Year : 2019 | Volume
| Issue : 4 | Page : 377-389
Motivational Crowding in Payments for Ecosystem Service Schemes: a Global Systematic Review
Jordan Frederick Akers, Maï Yasué
Quest University Canada, British Columbia, Canada
Quest University Canada, British Columbia
Source of Support: None, Conflict of Interest: None
|Date of Submission||15-Jul-2018|
|Date of Acceptance||02-May-2019|
|Date of Web Publication||14-Oct-2019|
| Abstract|| |
We contribute to the growing body of literature on the ecological and socio-psychological impacts of providing payments as rewards for conservation. We conducted a systematic review of 74 payments for ecosystem services (PES) schemes and identified contextual factors that correlate with psychological mechanisms that enhance (”crowd-in”) or erode (”crowd-out”) autonomous motivation. Such indicators of crowding-in were more likely when schemes empowered local participants, provided in-kind non-monetary community benefits, and aimed to foster feelings of autonomy. Schemes that thwarted feelings of autonomy correlated with indicators of motivational crowding-out. Although motivational crowding had no effect on ecological success, indicators of crowding-in positively predicted social success (χ2 = 8.60, n = 48, p = 0.003) and crowding-out negatively predicted social success (χ2 = 9.59, n = 47, p = 0.002). Compared to past studies highlighting the negative impacts of extrinsic rewards on autonomous motivation, our study provides a more nuanced perspective and demonstrates that extrinsic incentives such as payments can promote crowding-in of autonomous motivation if schemes are designed equitably and provide opportunities for autonomous decision-making. Our study demonstrates how the application of psychological theories can contribute to the design of fair and effective PES schemes.
Keywords: self-determination theory, community-based conservation, meta-analysis, overjustification effect, motivational crowding, payment-based conservation, PES
|How to cite this article:|
Akers JF, Yasué M. Motivational Crowding in Payments for Ecosystem Service Schemes: a Global Systematic Review. Conservat Soc 2019;17:377-89
| Introduction|| |
Conservationists have used a variety of tools to engage with local communities. In the past, the most prevalent tools provided non-material (e.g., support for marketing alternative livelihoods such as ecotourism) and material benefits (e.g., construction of a new school) to the local community to promote sustainable use of resources (Alcorn 1993; Wester et al. 1994). In recent years, however, an increasing number of conservation projects have shifted to more direct monetary incentives in exchange for conservation outcomes (Ferraro 2001; Thompson et al. 2011; Wunder 2015). These monetary incentives, broadly termed payments for ecosystem services (PES), compensate individual or groups of landowners or rights-holders for maintaining ecosystem services (Wunder 2015) such as biodiversity conservation, carbon sequestration, watershed services, or landscape beauty (Costanza et al. 1997; Gould et al. 2015). Advocates of PES schemes have suggested they may help reduce administrative costs, enhance efficient use of conservation funds, and empower individual landowners (Ferraro 2001; Ferraro and Kiss 2002). Indeed, studies on the impacts of PES schemes have indicated both ecological and economic benefits (Fitzsimons and Wescott 2001; Kleijn and Sutherland 2003; Iftekhar et al. 2014; Blundo-Canto et al. 2018).
In addition to such ecological and economic benefits, PES schemes can also have socio-psychological impacts on the community. Specifically, paying participants can affect the nature of their motivation for engaging in prosocial or pro-environmental activities (Gneezy and Rustichini 2000; Frey and Jegen 2001). Although past research has identified the importance of several contextual factors in influencing the socio-psychological impacts of extrinsic motivators such as payments and fines (Deci et al. 1999; Rode et al. 2015), there have been few extensive systematic reviews (see Rode et al. 2015) addressing the socio-psychological impacts of PES schemes.
