><td></td><td></td></tr></tbody></table>

relying on e-H₂-consuming sectors). The effects of the e-H₂ shock on the other sectors are primarily indirect and generally rather modest (lower than 0.06).

We complement the analysis of the effects of the e-H₂ shock on industries with indicators by assessing linkages between sectors employing BLI and FLI linkages. The BLI measures an industry's potential to pull other industries upwards through its own intermediate consumption. In other words, an increase in a sector's output with high BLI can result in a significant increase in upstream industries' output. The e-H₂ sector once again ranks first among the top 20 sectors (Table 7), which aligns with our expectations as BLI is calculated using the output multiplier where the e-H₂ sector ranks first. This result is due to the initial condition according to which e-H₂ is produced using French electricity and maintenance is operated in France. The BLI for the e-H₂ sector could be even higher in a scenario in which e-H₂ requires additional power

<table><caption>**Table 7**
Backward Linkage Index.</caption><thead><tr><th>Sector</th><th>BLI</th><th>Rank</th></tr></thead><tbody><tr><td>e-H<sub>2</sub></td><td>1.725</td><td>1</td></tr><tr><td>Water transport services</td><td>1.416</td><td>2</td></tr><tr><td>Insurance, reinsurance and pension funding services, etc.</td><td>1.326</td><td>3</td></tr><tr><td>Wood and of products of wood and cork, excl. furniture; etc.</td><td>1.151</td><td>4</td></tr><tr><td>Food, beverages and tobacco products</td><td>1.150</td><td>5</td></tr><tr><td>Basic metals</td><td>1.130</td><td>6</td></tr><tr><td>Other non-metallic mineral products</td><td>1.122</td><td>7</td></tr><tr><td>Mining and quarrying</td><td>1.119</td><td>8</td></tr><tr><td>Paper and paper products</td><td>1.107</td><td>9</td></tr><tr><td>Accommodation and food services</td><td>1.101</td><td>10</td></tr><tr><td>Machinery and equipment n.e.c.</td><td>1.097</td><td>11</td></tr><tr><td>Motion picture, video, television programme production services; etc.</td><td>1.097</td><td>12</td></tr><tr><td>Fish and other fishing products; aquaculture products; etc.</td><td>1.095</td><td>13</td></tr><tr><td>Motor vehicles, trailers and semi-trailers</td><td>1.091</td><td>14</td></tr><tr><td>Wholesale trade services, excl. of motor vehicles and motorcycles</td><td>1.083</td><td>15</td></tr><tr><td>Natural water; water treatment and supply services</td><td>1.080</td><td>16</td></tr><tr><td>Services auxiliary to financial services and insurance services</td><td>1.074</td><td>17</td></tr><tr><td>Advertising and market research services</td><td>1.057</td><td>18</td></tr><tr><td>Other professional, scientific, technical services, veterinary services</td><td>1.055</td><td>19</td></tr><tr><td>Printing and recording services</td><td>1.053</td><td>20</td></tr></tbody></table>
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<table><caption>**Table 8**
Forward linkage index.</caption><thead><tr><td>Sector</td><td>FLI</td><td>Rank</td></tr></thead><tbody><tr><td>Services auxiliary to financial services and insurance services</td><td>1.652</td><td>1</td></tr><tr><td>Printing and recording services</td><td>1.619</td><td>2</td></tr><tr><td>Employment services</td><td>1.532</td><td>3</td></tr><tr><td>Electricity, gas, steam and air conditioning</td><td>1.462</td><td>4</td></tr><tr><td>Legal and accounting services; services of head offices; etc.</td><td>1.459</td><td>5</td></tr><tr><td>Financial services, except insurance and pension funding</td><td>1.431</td><td>6</td></tr><tr><td>Warehousing and support services for transportation</td><td>1.418</td><td>7</td></tr><tr><td>Mining and quarrying</td><td>1.405</td><td>8</td></tr><tr><td>Other professional, scientific, technical services, veterinary services</td><td>1.390</td><td>9</td></tr><tr><td>Security and investigation services; services to buildings etc.</td><td>1.334</td><td>10</td></tr><tr><td>Wood and of products of wood and cork, excl. furniture; etc.</td><td>1.323</td><td>11</td></tr><tr><td>Postal and courier services</td><td>1.307</td><td>12</td></tr><tr><td>Travel agency, tour operator and other reservation services etc.</td><td>1.300</td><td>13</td></tr><tr><td>Rental and leasing services</td><td>1.286</td><td>14</td></tr><tr><td>Sewerage services; sewage sludge; waste collection, etc.</td><td>1.282</td><td>15</td></tr><tr><td>Other non-metallic mineral products</td><td>1.270</td><td>16</td></tr><tr><td>e-H<sub>2</sub></td><td>1.239</td><td>17</td></tr><tr><td>Products of forestry, logging and related services</td><td>1.232</td><td>18</td></tr><tr><td>Fabricated metal products, except machinery and equipment</td><td>1.222</td><td>19</td></tr><tr><td>Advertising and market research services</td><td>1.220</td><td>20</td></tr></tbody></table>

