Who benefits from natural capital in Switzerland? An analysis of resource rents by groups of beneficiaries

1 Introduction

The phenomenon of economic rent from land and other resources, defined as income that is simply remuneration for ownership of an asset, independent of any labor, has been an important topic of inquiry since the beginnings of modern economics (Smith, 1776; Ricardo, 1817; George, 1912/1879; Hotelling, 1931; Sachs and Warner, 1995; Piketty, 2014). Questions about distribution—about who benefits from these rents—have also been of interest since early on. Adam Smith, for instance, noted, “As soon as the land of any country has all become private property, the landlords, like all other men, love to reap where they never sowed, and demand a rent even for its natural produce” (Smith, 1776, book 1, chapter 6). Who reaps the benefits from natural resources has remained an important and contested issue over time (Heller and Salzman, 2021).

Given the importance of natural resource rents as a source of wealth (Smith, 1776) and a potential curse (Sachs and Warner, 1995), there has been surprisingly little work on empirical measurement at the national level. The available estimates published by the World Bank (2025) have important shortcomings (Schläpfer, 2026). First, they cover only extractive resources – oil, natural gas, coal, minerals, and forest reserves, which limits the usefulness for analyses involving countries where other resources play an important role. Second, urban land is subsumed under produced capital. As a consequence, the importance of natural capital may be greatly underestimated, and estimates of the productivity of produced capital may be biased (Bakker, 2023). Third, the data include only rents from sales of resources. Gains from increasing values of the resource stock due to rising prices or from the transfer of property rights are not included. Finally, the indicator does not account for the (over)use of shared environmental resources (at the cost of third parties or future generations), which can also be counted as a resource rent.

Even harder to find are studies on natural resource rents that look beyond the total amount of rental income and examine the distribution of rents. Several aspects of distribution would be of interest: Among institutional sectors such as the state, private households, and corporations as defined in national accounts, among segments of the population based on the distribution of income or wealth, or among national vs. foreign owners. The current lack of empirical information is particularly problematic in the domain of land rents, where rapidly increasing property prices are increasing inequality and causing a housing affordability crisis in many regions of the world (Knoll et al., 2017; Wetzstein, 2017; Schwerhoff et al., 2020, 2022).

The objective of the present paper is to provide a framework for analyzing the distribution of natural resource rents and transfers and to apply it in an analysis of resource rents at the national level. The distribution we examine is among four main groups of beneficiaries, including private owners, public owners, citizens as equal shareholders of collectively held resources, and a so-called open-access sector comprising environmental polluters and similar beneficiaries of open-access regulations of environmental resources. The framework is applied to data from Switzerland, where data on resource rents at the national level are available from a recent report (Schläpfer and Lobsiger, 2024). The example is also interesting due to a rapid increase in land prices in recent years and decades. For instance, in the Swiss canton (state) of Zurich, average land prices for owner-occupied apartments more than tripled between 2000 and 2024 (Wüest Partner, 2025). The issues of affordability and distribution are therefore increasingly important on the political agenda (e.g., Wüest Partner, 2024a).

We examine three main questions about natural resource rents in Switzerland: (1) How large are the total resource rents? (2) What is the composition of the rents by resource category and rent category? (3) How are the rents distributed among groups of beneficiaries?

To answer the first two questions, we follow the conceptual approach developed in an earlier study (Schläpfer, 2026) but apply it to a more detailed dataset. The conceptual approach and empirical investigation of the third question is original.

The paper makes two main contributions. First, we provide a pragmatic conceptual framework for examining the distribution of resource rents, and, second, we present the first national-level empirical analysis of the distribution of resource rents by major categories of beneficiaries.

The paper is organized as follows. The next section develops the conceptual framework. Section 3 presents the methods. Finally, Sections 4 to 6 contain the results, discussion, and conclusions.

2 Conceptual framework

2.1 Natural and environmental resources

We use “natural and environmental resources” to refer to the physical basis of financial and non-financial rental income from natural capital. Natural and environmental resources have different economic characteristics and generate different types of rents (Section 2.3). We therefore distinguish between the two as follows. “Natural resources” includes land—used for buildings, infrastructure, agriculture and forestry—and other resources that are “divisible” in the sense that resource units can be relatively easily allocated to individual users or user groups. Examples are mineral resources and hydropower resources. “Environmental resources” include shared or “indivisible” resources that cannot be easily allocated to individual users or user groups but are enjoyed by many or even all individuals collectively. Examples are groundwater, air quality, biodiversity, and landscapes. Impacts on environmental resources are often caused by identifiable polluters or polluter groups, while the costs of the impacts are usually borne by many individuals or society at large (Schläpfer and Vatn, 2023). The distribution of the costs among the impacted individuals is usually difficult to measure.