In this systematic review, we applied two psychological theories of motivation (Self-determination theory and motivation crowding theory) to examine the socio-psychological impacts of PES. Self-determination theory (SDT) is a widely empirically supported psychological theory that differentiates between two types of motivation—intrinsic and extrinsic (Rigby et al. 1992; Ryan and Deci 2000). Intrinsic motivation represents motivation that is self-determined or autonomous (i.e., the tendency to act on inherent desires consistent with personal volitions). Extrinsic motivation tends to arise externally from the self, such as through rewards and punishments (Ryan and Deci 2000). We were interested in understanding PES schemes in the context of different types of motivation because, in comparison to controlled motivation, people who are motivated by autonomous motivation are more likely to develop long-term, stable environmental beliefs (Decaro and Stokes 2008; Sheldon et al. 2011), which can strengthen pro-environmental behaviour (Kollmuss and Agyeman 2002; De Groot and Steg 2009) and resolve to tackle more challenging pro-environmental tasks (Cooke et al. 2016; Yasue and Kirkpatrick 2018). Thus, SDT suggests that in order to create long-term sustainable conservation, people need to not only make behavioural changes but they need to make these changes for autonomous reasons. Indeed, community conservation projects that are designed to enhance autonomous motivation are more likely to have greater social or ecological success (Cetas and Yasue 2016). SDT research in different fields (including education, health, coaching, and environment) has suggested that autonomous motivation is enhanced when three basic and universal psychological needs are met—autonomy, competence, and relatedness (Ryan and Deci 2017) ([Table 1], variable 4b).
|Table 1: Details of project design characteristics coded for each case study|
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Motivation crowding theory suggests that monetary compensation can undermine (”crowd-out”) or enhance (”crowd-in”) autonomous forms of motivation (Frey and Jegen 2001; Gneezy and Rustichini 2000). Crowding-out may occur if payments lead participants to shift their personal moral reasons for engaging in conservation from autonomous to that of controlled motivation. For example, a participant who initially planted trees because they enjoyed planting trees and felt that it was contributing to the well-being of future generations may, after receiving payments, shift their perspective to view their own pro-environmental behaviour as a way to make money (Van Hecken and Bastiaensen 2010; Kerr et al. 2012), and subsequently they may no longer feel personally motivated to plant trees when the payment scheme is removed. While many conservationists warn that compensation could result in crowding-out (Kosoy and Corbera 2010; Vatn 2010; Gómez-Baggethun and Ruiz-Pérez 2011; Muradian et al. 2013), some payment-based field experiments have also suggested that payment schemes result in “crowding-in” or enhancing autonomous motivation (Rodriguez-Sickert et al. 2008; Narloch et al. 2012). Previous research in motivational crowding (Cameron et al. 2001; Narloch et al. 2012; Midler et al. 2015; Rode et al. 2015) indicates that through careful design and consideration for cultural and socio-psychological contexts, external incentives may allow payments to reinforce autonomous forms of motivation. At present, however, few PES studies (Agrawal et al. 2015; Rode et al. 2015; Cetas and Yasué 2016) have rigorously explored contextual factors that affect motivational crowding.
In addition to the potentially adverse psychological impacts of PES schemes, there is much research that has highlighted the negative or neutral social, economic, and ecological impacts of PES schemes (Pagiola et al. 2005; Pascual et al. 2014; Alarcon et al. 2017; Blundo-Canto et al. 2018). Thus, in order to work towards the goal of creating more effective PES schemes, our research aims to identify contextual factors that influence the socio-economic or ecological success of PES schemes.
In this systematic review of 74 PES case studies, we added to past reviews of community conservation projects (Brooks et al. 2012; Adhikari and Agrawal 2013; Ojea et al. 2016) by conducting a two-part analysis of motivation crowding in PES schemes. First, we identify contextual factors that correlate with psychological mechanisms demonstrated in the literature (Rode et al. 2015) to lead to crowding-out or crowding-in. Second, we examine how crowding-out or crowding-in relates to the social and ecological success of the PES schemes. Contextual factors considered in this review include a range of variables that have been identified in other reviews as being important (Waylen et al. 2010; Andrade & Rhodes 2012; Brooks et al. 2012; Cetas and Yasue 2016)—number of scheme participants, degree of local participation in scheme design, payment type and method, PES mechanism, and lastly, participant motivation. Due to the wide range of goals in PES schemes, we chose to analyse both social success—increases in social cohesion, wealth, and equity and ecological success—increases in ecological services targets such as water quality, deforestation rates, and replanting amounts. This research aims to identify the best practices of designing PES schemes, which can be used to maximise the long-term environmental and social benefits by leveraging participants' intrinsic motivation.
