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GLOBAL LAND USE DATASET

Data is critical to assess the reliance of companies on natural capital. I propose to fill the existing gap of availability of data with a new set of data for land use with a global coverage.

 

 

 

 

Land is one of the most precious assets we have. Soil in particular is one of the resources we rely on to produce the food we eat. All our activities involve land use to some extent, food production being one of the most intensive land users.

 

In order to better capture and measure the externalities of companies, or alternatively their “environmental profit and loss accounting”, we rely on good quality data. It is particularly an issue with global companies having a complex value chain. The collection of primary data is complex along their entire lifecycle; as a result, they rely on secondary datasets that they link to data they know. For example, instead of collecting data at a farm level, they will use data from an average farm, expressed per unit of production, and multiply it by the amount they purchase from this farm. This is a common practice within the industry.

Land use data is particularly difficult to model as it varies depending on the location and a variety of parameters. Currently, most companies use one single value for their entire value chain.

 

I introduce here a new model providing worldwide coverage of externalities values related to land use, based on scientific publications, in particular one from the CIRAIG (the International Centre for the Life Cycle of Products, Processes and Services). Cao et al. 2015 developed the framework for an application within LCA methodology, although its use can easily be extended for this particular purpose. This model measures the externalities linked to land use for five different ecosystem services (see figure below, credit: TEEB icons).

 

 

 

The valuation of ecosystem services is based either on replacement costs or avoided costs techniques. It accounts for:

  • The type of occupation (forest, shrubland, grassland, pasture, permanent and annual crops, urban and urban green areas)

  • The soil functions defined in the figure above

  • The exposure of communities to these externalities

  • The fact that it is a long term occupation or a transformation of land

The open model and data allows to easily adjust the various parameters to adapt the results to the particularities of companies or to further regionalize the data with primary data. For example, the pricing of carbon might be adapted, as could the productivity of different crops, or even agricultural practices such as agroforestry, no-tillage, etc.

 

The results for a few countries are reported below. The first figure represents the total value of externalities per hector of land used in different countries in USD. The second figure details the contribution of the different externalities linked to the ecosystem services considered for Italy.

 

 

 

 

 

 

I am working on adapting the model to fit the purposes of corporate and project based accounting. Further information about this data can be obtained by contacting me.

 

Secondary data sources of good quality, which are regionalized and transparently built, will without doubt support the deployment of natural capital accounting methodologies. It will lead to a reduction of costs for such studies, while at the same time improving their credibility by using scientific and reliable data.

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