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Economie Publique

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Schéma du projet

Schéma et descriptif des tâches du projet STIMUL
scheme

Schéma STIMUL

Task 1: Design of baseline, scenario and consistency check of various modelling tools

Task 2: Supply response to the policy scenarios

Task 3: Direct impacts of policies on land use and production in Europe

Task 4: Global price vector, feedbacks, net displacement factor and indirect land use changes

Task 5: Indicators and impact assessment

Task 6: Linking with and supporting similar initiatives at the international level

Task 1: Design of baseline, scenario and consistency check of various modelling tools

A first task is to collectively define a baseline scenario (i.e. a mid-term projection of the main
exogenous drivers, such as climate, population, productivity) and two "shock" scenarios (i.e. policy
actions that are treated as strong deviations from the baseline). The baseline and shock scenarios will
make it possible to gauge the compatibility and complementarity of the different models developed
by BASC under a range of conditions. Putting together a baseline scenario is complex since it is
necessary to ensure consistency between a variety of parameters (e.g. resource growth, productivity
and GDP). As decided in preliminary workshops, the baseline scenario will be based on a "business-
as-usual" Shared Socio-economic Pathway (SSP) developed in the context of the IPCC (most likely the
SSP2 scenario). For climate change, we will use CMIP5 climate projections that are consistent with
the SSP, most likely based on the RCP 4.5 emissions pathway. This SSP2 scenario will be modified
with additional scenarios exploring two different idealized "disruptive policy shocks". The first shock
is a large-scale reduction of nitrogen fertilizers corresponding to European policies to reduce
nitrogen pollution and potential increases in N fertilizer prices. The second shock is a large-scale
expansion of bioenergy in Europe corresponding to policies that could be put in place to meet strong
climate change mitigation targets set in the Paris Agreement on climate. These "shock" scenarios
should be viewed as idealized extreme scenarios used to test the coupling between models.

1. Scenario 1: Baseline scenario + 50% reduction of nitrogen fertilizers in Europe

2. Scenario 2: Baseline scenario + large expansion of bioenergy in Europe. The extent and type of
bioenergy deployment will be determined early in the project.

A list of input and output variables for each model, with their spatial dimension, units and formats
has been put together (see the appendix). It shows that there are a number of obstacles and gaps
that need to be overcome to develop a fully integrated suite of models. Issues include differences in
spatial resolution and extent, as well as differences in temporal resolution. Making the modelling
tools more consistent and building coherent "handshakes" between models requires both pragmatic
approaches and methodological innovation. Software for disaggregation and aggregation (e.g., using
enthropy methods) will be necessary to reconcile the framework used for trade, fiscal and
environmental policies (MIRAGE model environment) with the representation of agricultural policy
instruments (AROPAj and NLU) as far as spatial scale and entry inputs are concerned. The articulation
of biophysical models (ORCHIDEE) with these models raises less technical problems, but experience
in using ORCHIDEE and AROPAj together shows that there are several bottlenecks that need to be
resolved. One is the articulation between different spatial and temporal scales. A second is the
consistency with the animal and crop part of the models since preparatory work indicated a lack of
consistency between modelled feedstuff production and consumption in the livestock sector across
models, as well as lack of consistency with soil nutriments exports and manure/dejections between
the crop and livestock sector.

Task 2: Supply response to the policy scenarios

In order to assess land use changes induced by the above defined policy scenarios, it is necessary to
model the direct impact of the scenarios on agricultural yields and outputs, accounting for the
biophysical drivers included in the baseline scenario, in particular with respect to climate change.
While the supply response is not only driven by technical determinants (producers adjust the use of
their variable inputs and the expected level of output to input and output prices), modeling the
technology and in particular the change in response to the constraints imposed under the scenario is
a crucial step. Hence, Task 2 includes the identification of the different crop and livestock production
systems that would make it possible to cope best with the constraints imposed on EU agriculture. For
this purpose, STIMUL will rely on the large set of knowledge developed by the "Agronomie Globale"
team (large-scale agronomic databases, statistical models derived from meta-analyses, and
simulations regarding the performances of crop systems at different scales). It is noteworthy that, in
the current consortium, there are issues whose coverage is insufficient, in particular in the livestock
sector and that some resources will need to be devoted to further analysis. A part of the solution
could be the development of simulations using crop models (STICS, ORCHIDEE-CROP) to assess the
potential production response to input changes. This task builds on previous work of BASC to specify
the feedbacks between land use change and regional climate, and on the ongoing work on climate
change impacts on biodiversity (see above).