plants to generate the electricity needed for electrolysis, which is not modelled here. The electricity supplying the e-H₂ sector is assumed to be generated by pre-existing power plants.

The FLI results reveal that the e-H₂ sector is ranked seventeenth, indicating that the demand for e-H₂ is moderate in the overall economy (Table 8). This result is based on the assumption that e-H₂ is only used in two sectors (CRP and CC) and e-H₂ is an intermediate consumption good rather than a final consumption good.

As illustrated in Fig. 6, combining all BLI and FLI indicators shows that the e-H₂ sector has a specific position with very high BLI, and interestingly, has a downstream index that is concentrated around moderate levels (value > 1). This is specific to sectors that supply only one good (in this case, e-H₂) to few sectors with no exports (in this case, refining and ammonia). These align with O'Connor (2021), i.e. high BLI and moderate FLI but contradict Gupta et al. (2023), who find high BLI but low FLI (value < 1). This difference is attributable to differing assumptions concerning e-H₂ usage as final demand for transport rather than the industrial use examined in this study and the linkage index in our study being calculated using a mixed model with OTO multipliers.

A limitation regarding OTO multipliers and linkages is that they do not consider the inter-sectoral dependence issue. For instance, if e-H₂ production drops, it will predominantly affect the electricity sector (upstream effect) and the CRP and CC sectors (downstream effects). These sectors supply essential inputs to the economy, such as fertilisers (produced with H₂-based ammonia) and gasoline (from oil refining). These products are essential as intermediate inputs for key sectors such as the agriculture and transport sectors, which also have an essential role in the overall economy. General equilibrium approaches rather than IO models could consider these effects; however, these models do not allow us to identify the sectoral needs for the construction of e-H₂ infrastructure in detail.
<figure><img src="image_10.png" /><figcaption>Fig. 6. Backward and forward linkage indices for the 65 sectors.</figcaption></figure>

**5. Policy recommendations**

Our results suggest that a relatively modest production of e-H₂ requires significant equipment, industrial capacity and labour force resources, indicating that sound policy planning is necessary to anticipate 1) industry inter-dependence, 2) labour market challenges, 3) power sector scheduling and 4) the country's role in the worldwide race for low-carbon H₂.

**1) Inter-industry dependence**

Linkages demonstrate that domestic production of e-H₂ strongly stimulates the equipment and construction sectors; however, France has experienced a decades-long process of de-industrialisation, with a decline in the share of the industrial sector's added value in the GDP from 25 % in 1980 to 12 % in 2017 (France Stratégie, 2020). This trend has deteriorated the trade balance and increased the country's dependence on imported goods (manufactured products in particular). These effects have been sharply highlighted by the COVID-19 pandemic and the Ukrainian crisis. The loss of sovereignty in intermediate goods sectors has weakened the overall French industry, which currently experiences difficulties in meeting the expectations of the government's re-industrialisation objectives.

