DOI: 10.5281/zenodo.17434594
title: LAMASUS NUTS-level forest land rents

description: This dataset provides spatially explicit estimates of forest land rents, expressed as the Net Present Value (NPV) of forestry operations in EUR per hectare. The estimates are based on biophysical and economic parameters describing wood production, planting costs, and forest productivity across Europe, and are aggregated to different NUTS levels (NUTS0–NUTS3).Methodology

Forest land values were estimated using a perpetual NPV model, assuming steady-state forest management under constant economic and ecological conditions. The model incorporates revenues from the sale of sawn logs and pulpwood and subtracts regeneration and planting costs, discounted at a risk-adjusted rate following Benitez et al. (2007) and Kindermann et al. (2006).

 

To reflect typical sustainable forestry practices, it is assumed that 70% of the Mean Annual Increment (MAI) is harvested annually, with harvested areas regenerated in the same year. This approach captures the long-term equilibrium profitability of managed forests while considering regeneration and growth cycles.

 

The NPV was computed at a 0.5° grid resolution and subsequently aggregated to the NUTS3, NUTS2, NUTS1, and NUTS0 levels using area-weighted averages, excluding grid cells without long-term forest production potential.

 

All NPV values are expressed in 2020 Euro, consistent with wood price data from 2015–2022 (LUKE 2022) and purchasing power parity indices from Eurostat (2023).

 

File description

The dataset consists of four CSV files, corresponding to four territorial aggregation levels according to the 2016 NUTS classification.

 

Each file contains the following columns:



NUTS — the unique NUTS (2016) regional identifier

value — the Net Present Value (NPV) of forest land in EUR per hectare


 

Data sources



Wood prices and planting cost data were derived from LUKE (2022) for Finland and adjusted to other EU Member States using purchasing power parity (PPP) indices from EUROSTAT.

Forest productivity data (MAI) were sourced from pan-European forest inventory datasets, following Kindermann et al. (2006).

Population density and forest deficit indices were used to regionalize wood price estimates, accounting for differences in accessibility and market demand (CIESIN, 2005).


 

Key assumptions



Infinite rotation period under constant prices, costs, and productivity.

70% of MAI harvested annually; harvested area regenerated in the same year.

Discount rate adjusted by country-specific risk and purchasing power parity.

Exclusion of non-managed forest and alpine/no-growth areas.


 

Source information

·         Benítez, P.C., Murray, B.C. and Pascual, U. (2007) ‘Global forest land-use change: economic aspects of forest conversion’, Ecological Economics, 62(3-4), pp. 372–382.

·         Kindermann, G.E., Obersteiner, M., Rametsteiner, E. and McCallum, I. (2006) ‘Predicting the deforestation–reforestation dynamics of the world’s forests’, Forest Ecology and Management, 217(2-3), pp. 221–230.

·         Natural Resources Institute Finland (LUKE) (2022) Forest Statistics Database 2022. Available at: https://stat.luke.fi/en.

·         Eurostat (2023) Purchasing Power Parities (PPPs), Price Level Indices and Real Expenditures for ESA 2010 Aggregates. Luxembourg: Publications Office of the European Union.

·         Center for International Earth Science Information Network (CIESIN) (2005) Gridded Population of the World, Version 3 (GPWv3): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).

 

This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 39-45, belonging to section 3.2 Forest Land Rents. The full text of the deliverable can be accessed via: https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.