Using a Human-Centered Approach to Design Restoration Projects
Beginning in 2021, United Nations Decade of Ecosystem Restoration The ambitious call to prevent, halt and reverse global ecosystem degradation by 2030 coincides with sustainable development goals timeline. However, a resounding question is how to identify these restoration sites and projects.
A new study led by restoration scientists Puja Choksiwho just graduated from Columbia University Department of Ecology, Evolution and Environmental Biology The Ph.D. program, along with 10 other recovery experts from nine institutions in India and abroad, came up with a “people-centred” approach to decision-making.Use India – it’s one of the highest recovery targets in the world 26 million hectares by 2030– As a case study, Choksi and colleagues examined socioeconomic and land ownership (whether public or private) carbon sink and biodiversity conservation efforts across the country.The findings have just been published in the journal npj biodiversity.
Some forests in the Mandala district of central India have high densities of the invasive lantana species, which can become a hindrance to the livelihoods of local people. Photo: Pooja Choksi
Combining these three sets of data allows for a more holistic approach to designing recovery plans that doesn’t “put people off the map” as many previous approaches do, according to the authors. “If these recovery efforts don’t work for people, we likely won’t see their long-term benefits,” Choksi said.
“This paper illustrates the complexities of restoring forests in a place as densely populated as India,” added Ruth de VriesCo-Founding Dean of Columbia Climate School and authors of the study.
In their analysis, the authors looked at 579 districts in India and found that areas with high levels of poverty and areas with the greatest potential for restoration benefits (defined as sites that could contribute the greatest possible carbon sequestration and enhanced biodiversity levels) Largely overlapping, most of the land available for restoration in these areas is privately owned. This, they suggest, indicates an opportunity to focus on reducing methane emissions from crop and livestock production—for example, by restoring native grasses to pasture—rather than acquiescing to the dominant mode of carbon and forest restoration projects in these regions.
However, in the top 20 percent of the poorest areas of the country where restoration benefits are greatest, public lands make up the majority of these sites—an attribute that needs to be considered in planning. According to the study, restoration strategies for different socio-environmental conditions should be tailored to each project area, while giving priority to local demographic characteristics.For example, in areas where significant ecological improvements are possible but poverty rates are high, traditional Agriculture and Forestry Practice can be combined with economic policies and initiatives that improve living standards; if these areas are largely on public land, they write, the authors also recommend alternative restoration measures, such as invasive species management, and provide greater community rights to manage this piece of land.
Cassia is a grass used for many purposes. They can be found throughout forests and open ecosystems in parts of India that have historically been undervalued and referred to as wastelands. Photo: Pooja Choksi
Essentially, there is no one-size-fits-all model. “Our study took a people-centred approach to planning restoration, rather than prescribing restoration priority areas,” Choksi explained.
While local needs assessments and consultations will continue to play an important role in restoration decision-making, the authors acknowledge that combining socioeconomic and land ownership data can be used as an initial filter for identifying restoration goals at a global scale, incorporating people as a key variable The process.
“Intentional restoration of improved living standards may be the link between broad global goals and local realities,” De Vries explain.