We would like to highlight that given the paucity of empirical research that identifies the psychological impacts of conservation interventions in general and PES schemes specifically (Selinske et al. 2018; Yasué and Kirkpatrick 2018), in order to aggregate the results of a vast range of PES schemes, we used proxies for autonomous motivation in this study. These proxies were selected based on SDT and motivational crowding theory as well as research describing the psychological mechanisms leading to motivational crowding (Rode et al. 2015). Thus, throughout the manuscript we are using “motivational crowding” in place of the variables that we actually measured—which are psychological mechanisms that lead to motivational crowding.
| Methods|| |
Following established conservation systematic review protocol (Pullin and Stewart 2006; Collaboration for Environmental Evidence 2018), we completed a four-part article search process to identify case studies that contained sufficient information for inclusion: 1) An initial keyword search on Science Direct in April 2016 for articles written after 2000 for “(”payment for ecosystem services” OR PES OR “payment* for environmental services” OR “direct payment” OR “agr*-environ*”) AND (”ecosystem service*” OR biodiversity OR conservation OR “carbon sequestration” OR watershed OR landscape) AND (success OR participation OR attitude OR perception)”; 2) a 'snowball search' reviewing reference lists of already identified articles for additional sources; 3) a repeated search using the original keywords (listed earlier) on Science Direct in January 2018 to account for any papers that were published in the 20 months since the first search; and 4) an updated keyword search used on Science Direct in January 2018. For the second updated keyword search, we first conducted a search within titles and abstracts for “payment for ecosystem services” OR PES OR “payment for environmental services” OR “direct payment” OR “agr*-environ*” OR REDD OR incentive*, and then within this subset of papers we also searched all fields for success OR participation OR attitude OR perception* OR outcome* OR impacts OR engage AND “case study”. Multiple searches were necessary due to low numbers of schemes with sufficient information to code (n = 49, 12, 6, and 7, respectively, for each step). Overall, more than 1300 articles were filtered based on titles and/or abstracts. 161 articles were identified to be read in full, from which only 32 papers had all contextual information necessary to code for our review. However, in addition to these 32 papers, we were able to obtain sufficient information for an additional 42 case studies by identifying other papers that were written about the same case studies in a 10-year window (Supplementary Material 2[Additional file 2]; Akers and Yasué, 2019). Articles read in full but not coded included schemes that did not meet our criteria of a PES scheme (such as not containing private ecosystem services providers), schemes in which there was insufficient information to code, or papers that focused on schemes already coded.
Some programs existed across multiple nations. For example, World Bank or Global Environmental Facility schemes such as the Regional Integrated Silvopastoral Approaches to Ecosystem Management Project simultaneously created schemes in Nicaragua, Costa Rica, and Colombia (Pagiola et al. 2016). Where this occurred, these schemes were coded as different case studies because the implementation of schemes funded by the same source still varied between countries. However, if we found multiple schemes which were part of the same broader program or international funding source within the same country that had sufficient data available to include in this analysis, we randomly included one of the case studies to minimise pseudo-replication.
All the schemes included in this study had some form of payment (monetary or in-kind) and listed the improvement of ecosystem services as a priority. However, it is important to note that beyond these payments, many schemes simultaneously provided different types of incentives and included additional objectives such as alleviating poverty, enhancing social capital, or strengthening cultural values (Buch and Dixon 2009; Börner et al. 2017).
For each case, we recorded the following design characteristics of the scheme as well as other contextual factors that have been found to predict the success of community conservation schemes (Wunder 2005; Andrade and Rhodes 2012; Sattler et al. 2013; Cetas and Yasué 2016): the PES target or goal (biodiversity conservation, watershed services, landscape beauty, and carbon sequestration), the PES mechanism ([Table 1], variable 1), the scheme size (the number of participants enrolled in a scheme, log-transformed), payment method, and payment distribution method ([Table 1], variables 2 and 3).
We coded participant motivation as either controlled or autonomous (self-determined) using two different approaches. First, if open-ended interviews were used to understand participants' initial motivations for engaging in a program, we coded the type of motivation as controlled or autonomous by applying the SDT framework created by Ryan and Deci (2000) ([Figure 1]; [Table 1], variable 4a). This approach was used to code 53% of the studies. We relied only on open-ended interviews because there are few studies that have used SDT to examine motivation in the context of PES and we were unable to use existing closed-ended survey responses about PES to assess controlled or autonomous motivation because the survey itself might prime for more extrinsic motivations. Second, for the remainder of studies ([Table 1], variable 4b), because there were no empirical studies that we could use to assess initial motivations, we used an approach similar to Cetas and Yasué (2016). As a proxy for initial autonomous or controlled motivations, we recorded whether the scheme was designed to foster or thwart autonomy, competence, and relatedness (Ryan and Deci 2000). Schemes that are designed to promote these three basic psychological needs may enhance autonomous motivation, whereas schemes that thwart these three basic psychological needs may enhance controlled motivation (Cetas and Yasué 2016) (Supplementary Material 1)[Additional file 1]. Although there are limitations of using such proxies, numerous studies in the fields of health, education, and work-environments that use SDT as a framework have demonstrated a strong correlation between basic psychological needs and autonomous motivation (Black and Deci 2000; Ryan and Deci 2017). Furthermore, preliminary analyses that included a binary random effect variable indicating whether or not a proxy was used to determine autonomous motivation, did not influence the final model.