Public policies must be revised to reinforce key industries as any newly entering sector is dependent on other sectors' output. Domestically produced H₂ is a relevant example of an emerging product that must be urgently developed as a strategic component in reaching national objectives and potential export goods. Policy actions have already been implemented such as the installation of giga-factories by McPhy, Genvia and Elogen, and the wide development of H₂ ecosystems and projects across the country.<fnref n="9" /> Moreover, the French government announced a plan to invest €7 billion in H₂ infrastructure and research by 2030 (Ministry for industry, 2023). In the downstream H₂ chain, subsidies and tax credits are being used to incentivise consumers substitution of grey H₂ with e-H₂, more specifically with

<footnote n="9">⁹ https://www.france-hydrogene.org/en/magazine/un-ipcei-a-plus-de-5-milliards-pour-lhydrogene/ (In French.)</footnote>
domestically produced low-carbon H₂. However, the administrative procedures associated with launching H₂ projects and applying for subsidies are rather long and complex; thus, it is essential to simplify these procedures.

## 2) Labour market planning.

Due to France's significant fall in industrial activity, the number of associated jobs has also sharply decreased, from 25 % of the total workforce in 1980 to just 9 % in 2017 (France Stratégie, 2020). Recent public policy actions implemented to re-industrialise the French economy raise the need for labour and introduce even more pressure on the industrial and construction sectors, which are already confronting recruitment difficulties (DARES, 2020). Moreover, jobs with the most re-cruitment problems are skilled and technician jobs in the construction and industry sectors, which are characterised by difficult working conditions and specific training needs. Public policies should focus on making these sectors more attractive; for example, by facilitating access to training and lowering employers' labour costs. Measures have been adopted in favour of low-wage jobs; however, the industrial sector has drawn limited benefits from them since the average wage in this sector is currently higher than the wage threshold set by the policy (France Stratégie, 2020).

## 3) H₂ dependence on the power sector.

The strategy of the French government is to prioritise the production of e-H₂ (Ministry of Ecological Transition, 2020). However, France has confronted power supply issues due to temporary shutdowns of some of its nuclear reactors for maintenance and the closure of some dis-patchable power stations. Moreover, new nuclear power plants will not be operational until 2035 and renewable energy projects are not rolled out fast enough (RTE [3], 2020; RTE, 2023). Achieving de-carbonisation targets necessitates accelerating the implementation of these projects or decreasing the power demand as otherwise there might not be a sufficient supply of electricity to produce green H₂. Public policies could dedicate a specific proportion of renewable power gen-eration to producing H₂ without generating any conflict with other users such as the industrial and residential sectors. Subsidies and tax credits could also incentivise industrial H₂ consumers to integrate the additional power plants needed to produce H₂ into their investment strategies (see bilateral contracts for green energy signed by the shipping company Maersk in Spain).<fnref n="10" />

## 4) The hydrogen development race.

H₂ will be a key component in the worldwide energy transition as a necessary input for the production processes of the industry, transport and energy sectors; therefore, planning H₂ production efficiently is essential for ensuring any country's economic and political sovereignty. Despite industrial difficulties, France has a number of notable strengths such as the development of industrial energy champions across the entire value chain and several projects to build H₂ infrastructure (for example H₂ ecosystems,<fnref n="11" /> large industrial projects<fnref n="12" /> and giga-factories<fnref n="13" />). The coming decade will be critical for increasing market shares on a regional scale. Within the European Hydrogen Backbone initiative<fnref n="14" /> and more locally, H₂ eco-system projects could support large-scale projects. To stay in the race, public policies could more

<footnote n="10">¹⁰ https://www.maersk.com/news/articles/2022/11/03/maersk-and-the-spanish-government-to-explore-large-scale-green-fuels-production.</footnote>

<footnote n="11">¹¹ https://s3.production.france-hydrogene.org/uploads/sites/4/2021/11/AFHYPAC_H2regions_2017-06-09_web.pdf.</footnote>

<footnote n="12">¹² https://france.arcelormittal.com/actualites/vers-une-production-d-acier-sans-co2-en-france.</footnote>

<footnote n="13">¹³ https://www.lesechos.fr/industrie-services/energie-environnement/la-france-va-cofinancer-la-construction-de-dix-usines-misant-sur-lhydrogene-1852460.</footnote>

<footnote n="14">¹⁴ https://ehb.eu/.</footnote>
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actively support the development of H₂ distribution infrastructure and equipment for consumers. Upstream, the dynamics for creating electrolyser infrastructure is already effective and could enable national industries to become leaders in the low-carbon H₂ market.