Task 3: Direct impacts of policies on land use and production in Europe

This task includes the modeling of land allocation in Europe among its different uses that results from
the baseline and shock scenarios. Land use changes will be based on the technical relations modeled
in Task 2, the changing price environment, and the different drivers embedded in the baseline and
shock scenarios. Agricultural product supply that is driven by profitability and relative prices will play
a key role in these scenarios. The relative prices result from the matching of supply and demand
responses in Europe to the exogenous shocks introduced in the "shock" scenarios. This matching
results from changes in consumption and production that balances supply and demand at the
European level, i.e. with exogenous world prices.

The AROPAj model provides results in agricultural land allocation among series of crops and fodders
or grasslands associated to the different scenarios. Scenarios include EU Common Agricultural Policy
(CAP) and environmental policies impacting the agricultural sector, such as policies promoting
bioenergy. In the latter case, perennial bioenergy crops may be introduced in the model. Production
changes result from land allocation (extensive margin effect) and from change in marginal
productivity of the land in case of price change. Livestock may be directly affected by meat and milk
prices as well as by change of marketed feed price and by change of implicit value of on-farm sourced
feed.

Econometric models developed within UMR Economie Publique will be used in this task. These
models provide results of the impacts of different policy and climate change scenarios on land use
(Chakir and Parent, 2009; Chakir and Le Gallo, 2013). Those models were developed at the French
scale and are now extended to the EU scale within the TRUSTEE-ERANET project in collaboration with
researchers from CESAER-INRA-DIJON. The econometric models include land rents and pedoclimatic
variables as input and provide the distribution of land use for agriculture/forest/urban under
different scenarios: Climate Change, public policy, price shocks.

Task 4: Global price vector, feedbacks, net displacement factor and indirect land use changes

The modeling of global economic effects makes it possible to take into account changes in world
prices when developing scenarios for Europe. The distortion of agricultural, food and energy
component of world prices results in a feedback on both supply and demand. At the world level, this
requires the simulation of a new equilibrium characterized by prices, trade, production, consumption
and therefore land use. The indirect consequences on land use (iLUC for indirect Land Use Changes)
will therefore be added (or subtracted) to the direct effects obtained under T3. This step requires a
comprehensive representation of the new equilibrium, and hence the calculation of changes in
demand, trade and production. The combination of MIRAGE and NLU would therefore make it
possible to add the "indirect" dimension through global effects, with consequences in terms of land
use changes. It will make it possible to obtain a spatial representation of the changes in land use both
between agriculture, forestry and other uses as well as between the different agricultural uses and
land intensification. To represent land-changes mechanisms as explicitly as possible, indirect
consequences will also be decomposed among production changes (through international trade,
changes in intermediary and final demand) and yield changes (land vs non-land inputs substitution).

Task 5: Indicators and impact assessment

The consequences of dLUC and iLUC on European land use that results from the baseline and shock
scenarios will be assessed using a set of biophysical, economic and environmental indicators. This will
be combined with the impacts of climate change based on one or more coherent sets of climate
projections (see above).

On the economic side, indicators will include use of agricultural inputs (e.g., fertilizers and pesticides),
agricultural outputs, trade and global welfare (equivalent variation/consumer surplus).
Changes in greenhouse gases will be matched to LUC at a disaggregated level (based on the NLU
disaggregation). Changes in water pollution will be modelled based on the input use based on the
"water" module of the AROPAj model.

The ORCHIDEE model will be use to evaluate impacts of land use and climate change on carbon and
water cycles at national and European scales, potential for carbon storage, as well as impacts on
forest productivity, crop and pasture productivity. Biodiversity and ecosystem impacts of land use
and climate change will be examined at several levels including the level of habitats (≈ land cover
change), plant functional types (from ORCHIDEE) and species diversity including birds, local scale
species richness, species turnover, biodiversity intactness (difference between modified and
"natural" system) and large scale species richness.