|Figure 1 (a-d): (a and b) Participation Score (Arnstein 1969), by region (a, left, top), and by HDI (b, right, top) (UNDP 2014); 1 indicates limited effort to fully engage the local community and 6 indicates self-mobilisation (c and d) Social success (c, left, bottom) and environmental success (d, right, bottom); categorised as failure (F), limited Success (LS), and high levels of success (S) by motivation crowding; “Yes” and “No” indicate the occurrence of crowding-in and crowding-out (not mutually exclusive)|
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Additionally, because the schemes varied substantially in whether and how they were initially designed to engage and empower the local community in decision making, we chose to code this characteristic using a modified version of Arnstein's ladder of citizen participation (Arnstein 1969). To enhance the reliability of our coding with limited information, we modified a pre-existing typology of participation inspired by Arnstein's ladder (Pretty 1995) into a six-category scale ([Table 1], variable 5). Schemes were scored according to different methods to engage the community, with 1 involving low levels of community involvement and 6 involving citizen control of the resource. Although Arnstein's ladder is usually used to assess the type of participation, Pretty's (1995) modification of the ladder relates specifically to land-use scenarios that focus on how schemes can be designed to distribute power and enhance community participation.
Finally, we included contextual factors such as the scheme's start date as well as the nation's Human Development Index (HDI) in the year the scheme was introduced (Brooks et al. 2012; Jones et al. 2013; Cetas and Yasué 2016). HDI is a ranking given by the United Nations that indicates a country's development through the consideration of a nation's wealth, average life expectancy, and level of education (UNDP 2014).
The dependent variables, motivation crowding-out and motivation crowding-in, were coded based on both quantitative and qualitative empirical findings from papers on the case studies that indicated the participants' experiences during and after the scheme. Measurements of motivation crowding-out and motivation crowding-in in PES frequently involve surveys relating to motivation and conservation attitudes (Handberg and Angelsen 2019; Kaczan et al. 2019; Moros et al. 2019), interviews (Kolinjivadi et al. 2019), or behaviour analyses (Bose et al. 2019; Van Hecken et al. 2019). These direct measurements of motivation crowding focus on observation or self-reporting from participants regarding changes following PES participation. Where possible, this systematic review made use of direct measurements of motivation crowding. However, many schemes do not include motivation crowding evaluation in their design. In fact, remarkably few studies measured much more than very basic data on the psychological dimensions of a project. Therefore, we developed a proxy by applying past research on motivation in PES to interview and survey responses (not directly focusing on motivation) that assessed participants' experiences in PES (Lapeyre et al. 2015; Rode et al. 2015; Ezzine-de-Blas et al. 2019). Specifically, we coded motivational crowding, based on psychological mechanisms that have been found to correlate with motivational crowding ([Table 1], variables 6a and 6b; Rode et al. 2015). As indicated earlier, there are limitations with using proxies. Measuring the presence of psychological mechanisms that could lead to motivational crowding is not the same as direct measurements of motivational crowding. Given that motivation crowding is measured at the community level, both crowding-in and crowding-out can occur simultaneously with different people in the same community (Bose et al. 2019). As such, these two variables were recorded independently as occurring or not occurring. In this study, 11 schemes were recorded as having both crowding-in and crowding-out. In addition, for 9 and 11 of the schemes (respectively), we were unable to code whether crowding-in and crowding-out occurred.
