Ecological Indicators 121 (2021) 107056
Available online 9 November 2020
1470-160X/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
A decision making approach for assignment of ecosystem services to forest
management units: A case study in northwest Turkey
˙
Inci Caglayan
a,*, Ahmet Yes¸il
a, ¨Ozgür Kabak
b, Pete Bettinger
c aIstanbul University-Cerrahpas¸a, Faculty of Forestry, Istanbul, TurkeybIstanbul Technical University, Faculty of Management, Istanbul, Turkey
cUniversity of Georgia, School of Forestry and Natural Resources, Athens, GA, USA
A R T I C L E I N F O Keywords: Ecosystem services Decision making Delphi method Assignment problem Mixed integer programming
A B S T R A C T
In general, studies are carried out to determine the location and to resolve trade-offs in the management of forests according to ecosystem services. The government of Turkey also has been advancing this type of analysis in the practical management of forests. The creation of ecosystem services (ES) suitability map for forests through a participatory approach can provide forest managers valuable information that informs ecosystem- based management plans. The objective of this paper is to formulate and solve the problem of assigning man-agement actions to forest stands in order to optimally provide ecosystem services that are consistent with, and based on Sustainable Development Goals (SDG). We outline a model for a participatory process that has seven steps: (1) determine the possible ES that can be supported on the landscape, (2) create a suitability map for each management unit (stand of trees), (3) define SDG for each ES, (4) create the stakeholder groups, (5) determine the importance of each SDG, (6) determine the contribution of ES to each SDG, and (7) assign ES to each management unit in an optimal manner. We used pairwise comparisons and the Delphi method to determine stakeholder preferences. This information guided the assignment of management actions to management units when optimizing ES outcomes. This approach was applied to a case study centered on the Belgrad Forest, located in northwest Turkey. The percent of land assigned to ES was respectively, water regulation (71%), cultural heritage (17%), education (9%), water supply (2%), and carbon (1%). Through this process, for this case study area, there were no assignments for recreation and aesthetic quality services. As a result, the Belgrade Forest was zoned into management classes unlike the current situation. The pairwise comparisons and the Delphi method can help formalize public participation in the decision-making process. Results from these can inform a resource allocation optimization model through the development of coefficients and constraints.
1. Introduction
In recent decades, there has been a shift world-wide in philosophical approaches to forest planning, from timber harvest sustainability to broader sustainability concerns. Even though sustainable timber man-agement (sustainable wood flow) is still a valid and practiced manage-ment philosophy, multi-purpose forest managemanage-ment, sustainable forest management, ecosystem management, and the sustainability of ecosystem services (ES) are increasingly becoming the basis for forest planning (Raum, 2017). Many international organizations (Millenium Ecosystem Assessment, 2005; Schauer et al., 2008) and researchers (Costanza et al., 1997; Kindler, 2016; La Notte et al., 2017; Maes et al.,
2013) have described how to classify ES derived from a forested land-scape. ˙In addition, a number of studies concerning the identification and quantification of ES (Egoh et al., 2012; Heal et al., 2005; Kindler, 2016) and trade-offs among ES (Bennett et al., 2009) have become available to society. Research concerning the mapping of ES across broad landscapes is also slowly progressing (Andrew et al., 2015; Egoh et al., 2008).
Many challenges remain for integrating ES into forest management planning processes, and analyzing how ES will be affected by forest management activities (Carpenter et al., 2009). The trade-offs between competing ES may be poorly understood, and spatial modeling of these may be challenging (Filyushkina et al., 2016). Therefore studies con-cerning the functional relationships among ES and broad-scale * Corresponding author.
E-mail addresses: inciyaylaci@istanbul.edu.tr (˙I. Caglayan), ayesil@istanbul.edu.tr (A. Yes¸il), kabak@itu.edu.tr (¨O. Kabak), pbettinger@warnell.uga.edu
(P. Bettinger).
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolindhttps://doi.org/10.1016/j.ecolind.2020.107056
attainment of ES across landscapes will help advance our ability to achieve sustainable management (Bennett et al., 2009). The ES concept is gradually gaining acceptance as a tool to guide the structure and function of forest management plans, and to involve stakeholders in the management of forest lands (Smith et al., 2011). However, as timber production is a provisioning service and as recreation is a cultural ser-vice, both long integrated into forest plans, the current efforts seem more accurately aimed at expanding the suite of ES that are acknowl-edged in forest plans, rather than exploring the trade-offs among ES. A key challenge of forest planning is determining how to manage multiple ES across a landscape and effectively implement planning decisions based on ES (Geneletti, 2011).