2.2 Natural capital

We use “natural capital” for natural and environmental resources that are at least occasionally exchanged in transactions. “Transaction” is understood in a wide sense that includes both sales and leases through market exchanges and exchanges through other institutions. An advantage of this definition of natural capital is that it encompasses not only extractive natural resources and land but also (rights to) environmental resources such as clean air and water. For example, rights to pollute environmental resources are sometimes exchanged through pollution charges or markets for pollution permits. The involved environmental resources therefore satisfy our definition of natural capital.

2.3 Rents and categories of rent

We use “rent” in the wide sense of “remuneration for ownership of the asset, independent of any labor” (Piketty, 2014; Schwerhoff et al., 2020). This definition implies that capital invested in the mere ownership of natural capital is not treated as a cost. In the case of “unimproved” land, for instance, the rent is simply the (competitive) lease price of the land obtained by the landowner, without any deduction of capital costs, as no labor is required to produce the natural resource that is exchanged in the market. Mineral resources, in contrast, must be mined before they can be sold and used. The rents from mineral resources are therefore given by the revenues from resource sales, minus the costs of resource extraction.

We distinguish four categories of rental income from natural capital that flows to recipients or beneficiaries (Table 1; cf. Schläpfer, 2026).

Table 1. Classification of rents and transfers.

^aFollowing Schläpfer (2026).

^bRent or income for receiving party/loss or cost for contributing party. For simplicity, it is assumed that the benefits to the receiving party equal the loss of the contributing party.

The first category, for which we use the term “capital income,” is the traditional natural resource rent. In the case of unimproved land, as mentioned earlier, the rent is given simply by the lease price. In the case of a mineral resource, for instance, the rent is equal to the revenue from the sale of minerals, minus the extraction costs. In the case of a hydropower resource, the rent is given by the (competitive) water fees hydropower operators pay for the undeveloped resource or, alternatively, by the revenue from the sale of electricity, minus the cost of electricity generation. Much work on rents from natural resources looks only at this specific component of totals rents from natural capital (Schläpfer, 2026).

The second category, “capital gain,” is defined as the change in the resource price that is due not to any improvements of the resource but to general economic development. In the case of hydropower, this could be a technological development that lowers the cost of electricity generation or increased electricity demand that increases the price of electricity.

The third and fourth categories of rent include two types of non-monetary transfer. Transfers, more generally, are one-sided contributions from one party to another without any goods or services being received in return. The transfers are rents from the perspective of the receiving party, while they are costs (or losses) from the perspective of the contributing party.

The third category, “capital transfer,” is defined as a transfer of rights to natural resources from one owner or owner group to another, usually through some government ruling. For instance, a government makes a decision to rezone private agricultural land as building land. The decision defines new rights to use the natural resource (land), which are then transferred to the landowner. Another case is regulatory changes in the tolerated level of (uncompensated) pollution.

The fourth category, “cost transfer,” is defined as a transfer of costs through environmental resource regulations that allow one party to impose costs on another party without paying full compensation. For instance, an environmental legislation allows farmers to pollute groundwater with harmful pesticides and does not require them to compensate those harmed. In economics, these costs are referred to as “external costs.”

Figure 1 summarizes the examined categories of resources, rents, and beneficiaries described in the preceding subsections.

2.4 Valuation of rents

The transactions used to delineate the tradeable natural capital also provide the prices required for their quantitative assessment (cf. Section 2.2). In the case of market transactions, prices and quantities are readily available. Parcels of land change hands at specific prices per unit of land. When land is rezoned from agriculture to urban use, the value is reflected in increased market prices. However, the transactions can also occur in the administrative and political domain. For instance, when governments that own water resources auction off licenses for hydropower, the prices paid by the hydropower producers can be used as a proxy of the resource rent (capital income). Another example of a non-market transaction is costly legislation to protect environmental resources from specific harmful impacts from which a valuation per unit of the impact can be derived (e.g., Schläpfer, 2020). Estimation of the total resource rent can then follow the standard accounting approach, in which total values are determined by multiplying the quantity of the resource (stock or flow) by the price per unit of the resource.

2.5 Categories of beneficiaries

Beneficiaries are defined as those who own or have access to the benefit streams flowing from the examined natural capital. We distinguish four basic groups of beneficiaries: public entities (“public sector”), private entities (“private sector”), citizens collectively (“citizen sector”), and polluters as beneficiaries of open-access environmental resources (“open-access sector”).

The private sector includes natural and legal persons. The legal persons (or entities) are further subdivided into profit-oriented companies and non-profit-oriented companies (as distinguished in national statistics).