France's nuclear programme is a relevant example of successful industrial planning. Two key factors of extra-economic criteria and very strong government support have primarily contributed. Economic relevance was not a decision structuring factor since the economic model of the nuclear power sector has not proven to be viable at the development stage (Hadjilambrinos, 2000). Non-economic criteria prevailed when launching the nuclear programme, which included energy independence, the desire to restore France's excellence, military interests and the need for an essential energy source to support the nation's ambitious industrial programme (Mendl, 1965). In the absence of market signals, industrial planning was used instead, by means of significant State financial aid and investments for technology development (Jasper, 1992). The State has also used a proactive policy on the labour market. The main energy company (EDF) being nationalised, labour unions were considering the nuclear power development as being working class oriented, which thus facilitated the achievement of the sector's manpower objectives (Fagnani & Moatti, 1984).

**6. Conclusions**

This study provides a methodological framework for addressing the issue of hydrogen production in France and formulates a policy for replacing grey H₂ that is produced with imported natural gas, with domestically produced low-carbon H₂. We use IO models to estimate the human and material resources needed to develop a domestic e-H₂ sector that could supply two key sectors (ammonia production and oil refining). The results show that the H₂ sector's construction phase generates significant economic activity (€1.3 Bn of GDP during the construction period) and creates jobs (more than 3600), and the operation phase generates almost €1 Bn of GDP per year.

The use of an IO model makes it possible to estimate upstream and downstream industry connections using forward and backward linkage indices. Numerically, H₂ production leads to very high BLI while stimulating the electricity sector but moderate FLI as H₂ is examined as input in the production processes of only two sectors. However, these two sectors produce essential goods for the economy such as gasoline and fertilisers; therefore, H₂ production is indirectly essential to economic activity as any decrease in H₂ output could significantly impact all other sectors.

Two main sector vulnerabilities are identified, one upstream, which is related to the availability of human capital needed for the construction phase and for H₂ infrastructure maintenance, and one downstream, which is related to H₂ production's dependence on electricity supply. Both issues require a high level of industrial planning and anticipation. First, measures must be implemented to make the industry more attractive for workers and to dispel the negative image of industrial jobs, which is partially attributable to the negative relationship between effort and reward, particularly in machine tool manufacturing, energy, chemicals and construction sectors. The downstream dependence issue can be addressed by increasing the electricity sector's capacity to respond to the requirements of e-H₂ production. This cross-sector dependence is also highlighted by the French system operator (RTE 1 2022), which plans new nuclear reactors and large capacities of renewables to meet the demand of e-H₂ (scenario N03 H₂ high). If no plan is adopted to considerably extend the electricity sector, our prospective scenario for e-H₂ cannot be a possible solution.
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Methodologically, IO models have intrinsic limitations due to the use of linear calculations, a static representation of the impacts of the transition and exogenous innovation. However, the IO model provides useful insights into the role of energy in the economy, and the cost-benefit structure associated with producing domestic resources (electricity) and vectors ($H_2$) that will reduce the country's energy dependence. State intervention is crucial for industrial development at this stage (Van der Spek et al., 2022) and the French government has elaborated and partially implemented massive support initiatives (€7 Bn) and set ambitious milestones (10% clean $H_2$ for 2023 and 20-40% for 2028; NHS National Hydrogen Strategy, 2020).

This study opens avenues for future research in the field of industrial planning. The necessary infrastructure for developing a national industry will be made up of local clusters of $H_2$ production, transport and distribution for other use such as heavy transport. At the industry level, firms in the fields of steel production (700 $H_2$/year) and small-scale industrial activities (hexamethylenediamine, hydrogen peroxide, glass, metal surface treatment; 65 kt of $H_2$) can transition to low-carbon $H_2$. Future research could focus on extending the model to these industries to precisely describe the production process (storage and distribution) along with the integration of new modes of production ($H_2$ from biomass). At a more de-centralised level, further research is necessary to understand the factors that stimulate synergies between sectors. Studies on $H_2$ deployment based on concepts of innovation systems (Piirainen et al., 2017) and clusters (Madsen & Andersen, 2010) can complete the macro-economic vision of the $H_2$ sector. Such an integrated approach could better illustrate the technological and spatial dynamics of $H_2$ supply and demand, the function of the existing infrastructure, the evolution of the industry and of the level of regional specialisation.