Lastly, environmental success and social success were assessed based on explicitly stated goals of the scheme (Brouwer et al. 2011; Brooks et al. 2012; Cetas and Yasué 2016). For quantitative studies, success was determined based on the percentage completion of various goals stated in a specific scheme ([Table 1], variable 7a). For case studies with a qualitative evaluation method, we followed similar proportions of stated achievement of goals ([Table 1], variable 7b). Following Brooks et al. (2012), when no explicit statement of goals, or subsequent achievement of goals, could be found in additional sources, we determined a level of success based on our judgement. For 20 case studies, we had to evaluate the possible aims of the scheme based on the description of the scheme and the potential effectiveness of the scheme based on the reporting in various papers.
Following Pullin and Stewart (2006), we examined inter-coder reliability using a kappa analysis conducted at the preliminary stages of article coding. Comparing the coding of 15 articles between one of the authors and a trained assistant resulted in a moderate average Cohen's kappa coefficient of 0.44 ± 0.26 (SD). Following this initial assessment, we used a more structured approach to coding methods ([Table 1], variables 4–7).
Although we were able to code almost all the independent variables, there were four missing values for the size variable. We used the Multivariate Imputation by Chained Equations (MICE) method to simulate these four missing values. The MICE method utilises various regression models where missing data is modelled via other predictor variables included in the data (Azur et al. 2011).
We used Binary Logistic Regression (GLM) in R version 3.3.2 (R Core Team 2016, www.r-project.org), using the glm() function, to identify factors that predict crowding-in and crowding-out, independently. All predictor variables were included within full or saturated models. All models were fixed. Mixed models were initially explored but none of these models explained greater variance. The saturated model for crowding-out was reduced to an optimal model via backwards selection using Akaike Information Criterion (AIC) and the stepAIC() function from the package MASS (Venables and Ripley 2002). However, because there was complete separation within the saturated model for crowding-in (Albert and Anderson 1984), we used an alternative method for model selection (Least Absolute Shrinkage and Selection Operator, LASSO) (Fonti and Belitser 2017), specifically the glmnet() function from the package glmnet (Freidman et al. 2010). Within our models we checked for multicollinearity. Variance inflation factors were > 3.2 and below the threshold for multicollinearity (Hair et al. 2009). Preliminary analyses using an accuracy variable of the authors' confidence (coded at three levels) in available scheme data, had little effect on the results of the final models. Thus, this accuracy proxy is not currently presented in our final models.
Lastly, Chi-Square tests were used to assess whether schemes resulting in crowding-in or crowding-out were more likely to have higher levels of ecological and social success. Due to low frequency of 'failure' for social and ecological success in the reviewed studies, this analysis combined the cases with limited success together with cases with failure.
| Results|| |
Geographic distribution and scheme characteristics
The 74 PES schemes identified in this study were located in countries across the Global North and the Global South ([Figure 1]; [Table 2]; Supplementary Material 2; Akers and Yasué, 2019). There were several schemes identified in countries such as Australia (n = 7), Mexico (n = 6), Kenya (n = 5), China (n = 5), and Brazil (n = 5), where regional governments have implemented many schemes. The review also included schemes that engaged a wide range of participants [Table 2]. The schemes targeted biodiversity conservation (62%), watershed services (42%), and carbon sequestration (23%), as well as a combination of services (27%). For example, schemes enhanced forest cover to simultaneously achieve multiple targets (Robinson and Redford 2004). The most common mechanisms used to promote services were asset building (57%), use restricting (29%), and area-based capping (18%). Some schemes (11%) involved monitoring that recorded (for example) bird-nests or illegal logging activities (Sommerville et al. 2010; Clements et al. 2013).
Over 85% of the schemes incorporated cash payments, with the remainder offering compensation in the form of infrastructure (27%) or items/resources (12%). These payments were most frequently distributed via individual payments (76%), but some schemes incorporated group (14%) or community payments (30%).
Most schemes had low Arnstein scores [Table 2]. The Arnstein score varied by continent [Figure 1]a and by HDI ranking [Figure 1]b. For example, Europe had no occurrences of manipulative participation, but manipulation occurred more frequently in the Global South.
Predicting motivation crowding outcomes
Psychological mechanisms that lead to crowding-in were most likely to occur in larger schemes and in schemes that included infrastructure payments, high Arnstein scores, and were designed to enhance autonomous motivation. Scheme characteristics that negatively correlated with psychological mechanisms leading to crowding-in included area-based capping, monitoring, and items/resources compensation [Table 3].
|Table 3: Parameter estimates for the Binary Logistic Regression model predicting the occurrence of motivation crowding-in/-out|
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Psychological mechanisms leading to crowding-out were more likely to occur when payments were made to the community [Table 3]. Schemes in which participants were coded as autonomously motivated were also less likely to crowd-out autonomous motivation.
