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Baulenas, E., T. Baiges, T. Cervera, and C. Pahl-Wostl. 2021. How do structural and agent-based factors influence the effectiveness of incentive policies? A spatially explicit agent-based model to optimize woodland-for-water PES policy design at the local level. Ecology and Society 26(2):10.
https://doi.org/10.5751/ES-12325-260210
Research

How do structural and agent-based factors influence the effectiveness of incentive policies? A spatially explicit agent-based model to optimize woodland-for-water PES policy design at the local level

1Albert-Ludwigs-Universität Freiburg, 2Centre de la Propietat Forestal, 3Institute of Environmental Systems Research, Osnabrück University

ABSTRACT

A key factor in the resilience of water and forest ecosystems in the face of climate variability is the management decisions taken by the individuals responsible for them, from public officials to private owners. The presence of economic and other non-material incentives can modify the decision-making processes of these individuals and thereby avoid current socioeconomic trends in Mediterranean forested areas such as land abandonment and its detrimental consequences for both social and ecological systems. In this article, we created a spatially explicit agent-based model to observe the effects of the implementation of a woodland-for-water payment for ecosystem services scheme in a local area in Catalonia (NE Spain). The results of the model show that the policy design that supports recurrent management practices obtains the same results at the 25-year mark that other policy designs at the end of the modeled period in number of managed hectares. This design entails the presence of a local intermediary, financial coverage of the management changes to improve water conditions, and the targeting of only one environmental goal, thereby avoiding the ecosystem trade-offs that can arise when two or more goals are targeted. In this design, the first generation of forest owners engaging in behavior change would benefit from their actions, which is also key for maintaining their engagement with the payments for ecosystem services scheme.
Key words: agent-based model simulation; forest; payment for ecosystem services; policy integration; water

INTRODUCTION

Forest cover in Spain has increased from a 12.5% in the 19th century to a current 30% of total land area. Although this increase was partially due to administration-led reforestation and afforestation programs (Vadell et al. 2016), land abandonment has also been a major contributor since the 1960s (Cervera et al. 2019). This land use change is said to be due to low profitability of forests and the abandonment of management practices, as well as interrelated rural-urban migration dynamics (Górriz-Mifsud et al. 2016). This situation is not idiosyncratic of Spain but rather common across Mediterranean countries (Feranec et al. 2010), Eastern Europe (Kuemmerle et al. 2011), and it is an increasingly foreseeable scenario in continental Europe (MacDonald et al. 2000). Although forest cover increase could be a mitigation measure against climate change (Fox 2019), in arid and semi-arid climates the advance of untargeted revegetation can have negative impacts at ecological, landscape, and socioeconomic levels (Lasanta et al. 2017). Among such, forest abandonment implies forest densification with higher impact on drought stress (De Cáceres et al. 2015), land degradation (Symeonakis et al. 2007), and it can lead to a decrease of water availability (García-Ruiz and Lana-Renault 2011) in an already general context of water scarcity (Mekonnen and Hoekstra 2016). Under these scenarios, abandoned land area is easily converted into shrubland, which increases the risk and intensity of wildfire events (Moreira and Russo 2007, Badia et al. 2019) and limits the resilience of the ecosystems because of the slowness of passive regeneration processes (Navarro and Pereira 2015).

There are several policy responses available to counteract the consequences of land abandonment, among which is the potential loss of ecosystem services (Mansourian et al. 2005). One of these, promoted by international (OECD 2010) and European (EC 2012) institutions alike, is the use of payments for ecosystem services[1] (PES) to alter natural resource management decisions to include environmental objectives. Despite attracting some criticism (Fletcher and Büscher 2017), this instrument has been implemented around the globe with enough mixed results to enable an array of possible policy designs (Sattler and Matzdorf 2013, Engel 2016). These recommendations have greatly advanced our understanding of PES schemes, but they rely heavily on examples from developed countries (see, e.g., Wunder 2005, Engel et al. 2008, Muradian et al. 2010). Because the context in which PES are embedded is seen as key for their performance (Lundberg et al. 2018, Wunder et al. 2018), in the current article we distil the lessons obtained from EU studies with special focus on the Mediterranean area[2], which is the location of our case study. Our main aim is to analyze the interlinkage between PES policy design, provision of ecosystem services, and forest abandonment/management dynamics adopted by landowners, whose long-term behavior modification is key to reaching the ecological objectives of the policy in place (Arriagada and Perrings 2013).