The public sector encompasses public entities at all levels of government. When rents from natural capital flow to the public sector, the ultimate beneficiaries are, of course, private entities, and the distribution among these entities depends on how the rental income is used. For instance, if the public sector uses income from its natural capital (such as hydropower resources) for reducing taxes, then the distribution of the benefits is implied by the tax schedule (tax distribution).

The citizen sector is defined as the total population of citizens who enjoy equal rights to natural capital that they collectively own and to the benefits that flow from such capital. The same collectivity is also a contributor of capital transfers and cost transfers to other sectors. (Alternatively, the citizen sector could also be defined as the residential population).

The open-access sector encompasses the polluters who benefit from open-access environmental resources. We also use “polluters” to signify the beneficiaries of harmful (and, hence, costly) pollution of open-access environmental resources. Due to the problem of allocating environmental impacts at the individual level, the external costs of unregulated pollution are allocated to the citizen sector.

2.6 Flows of rents and transfers

Table 2 provides an overview of how the groups of beneficiaries benefit from and/or contribute to flows of rents and transfers. The first row identifies the beneficiaries of rents and transfers that flow to the respective sector (columns). For instance, the private-sector beneficiaries of rents from natural capital are the private owners of rights. The second row identifies the sector or group that contributes to any transfers obtained by the respective sector. For instance, the contributors to transfers obtained by the private sector are the citizens who are the collective owners of shared resources that have not been transferred to a private or public owner. The last two rows show how the benefits and costs of capital transfers and cost transfers are distributed within the groups of beneficiaries. Regarding capital transfers flowing to the public sector, for instance, the distribution depends on how the public revenue is used. Regarding the cost transfer through unregulated externalities, the costs are borne by the citizen sector, and the distribution of costs within that sector depends on the distribution of harmful environmental impacts among citizens.

Table 2. Groups of beneficiaries and flows of rents and transfers.

^aAll costs allocated to citizen sector by definition (cf. Section 2.5).

3 Methods

3.1 Identification of effective rights to natural capital

The empirical assessment of flows of rents as defined in the conceptual framework requires an interpretation of who are effectively the beneficiaries of environmental capital. This is not trivial, since the beneficiaries cannot be usefully identified based on legal entitlements such as private property rights alone. Who effectively benefits from natural capital depends on a combination of private property rights and public-law (including common law in common-law jurisdictions) restrictions on private property, on the enforcement of these laws, and on social norms (Ostrom, 1990; Bromley, 1991; Gerber et al., 2020). For instance, when the owners of a commercial property charge higher rents than would be legally admissible, the relevant distribution of benefits is not that implied by the legally admissible rents but that implied by the actual rents. When an environmental law requires that polluters compensate the victims of environmental damage but the law is not enforced, the relevant distribution of benefits is not that implied by the formal legal requirement but, rather, that implied by the actual policy outcome.

Table 3 describes how we interpreted the effective rights to natural capital in Switzerland under existing laws, enforcement, and social norms. For building land, the zoning-compliant rights are held by either private or public entities. The complementary rights, i.e., the public-law restrictions on private and public rights, are effectively held by citizens, as any benefits of restrictions such as limitations on building density or height accrue to all citizens (or even the entire residential population). The situation with the rights for building land outside the regular building zones is similar. The only difference is that the existing building rights do not comply with zoning regulations but are instead based on exceptional permits or legal entitlements that predate the zoning legislation.

Table 3. Distribution of effective rights to natural capital.

^aDefined as “all citizens with right to vote” or “the residential population.”

For other divisible natural resources, private rights are more restricted. In the case of agricultural land, the rights of the mostly private landowners include only limited agricultural uses. They do not include the right to construct buildings, for instance. Furthermore, concerning access, or the right of way, the agricultural land in Switzerland is under a restricted open-access regime. Cropland in winter or freshly mown meadows can be accessed. On forest land, the rights held by public or private entities are even more restricted. For instance, citizens retain the right to light a fire or to collect mushrooms.

Shared environmental resources are mostly under a restricted open-access regime. Any constraints on these uses are effectively owned by citizens, as everyone enjoys the same rights to the benefits of the restrictions on open-access pollution and other harmful impacts. An important exception are environmental resources for which the government has introduced charges for harmful uses that cover the costs of harm to other parties. The implicit rights here depend on how the revenue from the charges is used. If the revenue from charges on harmful uses of a resource that affect the whole population are recycled only among the harmful users, then the resource rights are effectively held by the private users. An example is farm-emissions taxes that are recycled to farmers only. If the revenue is not recycled but used by the public sector, then the rights are effectively held by public entities. If the revenue is fully recycled to the citizens on a per-capita basis, then the right to the harmed resource is effectively held by the citizens with their equal individual rights to the resource. An example in Switzerland of this particular distribution of effective rights to natural capital is air quality with respect to one class of harmful air pollutants called volatile organic compounds (VOCs; cf. Section 3.2.3).