## Acknowledgment

The authors gratefully acknowledge the Editor and the anonymous referees for their very constructive comments that improved the initial version.

## References

* Alkemade, F., & Suurs, R. A. (2012). Patterns of expectations for emerging sustainable technologies. *Technological Forecasting and Social Change*, 79, 448-456.

* Alcántara, V., & Padilla, E. (2009). Input-output subsystems and pollution: An application to the service sector and CO2 emissions in Spain. *Ecological Economics*, 68(3), 905-914. https://doi.org/10.1016/j.ecolecon.2008.07.010

* Bae, J. H., & Cho, G. L. (2010). A dynamic general equilibrium analysis on fostering a hydrogen economy in Korea. *Energy Economics*, 32(1), S57-S66. https://doi.org/10.1016/j.eneco.2009.03.010

* Ball, M., & Weeda, M. (2015). The hydrogen economy – Vision or reality? *International Journal of Hydrogen Energy*, 40(25), 7903-7919. https://doi.org/10.1016/j.ijhydene.2015.04.032

* Blackburn, J. C., & Moreno-Cruz, J. (2021). Energy efficiency in general equilibrium with input-output linkages. *Journal of Environmental Economics and Management*, 110, Article 102524. https://doi.org/10.1016/j.jeem.2021.102524

* Brandon, N. P., & Kurban, Z. (2017). Clean energy and the hydrogen economy. *Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences*, 375(2098), 20160400. https://doi.org/10.1098/rsta.2016.0400

* Bresnahan, T. (2010). General purpose technologies. In Bronwyn H. Hall, & Nathan Rosenberg (Eds.). *Handbook of the economics of innovation* (pp. 761-791). (edition 1, 2).

* Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies ‘engines of growth’? *Journal of Econometrics*, 65(1), 83-108. https://doi.org/10.1016/0304-4076(94)01598-T (ISSN 0304-4076).
R. Guionie, L. Lemiale and R. Loisel

Journal of Policy Modeling xxx (xxxx) xxx-xxx

Cellura, M., Gangi, A. D., Longo, S., & Orioli, A. (2013). An Italian input-output model for the assessment of energy and environmental benefits arising from retrofit actions of buildings. *Energy and Buildings*, *62*, 97-106. https://doi.org/10.1016/j.enbuild.2013.02.056

Chun, D., Chungwon, W., Seo, H., Chung, Y., Hong, S., & Kim, J. (2014). The role of hydrogen energy development in the Korean economy: An input-output analysis. *International Journal of Hydrogen Energy*, *39*, 7627-7633. https://doi.org/10.1016/j.ijhydene.2014.03.058

DARES. (2020). *Tensions on labor market in 2019*. in French.

DGE. (2024). *Where stands the reindustrialisation of France? Direction Generale des Entreprises*. Technical note. In French.

Espegren, K., Damman, S., Pisciella, P., Graabak, I., & Tomasgard, A. (2021). The role of hydrogen in the transition from a petroleum economy to a low-carbon society. *International Journal of Hydrogen Energy*, *46*(45), 23125-23138. https://doi.org/10.1016/j.ijhydene.2021.04.143

European Commission. (2023). *Renewable hydrogen production: New rules formally adopted*.

EC European Commission. (2020). *A hydrogen strategy for a climate-neutral Europe*.

Eurostat. (2008). *Nace Rev. 2, Statistical classification of economic activities in the European Community*.

Fagnani, J., & Moatti, J.-P. (1984). The politics of French nuclear development. *Journal of Policy Analysis and Management*, *3*, 264-275. https://doi.org/10.1002/pam.4050030208

France Stratégie. (2020). *Industrial policies in France. Developments and international comparisons*. In French.

Ghosh, A. (1958). *Input, output approach to an allocation system*. *Economica*, *25*(97), 58-64.