Studies from industrialized countries show the influence from micro-level socio-demographic factors on landholder PES participation. Studies point out age, training, farm size, and strength of the tenure as having an effect on the adoption of environmental practices at least in agriculture and with discrepancies on the magnitude and direction of effects (Cranford 2014). There is a similar lack of consensus with respect to exogenous factors such as the effect of economic coverage. Studies on woodland-for-water PES schemes and nature conservation suggest that financial incentives need to surpass the actual costs of the management change for landowners to participate (Kline et al. 2000, Pujol et al. 2006, Ferranti et al. 2017). Other studies claim that financial incentives are not sufficient (Kilgore et al. 2007) and that all alternatives were rejected by landowners if they required a change in their practices (Serbruyns and Luyssaert 2006). Apparently, the reason behind these differences is the presence of owner typologies among the forest owner community of a given country, with some owners always willing to change their behavior, while others would not change it under any condition (Boon et al. 2004, Hogl et al. 2005, Hujala et al. 2007, Ní Dhubháin et al. 2007, Makkonen et al. 2015).

However, even when a high percentage of landowners adhere to a PES policy, it is uncertain what the long-term effects of the policy will be on the ecological system (Pahl-Wostl 2007). The main reason is the effects of climate change at local, regional, and global scales make likely that future conditions of the managed ecosystem will be different from those under which the policy was designed (Millar et al. 2007). In coupled social-ecological systems (SES), uncertainties regarding outputs are accepted as the norm and they should be incorporated in the design of policy and management practices (Folke 2006). Disregarding this could lead to policy failure independent of wide stakeholder participation (Medema et al. 2008). In summary, PES effectiveness is not only based on human actions, but also on the capacity of the program design to accommodate two additional aspects: the ecological variability of the targeted geographical area (Chen et al. 2014), and the uncertainty around how the ecosystem will respond to changes in management practices (Jack et al. 2008). Some PES scholars frame it as the interplay between context, design, and implementation (Wunder et al. 2018).

Because the study of PES cannot be decoupled from either social or ecological systems, nor from its context, with this study we aim to contribute to the debate through the design of a spatially explicit agent-based model (ABM). ABM has been found useful to study the adequacy of different policy instruments in the case of farmers (Janssen et al. 2000), on the management of lake eutrophication (Janssen 2001), and also for non-spatially targeted PES schemes (Lundberg et al. 2018) and spatially targeted schemes in China (An et al. 2005). ABM has a growing presence in the literature on coupled social-ecological systems because it allows simplified representation of complex real-world issues while at the same time introducing a variety of intervening elements (An et al. 2005). One such element is agent heterogeneity, by which there are groups with different characteristics, interests, and behaviors (Pahl-Wostl 2002), but also the interaction of processes that happen at different scales (An 2012). Participatory ABM, specifically, includes the stakeholders at different stages of the modeling process (Guyot and Honiden 2006). In our case, stakeholders were included at the outset of the model design and the discussions addressed design options to increase the likelihood of PES acceptance (Horne 2006, Getzner et al. 2018).

With our model, we simulate the effects of different PES scheme designs on individual behavior (the social) and on water and forest conditions under variable climate conditions (the ecological). Our research question is, how do structural and agent-based factors influence the effectiveness of an incentive policy? To address it, we first conducted a literature review on EU-based PES schemes to extract best practice on PES policy design. Second, we organized a workshop on a river basin in NE Spain in which the design of a woodland-for-water PES scheme was discussed with regional authorities and local stakeholders, including forest owners. We used the information obtained from the workshop to contrast the results of the literature review. Third, we developed a spatially explicit ABM[3] for this geographical area, to study how different design characteristics of PES schemes influence long-term effects on both social and ecological systems. The ABM is based on three sub-models. (1) The social, based on behavioral models of forest owners; (2) the ecological, based on climate models for the local area studied using the GOTILWA+ model (see Methods); and (3) the policy-structural, which contains different PES design characteristics obtained from the literature review on EU PES schemes and contrasted with the results of the workshop.

METHODS

Study area

We chose the geographic area of the Rialb River Basin in Catalonia (Fig. 1), NE Spain, to develop our ABM, because it allowed us to observe abandonment/management dynamics as part of our research aim, which includes the exploration of interlinkages among policy design, ES provision, and behavioral responses. This area, specifically, is exemplary of a case of land use abandonment (Consorci Segre Rialb 2018). Additionally, we had access to information required for our model development.

The basin has an arid continental Mediterranean climate characterized by two distinct seasons: a semi-arid season (~130mm/av 20 °C) and a dry-cold season (~557mm/av 2.7 °C in the coldest months and north of the area because of the influence of the Pre-Pyrenees geology). At the socioeconomic level, the main city Baronia de Rialb has 231 inhabitants, a decline in inhabitant number since 2007 when 285 inhabitants were registered (Idescat 2018). This reflects the overall socioeconomic trends of depopulation and an aging population present in many rural areas of Spain (Serra et al. 2014). Land tenure is predominantly private and only 6.6% of the area is for public utility and selected to be managed to be “conserved and improved in relation to the influence of forest on hydrology” (§11, Catalan Forest Law 6). The number of landowners in the area is estimated at 1098 as of official records from the 2010 register[4]. The area extends across 350.3 km² and around 80% of it is covered by forest mass (FTiP 2003). The majority of the forested land continues to be private, with approximately 260 km² of the area. The codes (colors) in Fig. 1 indicate the land cover changes that occurred between the years 1993 and 2009, with an increase of forested areas as well as the main illustration of the land abandonment processes experienced in the region.