3.2 Data

3.2.1 Building land

The main datasets used in the calculations for building land within and outside of the building zones of the spatial planning legislation are shown in Table 4.

Table 4. Dataset used for building land.

^aFOS, Federal Office of Statistics; FOSD, Federal Office of Spatial Development; FHO, Federal Housing Office.

Land price information is published twice a year for 106 labor-market regions by the consulting firm Wüest Partner. According to information from Wüest Partner, prices per square meter of undeveloped land are modeled as the difference between transaction prices of real estate and estimated prices of buildings (called the residual method). The available data were 10th, 50th, and 90th percentile prices for each of four categories of building land: “single-family houses” (SFH), “multifamily houses” (MFH), “commercial land” (Geschäftsflächen), and “industrial land” (Gewerbeflächen). According to information from the data provider, the transaction prices of SFH land are based on about 12,500 observed transaction per year. Transaction prices of MFH land are based on 300,000 rental contracts and 10,000 properties based on municipality-specific market rents and capitalization factors for investment properties. Commercial and industrial land prices are likewise based on rental contracts. The commercial category includes businesses like physical therapists or hairdressers. Industrial includes everything from small craft shops to heavy industry.

From the regional land price data we first computed weighted national-level estimates. For this purpose, regional prices were weighted with the regional quantity of the respective land uses based on the area statistics. The SFH prices were weighted with the quantity of land in the land-use categories SFH, semi-detached houses, and terrace houses (as categories 3 through 6). The MFH prices were weighted with the land in the land-use categories for MFH (categories 7 and 8). The prices for industrial and commercial land were weighted with the land in the land-use category “industry and commerce” (categories 1 and 2). Finally, to compute mean values from the percentile values, we assumed that the log of the prices followed a normal distribution. Descriptive information (means and quantiles) is documented in Supplementary Tables S1, S2.

Linking the price data with the quantity data required a series of assumptions, which are described in what follows. A first set of parameters was defined to link the categories of the land-price data with the categories of the area statistics. The price data for “industrial space” and for “commercial space” was linked to the land-use category “industry and commercial area” of the area statistics using the assumption that 90% of the area was in “industrial” and 10% in “commercial” space. For the land-use category “non-specified buildings” we assumed a land price based on a composition of 25% each of industry, retail, office, and MFH land. Further assumptions were required for land prices of land uses not represented in the price data, including land with “agricultural buildings” (value in 2021 of CHF 1,000/m²), “traffic area” (CHF 1,000/m²), and “other uses” (within the building zones) (CHF 500/m²). The parameters are documented in Supplementary Table S3. A second set of parameters was defined to link the categories of the area statistics with the categories of the zoning statistics. These parameters were obtained through a cross-tabulation in which we used the geo-datasets to calculate the percentage of each of 8 land uses in each of 11 land zones (Supplementary Table S4). Additional assumptions were used for the price of land with buildings outside the building zone (value in 2021 of CHF 500,000 per building with and CHF 100,000 for buildings without residential use), for which we did not have statistical information (parameters in Supplementary Table S5).

Parameter values for the net return on capital were defined for two model variants, one with 2% and the other with 3% net return on capital. These parameters were chosen based on the legally allowed maximum for property owners' return on private capital (3.25% in 2021) and estimates for the rate of return of commercial property published by real-estate research firms. The reason for the use of model variants was the high degree of uncertainty regarding the true parameter values. These parameters were applied to all land uses for buildings or transportation. We thus assumed that the ongoing reallocation of land to different uses equaled the (financial or real) return on land capital and that the return on land capital was independent of the type of land tenure. For the “other uses/areas,” we assumed a net return of 1% and 1.5%, respectively, for the two model variants. The parameter values are documented in Supplementary Table S6.

Parameter values for capital gains were likewise specified for two model variants, one based on the land price data of the real-estate consulting firm Wüest Partner in 2016 and 2021 (Table 4) and the other based on price indices for the same period published by another real-estate consulting firm (Fahrländer Partner, 2024) for the same period. The reason for this approach was that the data published by the two firms differed substantially, and we did not have a basis for determining which of the two was closer to the true values. Empirical parameter values from the two sources were available for the main residential and commercial land uses. For the other land-use categories we used the parameters of the most similar categories for which empirical estimates were available. For those land-use categories for which the 2021 land prices—due to the lack of empirical information—were based on assumptions, we computed the 2016 land prices “backwards” from the 2021 prices and the price changes (Supplementary Table S5). The parameters are documented in Supplementary Table S7.

The parameters used to compute capital transfers through zoning of land from agriculture to building land are documented in Supplementary Table S8. For simplicity, we assumed that the distribution of the new building land among the different zones in each labor-market region was equal to the national average. The data used to compute capital transfers through densification are documented in Supplementary Table S9.

