Abstract
The synergies and trade-offs between human well-being, biodiversity, and ecosystem services are under debate for the design of more sustainable public policies. In that perspective, there is a need of quantitative methods to compare all these outcomes under alternative policy scenarios. The present paper provides scenarios at the horizon 2053 for the New Aquitaine region in France. They rely on spatio-temporal models derived from individual land-use choices under climate change. The models are estimated at the national level from 1993 to 2003 fine-scale data. We focus on farming, forestry, and urban land uses along with bird biodiversity scores and a basket of ecosystem services, namely carbon sink, recreation, and water quality. A “climate-economic adaptation” scenario shows that climate-induced land use worsens the negative effects of climate change on biodiversity and several ecosystem services in the long run as compared to a “status quo” scenario. Another scenario with an incentive policy based on a payment for pastures slightly mitigates these impacts on biodiversity and water pollution. However, this turns out to be detrimental for other ecosystem services. This confirms that the design of sustainable policies cannot be limited to uniform strategies and should account for the complexity of ecosystem management.
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Notes
Typical instances of strategic, prescriptive, and integrating plans at regional scale in France are SRADDET (Schémas régionaux d’aménagement, de développement durable et d’égalité des territoires) https://www.ecologie.gouv.fr/sraddet-schema-strategique-prescriptif-et-integrateur-regions.
Representative landowners or agents in every location q are potentially farmers, foresters, or urban landowners depending on the land use in each plot q. They are rather private agents.
In the European Common Agricultural Policy, a significant amount of agri-environmental schemes are payments depending on land use. Since 2007, the French government has taken over an acreage payment of 76 euros by ha and by year for pastures. Our stylized payment is close to a rather ambitious version of this, over doubling the payment.
For nitrate and phosphorus, we use the indicators \(I_{NO}(t,q)=\exp (-NO(t,q))\) and \(I_{PHO}(t,q)=\exp (-PHO(t,q))\).
These values are however subject to the assumption of maintaining the land-uses; they do not take into account the sequestration flows emanating from changes in land use. A proposal for the future would be to add this flow as in Bateman et al. (2013) who propose a method for estimating this flow by calculating the long-term equilibrium carbon stock.
The curse of dimensionality underlying the Dijkstra algorithm can be problematic and costly (in time and space) from the numerical viewpoint in particular with a dense transportation network.
For the numerical implementation, we here use the scientific software R and in particular the cppRouting package.
We use again the exponentiel with the indicator \(I_{REC}(t,q)=\exp (-c^*(t,q))\).
Given the uncertainties \(\epsilon (t,q)\) underlying the utility model of Eq. (2) or the probabilities of transition underpinning LUC (8), confidence intervals could be potentially derived for the different outcomes and figures. However, for the sake of clarity and simplicity, we choose to only show the expected values based on land use Eq. (8).
Thus, in more mathematical terms, the normalized values of the radar chart for the different scores \(I^{\text{ s }cenario}_k(t,q)\) for each scenario (sqs, ceas, bcs) are defined by:
$$\begin{aligned} \widetilde{I}^{\text{ s }cenario}_k= \frac{\displaystyle \sum \limits _{q} I^{\text{ s }cenario}_k(2053,q)- I_k^{\min } }{I_k^{\max }- I_k^{\min } }, \end{aligned}$$where the different scores k refer to three biodiversity indicators (aggregate bird, trophic, Shanon) and carbon sink intensity, recreational service, nitrate, and phosphorus quality respectively. Extreme values \(I_k^{\min }\) and \(I_k^{\max }\) are defined by
$$\begin{aligned} I_k^{\min }= & {} \underset{\text{ s }cen=\textsc {sqs}, \textsc {ceas}, \textsc {bcs}}{\min }\,\sum \limits _{q} I^{\text{ s }cen}_k(2053,q),\\ I_k^{\max }= & {} \underset{\text{ s }cen=\textsc {sqs}, \textsc {ceas}, \textsc {bcs}}{\max }\,\sum \limits _{q} I^{\text{ s }cen}_k(2053,q). \end{aligned}$$For the sake of clarity, the minimal values \(I_k^{\min }\) are not plotted at the centroid of the radar but arbitrarily correspond to level 5 of the radar.
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Acknowledgements
This work was made possible with the dedicated help and the production of data of several institutions and scientists including the volunteer ornithologists, the French Museum of Natural History (MNHN), the French Ministry of Agriculture (Service de la Statistique et de la Prospective), and Météo France.
Funding
This study was carried out with a grant of the New Aquitaine region through the project entitled BIRDLAND (Convention 2018 1R40115).
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Andriamanantena, N.A., Gaufreteau, C., Ay, JS. et al. Climate-dependent scenarios of land use for biodiversity and ecosystem services in the New Aquitaine region. Reg Environ Change 22, 107 (2022). https://doi.org/10.1007/s10113-022-01964-6
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DOI: https://doi.org/10.1007/s10113-022-01964-6