# Couplage de modèles dynamiques et environnementaux pour une évaluation prospective et spatialisée de l'agrivoltaïsme: apport méthodologique en ACV

Pierre Jouannais, Lia Rapella, M. Douziech, Mathilde Marchand

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Pierre Jouannais, Lia Rapella, M. Douziech, Mathilde Marchand. Couplage de modèles dynamiques et environnementaux pour une évaluation prospective et spatialisée de l'agrivoltaïsme: apport méthodologique en ACV. Congrès MCV 2025, Nov 2025, Bordeaux, France. hal-05382045

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Submitted on 25 Nov 2025

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# Couplage de modèles dynamiques et environnementaux pour une évaluation prospective et spatialisée de l'agrivoltaïsme : apport méthodologique en ACV

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Pierre Jouannais¹, Chargé de Recherche

Lia Rapella², Mélanie Douziech³, Mathilde Marchand-Lasserre⁴

¹ ITAP-ELSA Team, INRAE, Montpellier France

² Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France

³ Agroscope, Life Cycle Assessment Research Group

⁴ Centre Observation, Impacts, Energie (OIE), MINES Paris – PSL University, Paris, France, CH-8046, Zurich, Switzerland
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# Introduction
# Agrivoltaism : a polymorphic system under debate

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https://justenergy.com/blog/agrivoltaics/

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Allemagne, https://www.lightzoomlumiere.fr/article/agrivoltaisme-velux-et-baywa-r-e-innovent-en-espagne/

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Alhendin, Spain,
https://www.lightzoomlumiere.fr/article/agrivoltaisme-velux-et-baywa-r-e-innovent-en-espagne/

* Many different configurations

* Different performances

* For crops

* For PV production

* Dependent on technology, crop type, climate etc.

<table><thead><tr><th>Crop</th><th>Location</th><th>Shading rate</th><th>Yield change</th><th>References</th></tr></thead><tbody><tr><td>Lettuce</td><td>Santiago, Chile</td><td>30%</td><td>(–) 8%</td><td>[42]</td></tr><tr><td>Broccoli</td><td>Santiago, Chile</td><td>30%</td><td>(–) 29%</td><td>[42]</td></tr><tr><td>Winter Wheat</td><td>Heggelbach, Germany</td><td>35%</td><td>(–) 19% (2017)<br>(+) 3% (heat summer 2018)</td><td>[10]</td></tr><tr><td>Potato</td><td>Heggelbach, Germany</td><td>35%</td><td>(–) 18%<br>(+) 11%</td><td>[10]</td></tr><tr><td>Celery</td><td>Heggelbach, Germany</td><td>35%</td><td>(–) 19%<br>(+) 12%</td><td>[10]</td></tr><tr><td>Clover Grass</td><td>Heggelbach, Germany</td><td>35%</td><td>(–) 5%<br>(–) 8%</td><td>[10]</td></tr><tr><td>Lettuce (varieties Kiribati and Madelona)</td><td>Montpellier, France</td><td>Half density, solar tracking, controlled tracking</td><td>(–) 5% to (–) 30% with fewer losses on controlled, that is, crop friendly tracking</td><td>[46]</td></tr><tr><td>Chiltepin pepper</td><td>Tucson, Arizona, USA</td><td>70%–80%</td><td>~ (+) 150%<sup>a</sup></td><td>[40]</td></tr><tr><td>Jalapeno</td><td>Tucson, Arizona, USA</td><td>70%–80%</td><td>~ (–) 15%</td><td>[40]</td></tr><tr><td>Cherry Tomato</td><td>Tucson, Arizona, USA</td><td>70%–80%</td><td>~ (+) 90%</td><td>[40]</td></tr></tbody></table>
➤ A promising system to adapt agriculture to climate change

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(Rapella et al. 2025)

ONLY LOCAL AGRO-CLIMATIC FACTORS

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+ PV model

ORCHIDEE
LAND SURFACE MODEL

Surface Temperature

<figure><img src="image_11.png" /></figure>

* Higher NPP for AV systems than control conventional cultures in the Mediterranean area.

* Mixed results in other areas, depending on the annual meteorological conditions.
> Research question

Which environmental impacts and benefits for AV deployment in the Mediterranean basin under different socio-economic pathways and climate trajectories?
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# Methods
> Combining prospective LCA modeling with prospective climatic modeling

Local Agro-
climatic factors

(Rapella et al., 2025)

<figure><img src="image_14.png" /></figure>

ORCHIDEE
LAND SURFACE MODEL

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<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

AV structures,
SIRTA Observatory, Palaiseau

electricity

FU: 1 kWh

VS

Conventional
PV electricity
system

X

X'

wheat

wheat

Local (or global)
conventional
crop production

Y'

Parametrized LCA model for AV

(Jouannais et al., 2025)

<figure><img src="image_18.png" /></figure>

AV LCA impacts

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

Prospective
consequential
LCA databases

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

premise

(Maes et al., 2023)

Global socio-economic
factors

<figure><img src="image_23.png" /></figure>
## Scope of possibilities for the future