A final set of data sources and assumptions were used to break down the different land-use categories by owner categories (groups beneficiaries, cf. Section 2.5). The category “private” was further disaggregated into “natural persons,” “profit-oriented enterprises,” and “non-profit enterprises.” For SFH, shares were based on the Federal Statistical Office's (FSO) “buildings by owner type,” the Federal Office for Housing's (FHO) “non-profit residential buildings by building category,” and our own assumptions. For MFH, shares by owner type were calculated separately for rental apartments (based on the FSO's “apartments by owner type,” FHO's “non-profit residential buildings by building category”), and for owner-occupied apartments (based on FSO's “buildings by owner type”), as well as our own assumptions. The proportion of owner-occupied apartments and rental apartments in the total number of apartments was calculated on the basis of FSO's data on “housing conditions by type of occupant.” For industry land: 90% for-profit enterprises and 10% public; agricultural building land: 100% natural persons; agricultural land: 90% natural persons and 10% public; non-specified buildings: 80% for-profit enterprises and 20% public. For “other land uses/areas,” the proportions of the owner categories were assumed to correspond to the distribution of the total areas of all other categories. For buildings outside the building zone, it was assumed for the sake of simplicity that the proportions of the owner categories corresponded to those within the building zone. The parameters are documented in Supplementary Table S10.

3.2.2 Other divisible natural resources

Quantities and prices for other resources were compiled from the following sources (for the reference year 2021). Information on the amount of agricultural land in crops, grazing lands, other uses, and summer pasture was available from the annual statistics of the Federal Office of Agriculture (FOA, 2023). Rental prices are from the Canton of Aargau (2024) and supplemented with the authors' own assumptions (Supplementary Table S11). Hydropower rents were available from the Federal Office for the Environment (FOEN, 2023). Revenues from the auctioning of mobile-phone concessions (for the period from 2012 through 2034) were obtained from the Federal Office of Communications (OFCOM, 2012, 2019). Income from royalties is from the Federal Finance Administration (FFA, 2023).

3.2.3 Environmental resources

Cost transfers mediated through environmental resources were obtained from the available sectoral assessments of external costs. For the transport sector, we used the official estimates (for the year 2020) for the environmental impact categories air pollution, noise, climate, and nature and landscape published in FOSD (2023). Estimates for electricity and heat production include costs of particulate matter (PM₁₀) emissions from a study (for 2015) by Ecoplan (2022). Additionally, information on costs of CO₂ emissions was included based on energy use and emission factors published by the FOEN and a CO₂ price of CHF 140/t (FOSD, 2023). The data and sources are documented in Supplementary Table S12. Estimates of PM₁₀ emissions from (non-agricultural) production were taken from Ecoplan (2022). Estimates for agricultural production including greenhouse gas, ammonia, and nitrate emissions, soil erosion, and pollution with herbicides and fungicides (in 2018) were available from an earlier study on the external costs of Swiss agriculture (Schläpfer, 2020). Finally, the external costs of urban expansion (in 2021) were also taken from an earlier study by one of the authors (Schläpfer, 2024). All estimates were converted to (January) 2022 prices using the inflation calculator of the Swiss Federal Office of Statistics.

Statistical information on charges (levies) for harmful uses of environmental resources (in 2021) was obtained from FOSD (2023) and FOEN (2024). The data include (a) the total revenue from the levies and (b) shares received by each institutional sector (group of beneficiaries). The information was based on how the revenue was used or recycled (cf. last row in Table 3). The charge on CO₂ from heating fuels yielded revenues of CHF 1,155 million, of which CHF 639 million was recycled to the population (equal amounts per capita) and about CHF 106 million was recycled to companies (per CHF 100,000 of wages paid). The remaining CHF 410 million was used by the government. The charge on VOCs yielded CHF 114 million, which were fully recycled to the population. The environment-related charges on heavy-duty road transport yielded CHF 639 million, which was used by the government.

The external cost estimates for the five domains were allocated to impact categories air, (drinking) water, noise, climate, biodiversity and nature and landscape based on sub-categories in the original studies and the following own assumptions. Transportation impacts on “nature and landscape” (FOSD, 2023): 25% on biodiversity and 75% on nature and landscape; impacts of agriculture (Schläpfer, 2020) for ammonia: 25% on air, 75% on biodiversity; for nitrate: 75% on water, 25% on biodiversity; for soil erosion: 100% on nature and landscape; for herbicides and fungicides: 50% on water and 50% on biodiversity. The parameters are documented in Supplementary Table S13.