Sustainable development goals (SDG) have been proposed by United Nations member states as guideposts for the international community to follow toward sustainable development (Le Blanc, 2015). Forests play an important role in achieving SDG that focus on water, health and food, climate, biodiversity, and sustainable land use (New York Declaration on Forests Global Platform, 2018), thus forests provide a wide range of ES that may contribute to the SDG (De Jong et al., 2018). One way to define the importance of SDG and how ES can contribute to these is to elicit the opinion of stakeholders on this issue. Stakeholder analysis literature focuses on understanding interests and relationships (Mitchell et al., 1997). There are a number of studies in the literature on how group decision-making techniques are used to determine the importance of ES. For identifying stakeholders in a system under study, one could use expert opinion, focus groups, semi-structured interviews, snowball sampling, or a combination of these methods (Reed, 2008; Reed et al., 2009). Raum (2018), for example, presents an approach for the sys-tematic application of stakeholder input in ES research. Palacios- Agundez et al. (2013) used World Caf´e methodology to structure a questionnaire and a workshop involving participatory methods, and to present scenarios for planning. De Meo et al. (2018) analyzed stake-holders’ opinions and preferences for forest ES using a questionnaire provided to forest planners. Scolozzi et al. (2012) presented a study using a Delphi-based estimation of land use potential for determining
the importance of ES. And, Filyushkina et al. (2018) studied the effects of five potential forest management regimes on the preservation of biodiversity and wildlife habitat using the Delphi method. However, few studies have focused on broader suites of forest ES.
Land suitability can be described as the fitness of a given area of land for some defined use (Steiner, 1983). Based on how it is described in some of the current literature, land suitability mapping for ES purposes might be classified as hotspot mapping. However, within the literature there seems to be no consensus concerning the definition of hotspot maps (Schr¨oter and Remme, 2016). With respect to ES, hotspots have been described as “areas which provide large proportions of a particular service” (Egoh et al., 2008, p. 136). Using this definition as a guide, a large proportion of an ES may also refer to the provision of high suit-ability of an ES within an area. Hotspot mapping has been widely used in identifying suitable areas for ES (Schr¨oter and Remme, 2016). There-fore, ES suitability mapping may be similar to hotspot mapping although applied delineation methods differ. The development of maps repre-senting suitable areas for certain ES within forests has also become an important consideration for achieving SDG.
Multi Criteria Decision Analysis (MCDA) is a methodology for designing a decision-making problem, describing the priorities of the problem, combining and assessing the alternatives, and making rec-ommendations (Guitouni and Martel, 1998). MCDA has been used in land use assessments that have many different foci. For instance, MCDA methods have successfully been applied in analyzing land-use changes and impacts on ES (Fontana et al., 2013), forest management (Ananda and Herath, 2009; Uhde et al., 2015), sustainable forest management (Wolfslehner and Vacik, 2011), sustainable agricultural management (Talukder et al., 2018), wetlands (Rodríguez-Merino et al., 2020), land use planning (Ioki et al., 2019) and suitability mapping for ES (Egoh et al., 2012; Grˆet-Regamey et al., 2015). Different methods of MCDA, like MAUT (Ananda and Herath, 2005), AHP (Khadka and Vacik, 2012), PROMETHEE (Huth et al., 2005; Talukder and Hipel, 2018), ELECTRE (Kangas et al., 2001), are being used in this context (Kangas et al., 2001; Uhde et al., 2015). MCDA approaches based on GIS are most often used
for land suitability mapping (Malczewski, 2006). Among these decision processes, AHP is useful in many situations (Guitouni and Martel, 1998), especially in land suitability assessment (Bunruamkaew and Murayam, 2011). In addition, MCDA is a powerful tool to support participatory and collaborative processes associated with planning (Marttunen et al., 2015). Participatory ES assessment is important for the development of plans aimed at managing the sustainability of ES. Boj´orquez-Tapia et al. (2001) illustrated outcomes of land suitability assessments based on GIS, using a participatory approach.
However, to our best knowledge, there is no study in the literature that uses MCDA methods with participatory approach for assessment of assigning ES priorities to forest management units. In this study, we created sophisticated approach for assignment of ES priority to forest management units based on SDG, suitability values of ES, MCDA that is composed of integer programming, and group decision making approaches.
This paper outlines an approach for a participatory, seven-step pro-cess to determine the value of ES to stakeholders in the management of a forest. Using SDG as a guide, the objective of this paper is to formulate and solve a forest management problem that makes trade-offs among ES and optimally assign ES classes to each forest management units, using integer programming. The development of the mathematical formula-tion is informed by a suitability mapping approach, and by preferences for ES obtained from stakeholders of a forested area, through the use of pairwise comparisons and the Delphi method. The objective of the
research was to determine whether the combination of social and mathematical processes for describing the forest management problem could result in a feasible allocation of ES to forest management units in a manner that faithfully reflected the values local society held for the forest.
