A. Relevance and sustainability context
A.1 Policy relevance
The Biodiversity Footprint for Natural Ecosystem Extent captures the extent to which global consumption patterns drive changes in natural ecosystems by linking biodiversity-relevant land-use change to consumption sectors. In doing so, it connects economic development with ecosystem conservation and provides a means of assessing the biodiversity impacts of consumption. The indicator is directly relevant to the German Biodiversity Strategy 2030, published by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV), particularly Target 21.1, which aims to reduce the negative impacts of globally organized economic activity and international supply chains on biodiversity. As progress towards this target is currently considered not measurable ("derzeit nicht messbar"), the biodiversity footprint has the potential to fill an important monitoring gap by providing a quantitative and policy-relevant assessment of biodiversity impacts associated with German consumption.
A.2 Sustainability and / or comparative evaluation options
The biodiversity footprint can be evaluated using different approaches depending on the intended application. As an intensity indicator, expressed as ecosystem-area loss per unit of gross domestic product (GDP), it enables comparisons across commodities, sectors, or economic activities by accounting for differences in economic output. Alternatively, the indicator can be presented as a cumulative footprint, representing the total area of natural ecosystems converted over a defined period, thereby allowing the assessment of temporal trends and long-term developments. Both approaches can be derived from the same underlying datasets, ensuring methodological consistency and facilitating comparisons across spatial and temporal scales.
B. Data availability
B.1 Stage of development
The indicator is at an advanced stage of development. All required data sources are available, and the computational framework has been established. The code for calculating the global indicator as well as commodity- and country-specific sub-indicators is fully implemented and operational, enabling consistent and reproducible assessments across different spatial and thematic scales.
B.2 Data sources
- LandSHIFT: pixel-level data from on agricultural area and country-level commodity shares
- GlobES: pixel-level data from on shares of ecosystem types according to the IUCN habitat classification scheme
- GLORIA: country-level data on production shares attributable to German consumption
B.3 Coverage over time and replicability
The biodiversity footprint is calculated for the time span 1995 to 2015 at the moment but can easily be replicated for upcoming time spans as long as the data sources described above are stored in a similar fashion. Given the size of the spatial data, a High Performance Cluster is usually required for running the code.
B.4 Coverage across scales and sectors; compatibility
The indicator is designed to be applicable across all spatial scales for which the required input data are available. Its flexible framework allows for the calculation of commodity-specific and ecosystem-specific footprints, enabling more detailed analyses of biodiversity impacts. For example, the indicator can be used to estimate the biodiversity footprint associated with German consumption of a specific commodity, such as maize, or to quantify impacts on particular ecosystem types, such as tropical dry forests. This scalability and level of disaggregation make the indicator suitable for a wide range of monitoring and policy applications.
C. Considerations for operationalization
C.1 Feasibility: Fit for monitoring
It is ranked as sufficient because while all required data sources are available, the computational framework has been established and the code is operational, experiences with testing and review are currently limited as it is at the forefront of new methodological development.
C.2 Institutions
The German statistical agency (Destatis, already in charge of environmental-economic accounting) could play a role in the future, as well as so called "Ressortforschungseinrichtungen" (Thünen Institute, Umweltbundesamt, Bundesamt für Naturschutz) could also play a role in the future
- Maybe partnerships between research institutions (e.g. iDiv) and Bundesämter) could help to establish the indicator
- Institutional requirements: access to data and calculation process/code
C.3 Future prospects
Further steps are needed to test and further develop the potentials.
C.4 Costs, considerations and reproducibility
This is to be updated in the future, further steps are needed to insert valuable information.
C.5 Potential for automation
The indicator has a high potential for automation, as all required input data and the computational code are already available and accessible. Once the underlying datasets are updated, the calculation process can be repeated, enabling reproducible indicator production. The potential for automation is expected to increase further as data availability, standardization, and computational infrastructure continue to improve.
C.6 Presentation options and breakdown / sub-indicator needs
Diverse presentation options exist as the indicator can be disaggregated across different regions, commodities, ecosystems. One can display both temporal trends and spatial allocation maps and use the data for further analysis.
C.7 Potential for scenario integration
The indicator has the potential to be integrated into future scenario analyses, provided that reliable projections of ecosystem extent become available. Such applications would require research to develop methods for linking projected land-use changes and ecosystem dynamics with biodiversity footprint calculations.
C.8 Interlinkages and alignment with other monitoring activities
The biodiversity footprint complements existing biodiversity monitoring activities by addressing the gap in the assessment of the German Biodiversity Strategy 2030. It has the potential to provide a quantitative measure for Target 21.1, for which no suitable monitoring indicator currently exists. The indicator may be aligned with other national and international monitoring frameworks through its shared methodological basis and data sources. For example, it offers clear synergies with ecosystem extent indicators, including Headline Indicator 2A of the Kunming–Montreal Global Biodiversity Framework (GBF).