3.3 Calculations

3.3.1 Value of building land

For building land, we first computed the capital values of the land in 2016 and 2021 and then derived the rents and transfers based on these values. We started with the quantity of land in (eight) land-use categories based on the official area statistics. Using the most detailed available housing and ownership statistics and simple complementary assumptions, the quantity in each land-use category was allocated to the different categories of beneficiary (Supplementary Table S10). The quantities in these land-use-by-beneficiary categories were then linked with the land prices using the parameters and assumptions described in Section 3.2.1. To compute the land price of undeveloped land within the building zones, we first translated the land prices for the eight categories of the area statistics into prices for the nine categories of the zoning statistics (see parameters in Section 3.2.1). We then calculated the price of the undeveloped land within the building zones as the weighted price based on the prices of the eight land zones and their shares of the undeveloped land.

3.3.2 Rents and transfers from building land

This basic quantity and price information was used to compute the four categories of rent and transfer income from building land: capital income, capital gains, and capital transfer.

The capital income for the year 2021 was computed simply by multiplying the price and quantity information for 2021 by the parameters for the percentage return on capital (Section 3.2.1). The annual capital gains between 2016 and 2021 were computed using two sets of parameters (Section 3.2.1). The annual capital gain was computed as the mean annual capital gain in the period from 2016 to 2021. The calculations were made based on the land-price data for 2016 and 2021 for nine land-use categories within the building zones—the eight categories referred to earlier, with the land in MFH disaggregated into land with rental and owner-occupied buildings. Furthermore, the calculations were made for two categories of land outside the building zones—land occupied by buildings with and without dwellings.

The estimation of annual capital transfers considered three types of transfer: (1) transfers through zoning of land from the agriculture zone to building zones, (2) transfers through rezoning of land from lower to higher maximum building density, and (3) transfers through permits for new residential and other buildings outside the building zone. The agriculture-to-urban zoning transfer was calculated at the level of the 106 labor-market regions by multiplying the quantity of land transferred to the building zone by the price of the building land. The up-zoning (or densification) transfer was computed by multiplying the percentage increase in density by the aggregate capital value of land. The increase in density was quantified based on gross floor area per unit of land in the building zone (see parameters described in Section 3.2.1). The transfer through permits outside the building zone was computed by multiplying the price of the residential and other building land in 2021 by the change in the number of residential and other buildings between 2016 and 2021.

3.3.3 Rents and transfers from other natural and environmental capital

The other parts of (divisible) natural capital—including agricultural land, forest land, hydropower, air waves, and royalties—required only minor calculations to compute aggregate annual values for 2021. For this type of natural capital, we only considered capital income, since the other three categories of rents and transfers were not quantitatively important.

For (indivisible) environmental capital, the external cost estimates from the sectoral studies were allocated to the affected resource categories using the data and parameters described in Section 3.2.1.

3.3.4 Additional calculations

In a final step, we used official population statistics, voter statistics, and aggregate economic indicators to relate the estimates to population size and gross domestic product (GDP).

4 Results

4.1 Rents and transfers from building land

The calculations were carried out for alternative assumptions on return to capital and (data) on price development, as mentioned in Section 3.2.1. In what follows, we present the results based on the lower (2%) assumed return on land capital and price data from Wüest Partner. The sensitivity of the estimates to these assumptions is examined in the final section of the results.

Table 5 shows the estimated annual rental income of landowners and its composition by rental categories (cf. Table 1) and land categories based on Swiss area and zoning statistics for the reference year 2021. Of the total annual income of CHF 129.9 billion, about a third (34%) comes from capital income, half (55%) from capital gains, and a tenth (11%) from capital transfers. Residential land uses contributed CHF 74.5 billion, or 57%, to the total.

Table 5. Rental and transfer income from land by income and land category (million CHF/year).

Data sources: see Section 3.2, data 2016 through 2021.

Figure 2 disaggregates the total rental and transfer income by group of beneficiaries. Almost 80% (79%) of the income was received by private entities. Among these, natural persons received 58%, for-profit enterprises 20%, and non-profit enterprises 1%. Public entities received 21%. The citizens collectively received no rental or transfer income but contributed CHF 14.6 billion in capital transfers to the other sectors (cf. Table 5), which implies a net loss of CHF 14.6 billion.

4.2 Rental income from other natural resources

Table 6 presents the rental income from other divisible natural resources by resource category. The total rental income amounts to CHF 1.3 billion. The largest contributions came from agricultural land and hydropower. The commercial use of forests did not generate a positive rental income.

Table 6. Rents from other natural resources (million CHF/year).

Sources: see Section 3.2. Data 2021.

The rental income from agricultural land accrued largely to private beneficiaries, and the rental income from other resources went to public entities. Thus, public entities received 51% of the resource rent, private entities 49%, and citizens collectively none (0%).