2. Materials and methods
The materials and methods that are described below are divided into two main distinct parts. First, we selected a representative study area to apply the methodology. Second, we explained the methodology involved in assignment of ES to forest management units, which is noted in the seven substeps from Section 2.2.1 to Section 2.2.7.
2.1. Study area
Nearly all of the forests in Turkey are state-owned and administrated by the General Directorate of Forestry (GDF). Private landowners own less than 1 percent of the total forest area. The management plans for state-owned lands are created by the planning department within the GDF and are developed to provide tactical level guidance. The study area encompasses the Belgrad Forest, with an area of 5660 ha, located in the northwest part of Turkey (Fig. 1). The Belgrad Forest is high forest (developed from seeds or seedlings, rather than from coppice), and is dominated by broadleaved tree species, mainly beech (Fagus orientalis)
and oak (Quercus spp.). Hornbeam (Carpinus spp.) can also be found scattered in all forest stands, and conifer plantations are often mixed with deciduous trees. The forest area includes historical water reservoirs that originated during the Ottoman Empire period.
2.2. Methodology
The methodology consists of seven general steps as given in Fig. 2. The details of the steps are given in the following subsections. 2.2.1. Step 1: Determine the possible ES
Ecosystem services are processes, functions, and products that are beneficial to society. They can be groups into categories such as cultural services (recreational, religious, etc.), regulating services (water purifi-cation, disease regulation), provisioning services (outcomes directly used by society, such as wood and water), and supporting services (e.g.,
primary production) that are needed by other services (Grebner et al., 2013). The dependency of one group of services on another can be quite complex (Smart et al., 2010). The GDF in Turkey has recognized some broader classifications of ES (Eraslan, 1982) that include public health, climate regulation, erosion control, food production, hydrologic sys-tems, raw materials, education and science, aesthetic quality, and rec-reation opportunities. For this analysis, we decided to use a more extensive, non-categorized classification of ES (Table 1) that follows the work of Kindler (2016).
The identification of the ES for the Belgrad Forest was based on the following guiding interview questions directed toward a sample of 31 stakeholders:
Question 1: Thinking about the ES provided by the Belgrad Forest among those listed above, which ecosystem services are more suitable for the Belgrad Forest?
Question 2: In which ES are you interested and how would you assess the degree of suitable ES? (Very strongly (5), strongly (4), moderately (3) weakly (2) very weakly suitable)?
The 31 stakeholders held master’s or doctorate degrees involving forestry science. From the responses we received, we selected the top seven ES according to their scores from question 2 (Table 2).
2.2.2. Step 2: Create a suitability map for each management unit In order to illustrate the potential of the top seven ES for the Belgrad Forest, we collected criteria related to each ES set forth in the published literature. Each management unit or stand (collection of trees of similar age, species mix, and management history) in the forest was assigned a value on a scale of 0 to 1 for each ES. Geographical analysis was used to assign a value for each ES to each stand according to raster, vector, and point data analysis processes (Figs. 3–5). In these figures, a gradient of green to red colors corresponds to the range of low (green) to high (red) values for each ES. These sets of suitability scores for each stand range in value from 0 to 1 (Table 3). Although there were similarities among the criteria for different services (e.g., water regulation and water supply have the same criteria), mapping each stand’s potential ES value was distinct because of the different weights of the criteria. Some of the ES were logically spatially clumped or clustered in relation to social (e.g., intensive activity area) and ecological (e.g., slope, elevation) factors. For example, higher recreation values are found in the intensive activity
Table 1
Full set of ES considered in this analysis (Kindler, 2016). Ecosystem services
1) Gas regulation 9) Waste treatment 17) Education and science 2) Climate regulation 10) Food production 18) Water purification 3) Disturbance regulation 11) Genetic resources 19) Soil formation 4) Nutrient cycling 12) Water supply 20) Refugia 5) Pollination 13) Sense of place 21) Cultural heritage 6) Biological control 14) Raw materials 22) Spirituality and religion 7) Water regulation 15) Biochemical 23) Aesthetics/inspiration 8) Erosion control 16) Fuel wood 24) Recreation
Table 2
Rating of ES as determined by experts.
Ecosystem service Score
1 Education and science 4.42
2 Culture heritage 4.29 3 Aesthetics/inspiration 4.29 4 Water regulation 4.19 5 Water supply 4.03 6 Gas regulation 3.94 7 Recreation 3.87
areas of the forest, while higher cultural heritage are found clumped around old forest areas, and higher aesthetic values are preferentially clustered on the edges of the lake (Caglayan et al., 2020). However, we
observed from this processing step that a single stand could have the same value for different ES. Since the goal of this step in this process was to create a map illustrating the highest values ES for each stand, this
Fig. 4. ES suitability maps for each stand’s potential contribution to a) aesthetic b) recreation services (Caglayan et al., 2020).