An additional effort to assess the impact of the baseline and shock scenario on local pollution will be
carried out at the national scale (France) using land use change projections from spatial
econometrics (Ay et al. 2014). This will provide a much more rapid means of interaction between
land use change and impacts models compared to using the full chain of more mechanistic economic
models outlined in Tasks 1-4. This will play a key role in identifying the difficulties to overcome, as
well as possibilities in terms of model interactions. It will also provide an evaluation of these
couplings at fine spatial resolutions and make it possible to use detailed data for France.

Task 6: Linking with and supporting similar initiatives at the international level

Participants in this project are involved in coordination and in contributions to several international
programs that include development and use of similar types of integrated modelling. This includes
coordination and participation in activities within in Future Earth (e.g., bioDISCOVERY and iLEAPs
projects, Future Earth modelling "clusters"); initiatives to develop ties between the climate and
biodiversity scenarios and modelling communities supported by the Convention on Biological
Diversity (CBD), IPBES (Intergovernmental Platform on Biodiversity and Ecosystem Services) and
UNESCO; and activities to support IPCC assessments including SSP development and use for impact
studies as well as collaboration with AgMIP (Agricultural Model Intercomparison Project) and EMF
(Energy Modelling Forum - Stanford University).

References:

OECD. (2016), Alternative Futures for Global Food and Agriculture, OECD Publishing, Paris.
DOI: http://dx.doi.org/10.1787/9789264247826-en

Al-Riffai, P., Dimaranan, B. and Laborde, D. (2010). Global Trade an Environmental Impact Study of
the EU Biofuels Mandate. , International Food Policy Research Institute (IFPRI).

Alkemade, R., Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M. and Brink, B. ten (2009).
GLOBIO3: A framework to investigate options for reducing global terrestrial biodiversity loss.
Ecosystems 12: 374–390.

Ay, J.-S., R. Chakir, L. Doyen, F. Jiguet and P. Leadley (2014). Integrated models, scenarios and
dynamics of climate, land use and common birds. Climatic Change 126.1-2 , pp. 13–30.

Ay, J.-S.; Chakir, R. & Le Gallo, J. (2015), 'Aggregated versus individual land-use models: Modeling
spatial autocorrelation to increase predictive accuracy', revised and resubmitted to Environmental
Modelling and Assessment.

Ben Fradj, N., Jayet, P. A., & Aghajanzadeh-Darzi, P. (2016). Competition between food, feed, and
(bio)fuel: a supply-side model based assessment at the European scale. Land Use Policy, 52, 195-205.

Brunelle, T., Dumas, P., Souty, F., Dorin, B. and Nadaud, F. (2015), Evaluating the impact of rising
fertilizer prices on crop yields. Agricultural Economics. doi : 10.1111/agec.12161.

Brunelle, T., Dumas, P., and Souty, F. (2014). The impact of globalization on food and agriculture : The
case of the diet convergence. The Journal of Environment & Development.

Bourgeois, C., Habets, F., Jayet, P.-A., & Viennot, P. (2016). Estimating the marginal social value of
agriculturally-driven nitrate concentrations in an aquifer: a combined theoretical-applied approach.
Water Economics and Policy. accepted

Bourgeois, C., Ben Fradj, N., & Jayet, P.-A. (2014). How cost-effective is a mixed policy targeting the
management of three agricultural N-pollutants? Environmental Modeling and Assessment, 19(5),
389-405.

Cantelaube, P., Jayet, P.-A., Carré, F., Zakharov, P., & Bamps, C. (2012). Geographical downscaling of
outputs provided by an economic farm model calibrated at the regional level. Land Use Policy, 29,
35-44.

Chakir, R. (2009) "Spatial downscaling of Agricultural Land Use Data : An econometric approach using
cross-entropy", Land Economics, 85(2), 238-251.

Chakir, R. and Le Gallo, J. (2013) : "Predicting land use allocation in France : a spatial panel data
analysis", Ecological Economics, vol 92, 114–125.

Chakir, R. et Vermont, B. (2013): "Analyse des changements d'allocation des sols et des émissions de
gaz à effet de serre liées au développement des biocarburants en France". Rapport d'une étude
financée par l'ADEME. Rapport final, 72 pages.