4.3 Transfer income from environmental resources

Table 7 shows the cost transfers mediated through environmental resources such as air and water. The first column shows the total transfers from the citizen sector (donor of transfer) to the other sectors (recipients of transfer). Uncompensated environmental cost transfers imply a transfer from citizens to polluters (second column). Environmental cost transfers that are compensated imply a transfer from the citizen sector to the sector receiving the compensation. Of the environmental charges in place in Switzerland in 2021, an estimated CHF 979 million went to the public sector (third column), CHF 106 million to the private sector (fourth column), and CHF 753 million to the citizen sector (last column).

Table 7. Transfers through resource externalities (million CHF/year).

^aSee explanation in the text (Section 4.3).

^bValues in parentheses are net transfers (excluding transfers from citizen sector to polluter sector that are compensated). Sources: see Section 3.2. Data 2021.

In this sectoral perspective, when environmental cost transfers are compensated and the compensation is made to the citizen sector (last column), the transfer and the compensation cancel out (resulting in a net transfer of zero in parentheses).

4.4 Total rents and transfers

Table 8 summarizes the rental and transfer income from the total natural capital, including building land, other natural resources, and environmental resources. The numbers are broken down by resource category and rental category. Of the total rental income of CHF 159 billion, 82% was generated by building land, 1% by other divisible natural resources, and 18% by environmental resources. With respect to income categories, 29% of the total was capital income, 45% capital gains, 9% capital transfers, and 18% income transfers.

Table 8. Total rents and transfers (billion CHF/year).

Sources: see Section 3.2. Data 2016/2021. Dashes (–) indicate cells that were not examined.

Figure 3 shows the composition of the rental and transfer income by sector (category of beneficiary). The private sector received about two-thirds (65%) of the income. Of the remainder, about half (18%) went to the public sector and the other half (17%) to the users (polluters) of open-access environmental resources. The citizen sector incurred a loss of CHF 42.4 billion through transfers to the other sectors. This transfer corresponds to 27% of the total rents and transfers.

Figure 4 presents the rental and transfer income by rent category per capita. Dividing the total rents in the reference year 2021 by the residential population of Switzerland yields a mean rental income of CHF 18,546. Dividing by the population of citizens able to vote (citizens above 18 years) yields a mean income from rents and transfers of CHF 29,272.

4.5 Sensitivity to data and assumptions

The analysis with different sets of parameters for capital income and with different data sources for the development of land prices between 2016 and 2021 can provide some information on the uncertainty of the estimates (Table 9). The first column shows the estimates presented in the preceding sections. The other columns present the estimates for the other three combinations of assumptions on parameter values and choice of data. Assuming land price changes based on the price indices of Fahrländer Partner rather than calculations based on the data of Wüest Partner increased the annual rent from building land from CHF 129.9 billion to CHF 171.4 billion. Assuming a capital income of 3% instead of 2% increased the annual rent from building land from CHF 129.9 billion to CHF 152.1 billion. Combining both alternative assumptions yielded a total annual rent from building land of CHF 193.5 billion (fourth column). Compared with the baseline results presented in the first column, the estimates based on alternative assumptions are between 14% and 40% higher (third and fourth columns, respectively).

Table 9. Sensitivity of rents and transfers to data and assumptions.

WP, calculations based on data from Wüest Partner; FP, calculations based on indices published by Fahrländer Partner.

5 Discussion

Our main findings are twofold. First, we found that it was possible to organize and examine the distribution of a wide range of rents from natural capital within a single framework. Second, using this framework, we provided empirical estimates for the distribution by group of beneficiaries of total resource rents and transfers at the national level. These findings extend existing approaches and studies, and they suggest further directions in research toward more transparent information about the economic beneficiaries of current natural and environmental resource regulations.

Regarding the conceptual approach, natural resource accounting has traditionally focused on the assessment of resource values or rents without consideration of ownership or beneficiaries (World Bank, 2024; Schläpfer, 2026). This approach is sufficient if the objective is only to track or examine the total amount and value of natural resources through time but not if the objective is to understand and predict distributional effects of regulations involving natural resources. Existing approaches provide information on both quantity and price but neglect resource ownership as an important third piece of information in the analysis of natural resource systems and policies (Gerber et al., 2020; Özdilek, 2024). This same lack of information on ownership is also evident in environmental economic national accounting or assessments of ecosystem services (United Nations et al., 2014; King et al., 2024). The analysis of natural and environmental resources and capital by owners or beneficiaries addresses an important policy need and gap in official statistics and academic literature. While the data available for this purpose are limited, this study shows that analyses that account for ownership are feasible, and they can provide novel and policy-relevant insights.