Fig. 5. ES suitability maps for each stand’s potential contribution to a) carbon b) water regulation c) water supply services. Table 3
ES suitability values for ten example stands in the Belgrad Forest (in model:Sij).
Stand ID Education Cultural heritage Aesthetics /inspiration Water regulation Water supply Gas regulation Recreation
1 0.1 0.0 0.0 0.7573 0.7703 0.3024 0.3939 2 0.1 0.0 0.0 0.7450 0.7670 0.4915 0.5726 3 0.1 0.0 0.0 0.7845 0.7715 0.5511 0.4183 118 0.1 0.5 0.0405 0.8135 0.7578 0.6166 0.4418 143 0.1 1.0 0.0395 0.5344 0.8794 0.0233 0.2820 363 0.1 1.0 0.0928 0.7937 0.7471 0.6120 0.3149 375 1.0 1.0 0.0685 0.7683 0.7724 0.4239 0.3340 1000 0.1 0.5 0.0001 0.7993 0.7647 0.6901 0.4235 1168 0.1 0.0 0.0 0.7285 0.7684 0.6701 0.2926
posed a problem concerning the selection of the appropriate ES to map, as there were several stands where the highest suitability scores were equal between two ES classes. Fig. 6 illustrates the suitability values for selected stands with respect to the seven ES. For example, Stand ID 375 (the yellow line) had the same value (1.0) for education and cultural heritage services and lower values for the other five ES. Because of this issue of non-dominating ES preferences, we concluded that we should conduct a broader participatory process to inform the mathematical optimization (assignment) problem.
2.2.3. Step 3: define SDG for each ES
Because all ES are relevant to SDG (Geijzendorffer et al., 2017; Wood et al., 2018), an approach was developed to connect selected SDG with forest ES. We compared the management goals of each management unit with each SDG. As a result, we found that goals for the management of ES are highly related to one or more of the SDG. Therefore, we accepted as management goals the 5 SDG noted below.
SDG15: Life on Land represents protection of nature for sustainability and is directly related to forest conservation. The goal suggests one should protect, restore and promote sustainable use of terrestrial eco-systems (15.1), sustainably manage forests (15.2), combat desertifica-tion (15.3), and halt and reverse land degradadesertifica-tion and halt biodiversity loss (15.4, 15.5, 15.8).
SDG6: Clean Water & Sanitation represents a need for clean, acces-sible water. The goal suggests one should ensure the availability and sustainable management of water and sanitation for all (6.1, 6.3, 6.4, 6.6).
SDG3: Good Health & Well-Being is related to a healthy life. The goal suggests one should ensure healthy lives and promote well-being for all at all ages (3.3, 3.4, 3.9).
SDG13: Climate Action represents actions to combat climate change and its impacts (13.1).
SDG8: Decent Work & Economic Growth aims to promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all (8.2, 8.4, 8.9).
We need to describe the relationship between human well-being and ES to understand the relationship between SDG and the ES, because ES contribute directly or indirectly well being (Díaz et al., 2018; Wood and DeClerck, 2015). Therefore, it is important to identify the measurable determinants of well-being that can be associated with the delivery of ES (Smith et al., 2013). Some studies have investigated these linkages. For example, it has been shown that climate change affects the achievability of human well-being (Nerini et al., 2019). Furthermore, a failure to
sustain ES has been shown to have a negative effect on human well- being (Millennium Ecosystem Assessment 2005; Raudsepp-Hearne et al., 2010; Wood and De Clerck, 2015). On the other hand, tourism (Urosevic et al., 2018) and education (Vladimirova and Le Blanc, 2016) may contribute positively to SDG. Therefore, the relative contribution of each ES in achieving SDG needs to be estimated (Costanza et al., 2016; Wood et al., 2018). The potential linkages between ES and human well- being helps us understand the interrelated dynamics between ES and SDG. Forest management can contribute to the achievement of SDG (Baumgartner, 2019; Mancini et al., 2019), and therefore the contribu-tions of forestry (and associated ES produced) to SDG were determined in a workshop using Delphi methodology, which is described below. 2.2.4. Step 4: create the stakeholder groups
We needed to find a way to canvass opinions and achieve consensus on priorities. Therefore, we organized a workshop to obtain consensus the about SDG from stakeholders. A working group session of stake-holders was convened in 2018 in Istanbul, Turkey. Sarkissian et al. (1997) suggest the typical stakeholder groups for environmental de-cisions are: client groups, industry, the public, politicians, state agencies, local agencies, local councils, business/traders, media, and community activists. Raum (2018) classified stakeholders groups ac-cording to their interests in ES: governors, commercial, non- governmental organizations (NGOs), investors, professional organiza-tions, university employees (scientists and educators), woodland owners, communities, and individuals. Yes¸il et al. (2003) listed stake-holders as: municipalities, NGOs, GDF, forestry research institutes, universities, hunter associations, forest products industry owners, champers of agriculture, forest and landscape architects, forest villages and cooperative representatives, urban people, forest worker unions, farmer representatives, miners, tourism professionals, and the media. In this study, this latter classification of stakeholders was adopted to un-cover their interest in forest ES. The final identification of the stake-holder groups was based on guidance from the initial sample of 31 experts. The following question was posed to them.