Model overview

To describe our model we used the Overview, Design Concepts and Details (ODD) protocol[5] by Grimm and colleagues in their 2010 updated version (Grimm et al. 2010). The protocol is increasingly used among the ABM community to improve communication as well as replication and extensions of the model.

The purpose of the proposed agent-based model is to contribute to the PES policy design, implementation, and context debate (Wunder et al. 2018). We used the case for a woodland-for-water payment for ecosystem services (PES) and modeled its implementation in a local area of Catalonia (NE Spain). Our research question was the following: how do structural and agent-based factors affect the effectiveness of an incentive policy to integrate the forest and water sector? By structural factors, we meant different designs of a PES policy. For agent-based factors, we used the literature on landowner behavioral studies about reception and reaction to incentive policies from European-focused studies. By success, we understood that both the ecological but also social goals of the policy are reached effectively[6]. Our focus in Europe surges from the general context of land abandonment that many Mediterranean areas and Eastern countries are experiencing, and the growing interest from policy makers and practitioners on the implementation of PES schemes to ameliorate this situation. Specifically in woodland-for-water, the various services provided by forest and water are categorized in policy and the literature as provisioning, regulating, supporting, and cultural (Báliková et al. 2020). In this article, we focus on provisioning services (recharge of groundwater).

Structural sub-model: PES policy design

To select the literature for the review of EU-based best practices, we searched in Web of Science for the keywords “payment for ecosystem services” or “payment for environmental services” (Wunder 2015) and named each EU member state as well as “Europe” (including UK). From this search, we obtained 236 articles. We first examined the abstracts of the articles and discharged those deemed outside our scope following our definition of PES, e.g., Natura 2000 payments or forest subsidies. In a second step, which was performed by two of the authors in parallel, 59 studies were deemed relevant for the current study. We obtained the following information: country/ies, type of scheme (PES or Agri-environment schemes, see footnote 2), field (e.g., forest, agriculture), method, conclusion, summary, and if the PES was in place or it was a theoretical article. These criteria allowed us to select those articles that exclusively addressed PES from empirical cases only. Following the final selection, we summarized best practices based on 16 of the studies. The results were contrasted with those obtained during the workshop with stakeholders.

The half-day workshop was the second held in the Rialb river basin area in the context of an EU-funded project. The organizers were the Forest Ownership Centre of Catalonia (CPF) in coordination with the Catalan Forest Research and Technology Centre (CTFC). There were 25 participants from the local, sub-regional, and regional administrations, forest owners, and representatives of agricultural and tourism associations. The participant selection was based on a stakeholder analysis performed in the first stage of the EU project. The workshop started with a presentation of the goals—the pilot implementation of a PES scheme in the area—and presented different ways in which a PES can be designed. Following this, the discussions among participants were designed in two main lines of inquiry: who pays and who receives it, as well as how. Two workshop coordinators were present in each group, one for taking meeting minutes and a moderator. The discussion raised within each group was summarized in a final report[7]. In general, the participants were very supportive of an incentive mechanism to support forest management to improve water resources, but highlighted strongly the need to implement several communication campaigns to improve adherence. Table 1 shows the factors identified in the literature as best practice in PES design and the workshop conclusions in relation to each of the practice.

The design of the policy is expected to trigger different behavioral responses in forest owners. These responses are the types of forest management forest owners will choose. In this study, these decisions are based on the forest management guides called ORGEST (Sustainable Forest management Guides of Catalonia; Piqué et al. 2017). In these, there are two general management models per tree species considering improvement of water quantity. From the recommendations in these guides, we use the short- and long-term periods for selected thinning (15 and 35 years for conifers and broadleaf, respectively) and 50 and 100 years for end of rotation cutting with natural regeneration. Thinning implies selective removal of trees to allow the growth of others, and in the end of rotation, more trees are removed. The third alternative is management only activated sparingly (50–100 years) following close-to-nature approaches.

Agent sub-model: landowner behavior

Forest owners are identified as primary agents of forest ecosystem services provision (Sotirov et al. 2019). The literature on private forest owners shows that their decision-making process with regard to the management of their forests is not only based on economic factors, but also includes feelings of moral responsibility and pride (Oliva et al. 2016). Moreover, others have argued that the intention of forest owners is to do “good” in their forests (Domínguez and Shannon 2011). Several scholars suggest the need to tailor policy design to the forest owner character type (Boon et al. 2004, Layton and Siikamäki 2009, Primmer et al. 2014) as a factor more relevant than financial incentives (Serbruyns and Luyssaert 2006). The importance of identifying forest owner typologies for the effectiveness of PES has been studied by Ferraro (2008), who recommends collecting information on observable landowner attributes before implementing the policy. At the same time, forest owners have long planning intervals that should be accounted for in the design of any policy.