Goldfarb, A., Taska, B., & Teodoridis, F. (2023). Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings. *Research Policy*, *52*(1), Article 104653. https://doi.org/10.1016/j.respol.2022.104653

Gupta, R., Guibentif, T., Friedk, M., Parra, D., & Patel, M. (2023). Macroeconomic analysis of a new green hydrogen industry using Input-Output analysis: The case of Switzerland. *Energy Policy*, *183*, Article 113768. https://doi.org/10.1016/j.enpol.2023.113768

Hadjilambrinos, C. (2000). Understanding technology choice in electricity industries: A comparative study of France and Denmark. *Energy Policy*, *28*(15), 1111-1126. https://doi.org/10.1016/S0301-4215(00)00067-7

Han, S. Y., Yoo, S. H., & Kwak, S. J. (2004). The role of the four electric power sectors in the Korean national economy: An input-output analysis. *Energy Policy*, *32*(13), 1531-1543. https://doi.org/10.1016/S0301-4215(03)00125-3

Heady, E. O., & Schnittker, J. A. (1957). Application of input-output models to agriculture. *Journal of Farm Economics*, *39*(3), 745-758. https://doi.org/10.2307/1234429

Heringa, P. W. W., Heide, C. M., & Heijman, W. J. M. (2013). The economic impact of multifunctional agriculture in Dutch regions: An input-output model. *NJAS - Wageningen Journal of Life Sciences*, *64-65*, 59-66. https://doi.org/10.1016/j.njas.2013.03.002

Hertwich, E., & Wood, R. (2018). The growing importance of scope 3 greenhouse gas emissions from industry. *Environmental Research Letters*. https://doi.org/10.1088/1748-9326/aae19a

IEA. (2023). *Global hydrogen review 2023*. Paris.

IEA. (2021). *Net zero by 2050*. Paris.

IEA. (2019). *The future of hydrogen*. Paris.

IPCC. (2022). *Climate change 2022: Impacts, adaptation, and vulnerability*.

IRENA (2020). *Green hydrogen cost reduction: Scaling up electrolysers to meet the 1.5C climate goal*. Abu Dhabi: International Renewable Energy Agency.

Ishaq, H., Dincer, I., & Crawford, C. (2022). A review on hydrogen production and utilization: Challenges and opportunities. *International Journal of Hydrogen Energy*, *47*(62), 26238-26264. https://doi.org/10.1016/j.ijhydene.2021.11.149

Jasper, J. M. (1992). Gods, Titans and mortals: Patterns of state involvement in nuclear development. *Energy Policy*, *20*(7), 653-659. https://doi.org/10.1016/0301-4215(92)90007-0

Johnson, T. G., & Kulshreshtha, S. N. (1982). Exogenizing agriculture in an input-output model to estimate relative impacts of different farm types. *Western Journal of Agricultural Economics*, *7*(2), 187-198. https://www.jstor.org/stable/40987575 (JSTOR).

Jokisch, S., & Mennel, T. (2009). Hydrogen in passenger transport: A macroeconomic analysis. *Transport Reviews*, *29*(4), 415-438. https://doi.org/10.1080/01441640802358059

Lee, B., Cho, H. S., Kim, H., Lim, D., Cho, W., Kim, C. H., & Lim, H. (2021). Integrative techno-economic and environmental assessment for green H2 production by alkaline water electrolysis based on experimental data. *Journal of Environmental Chemical Engineering*, *9*(6), Article 106349. https://doi.org/10.1016/j.jece.2021.106349
R. Guionie, L. Lemiale and R. Loisel

Journal of Policy Modeling xxx (xxxx) xxx-xxx

Lee, D. H. (2012). Toward the clean production of hydrogen: Competition among renewable energy sources and nuclear power. *International Journal of Hydrogen Energy*, 37(20), 15726-15735. https://doi.org/10.1016/j.ijhydene.2012.04.124

Lee, D. H. (2014). Development and environmental impact of hydrogen supply chain in Japan: Assessment by the CGE-LCA method in Japan with a discussion of the importance of biohydrogen. *International Journal of Hydrogen Energy*, 39(33), 19294-19310. https://doi.org/10.1016/j.ijhydene.2014.05.142

Lee, D. H., & Chiu, L. H. (2012). Development of a biohydrogen economy in the United States, China, Japan, and India: With discussion of a chicken-and-egg debate. *International Journal of Hydrogen Energy*, 37(20), 15736-15745. https://doi.org/10.1016/j.ijhydene.2012.02.152

