Full Length ArticleSpatial prioritization to achieve the triple bottom line in Payment for ecosystem services design
Introduction
The global social and environmental crisis is pushing hundreds of nations to follow a development agenda that promotes social welfare (U.N. 2018), while avoiding the loss of Earth’s ecological processes (Steffen et al., 2011). Payment for ecosystem services (PES) is an approach that promotes the protection of natural habitat, the maintenance of ecosystem services and aims to ameliorate societal issues such as poverty or social inequality (Ingram et al., 2014). For instance, a PES scheme implemented in Mexico led to a small but statistically significant reduction in poverty between 2000 to 2010, showing a 10–12% increase in poverty alleviation index (Sims and Alix-Garcia, 2017). Furthermore, this scheme led to an increase of ∼ 25% in forest cover over a similar timeframe (Sims and Alix-Garcia, 2017). PES schemes have thus a big potential to help preserve biodiversity while achieving social and economic benefits (Barton et al., 2009, Naidoo et al., 2006). Despite their potential, defining the scope, design and the spatial allocation for the implementation of PES schemes is not an easy exercise. Many PES schemes focus exclusively on one objective, such as the maintenance of ecosystem services (most commonly water and carbon services (Bremer et al., 2016, Martin-Ortega et al., 2013, Ojea et al., 2016)), whereas other PES schemes include contrasting objectives, such as biodiversity conservation and social equity (Pagiola, 2008, Raes et al., 2014). Because of the diversity of actions that can be considered in PES scheme design, a framework that aids setting up a clear formulation of objective(s), constraints, and possible actions to find optimal solutions is much needed (Possingham et al., 2000).
Accounting for multiple objectives in PES design brings challenges, as the spatial location of the provision of multiple ecosystem services might not overlap with the location of important areas for biodiversity. For example, Williams et al. (2020) found that the spatial location of ecosystem services, species persistence, and agricultural production in the Orinoco region of Colombia differed markedly. When the authors optimized for water retention, agricultural land should be allocated in the north of the region, whereas when they optimized for carbon sequestration, the distribution of agricultural land should be more scattered. Adding to the complexity, budgets are limited, so any PES scheme must consider not only its intended social and environmental goals but also how to achieve them most cost-effectively (Hails et al., 2019, Wunder et al., 2018), known as the triple bottom line (Ehrlich et al., 2012). Therefore, there is a need to spatially optimize PES schemes to target ecosystem service maintenance, biodiversity conservation, and social equity objectives within a budget constraint.
There are many factors that can influence the trade-offs between multiple objectives in PES schemes. Ecosystem services largely consider the provision of services to society and thus supply is only one aspect of the equation (Villarreal-Rosas et al., 2020), a key factor that has not been considered until recently, are the patterns of demand for ecosystem services. Different patterns of supply and demand of ecosystem services may change the location of priority areas for PES (Verhagen et al., 2016). Both large scale continental (Verhagen et al., 2016) and landscape scale (Watson et al., 2019) analyses have shown that the selection of priority regions to preserve ecosystem services is markedly different when only supply, as opposed to supply and demand, is considered. Another key aspect that influences PES schemes is that the economic incentive matches the economic loss that landholders face when setting aside areas for preservation or restoration (West et al., 2018, Wunder, 2008). In setting aside areas, landholders could lose the opportunity to expand their crops in the future, and thus payments need to consider this lost opportunity (Adams et al., 2010). It is key that the trade-offs among services, the spatial arrangement of supply and demand of ecosystem services, and landholders lost opportunities are included with any spatial prioritization of PES.
Another aspect that should be considered when promoting PES schemes is how they aid the achievement of social equity outcomes (Wegner, 2016). PES schemes have the potential to benefit low-income populations that live in rural areas (Pattanayak et al., 2010). However, without careful planning, their uptake could be skewed towards higher-income populations, exacerbating social inequality as opposed to improving it (West et al., 2018). This potential bias has been attributed to the limited dependence of big landholders on farm income, their increased education, knowledge to access PES schemes, and lower per area cost in establishing PES contracts (i.e., low transaction cost (Jost and Gentes, 2014, Wunder, 2008)). Therefore, a PES scheme that omits social equity factors could not achieve triple bottom line solutions.
Most PES schemes to date have been implemented at local or regional levels (Bremer et al., 2016, Salzman et al., 2018); however, policy for sustainable development is often considered at a larger scale (Bateman et al., 2013). A number of nations now have country-wide PES policies (e.g. Costa Rica, Mexico, Brazil, and China (Calvet-Mir et al., 2015, Chen et al., 2015, Zanella et al., 2014)). Understanding the biophysical and socio-economic trade-offs that affect ES priority area selection at a national scale may help guide these critical national investments in PES. In this paper, we present a spatial framework to evaluate multiple socio-ecological objectives of policies that seek to spatially prioritize areas for PES at a national scale with limited budgets. We account for the spatial variances of land opportunity cost to examine the trade-offs and synergies among biodiversity conservation, carbon and water services maintenance, and investments that are social equitable at the national scale. We illustrate our approach by modelling a national PES scheme in Colombia (Fig. 1A), a policy that the government introduced in 2017 in an attempt to prioritize regions for the payment for ecosystem services (Fig. 1B (Conpes., 2017, Ideam, 2017)). We also compare outcomes to the areas already prioritized within Colombia’s PES policy. The application of our framework will avoid wasted financial resources through the misallocation of important areas when investing limited PES funds at a national scale.
Section snippets
Colombian PES scheme and our framework
The Colombian PES policy accounts for four elements (Conpes, 2017): 1) the protection of important ecosystems (Fig. 2A), 2) consideration of social equity through weighting efforts towards regions of high multidimensional poverty and past armed conflict (Fig. 2B), 3) the need for water (water demand, Fig. 2D), and 4) the protection of ecosystems that are important for carbon storage (Fig. 2C). This policy aims to make annual investments from 2017 to 2030 to conserve a million hectares of
Spatial trends and trade-offs
Our spatial framework shows that the strength of the trade-offs among objectives depend on the scenario considered. Trade-offs were stronger when biodiversity and social equity were included compared to the first scenario where only ecosystems services were considered. Weak trade-offs are found between carbon and water, as the highest solution for either carbon services (max. ∼ 2665 carbon index (Fig. 5A square)) or water services (max. water index ∼ 323, Fig. 5A dot)) shows that the other
Discussion
Our spatial prioritization approach provides a unified framework to explore solutions for PES scheme design with one or multiple objectives, and in this way, it supports decision-making. Although our results show that it is possible to achieve triple bottom line solutions with PES schemes, we also see that trade-offs emerge. For instance, we show that key regions for biodiversity in Colombia (i.e., strategic ecosystems) and social equity exhibit a clear trade-off that may affect the selection
Conclusions
PES schemes are valuable tools to enhance the protection of nature and ecosystem services, while fostering social equity. As multiple objectives can be involved when designing a PES scheme, it is essential to navigate the potential synergies and trade-offs among these different priorities. Accounting for spatially explicit socio-environmental heterogeneity can highlight the optimal areas to invest in PES. We provided a flexible framework where different environmental and social objectives can
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We are thankful for all the information that Javier Rojas from the Colombian National Planning Department (DNP Spanish acronym) provided about the PES policy design. We also thank Rebecca Chaplin-Kramer, Taylor Ricketts, and Rachel Friedman for making comments on early versions of the manuscript and we also thank three anonymous reviewers. SLC was supported by a doctoral fellowship award from COLCIENCIAS (No. 728), the Research Training program provided by the Graduate School, along with BAW,
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