Forest owner behavioral models cluster agents in six different categories: optimizers, traditionalists, maximizers, passives, multi-functionalists, and environmentalists (Sotirov et al. 2019). These categories are theory led but have been contrasted with empirical evidence (Deuffic et al. 2018) and matched with other established categories from case studies around Europe (see Table 3 in Sotirov et al. 2019). Optimizers and maximizers are characterized by intensive forest-oriented forestry, generally large-scale, with or without respect for rules. The next four categories are rather associated with small-scale forest owners. Traditionalists are related to family ownerships that maintain traditional values. Passives barely manage their forests because of a preference for an urban lifestyle. Finally, multi-functionalists and environmentalists partake in either medium to low intensity management, with a focus on ecosystem services including wood production in the first and close-to-nature approaches in the latter. They generally comply with rules if they perceive these to be aligned with their values. These assumptions are similar to the work of Layton and Siikamäki (2009). Other references support the idea that some agents always participate, whereas others will not, independent of any factor (Boon et al. 2004, Hogl et al. 2005, Hujala et al. 2007, Ní Dhubháin et al. 2007, Makkonen et al. 2015).

From data for the area, we know the number of owners (1098), the approximate size of their forests, as well as if they have a forest management plan (FMP), which are technical documents documenting the activities that will be conducted during a certain period (Brukas and Sallnäs 2012). The base scenario for our model begins with 1000 forest owners and no optimizers or maximizers, because they are not present in our case (Fletas et al. 2012, and data from the 2010 register). Among them, we distributed a random number of hectares, which can be between 1 and 25 ha, as observed in the region and representative of scenarios of property atomization as the main regime in Catalonia (Icea 2019). The distribution is random and changes every time the model is run. From expert knowledge from the area, it is known that 40% of owners have an FMP and 60% do not. From the former, we divide 20% multi-functionalists and 20% traditionalists, because these categories generally tend to have a higher level of engagement in terms of forest practices, including, e.g., contact with forest authorities or request of subsidies. From areas without an FMP, a majority enter into the category of passives (40%) and some are environmentalists (20%), who held beliefs of passive management as a way of nature conservation. In the model interface, these distributions can be changed, but our results are based on the references. The actual distribution of forest owner categories for the area is unknown. The experts, who were from the forest administration in charge of private forests (CPF) in partnership with the researchers conducting the EU-funded project[8], agreed to the plausibility of the final distribution in global terms, of percentage of passives (40%) and actives (60%). Discussions with these experts were held once the second workshop officially concluded and the main outcomes were being summarized.

In the model, agents will not make errors in their decisions: they will always follow the style of management aligned to their values. To bring about agent compliance, the compensation should generally cover the cost of the actual change in behavior nonetheless. Finally, one policy scenario simulates the concept of attrition, by which the policy design demotivates participation in the scheme across the years.

Ecological sub-model: woodland-for-water under climate variability

For the ecological model we used two sources of information: GIS and the outputs of the model GOTILWA+ (Growth Of Trees Is Limited by WAter), developed and applied to study responses of different forest types to water availability in Mediterranean areas, but also applicable to temperate and boreal regions (C. Gracia, S. Sabaté, and A. Sánchez, 2003, unpublished manuscript; D. Nadal-Sala, S. Sabaté, C. Gracia, and CPF, 2014, unpublished manuscript). In the context of an EU-funded project, GOTILWA+ was implemented using the ORGEST guides for certain tree species (Pinus halepensis, Quercus ilex, Pinus nigra, Pinus sylvestis, and Quercus humilis), which are those present in our case study, with Pinus nigra at 40% of the total forested area and Q. ilex and Q. humilis at 26%, jointly following (FTiP 2003). Input data required to run the model ranges from climate change scenarios based on the Intergovernmental Panel on Climate Change, quality of the seasonality, meteorology, and soil quality. From GOTILWA+ we used its output as data for our sub-model, matched to the characteristics of our case study[9]. This output data includes percentage of evapotranspiration (ET) and runoff by tree species and impact of management on runoff and drought (D. Nadal-Sala, S. Sabaté, C. Gracia, and CPF, 2014, unpublished manuscript). Our model does not include seasonality but it does include the expected decrease in rainfall following known variation (FTiP 2003).

In the SESPES model, we grouped trees in two main species, conifers (Pinus halepensis, Pinus nigra, Pinus sylvestis) and broadleaf (Q. ilex and Q. humilis), and each underwent different physiological processes. These were grouped and distributed from raster data to develop a spatially explicit model using the GIS extension. The information contained is topography (altitude and slope), percentage of forested area, and the types of land cover in these areas. In our model, the total number of 32508.8 pixels from the interface is equal to 270 km², where each pixel in the map is equivalent to 0.8 ha. The total forested area is 281.2 km² and, from this, ~50 ha are shrubland. Among the forest owners, the model distributed an average of 260 ha in each run. This base scenario simulates the situation in the region with a majority of forest owners (95.2%) owning < 25 ha (Icea 2019). Table 2 shows the data used for the model and sources by sub-model.