The relationship between motivation crowding and scheme success
Schemes that we coded as crowding-in were more likely to achieve social success (57%) compared to cases where crowding-in did not occur (17%) ([Figure 1]c, χ2 = 8.60, n = 48, p = 0.003). In addition, schemes that were coded as crowding-out were less likely (31%) to achieve high social success compared to schemes that were not coded as demonstrating crowding-out (76%, χ2 = 9.59, n = 47, p = 0.002). However, crowding-in or crowding-out did not significantly correlate with environmental success [Figure 1]d.
| Discussion|| |
Implications of findings
This review demonstrates that psychological mechanisms leading to crowding-in and crowding-out can be predicted by scheme design characteristics with moderate to high levels of variance explained (Nagelkerke R 2 = 0.65 and 0.29 respectively; [Table 3]). Our research extends the results from previous studies on community conservation schemes (Andrade and Rhodes 2012; Cetas and Yasué 2016) that illustrated the value of designing schemes with careful consideration of how the scheme can enhance autonomy, competence, and relatedness for participants. Our regression model indicated that schemes designed to prioritise autonomous motivation were 6.3 times more likely to crowd-in autonomous motivation, and an increase in one level of participation on our modified Arnstein's ladder increased this likelihood of crowding-in by 2.2 times. Similarly, if schemes were designed to promote autonomous motivation, the odds of crowding-out reduced 5.5 times. In addition, our research demonstrated the importance of examining motivational crowding because the presence of psychological mechanisms that lead to crowding-in positively predicted social success and those that lead to crowding-out negatively predicted social success.
Although past laboratory experiments and theoretical studies have warned that payments can crowd-out autonomous motivation (Cardenas et al. 2000; Jack 2009; Adhikari and Agrawal 2013), our study demonstrated that such schemes may also crowd-in intrinsic motivation. In fact, roughly the same percentage of PES schemes appeared to crowd-in (57%) and crowd-out (53%) autonomous motivation. In conservation literature, few studies examine crowding-in (Sommerville et al. 2010; Narloch et al. 2012; Ezzine-de-Blas et al. 2015; Rode et al. 2015); however, in other fields such as education (Deci et al. 1999; Cameron et al. 2001; Rommel et al. 2015; Ryan and Deci 2017), studies have demonstrated that extrinsic motivators can crowd-in autonomous motivation if the incentives are designed, communicated, and framed in a way to reinforce basic psychological needs. For example, schemes that supported feelings of relatedness may be more likely to crowd-in autonomous motivation by engaging with informal social networks (Gutiérrez et al. 2011)—such as community organisations, involving charismatic and popular leaders (Escobar et al. 2013), and leveraging cultural values (Atela et al. 2015)—which enhances feelings of trust and reciprocity between communities and conservation organisations (Ezzine-de-Blas et al. 2015). Schemes supported autonomy by offering a variety of participation options (Zanella et al. 2014) and enhancing land security (Bremer et al. 2014). Finally, schemes that enhanced a sense of competence may have crowded-in more autonomous motivation through provision of educational opportunities and skills training (Swallow and Goddard 2013) as well as frequent positive feedback on and recognition of conservation accomplishments (Rode et al. 2015). We also found that schemes designed to distribute power to the local community likely lead to crowding-in by improving relationships and by increasing the likelihood that the schemes were perceived as fair (Pascual et al. 2014; Ezzine-de-Blas et al. 2015).
In addition to autonomous motivation and participation, other characteristics of schemes, such as the number of participants, paid monitoring activities, and payments via items/resources, were significant predictors of crowding-in. The number of participants may have correlated with crowding-in because larger schemes tend to engage people who are willing, thereby reducing feelings of resentment and frustration felt by those unable to partake (Kellert et al. 2000; Sommerville et al. 2010). The negative association between monitoring and crowding-in may result from the small number of jobs available for monitoring and the consequent envy towards people who are chosen to participate (Clements et al. 2013). In addition, monitoring may have had negative impacts on autonomous motivation because when monitoring for and after spotting illegal activities, participants did not have the necessary support to stop these activities from occurring, either due to lack of local authorities (Manzo-Delgado et al. 2014) or due to poachers being armed (Sheng et al. 2017). A potential explanation for the negative relationship between items/resources payments and crowding-in is that these payments were often distributed to small groups within the community (Midler et al. 2015), which can undermine existing social norms (Narloch et al. 2012). These factors must be further studied in order to elucidate the mechanisms of how they influence motivational crowding.