Lee, D. H., Hsu, S. S., Tso, C. H., Su, A., & Lee, D. J. (2009). An economy-wide analysis of hydrogen economy in Taiwan. *Renewable Energy*, 34(8), 1947-1954. https://doi.org/10.1016/j.renene.2008.12.006

Lee, D. H., Lee, D. J., & Chiu, L. H. (2011). Biohydrogen development in United States and in China: An input-output model study. *International Journal of Hydrogen Energy*, 36(21), 14238-14244. https://doi.org/10.1016/j.ijhydene.2011.05.084

Leontief, W. (1936). Quantitative input-output relations in the economic system of the United States. *The Review of Economics and Statistics*, 18(3), 105-125.

Leontief, W. (1941). *The structure of American economy 1919-1939*. Cambridge: Harvard University.

Liang, S., Wang, C., & Zhang, T. (2010). An improved input-output model for energy analysis: A case study of Suzhou. *Ecological Economics*, 69(9), 1805-1813. https://doi.org/10.1016/j.ecolecon.2010.04.019

Lipsey, R., Carlaw, K., & Bekar, C. (2006). *Economic transformations: General purpose technologies and long-term economic growth*. Oxford University Press.

Liu, L., Huang, G., Baetz, B., & Zhang, K. (2018). Environmentally-extended input-output simulation for analyzing production-based and consumption-based industrial greenhouse gas mitigation policies. *Applied Energy*, 232, 69-78. https://doi.org/10.1016/j.apenergy.2018.09.192

Lixon, B., Thomassin, P. J., & Hamaide, B. (2008). Industrial output restriction and the Kyoto protocol: An input-output approach with application to Canada. *Ecology Economics*, 68(1-2), 249-258. https://doi.org/10.1016/j.ecolecon.2008.03.001

Llop, M. (2013). Water reallocation in the input-output model. *Ecology Economics*, 86, 21-27. https://doi.org/10.1016/j.ecolecon.2012.10.020

Llop, M. (2020). Energy import costs in a flexible input-output price model. *Resource and Energy Economics*, 59, 101-130. https://doi.org/10.1016/j.reseneeco.2019.101130

Loizou, E., Karelakis, C., Galanopoulos, K., & Mattas, K. (2019). The role of agriculture as a development tool for a regional economy. *Agrotikos Systems*, 173, 482-490. https://doi.org/10.1016/j.agsy.2019.04.002

Madsen, A. N., & Andersen, P. D. (2010). Innovative regions and industrial clusters in hydrogen and fuel cell technology. *Energy Policy*, 38, 5372-5381. https://doi.org/10.1016/j.enpol.2009.03.040

Maggio, G., Nicita, A., & Squadrito, G. (2019). How the hydrogen production from RES could change energy and fuel markets: A review of recent literature. *International Journal of Hydrogen Energy*, 44(23), 11371-11384. https://doi.org/10.1016/j.ijhydene.2019.03.121

Mendl, W. (1965). The background of French nuclear policy. *International Affairs (Royal Institute of International Affairs 1944-)*, 41(1), 22-36. https://doi.org/10.2307/2611946

Miller, R. E., & Blair, P. D. (2022). *Input-output analysis: Foundations and extensions* (third edition). Cambridge: Cambridge University Press.

Ministry for industry. (2023). Accelerating the deployment of hydrogen, the keystone for decarbonation of industry. In French.

Ministry of Ecological Transition. (2020). *National low-carbon strategy*. In French.

NHS National Hydrogen Strategy. (2020). *French Ministry of ecology, transition and solidarity*. In French.

Noce, A. A. (2015). *Long-term economic growth: Modeling the race between energy and technology and the stratospheric effects of hydrogen as a general purpose energy source (PhD thesis)*. Concordia University.

Nikolaidis, P., & Poullikkas, A. (2017). A comparative overview of hydrogen production processes. *Renewable and Sustainable Energy Reviews*, 67, 597-611. https://doi.org/10.1016/j.rser.2016.09.044

O'Connor (2021). *The potential significance of the Australian hydrogen industry: An input-output analysis approach (Thesis of master)*.