Model parameters were chosen with the use of expert assessment or empirical data from the area. The distribution of forest owner categories was the only data for which we did not have validation via secondary prediction, and we used categorical calibration by observing changes in the number of managed ha through small changes in the distribution of forest owner typologies[10] (Railsback and Grimm 2020).

The simulations represented each of the effects of different PES policy designs on the behavior of forest owners. As results, we present four scenarios:

  1. Short-term, by which all best practices are activated and each forest owner proceeds with their own style of management aligned to the values as presented in the social sub-model;
  2. Attrition, by which every certain number of years the number of forest owners participating is reduced because of no recurrent payments and loss of motivation;
  3. Long-term, by which two ES goals are pursued simultaneously and trade-offs on management imply that forest owners only intervene in the forest under long periods; and,
  4. Base scenario, with only the ecological sub-system, equal to a policy that does not ensure financial coverage to forest owners.

Table 3 and Figure 2 map and illustrate the selected scenarios vis-à-vis the simulated owner behavior.

We ran each scenario 100 times[11] for a period of 150 years (each tick equivalent to one year) because of the stochastic elements of the model, which are the distribution of forest owners and their properties, including dimensions of these properties, as well as the reduction in water use efficiency rate from management. The time period incorporated at least two generations of forest owners (Schouten et al. 2013) and thus the assumption that the second generation will maintain the decisions taken by their precedents. To improve running efficiency, we followed the recommendations from Railsback et al. (2017).

Model verification and validation

Model accuracy was assessed by verification, replication, and validation. Verification is the fit between the conceptual model and the implemented model. Replication is the capacity of other researchers to implement the conceptual model. Finally, validation implies contrasting the model results with alternative predictions, either from the literature or empirical data (see, e.g., Wilensky and Rand 2015). For model verification, we introduced unit tests for the set-up of the model. One is applied to the spatially explicit model to ensure that all land-uses represent the original GIS data. The second tests for the even distribution of ha per forest owner. In both cases, the interface displays an error message to users in the case that either of the two conditions encounters an error. Additionally, we completed the code with pseudo-code, a.k.a. explanations alongside the code of what the model is expected to perform following the conceptual model. To enable model replication, we provide the model and ODD protocol online[12].

For model validation, we made use of empirical data. This involved testing our predictions in relation to number of managed hectares for the total period as well as the estimated average rates of water use efficiency at the end of the 150-year mark. For the first, we used IFN (National Forest Inventory) data and available mapping from the CPF. According to this data, and with the criteria of slope, accessibility, and state of the forest, the potential area to act would be between 3300 hectares (cutting only where there is more timber, minimal but profitable cutting) and 12,000 hectares (cutting where there is something to cut, seeking the maximum benefit in water). The cutting actions currently planned in the set of management plans in the area affect 5760 hectares. With these three data points we compared them vis-à-vis our model outputs. For the water use efficiency validation, we used simulated data for the case study generated in the context of the EU-funded project. The study evaluated the gains in blue water in a single point action that extracted 30% of the basal area (i.e., thinning of ~30% of trees). The simulation is done with the MEDFATE model (De Cáceres et al. 2021), which does not take into account growth (such as GOTILWA) but it is more realistic because it includes competition between trees and shrub and the results were obtained from data from our case study (80 plots in the IFN). From this data, the average annual gain was calculated assuming vegetation growth will be reduced every 15–20 years, as implemented in our model (thinning turns and 30% of thinning for MFU and TRA). The average gain in water use efficiency according to these calculations is 7% in conifers and 3.2% in broadleaves at the 150-year mark.

RESULTS

Policy and social sub-models

The social sub-model starts with 1000 forest owners with each of the small-scale categories with a 20% presence with the exception of passives with 40%. The model randomly distributes “territories” to each group, and thus the number of ha owned by individual and group varies at each run, as well as its location. This has effects on the actions taken by traditionalists, whose management is influenced by the actions of their neighbors[13] rather than stimulated by the policy design. Whereas location could not be tracked during the model runs, the model interface shows the amount of ha allocated to each of the forest owner types at initiation. Table 4 shows the average number of distributed hectares (ha) by forest owner category.

Multi-functionalists (MFU) manage for the short-term under (A) short-term and (B) attribution, and (C) long-term rotation (see Fig. 2, for the policy scenarios). Environmentalists (ENV) manage only long-term, and we can observe from the 150-year mark how these two forest owner categories display similar percentages in scenario C. Traditionalists (TRA) show similar outputs in each of the scenarios. Table 4 also shows in the last column the amount of forested mass at the end of the period, with a decrease up to 10 km² in the base scenario (D), which displays the evolution of the area without the presence of the PES scheme (see Fig. 3). This area is assumed to be degraded.