Question 4: Think about which stakeholder groups are more likely to affect forest management decisions. In which stakeholder groups are you interested in and how would you assess the degree of effective stakeholder groups on decisions (Strongly (3), moderately (2) weakly (1)?
Fig. 6. Suitability values of four selected stands for the seven ES (stands were
chosen randomly).
Table 4
Selection of stakeholders.
Stakeholders Score (range: 1–3)
1 University employees 3.00
2 General Directorate of Forestry 2.97 3 Non-governmental organizations (NGOs) 2.77
4 Forestry research institutes 2.68
5 Media 2.52
6 Champers of agriculture, forest and landscape architects 2.48
7 Urban people 2.48
8 Municipalities 2.32
Table 5
Homogenous stakeholder groups.
Stakeholders Group
1 Group 2 Group 3
University employees 1 1 1
General Directorate of Forestry 1 1 –
Non-governmental organizations 1 1 1
Forestry research institutes 1 1 1
Media 1 – –
Champers of agriculture, forest and landscape
architects 1 1 –
Urban people 1 1 1
Municipalities 1 1 –
From the responses we received, eight stakeholder groups seemed most important according to scores assigned to them. A set of stake-holders to be used in the Delphi method were identified and invited for the meeting. For this reason, university, GDF, and NGOs were found to be the main groups (Table 4). We then developed the list of stakeholders and periodically updated the list to reflect cancellations on the work-shop day. Still, some of the participants did not attend the workwork-shop day, and therefore some stakeholders in Group 2 and 3 were missing (Table 5).
2.2.5. Step 5: determine the importance of each SDG
Once the three groups convened, the Delphi method was used to understand stakeholder preferences for the assignment of ES to stands. The respondents to Delphi questionnaires focused on the weights of SDG and the contribution of ES to SDG. The Delphi method is a widely used framework for developing information on preferences from question-naires and surveys of stakeholders. The main properties of the method can be described as (Landeta, 2006):
•The experts or participants must be consulted repeatedly on the same question so that they can reconsider their answers.
•Anonymity of answers is maintained by the group coordinator. •Information exchange between the experts or participants can be
facilitated by the group coordinator.
•Final answers from groups are presented quantitatively and statistically.
The Delphi method began with a clear description of each SDG and its relation to the ES. The participants were asked to develop the weights associated with SDG and the contribution of ES to each SDG. We entered stakeholder’s scores into an Excel spreadsheet at the end of each round. We used a pairwise comparisons to assess consensus between the stakeholders. The results of the pairwise comparisons were presented to the group members, who wanted to review the group opinions (Table 6). The results did not circulate for the second round of the inquiry, because stakeholders group had reached a consensus during the first round. The results of the pairwise comparison of the groups for SDG are given in the appendix in Tables A1–A3. Then the geometric means of the values in the tables are calculated to aggregate the comparisons to the final
pairwise comparisons table given in Table A4 (Saaty, 1990). The con-sistency ratios of the three groups are less than 20% and the final table is less than 10%. Therefore, the results are found to be consistent. To find the weights of the SDG eigenvector of the final table is calculated as given in Table 6.
2.2.6. Step 6: determine the contribution of ES to each SDG
There is a need to generate a set of core outcomes of contributions to SDG of ES. To achieve this, consensus should be reached among the stakeholders. We therefore asked the three groups the following guiding interview question:
Question 5: “What degree contribute to SDG goals of ES (Table 7) using score from 1 to 10, where 1 is the lowest?”
From the responses we received, we were able to associate the contribution of each ES with each SDG. While all seven of the ES seemed to contribute to the five SDG, interestingly, we found that the education ES was most highly related to the life on land SDG (Table 7), the carbon and recreation ES were more highly related to the good health and well- being SDG, and the carbon ES was most highly related to the climate action SDG. As expected, the water regulation and water supply ES were most highly related to the clean water and sanitation SDG. Of the seven ES, recreation seemed to be more highly related to the decent work and economic growth SDG, although the other six ES seemed to similarly be related.