Chakir, R., De Cara, S. and Vermont, B. (2011) :"Emissions de gaz à effet de serre dues à l’agriculture
et aux usages des sols en France : une analyse spatiale", Économie et Statistique; N 444-445.

Chakir, R., Parent, O (2009) "Determinants of land use changes : a spatial multinomial probit
approach". Papers in Regional Science, 88(2).

Chakir, R. & Lungarska, A. (2016), 'Agricultural rent in land use models: Comparison of frequently
used proxies', under revision.

De Cara, S., Goussebaile, A., Grateau, R., Levert, F., Quemener, J. and Vermont, B. (2012). Revue
critique des études évaluant l’effet des changements d’affectation des sols sur les bilans
environnementaux des biocarburants. , ADEME.

De Cara, S. & Jayet, P.-A. (2011). Marginal abatement costs of greenhouse gas emissions from
European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement. Ecological
Economics, 70, 1680-1690.

Galko, E. & Jayet, P.-A. (2011). Economic and environmental effects of decoupled agricultural support
in the EU. Agricultural Economics, 42, 605-618.

Hertel, T., Steinbuks, J. and Baldos, U. (2013). Competition for land in the global bioeconomy.
Agricultural Economics 44: 129–138.

Humblot, P., Leconte-Demarsy, D., Clerino, P., Szopa, S., Castell, J.-F., & Jayet, P.-A. (2013).
Assessment of ozone impacts on farming systems: A bio-economic modeling approach applied to the
widely diverse French case. Ecological Economics, 85, 50-58

Jayet, P. A. & Petel, E (2016) Economic valuation of the nitrogen content of urban organic residue by
the agricultural sector. Ecological Economics, 120, 272-281.

Jayet, P.-A. & Petsakos, A. (2013). Evaluating the efficiency of a uniform N-input tax under different
policy scenarios at different scales. Environmental Modelling and Assessment, 18, 57-72

Laborde, D. and Valin, H. (2012). Modeling land-use changes in a global CGE: assessing the EU biofuel
mandates with the Mirage-BioF model. Climate Change Economics 3.

Lambin, E. F. and Meyfroidt, P. (2011). Inaugural article: Global land use change, economic
globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences 108:
3465–3472.

Leclère, D., Jayet, P.-A., & de Noblet Ducoudré, N. (2013). Farm-level autonomous adaptation of
European agricultural supply to climate change. Ecological Economics, 87, 1-14.

May, R. M. (2000). Species-area relations in tropical forests. Science 290: 2084–2086.

Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B. da and Kent, J. (2000). Biodiversity
hotspots for conservation priorities. Nature 403: 853–858.

Phalan, B., Onial, M., Balmford, A. and Green, R. E. (2011). Reconciling food production and
biodiversity conservation: Land sharing and land sparing compared. Science 333: 1289–1291.

Plevin, R. J., O’Hare, M., Jones, A. D., Torn, M. S. and Gibbs, H. K. (2010). Greenhouse gas emissions
from biofuels’ indirect land use change are uncertain but may be much greater than previously
estimated. Environmental Science & Technology 44: 8015–8021.

Plevin, R. J.; Beckman, J.; Golub, A. A.; Witcover, J. & O'Hare, M. (2015). Carbon accounting and
economic model uncertainty of emissions from biofuels-induced land use change Environmental
Science & Technology, 49, 2656–2664

Searchinger, T., Heimlich, R., Houghton, R. A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D.
and Yu, T.-H. (2008). Use of U.S. croplands for biofuels increases greenhouse gases through emissions
from land-use change. Science 319: 1238–1240.

Souty, F., Brunelle, T., Dumas, P., Dorin, B., Ciais, P., Crassous, R., Muller, C., and Bondeau, A. (2012).
The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic
dynamics to model competition for land-use, Geosci. Model Dev

Sutton, M., Howard, C. and Erisman, J. (eds) (2011a). The European Nitrogen Assessment: Sources,
Effects and Policy Perspectives. Cambridge University Press. See also Sutton, M. A., Oenema, O.,

Erisman, J. W., Leip, A., Grinsven, H. van and Winiwarter, W. (2011b). Too much of a good thing.
Nature 472: 159–161.