Figure 4 shows the evolution of managed forests across the 150 years. For the first two scenarios MFU show almost four times more the output of the active peers, and ENV and TRA show a very close-by pattern. Only in scenario D, all three groups show similar distinguishable paths. Provided TRA is triggered by the activity of their neighbors, their managed percentages at the 150-year mark are always below that of the peers even under different management styles between them, short-term and ENV long-term. Another interesting result is the development of policy scenario B considered to trigger attrition. Attrition implies that some forest owners participating in the scheme stop being interested and abandon the policy. In spite of this, the final outputs relative to the short-term policy design are very similar. This implies that the impact of attrition is not linear and appears to be low. Under increasing levels of attrition, thus, the intermediaries of the scheme could intervene by improving communication (external change). Additionally, given that the differences between A and B are recurrent payment vs. one-time payment at the end of the management activity, it would be possible for the intermediary to also modify this aspect to maintain the motivation of the participants. Additional characteristics of both policies are the presence of an intermediary, as suggested, as well as one ecosystem (water and possibly timber, but not water and fire) and the coverage of the costs of the change in management from traditional to additionally improving water bodies.

Noticing the differences between the 25-year mark, conceptualized as the first generation of targeted forest owners (Schouten et al. 2013), and the end of the period also shows important results. The overall percentage of managed forests in both the short-term and the attrition policy design are similar to the percentage reached in policy C at the 150-year mark. The fact that PES participants observe benefits from their participation in the scheme has been mentioned as a motivational factor to keep the policy. At the same time, it has very important implications for the effects on the ecosystem. In summary, policy A and B established a link between the social goal (active stakeholders) and the ecological goal (water efficiency) more clearly then the long-term design.

Policy and ecological model

The ecological model varies across the years, taking into account the effects of climate change. The two main simulated effects are a reduction in precipitation and an increase in the amount of water needed by forest stands. We added the effects of drought, an event that occurs with the decrease in precipitation and during which older trees show signs of stress either through dieback or converting into shrubland. The rate at which this occurs increases with the decrease in rainfall.

Figure 5 displays the evolution of the forest under the different policy simulations. The base scenario reflects the above-mentioned ecological circumstances and parallels the policy in which the cost of management change is not covered. The lack of financial support would impede forest owners to engage in a change in their management and thus the ecosystem evolves without the effects of a policy (D). The map in (C) represents the effects of the policy when it triggers long-term instead of short-term management (A), and the third scenario implies the context of attrition, but which displays similar results as commented above. These illustrations show the endpoint after 150 years. Brown dots simulate tree cover under stress, and light green dots, parcels that have been managed. In scenario A, the territories of some of the forest owners can be distinguished. Tree stress is in spite of management present in all scenarios, and it does not differ much across scenarios: with a simulated 8.7 km² loss in scenario A and 11.8 km² in scenario D.

The final output from the model is the expected average of blue water percentage across forest tree species (Fig. 6). Blue water is defined as the percentage of water after tree consumption, which corresponds to the run-off and drainage (D. Nadal-Sala, S. Sabaté, C. Gracia, and CPF, 2014, unpublished manuscript). These begin at an average of 35% for conifers and 15% for broadleaf and decrease by 0.03% each year. Management is expected to improve the ratio by 0–10% and we simulated the effects whereby, when managed, tree stands would demonstrate an improvement of between 0 and 10%.

Results show that without any type of management, in 100 years, there is a 3-point reduction in blue water, whereas under all other types of policy design, this rate is improved, despite all of them being slightly less than the initial base scenario. The marginal difference between scenarios is minimal, but scenarios A and B show similar rates to the ones at the year-mark 0 (model initiation). Thus, with regard the water use efficiency output expected from the presence of the PES policy, the types of management do not show much difference among each other despite significant differences in the amount of managed stands.

Validation of the model outputs

Our main two outputs of the model are amount of managed forests and the improvement in the blue water percentage relative to green water. For amount of managed forests, the total managed hectares for policy B and C are very similar to the minimum and planned amount. The minimum amount of management in the area is estimated at 3300 ha taking into account only minimal cutting. Our policy scenario C obtained an average across runs of 3461.6 ha, which is a scenario accounting for close-to-nature management rotations characteristic of long-term interventions. Policy scenario B displayed a total output of 5760.4 managed ha. This output is casually exact to the planned number of ha that are planned to be managed in the Rialb river basin, following the information from the FMP, also estimated at 5760. This amount has a difference of ~560 ha to scenario A output set at 6321.7 ha on average. We thus consider this output realistic in terms of potential expected amount of forest management in the area. Finally, in relation to this, the maximum capacity of the forest is set at 12,000 ha which would imply cutting independently of the profitability and obtaining the maximum gains in water use efficiency. These scenarios could be reached if any of the forest owners acted with similar patterns as the MFU. However, in the context of our case study representing a Mediterranean area with low profitability of forest products and land abandonment is a very unrealistic scenario without public intervention and investment.