Comparison of the empirical findings with those of other studies is limited by the scarcity of empirical work on national-level resource rents and especially their distribution among groups of beneficiaries. One recent study estimated land rents in 14 OECD countries and found values between 7% and 24% of GDP (Bakker, 2023). In our base-case estimates, the total rents for land alone amounted to 17% of GDP, which is lower than Bakker's estimates for South Korea, Britain, and Australia but higher than the other national estimates. Furthermore, the results can be compared with those of a previous study that used more limited data and simpler assumptions (Schläpfer, 2026). The earlier estimate for total rents and transfers of CHF 298 billion was substantially higher than the present estimate of total rents of CHF 159 billion to CHF 223 billion per year.

Regarding policy implications, the empirical findings help to better understand the composition of natural resource rents and their distributional effects in Switzerland. The findings thus also provide a basis to better understand distributive implications of public investments and regulatory activity. Examples include public infrastructure investments, spatial planning decisions that affect public-law restrictions on land ownership, or taxation to capture land rents (Simon Büchler and Lutz, 2024; Gerber and Debrunner, 2022; Viallon, 2018). With respect to environmental resources, the results similarly provide information to better understand the distributional effects of current environmental regulations. The numbers can help to regulate (harmful) environment resource use in ways that correspond to desired distributions of rights (Schläpfer, 2020; Schläpfer and Vatn, 2023).

To what extent are these empirical findings transferable to other countries or regions? The aggregated rents and their distribution among beneficiaries depend firmly on context-specific parameters. For instance, the land prices in Switzerland are particularly high and increased rapidly in the examined period. Furthermore, the percentage of households that own residential property is very low—only 36% (FSO, 2026). Generalizations from the empirical estimates would have to be based on evidence of similar contextual parameters. A more promising approach for other regional studies may be to use the same or similar concepts together with local parameters to provide analogous estimates.

The empirical estimates are limited in several ways. A series of assumptions were needed to link the land-use, zoning, and property price statistics, all of which used different land classifications. Further assumptions were needed to complement the available official information on land ownership. Additional uncertainty was due to unobserved parameters such as the rate of return to owner-occupied property and property in the public domain. We assumed that parameters were similar to those for commercial property and examined the sensitivity of the results to alternative assumptions (Section 4.5). Another source of substantial uncertainty was the information on harmful impacts on environmental resources from studies of external costs. A strength of the valuations on which we drew was that they were derived from actual transactions in the political domain. Hence, the valuations have some political legitimacy even if they may not fully reflect informed preferences.

Regarding future work, an important current limitation concerns the lack of official statistical data on land ownership and value. In particular, information about ownership categories in official cadastral maps of Switzerland would be central for a more detailed analysis of who benefits from natural capital and related changes in regulations. More accurate estimates of the distribution of the rents from natural capital could then be obtained by linking models of natural resource value (van Dijk et al., 2016; Schläpfer et al., 2015) with the ownership information from these maps.

6 Conclusions

The empirical literature on national-level natural resource rents and their distribution among beneficiaries is extremely scarce. In this paper, we developed a framework based on a classification of beneficiaries to analyze the distribution of natural resource rents and transfers in Switzerland. We distinguished four main groups of beneficiaries, including the public sector, the private sector, citizens collectively, and polluters of open-access environmental resources.

Conceptually, our framework allowed us to examine the benefits flowing from both “divisible” natural resources such as land or hydropower resources and from “indivisible” or shared environmental resources such as groundwater in a single framework. Our analysis included urban land, which is often neglected in studies of natural resource rents or treated as produced capital together with buildings. Empirically, we thus presented what appear to be the first national-level estimates of comprehensive resource rents and transfers that distinguish groups of beneficiaries.

The findings show that the rental income from natural capital in Switzerland is substantial. The annual total in 2016 to 2021 of CHF 159 billion, which is based on the most conservative of four combinations of data sources and assumptions on capital income, corresponds to about 20% of the GDP and about 50% of the capital income as measured by Swiss national accounts. Of this total income, CHF 103 billion (65%) went to private entities, CHF 28 billion (18%) to public entities, and another CHF 28 billion to polluters of open-access resources. Citizens collectively received none of the rental income.

These estimates are coarse and subject to significant uncertainty, as seen in the parallel analyses with alternative data sources and assumptions on returns on capital. Nevertheless, they provide a novel approach and fresh, policy-relevant empirical insights on the distribution of the very substantial income from the non-produced (natural) capital and shared environmental resources in one country. Future research could provide more detailed estimates using more detailed data sources and apply our framework to the natural capital stocks of other countries.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frevc.2026.1787849/full#supplementary-material

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Footnotes

Edited by: Michael Getzner, Vienna University of Technology, Austria

Reviewed by: Binh Nguyen, University of Economics Ho Chi Minh City, Vietnam

Valentina Di Gennaro, University of East Anglia, United Kingdom