2.2.7. Step 7: assign ES to each management unit in an optimal manner In order to assign ES to stands a binary integer programing model is proposed. The assignment is based on the data collected and generated in the previous steps of the methodology (i.e., suitability scores (Sij), weights of SDG (Wk), and support of ES to SDG (Ujk)). The aim of the assignments was to achieve SDG that are prioritized in the previous steps. When a ES (j) is assigned to a stand (i), its contribution to a sus-tainable development goal (k) is computed by multiplication of Sij and
Ujk. Then the total achievement of goals was calculated using the
weighted sum of these contributions. Therefore, the total achievements of the goals are calculated by the following formula:
∑K k=1 Wk∑ I i=1 ∑J j=1
SijUjk for (i, j) such that ES i is assigned to stand j By using the above-given formula, the integer programming model is formulated as follows. Max∑ K k=1 Wk∑ I i=1 ∑J j=1 SijUjkXij+ε∑ I− 2 i=1 ∑I s=i+1 ∑J j=1 Yisj (1) Subject to ∑J j=1 Xij=1 ∀i (2) Xij+Xsj≥2Yisj ∀j, (i, s) ∈ N (3) Xij,Yisj∈ ( 0 1 ) ∀i, ∀j, ∀s (4)
In the problem above, the decision variables are assigned binary values, and the primary objective was to maximize total achievements of the goals by the assignments of the ES to stands. The secondary objective was to assign the same ES to neighboring stands. In Eq. (1), the first part is the primary objective; the second part is the secondary objective. ε is a
sufficiently small number that guarantees that model initially maximizes the primary objective and then maximizes second objective for the maximum value of the primary objective. The first constraint given in Eq. (2) is for assignment of exactly one ES to each stand. The constraint given in Eq. (3), ensures that if two neighboring stands i and s are assigned different ES (j) then the corresponding Yisj will forced to be 0.
Table 6
Weights, or importance, of SDG in the model (Wk).
Sustainable development goals (SDG) Weight
SDG15: Life on land 0.476
SDG6: Clean water & sanitation 0.176 SDG3: Good health & well-being 0.169
SDG13: Climate action 0.149
SDG8: Decent work & economic growth 0.030
Table 7
Matrices of the perceived level of support for ES-SDG contributions based on responses of surveyed stakeholders. ES-SDG values range from 1 (weak support) to 10 (strong support) (in model: Ujk).
Ecosystem
services (ES) Good health & well- being Clean water & sanitation Decent work & economic growth Climate action Life on land Education 5.72 5.56 4.11 6.28 8.11 Cultural heritage 6.39 5.00 5.00 5.67 6.39 Aesthetic 6.61 3.94 4.44 5.00 6.33 Water regulation 6.44 8.22 4.39 6.50 7.11 Water supply 6.56 7.83 4.72 5.61 6.17 Carbon 7.56 6.06 4.11 8.50 7.83 Recreation 7.50 3.72 5.72 4.22 4.11
Otherwise; if they are assigned the same ES then Yisj can be 1, which contributes to the secondary objective to maximize sum of Yisj. The types of the decision variables are defined in Equation (4). Please see Table 8 for the definitions of sets, parameters and decision variables in model.
3. Results
The resulting map of the Belgrad Forest case study area shows the spatial distribution of the ES assignment to stands (Fig. 7). Only five ES were assigned to the stands in this forest: water regulation, cultural heritage, education, carbon, and water supply to stands. Given the many and varied approaches for mapping ES, we argue there is a need for an
Table 8
Definitions for the binary integer programming formulation. Sets and Indices
i, s: stands (i = 1,…,I, s = 1,…I) j: ES (j = 1,…J)
k: SDG (k = 1,…K)
N: set of neighbor stands, N = (i,s) | i and s are neighbors Parameters
Sij: suitability score of stand i to ES j.
Wk: weights of SDG k.
Ujk:support of ES j to SDG k.
Decision variables
Xij:binary variable indicating assignment of ES j to stand i.Xij= {
1 if ES j is assigned to stand i. 0 otherwise
Yisj:binary variable indicating the neighboring stands are assigned the same ES. Yisj=
{
1 if i and s are neighbours and both assigned ES j.
0 otherwise
Fig. 7. Result of the assignment of ES to management units. Table 9
Summary of assignment of ES to stands. Assignment of
ES Number of stands Sum of area (ha) Percent of forest area (%) Water regulation 768 4010.3 71 Cultural heritage 201 962.9 17 Education 90 517.1 9 Water supply 49 85.8 2 Carbon 10 84.2 1 None 0 235.6 – Total 5895.9 – Total of forest Area 5660.3 100
assignment model such as this.