The second step of the validation implies the water use efficiency outputs. In this case, there was a parallel study performed to draw estimates of the % of blue water at the 150-year mark with a similar management regimen as performed in our model by MFU and TRA. This study showed a 7% (blue water ratio relative to green water at 28%) improvement in the blue water percentage in conifers and 3.2% (11.8%) in broadleaf. Our results for broadleaf are very similar, with a difference of -0.7, -0.5, and 0.2 respectively in scenarios A, B, and C. However, the differences are of up to -6 points in the case of conifers (-5.9; -5.7; -4.6). These differences could be explained by the fact that water use efficiency was a random process happening each time there was management: the range for conifers was 0–10% improve and for 0–5% for broadleaves, following the results from GOTILWA, and thus, the possible variance within the latter was smaller than in the case of the former. With these results we cannot validate our results in terms of the output for the water use efficiency gains.

DISCUSSION AND CONCLUSION

There is a gap between policy implementation and agent compliance. Policy design is expected to shorten this gap, by triggering different behavioral responses in the targeted agents. In a recent EU survey, these direct changes in behavior were signaled as the key factor influencing the effectiveness of woodland-for-water schemes (Báliková et al. 2020). For these reasons, PES policy design has generated an intense debate in the literature (Wunder et al. 2018, 2020, Wells et al. 2020). In this study, we have considered additionally the context of land abandonment and management dynamics with a Mediterranean case study.

Based on an EU-based PES literature review, we designed a spatially explicit agent based model to observe how such structural factors would affect the decisions of forest owners and influence the ecosystem. Results show that at the 25-year mark, which includes the assumption that the first generation of targeted forest owners will still be present, the management outputs of policy designs that fostered recurrent interventions in the forest are the same as those expected at the 150-year mark for other policy designs, particularly those that triggered long-term management. The considered optimal policy design included repeated payments, the presence of a local intermediary, and the targeting of one ecosystem goal. Long-term policy designs included the targeting of multiple ecosystem goals such as water protection and fire risk management. At the ecological level, nonetheless, the several scenarios showed similar outputs in terms of water use efficiency gains, which could imply the necessity for more aggressive forms of intervention that some stakeholders may always reject to implement because of contrary values of what “optimal values” are significant. Other studies performed in our area study and using the same management approach as the one simulated in the optimal scenario, nonetheless, showed significant differences in the water use efficiency. Our model could thus be underestimating the impact of the management on water.

Our findings additionally suggest that PES that include the figure of the intermediary could be beneficial for areas in which urban-rural dynamics have driven land abandonment. The choice of the intermediary is seen as key because it is expected to generate trust among targeted agents, some of whom would not otherwise adhere to the policy. In our model, the figure of the intermediary ensured that the first generation of targeted forest owners would be able to see changes in the system from the change in behavior. The literature has already highlighted that the intermediary is a key actor because it generates trust among stakeholders and can improve the likelihood that targeted agents will voluntarily enter the scheme (Báliková et al. 2020). This supports the literature on PES schemes that categorizes it as a hybrid system consisting of both market- and network-based modes of governance (Pahl-Wostl 2015). This is demonstrated in the fact that financial support is not sufficient by itself, but requires collaborative structures among stakeholders (from forest owners to authorities) to ensure that the PES policy influences the targeted social-ecological system. This intermediary could add communication policies to its repertoire to have better engagement with forest owners and find synergies between the values and preferences of stakeholders and the goals of the PES policy, as the results of the workshop also highlighted. Innovating in PES policy design and allowing for different forms of compliance has also been suggested by PES scholars as possible means of increasing compliance (Jack et al. 2008). Based on empirical studies on forest owners, some scholars suggest these communication activities should also be designed differently depending on recipients’ characteristics (Kuipers et al. 2013).

The model accounted also for the presence of different landowner typologies, which responded differently to the design of the policy. Provided this study’s and similar findings in related literature (Boon et al. 2004, Serbruyns and Luyssaert 2006, Ferranti et al. 2017), taking into account the characteristics of landowners and their socioeconomic context seems of key importance. This stakeholder network mapping would include information on the intensity of their management and engagement within the landowner community, main values about the ecological, recreational, and production views on forests (Nordlund and Westin 2011), and the main activities performed in them. This information could help assess the expected compliance and even their willingness to accept a conservation policy. In our case, the distribution of forest owner typologies was the primary assumption. Whereas we based the initial distribution in expert knowledge from the area, there are some discrepancies that we consider as limitations for our study. On the one hand, for the model we assumed forest owners without management plans were either passives or environmentalists, and forest owners with management plans were active categories. Moreover, we assumed that multi-functionalists would always engage in the PES scheme. These are non-validated assumptions, and for this reason we understand that either including the use of surveys or extending the participatory approach to the ABM at several points of model development would be of great benefit for these type of studies.

