Land

Land degradation adversely affects the businesses, communities, and species that depend on that land. It also contributes significantly to climate change and reduces land productivity. And it is the communities that are often already exposed to climate-related risks that are made even more vulnerable when the land they depend on is degraded. For instance, competition for scarce land resources is projected to increase social instability like conflict and migration (UNCCD, 2017).

To prevent land degradation and restore land that is already degraded, companies and cities need to take a holistic approach to setting science-based targets for nature. Companies’ and cities’ impacts on land come primarily from land transformation – changing from one land use category to another – and land occupation – the continuous use of an area of land for human-led purposes. We need to find more sustainable ways of using land so we can produce food, fiber, and fuel, transport it to market, and build infrastructure in a way that does not cause degradation.

By setting SBTs for nature, companies and cities can consider their impacts on land in relation to biodiversity, water, ocean, freshwater, and climate. Doing so will increase the return on investments made in the process of setting and taking action to achieve their science-based targets, thus supporting their goals in other areas, as well.

In early 2023, the Land Hub will launch initial science-based targets related to both land occupation and land transformation as part of SBTN’s SBTs for nature v1. These SBTs will allow companies to align with the directionality of the subsequent final targets and will bring working lands into the nature agenda. 

The final SBT methods for land will be informed by the needs of land systems in terms of the physical extent and functional condition required to remain resilient to impacts of people in the long term. As such, the Land Hub is working closely with the Earth Commission and a network of scientists to understand the place-based and locally-relevant thresholds which will underpin these targets.