The ES that has been assigned the most to land area within the Belgrad Forest is water regulation, as it is evident by the red polygons within the map (Fig. 7). The amount of land dedicated to this ES (768 stands and 4010.3 ha) seems to be suitable in terms of water regulation service (Table 9). The distribution of highest assignment is mainly related to service that has served beneficial towards the goals of clean water and sanitation and life of land. Initially, there were 258 stands that had the full score (1) for the cultural heritage service. Nevertheless, the result of the assignment model indicated that only 201 stands would be assigned to the cultural heritage service (Fig. 7, Table 9). Thus, 57 stands that initially had a full score for the cultural service were not later assigned to cultural service. These stands had the full score both culture and education services, and because the education service contributed mostly to the goal of life on land, and these 57 stands were therefore assigned to educational services.
There were initially 90 stands that had the full score (1) for the ed-ucation service, and at the end of the assignment problem, the same 90 stands (962.9 ha) were assigned the education service (Fig. 7 and Table 9). We think that the education service contributes the most to the goals of life on land, and therefore it had the highest weight among the SDG. At the end of the assignment, there were 49 stands assigned to the water supply service, and only ten stand assigned to the carbon service. There were no assignments for recreation and aesthetic services (Table 9).
Under the assumed assignment of ES conditions, the selection of an ES to best achieve the SDG was dominated by the ES values of each stand. Overall, our results demonstrate that SDG can influence the considered ES. Because life of land and clean water and sanitation SDG had the greatest weighted values according to the stakeholders.
4. Discussion
This study presented an assignment model for analyzing multiple ES, using pairwise comparisons of ES importance and a Delphi method to understand stakeholder preferences. Findings from the assignment model showed that stand suitability values of ES (rather than weights of SDG) were the most important characteristics for the assignment of ES to the landscape. It seems plausible to start with an initial state of ES in analyzing the landscape. However, our work is different from prior approaches in that others have carried out the mapping of ES at a pixel level, while we have assumed they should be assigned and mapped at the stand (polygon) level. This seems reasonable since forest planning is carried out on stand (or management unit) basis. Therefore, it seems reasonable that ES data should be described on stand basis.
In this study, expert opinions regarding the suitability of ES for the Belgrad Forest were obtained. Some variation in opinion among the experts was observed. For example, while some experts thought that recreation was the most suitable ES for Belgrad Forest, it was not strictly suitable for other respondents. One possible reason for this difference of opinion was that the Belgrad Forest has considerable historical impor-tance to Turkish society today, precluding the use of some areas for recreational purposes. However, a stand may have the same suitability value assigned to more than one ES. This clearly reveals the need to consider trade-offs between ES based on their defined suitability values. Thus, when the same levels of importance in two or more ES are sug-gested for a stand, the process to overcome this situation can be followed along the steps described above.
Preference for ES varies among society, given the cultural, social, and educational background each person has experienced. The importance of the SDG and their contribution to ES were brought forth from stakeholders in the management of the Belgrad Forest during a work-shop using pairwise comparisons and the Delphi method. Before the workshop, the stakeholders were identified and invited for the meeting. During the use of the Delphi method, groups should have been relatively homogeneous in character. However, in this study, we took the risk for
the third group, which had the most missing members. If the decisions of the third group were very different from the decisions of the other two groups, we would have cancelled the third group. Nevertheless, the re-sults were substantially similar among all three groups. It is unclear at this time whether more groups, or larger groups, would have resulted in different assessments of the importance of ES to SDG. This is one aspect of our work that we leave for others to pursue.
The findings from Q5 of this study suggested that two SDG (“Life of land” and “Good Health & Well-Being”) have a strong effect on the choice of ES, which was supported by outcomes of previous studies (Geijzendorffer et al., 2017; Wood et al., 2018). This suggests that to promote the preservation of forest ecosystems similar to the Belgrad Forest in northwest Turkey, managers should focus on objectives that include SDG. The general relationships between SDG and ES have been defined so far, but there are not enough studies describing the functional relationship between forest management and the achievement of desired levels of ES. This may also indicate that the relationships be-tween SDG and ES are not well understood. A management approach which attempts to maximize the value of an ES, usually leads to a decrease in the value of other ES (Bennett et al., 2009). While stake-holder group decisions can be used to overcome this challenge by prioritizing ES, the trade-offs remain. Nonetheless, stakeholder opinions may be important in ES trade-off analyses (Turkelboom et al., 2018).