In Europe, financial incentives for ES provisions other than provisioning services (e.g., timber) are more present in the agricultural sector than in the forest sector (Bösch et al. 2018). Nevertheless, there is increasing interest in implementing such schemes to support forest management, with the expectation that these would “provide significant benefits to a locality, community or industry” (Capodaglio and Callegari 2018), even if they were implemented in limited local areas. Innovating in policy design for the management of complex social-ecological systems is proposed by proponents of adaptive management and co-management approaches to governance (Armitage et al. 2009). In such schemes, building trust among stakeholders as well as continued monitoring of the implemented policies are also emphasized. In line with the results of our study, we consider this form of governance to be compatible with PES.

To conclude, an extension of the model could tap into the possibility for an innovative approach to PES design including differences depending on the targeted stakeholders, instead of being, as we have implied in this article, one-size-fits-all. The exploration of this possibility could yield further contributions to the debate on PES design. An alternative extension could be the incorporation of the “willingness to pay” (Primmer et al. 2014, Mäntymaa et al. 2018). Generally, PES schemes consider the involvement of private benefiting actors, such as tourism companies or other local providers. Involvement of these stakeholders in a participatory ABM could yield important insights into effective PES design.

__________

[1] PES can be defined as “(1) voluntary transactions (2) between service users (3) and service providers (4) that are conditional on agreed rules of natural resource management (5) for generating offsite services” (Wunder 2015:241). This definition was adapted from Wunder (2005) in response to critiques and implementation experiences. For more on PES definitions see Sattler and Matzdorf (2013).
[2] In Europe, compensation for ecological services from natural resources started in the 1970s with the agri-environmental schemes (AES) under the Common Agriculture Policy, as a policy closely resembling PES (Schomers and Matzdorf 2013).
[3] The ABM, designed with Netlogo (Wilensky 1999), can be downloaded from the Netlogo Modeling Commons website as well as COMSES network, to be found under the name: SESPES: socio-ecological systems and payment for ecosystem services model.
Netlogo: http://modelingcommons.org/browse/one_model/6398
COMSES: https://www.comses.net/codebases/1b1ac7b5-8015-4003-a838-2180586b8083/releases/1.1.0/
[4] Data obtained from the register of the forest administration, Centre de la Propietat Forestal, in charge of private forests.
[5] Reference to the ODD protocol from our model in: Baulenas, E. (2020, 20 December). “SESPES: socio-ecological systems and payment for ecosystem services model” (Version 1.1.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/1b1ac7b5-8015-4003-a838-2180586b8083/releases/1.1.0/
[6] Our understanding of effect on the ecosystem is informed by the literature on adaptive governance, which describes it as “restoring, sustaining, and developing the capacity of ecosystems to generate essential services.” (Olsson et al. 2006).
[7] The final report can be shared upon request. A press report from the meeting can be found in the project website (accessed December 2020): https://sincereforests.eu/rialb-2021-forests-and-water2/
[8] These same experts are involved in supporting the creation of a forest association in the area, and thus are in contact with the local community of forest owners.
[9] GOTILWA+ provides an average of all management made in a specific forest over 150 years (approx. 10 clearings), starting in 2000.
[10] We augmented or decreased active and passive forest owner typologies by 10%. Results show that a 10% increase in the number of active forest owner typologies has a greater effect on the total number of managed ha than the effect that it has on the contrary scenario of greater number of passives. However, these changes are minimal and they are inside the parameters given by the number of expected management.
[11] We base this number as an often used standard in ABM literature (see for discussion and recommendations: Lee et al. 2015).
[12] See note [3] or supplementary material for the protocol.
[13] Neighborhood is simulated as TRA noticing whether in a radius of 10 ha in any direction there is another forest owner active.

RESPONSES TO THIS ARTICLE

Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.

AUTHOR CONTRIBUTIONS

E.B. conceived the study and designed and developed the model. T.B and T.C. were in charge of conducting the activities and empirical studies in the case study. E.B. wrote the manuscript under supervision of C.P.-W. E.B. wrote the ODD protocol in consultation with T.B and T.C. C.P.-W supervised the development of the project.

ACKNOWLEDGMENTS

We would like to thank the research group at the University of Osnabrück for providing very valuable feedback on earlier versions of the model and the manuscript. We would like to also thank the anonymous reviewers who allowed this study to strengthen in multiple ways, but also, for their constructive and helpful style.

DATA AVAILABILITY

The data/code that support the findings of this study are openly available in modeling commons at: https://www.comses.net/codebases/1b1ac7b5-8015-4003-a838-2180586b8083/releases/1.1.0/

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Address of Correspondent:
Eulàlia Baulenas
Tennenbacher Str. 4 (4 Og)
Freiburg, Baden-Württemberg
Germany
79106
eulalia.baulenas@ifp.uni-freiburg.de
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Table1  | Table2  | Table3  | Table4  | Figure1  | Figure2  | Figure3  | Figure4  | Figure5  | Figure6  | Appendix1