Petralia, S. (2020). Mapping general purpose technologies with patent data. *Research Policy*, 49(7), Article 104013. https://doi.org/10.1016/j.respol.2020.104013
R. Guionie, L. Lemiale and R. Loisel

Journal of Policy Modeling xxx (xxxx) xxx-xxx

Piirainen, K. A., Tanner, A. N., & Alkærsig, L. (2017). Regional foresight and dynamics of smart specialization: A typology of regional diversification patterns. *Technological Forecasting and Social Change, 115*, 289–300. https://doi.org/10.1016/j.techfore.2016.06.027

RTE. (2023). *Forecast balance 2023–2025*. In French.

RTE [1]. (2022). *Report on future perspectives in 2050, Chapter 4. Electricity production*. In French.

RTE [2]. (2022). *Report on future perspectives in 2050, Chapter 9. The role of hydrogen and cross-sector coupling*. In French.

RTE [3]. (2020). *Transition to a low-carbon hydrogen*. In French.

Saba, S. M., Müller, M., Robinius, M., & Stolten, D. (2018). The investment costs of electrolysis – A comparison of cost studies from the past 30 years. *International Journal of Hydrogen Energy, 43*(3), 1209–1223. https://doi.org/10.1016/j.ijhydene.2017.11.115

Silva, C. M., Ferreira, A. F., & Bento, J. P. C. (2014). Impact of hydrogen in the road transport sector for Portugal 2010–2050. *Energy Procedia, 58*, 207–214. https://doi.org/10.1016/j.egypro.2014.10.430

Staffell, I., Scamman, D., Abad, A., Balcombe, P., Dodds, P., Ekins, P., Shah, N., & Ward, K. (2018). The role of hydrogen and fuel cells in the global energy system. *Energy & Environmental Science, 12*, 463–491. https://pubs.rsc.org/en/content/articelanding/2019/EE/C8EE01157E).

Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. *Research Policy, 47*(8), 1367–1387. https://doi.org/10.1016/j.respol.2017.01.015

Van der Spek, M., Banet, C., Bauer, C., Gabrielli, P., Goldthorpe, W., et al. (2022). Perspective on the hydrogen economy as a pathway to reach net-zero CO 2 emissions in Europe. *Energy & Environmental Science, 15*, 1034–1077. https://doi.org/10.1039/D1EE02118D

Velázquez, E. (2006). An input-output model of water consumption: Analysing intersectoral water relationships in Andalusia. *Ecology Economics, 56*(2), 226–240. https://doi.org/10.1016/j.ecolecon.2004.09.026

Waßenhoven, A., Rennings, M., Laibach, N., & Bröring, S. (2023). What constitutes a “key enabling technology” for transition processes: Insights from the bioeconomy’s technological landscape. *Technological Forecasting and Social Change, 197*, Article 122873.

Wei, R., Zhang, W., & Peng, S. (2022). Energy and greenhouse gas footprints of China households during 1995–2019: A global perspective. *Energy Policy, 164*, Article 112939. https://doi.org/10.1016/j.enpol.2022.112939

Wietschel, M., & Seydel, P. (2007). Economic impacts of hydrogen as an energy carrier in European countries. *International Journal of Hydrogen Energy, 32*(15), 3201–3211. https://doi.org/10.1016/j.ijhydene.2007.02.041

Wu, L., Li, J., & Zhang, Z. X. (2013). Inflationary effect of oil-price shocks in an imperfect market: A partial transmission input-output analysis. *Journal of Policy Modeling, 35*(2), 354–369. https://doi.org/10.1016/j.jpolmod.2012.01.008

Zhang, W., Fan, X., Liu, Y., Wang, S., & Chen, B. (2020). Spillover risk analysis of virtual water trade based on multi-regional input-output model – A case study. *Journal of Environmental Management, 275*, Article 111242. https://doi.org/10.1016/j.jenvman.2020.111242

Zhang, K., Xue, M. M., Feng, K., & Liang, Q. M. (2019). The economic effects of carbon tax on China’s provinces. *Journal of Policy Modeling, 41*(4), 784–802. https://doi.org/10.1016/j.jpolmod.2019.02.014