Just as there have been suitability studies for only one ES, suitability studies have been conducted for more than one ES. With this study, we were able to get ideas from many experts and participants. We proposed a start-to-finish methodology for the assignment of ES to the manage-ment units. While making assignmanage-ments with the mathematical model, we were able to consider the neighboring stands. In other words, when making assignments, we have taken into consideration not only the suitability value of ES in management unit, but also the neighboring management unit. While establishing the methodology, we integrated SDG and MCDA into the ES assignment process. Even if the original of our study is not MCDA, it is thought that SDG, MCDA and ES are inte-grated. It has been observed that it is effective to create assignment problems in complex studies such as zoning of forests according to ES. This methodology is better than other approaches for the purposes of this paper as it provides zoning of forest management units.
Several others have recently described how the Delphi method can assist in forest planning. From an operations perspective, Blagojevi´c et al (2019) describe how useful these processes might be for strategic and tactical planning. Although not specifically addressing ES, the guidance provided for forest operations may also be more broadly relevant to forest planning. Waldron et al. (2016) also used the Delphi method to determine important ecosystem management issues. Escribano et al. (2018) utilized the Delphi method similar in size and scope to the one described here to arrive at sustainability indicators for agroforestry programs Although aesthetics did not rank well in our study, Lim and Innes (2017) used a Delphi method of similar size as ours to understand the opinions of experts knowledgeable of forest aesthetics, and to recommend indicators for sustainable forest management. Further, we were able to locate one paper that used the Delphi method to validate a set of indicators for sustainable forest management (García et al., 2017). While MDCA has been used quite often in forest management (Uhde et al., 2015), the combination of MCDA and Delphi methods, like we have described here, is fairly unique.
5. Conclusions
This study used surveys of stakeholders of a forest in Turkey to identify the most important ES that the forest could provide. The stakeholders then ranked them in importance through the use of pair-wise comparisons and the Delphi method, and this information was used in an optimization procedure to assign a single ES to each forested stand. The results also provided information to map the ES assignment so that stakeholders can obtain broader geographical view of the outcomes. We
found that only five ES dominate this landscape, and these contribute directly to 5 SDG. The most prevalent ES mapped was related to water regulation, followed by cultural heritage and education services. This process of participatory involvement of stakeholders can be of value in informing the development of a forest plan. The assignment of ES to individual stands can guide the further assignment of management ac-tions to those stands.
CRediT authorship contribution statement
˙
Inci Caglayan: Conceptualization, Methodology, Data curation,
Writing - original draft. Ahmet Yes¸il: Supervision. ¨Ozgür Kabak: Methodology, Software, Validation, Writing - review & editing. Pete
Bettinger: Writing - review & editing. 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.
Acknowledgements
We would like to thank the all expert and participants of the Delphi study for their availability.
Appendix
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Table A1
Pairwise comparison of the first group for SDG.
SDG3 SDG6 SDG8 SDG13 SDG15 SDG3: Good health & well-being 1 1/6 8 1/5 1/9 SDG6: Clean water & sanitation 6 1 8 1 1/3 SDG8: Decent work & economic
growth 1/8 1/8 1 1/4 1/9
SDG13: Climate action 5 1 4 1 1/3
SDG15: Life on land 9 3 9 3 1
Consistency = 0.169
Table A2
Pairwise comparison of the second group for SDG.
SDG3 SDG6 SDG8 SDG13 SDG15 SDG3: Good health & well-being 1 1 9 3 1/9 SDG6: Clean water & sanitation 1 1 9 1 1/5 SDG8: Decent work & economic
growth 1/9 1/9 1 1/7 1/9
SDG13: Climate action 1/3 1 7 1 1/3
SDG15: Life on land 9 5 9 3 1
Consistency = 0.154
Table A3
Pairwise comparison of the third group for SDG.
SDG3 SDG6 SDG8 SDG13 SDG15 SDG3: Good health & well-being 1 6 9 2 1 SDG6: Clean water & sanitation 1/6 1 7 2 1/3 SDG8: Decent work & economic
growth 1/9 1/7 1 1/6 1/8
SDG13: Climate action 1/2 1/2 6 1 1/3
SDG15: Life on land 1 3 8 3 1
Consistency = 0.083
Table A4
Geometric mean of pairwise comparison of three groups for SDG. SDG3 SDG6 SDG8 SDG13 SDG15 SDG3: Good health & well-being 1.000 1.000 8.653 1.063 0.231 SDG6: Clean water & sanitation 1.000 1.000 7.958 1.260 0.281 SDG8: Decent work & economic
growth 0.116 0.126 1.000 0.181 0.116 SDG13: Climate action 0.941 0.794 5.518 1.000 0.333 SDG15: Life on land 4.327 3.557 8.653 3.000 1.000 Consistency = 0.043
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