Input–Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAPP) Database
Creators: name: Bunsen, Jonas
name: Coroamă, Vlad
name: Finkbeiner, Matthias
Publisher: name: Technische Universität Berlin
Year: 2023
Subjects: subject: 600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
subject: environmentally extended input–output analysis
subject: water consumption
subject: water footprint
subject: sustainable production and consumption

description: In many regions of the world, water consumption exceeds the limits of sustainable water use. A commonly used method to examine the relationship between global water consumption and production is input–output analysis. However, between approximately 70% and 90% of freshwater consumption occurs in agricultural primary production, which is often represented by only a small percentage of the total number of sectors in input–output databases. As a result, water-related assessments based on input–output analysis are limited in their accuracy and substance. In addition, the assessment of the impact of water consumption is usually carried out at the national level, which can further contribute to the imprecision of the results. Therefore, the primary objective of this work was to develop an approach to better assess water use and its impacts in input–output analysis. In order to achieve this objective, a novel approach was adopted by integrating a global spatial model of agricultural primary production (MapSPAM) into an existing input–output database via prorating. In addition, the utilisation of MapSPAM allowed the calculation of water environmental extensions with unprecedented accuracy. The resulting Input–Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAPP) approach includes (1) a novel input–output database and (2) novel environmental extensions for freshwater consumption and scarcity. The IO-GHAAPP database consists of 150 categories and 164 regions, resulting in a total of 24,600 region–category combinations. Forty-two of the categories are dedicated to agricultural primary production (28%). In comparison, the source input–output data consist of 120 categories and 164 regions, resulting in a total of 19,680 region–category combinations, of which 14 are dedicated to agricultural primary production (12%). The Python code and IO-GHAAPP database are openly available via Zenodo. The IO-GHAAPP approach is presented in a comparative analysis of agricultural primary production, along with the associated water consumption and water footprint, at both the global level and for the United States and India. Both countries are among the most important in the world in terms of agricultural primary production as well as associated water consumption and water scarcity. Furthermore, the IO-GHAAPP approach is applied in a simple case study of Germany, which stands in contrast as one of the largest importers of agricultural primary production on a global scale. The results show that the IO-GHAAPP approach adds a valuable layer of information to the disaggregated input–output data, allowing crop-specific analyses to be carried out that would otherwise not be possible, e.g., for specific leguminous or beverage crops. The results are relevant to practitioners of input–output analysis who are concerned with the impacts of agricultural primary production and who need highly resolved data, as well as to policy-makers who rely on such studies. The demonstrated IO-GHAAPP approach could be extended to other externalities relevant to agricultural primary production, such as land use, soil degradation or pollution.