EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
EM: Integrated assessment of regional approaches for biodiversity offsetting in urban-rural areas – A future based case study from Germany using arable land as an example (EM-1053)
Collapse All
Expand AllEM Identity and Description
EM Identification
|
EM ID
em.detail.idHelp
?
|
EM-1053 |
|
EM Short Name
em.detail.shortNameHelp
?
|
Biodiversity offsetting, Germany |
|
EM Full Name
em.detail.fullNameHelp
?
|
Integrated assessment of regional approaches for biodiversity offsetting in urban-rural areas – A future based case study from Germany using arable land as an example |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
None |
|
EM Source Document ID
|
499 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Sponagel, C., Bendel, D., Angenendt, E., Weber, T.K.D., Gayler, S., Streck, T. and Bahrs, E. |
|
Document Year
em.detail.documentYearHelp
?
|
2022 |
|
Document Title
em.detail.sourceIdHelp
?
|
Integrated assessment of regional approaches for biodiversity offsetting in urban-rural areas–A future based case study from Germany using arable land as an example |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript |
Software and Access
| Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
Christian Sponagel |
|
Contact Address
|
Department of Farm Management (410b), Institute of Farm Management, University of Hohenheim, Schwerzstraße 44, 70593 Stuttgart, Germany |
|
Contact Email
|
Christian.Sponagel@Uni-Hohenheim.de |
EM Description
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
Human interventions, i.e. settlement and construction activities, in the agricultural landscape including farmland but also natural and semi-natural habitats are a major driver of biodiversity loss. Consequently, their impacts on nature and landscape have to be compensated by no net loss policies in many countries around the world. However, their practical implementation often poses challenges with regard to the optimal spatial coordination and assessment of measures, especially in the case of eco-accounts or other habitat banking approaches. Against this backdrop, different approaches to offset biodiversity loss at regional level are analysed with due consideration of indicators of economy, ecology, landscape aesthetics and food production. We used an interdisciplinary modelling approach based on estimates for offsetting demand until 2030. In the integrated land use model, we associated a biophysical crop growth model with an economic optimisation model. The Stuttgart Region – an area with stiff competition amongst anthropogenic land use patterns in Germany – served as the study area. Our main focus was on arable land that has a high potential for nature conservation enhancement. In this context, farmers are deemed to be a major stakeholder group. We observed differing economic and ecological outcomes for the offsetting scenarios we considered. In urban areas with high population density and low biodiversity (e.g. Stuttgart city), compensation close to the site of intervention (on-site) may be more expensive than off-site compensation. However, further added value can be generated by on-site compensation in terms of visual landscape quality enhancement and habitat connectivity, provided that the measures lend themselves to establishing connectivity. Consequently, spatially unrestricted markets for eco credits may exacerbate ecological polarisation between urban and rural areas. Therefore, we concluded that offset site selection should not be driven solely by economics, as this may not optimise overall welfare from a societal perspective, resulting in the need for legal constraints. Our results show the trade-offs between the political goals of spatial planning approaches and compensation strategies. They can, therefore, thus provide valuable information that enables political decision-makers to more clearly weigh up the effects of policy measures in this area. |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
Demonstration of trade-offs between political goals of spatial planning approaches and compensation strategies to inform decision making. |
|
Biophysical Context
|
Stuttgart region, Germany |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
1. Partly off-site with coordination |
EM Relationship to Other EMs or Applications
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Model Run Associated with a Specific EM Application |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Partly off-site with coordination |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None |
EM Modeling Approach
EM Relationship to Time
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2022-2030 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
continuous |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable |
EM Spatial Extent
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Stuttgart Region |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
1000-10,000 km^2. |
Spatial Distribution of Computations
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
map scale, for cartographic feature |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
Not reported |
EM Structure and Computation Approach
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
Model Checking Procedures Used
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Yes |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Unclear |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Unclear |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Unclear |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
Yes |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Unclear |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| None |
Centroid Lat/Long (Decimal Degree)
|
Centroid Latitude
em.detail.ddLatHelp
?
|
49.12 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
9.71 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated |
Environments and Scales Modeled
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Terrestrial Environment (sub-classes not fully specified) |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Mixed urban, rural, and natural ecosystems |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| None Available |
EnviroAtlas URL
em.detail.enviroAtlasURLHelp
?
| GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day), Enabling Conditions, Employment Rate |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
?
|
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| None |
EM Variable Names (and Units)
Predictor
em.detail.variablesPredictorHelp
?
Intermediate
|
Intermediate (Computed) Variables (and Units)
view details (3 variables)
em.detail.intermediateVariableHelp
?
|
Response
em.detail.variablesResponseHelp
?