This dataset contains aggregated, temporally and spatially explicit data compiled from the public Kohesio database (download version 26 July 2023), covering the period from 2014-2020. The project-level data is presented in an aggregate form. The dataset consists of six files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification. For each file, the following columns are included the following: Identifiers: NUTS Code: The unique identifier for the NUTS (2016) region Year: The starting year of the projects considered for aggregation. Category: Project category, determined with regard to the potential impact on land use. Variable: 1. Total eligible expenditure: The monetary amount of funding that could be granted. The total eligible expenditure is usually larger than the realized policy payments. It can be interpreted as an upper bound. All values are expressed in Euro at current prices. The temporal dimension is yearly, ranging from 2014-2020. The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.- Each project can fall into one or more categories. The projects were grouped together with regards to their potential impact on land use. Construction: transport Construction: energy Construction: building Environment, incl. N2K Brownfield conversion Natural risk prevention and management The underlying project-level data on EU regional funds contains variables on the project itself (title, description, location, and project end and/or start date), the project’s beneficiary (name, location), the policy area to which the policy area contributes, and monetary information (type of fund used, co-financing rate, paid sums, eligible costs, etc.). The dataset contains observations for all EU member states, and in case of Interreg projects, on some EU neighboring states. The UK is not included in the dataset. This is (i) due to the needs of the project, and (ii) due to data availability. Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.