RCP
4.5
(+2.5-3°C in 2100)

SSP

<table><tr><td>SSP2_NPi</td></tr><tr><td>SSP2_base</td></tr><tr><td>SSP2_base</td></tr><tr><td>SSP5_NDC</td></tr><tr><td>SSP1_Base</td></tr><tr><td>SSP1_Base</td></tr></table>

IAM

<table><tr><td>Remind</td></tr><tr><td>Remind</td></tr><tr><td>Image</td></tr><tr><td>Remind</td></tr><tr><td>Image</td></tr><tr><td>Remind</td></tr></table>

Parameters for
marginal mix
identification

Slope-short
Long-area

Slope-short
Long-area

(Maes et al., 2023)

GCM-RCM

<table><tr><td>NCC-NorESM1-M-DMI-HIRHAM5</td></tr><tr><td>...</td></tr><tr><td>...</td></tr><tr><td>NCC-NorESM1-M-DMI-HIRHAM5</td></tr><tr><td>...</td></tr><tr><td>...</td></tr><tr><td>NCC-NorESM1-M-DMI-HIRHAM5</td></tr><tr><td>...</td></tr><tr><td>...</td></tr></table>

X 9

ACV foreground

<table><tr><td>Param 1</td><td>Param 2</td></tr><tr><td>2</td><td>3</td></tr><tr><td>1.5</td><td>4</td></tr><tr><td>3</td><td>1</td></tr><tr><td>2</td><td>1</td></tr><tr><td>...</td><td>...</td></tr></table>

X 20

<table><tr><td>Param 1</td><td>Param 2</td></tr><tr><td>2</td><td>3</td></tr><tr><td>1.5</td><td>4</td></tr><tr><td>3</td><td>1</td></tr><tr><td>2</td><td>1</td></tr><tr><td>...</td><td>...</td></tr></table>

...

Input
parameters
to

Orchidee AV

Background
LCA db

Background
LCA db

Orchidee AV

Foreground LCA

Model: remind | Scenario: SSP2-NPi

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

Model: image | Scenario: SSP1-Base

<figure><img src="image_25.png" /></figure>
## Scope of possibilities for the future

RCP
4.5
(+2.5-3°C in 2100)

SSP

<table><tr><td>SSP2_NPi</td><td>Remind</td></tr><tr><td>SSP2_base</td><td>Remind</td></tr><tr><td>SSP2_base</td><td>Image</td></tr><tr><td>SSP5_NDC</td><td>Remind</td></tr><tr><td>SSP1_Base</td><td>Image</td></tr><tr><td>SSP1_Base</td><td>Remind</td></tr></table>

IAM

Parameters for
marginal mix
identification

Slope-short
Long-area

Slope-short
Long-area

...

(Maes et al., 2023)

GCM-RCM

NCC-NorESM1-M-DMI-HIRHAM5

...

...

NCC-NorESM1-M-DMI-HIRHAM5

...

...

NCC-NorESM1-M-DMI-HIRHAM5

...

...

...

X 9

ACV foreground

Param 1
2
1.5
3
2
...

3

4

1

1

...

X 20

Param 1
2
1.5
3
2
...

3

4

1

1

...

...

...

Input
parameters
to

Orchidee AV

Background
LCA db

Background
LCA db

Orchidee AV

Foreground LCA

<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /></figure>

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

# Results
> Three distinct regions for AV performance

Interquartile range (Q1-Q3)
of relative GWP100 differences
between AV and PV in 2070

-12 consequential background databases:

- 3 SSPs

- 2 IAMs

- 2 marginal mix algorithms

-9 climate models

-Stochastic Climate and LCA
uncertainty

<figure><img src="image_29.png" /></figure>
> High sensitivity to market assumptions

**Local wheat substitution**
Compared
to sun-tracking PV

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

**Global wheat market substitution**
Compared
to sun-tracking PV

<figure><img src="image_31.png" /></figure>

SSP5-NDC REMIND

Mean relative difference of GW100 Impact per kWh

*Impact AV kWh - Impact PV kWh*

---

*Impact PV kWh*

* Global wheat substitution increases AV impacts due to higher yields and lower displacement benefits
Relative GW impact changes across years

> Main drivers of the impact evolution

* Agro-climatic factors drive the wheat impact.

* Socio-economic factors are the main drivers of AV impact.

SSP2-Base IMAGE

SSP1-Base REMIND

ecoinvent 3.10

(identical across years)

FU: Conventional wheat kg

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FU : AV kWh (reference structures)

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

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

Percent change in GW100 impact values between years

<figure><img src="image_35.png" /></figure>
<figure><img src="image_36.png" /></figure>

Conclusion
> Conclusion

* Prospective LCA combining prospective background (future economies) and foreground (future agro-climatic conditions) via model coupling.

* Extended assessment of the scope of possibilities and the associated incertitude for better decision-making.

* A complex interplay shaping AV's comparative performance in the 21st century

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Thank you for your
attention

pierre.jouannais@inrae.fr