Schemes that supported psychological mechanisms that promoted motivational crowding-in were not more ecologically successful. This result supports many conservationists' perspective that ecological and social goals are not necessarily congruous (Robinson 1993; Redford and Sanderson 2001; Brockington 2004). The support for this disconnect, while feared by some (Kronenberg and Hubacek 2016), highlights that PES scheme providers need to make explicit decisions about prioritising environmental versus social goals (Jindal et al. 2013), without assuming that the mechanisms needed to achieve these goals are the same (Robinson and Redford 2004). Overall, this finding suggests that even if participants have internalised motivation in these conservation schemes, other mechanisms that determine environmental success are likely hindering greater environmental benefit.
Limitations and future research
Our measures of success were based on the individual goals of schemes. We did not attempt to critique or evaluate the suitability or additionality relating to these goals. For instance, many schemes had goals that were enrolment oriented (e.g., enhancing the number of covenants on private land; Grillos 2017) rather than result oriented (e.g., payments dependent on the observation of returning wildflowers; Burton and Schwarz 2013; Russi et al. 2016). In general, most PES schemes we coded struggled to involve rigorous metrics to validate success, such as additionality—whether enrolment in a scheme actually changed habits (Ferraro and Pattanayak 2006; Ezzine-de-Blas et al. 2016; Börner et al. 2017)—or to incorporate randomisation testing of social impacts—comparing impacts between similar control sites (Agrawal et al. 2015).
Another limitation of this study is that most case studies described success and relatively few examined failures. As with systematic reviews, publication bias (and the greater likelihood that a study on a successful project will be published) may have influenced our results to some extent.
In addition, although we grounded our coding decision in extensive literature examining autonomous motivation and motivational crowding, it is important to note that we used proxies rather than standardised direct measures. For example, due to a lack of explicit qualitative or quantitative data on participants' initial motivation to engage in these schemes (cf. Chervier et al. 2019), we were compelled to focus on contextual information of whether basic psychological needs were being met and the inference of each researcher. Through this approach, despite our best attempts to be rigorous in our coding, there is potential to introduce some subjectivity in our analysis. We hope more research examining the impacts of PES schemes includes an assessment of initial motivation to engage in order to gauge changes in motivation over time. Such research would ideally involve some closed and open-ended questions to ensure that the questions do not prime the participant as well as more qualitative research to better understand contextual factors that influence participant motivation. We hope that with such research providing a better understanding of psychological impacts of these schemes, future reviews will need to rely less on proxy. Lastly, we failed to explicitly include measurements of equity in our review, as has been done previously (Brooks et al. 2012; Calvet-Mir et al. 2015) and was later identified as an important connection among many of our predictors (Pascual et al. 2014). In addition, one of the other limitations of this study is that we only systematically examined the impacts of different types of payment schemes. Future studies could explore the interaction between payment programs and other types of incentives that are simultaneously used to engage communities by enhancing social capital or strengthening cultural values. Overall, more research is needed to ensure that PES implementers standardise analyses of environmental additionality and evaluations of participant motivation. Developing these standards will likely increase the resolution at which future reviews can critically evaluate diverse and complex payment schemes.
| Conclusion|| |
In the current neoliberal climate of conservation (Igoe and Brockington 2007; Castree 2008; Holmes 2015), different types of market-based conservation interventions are being constantly developed—REDD+, agro-environmental schemes, water trusts, etc. Although market-based approaches often attempt to bypass the complexity of contextual factors (Ebeling and Yasué 2009), the results of this study emphasise the importance of understanding more about how social context and specific design characteristics of conservation interventions can impact motivation crowding and therefore the social success of these schemes. The shift from fortress conservation to pro-poor community-based conservation emphasised the need to better understand the experiences and perspectives of people who are directly impacted by conservation interventions (Shepherd 2004; West et al. 2006). As conservationists shift from community-based conservation towards market-based approaches, it is critical that the strengths of, and arguments for, community-based conservation—such as local community empowerment, just land distribution, and equitable sharing of costs and impacts of conservation—are not lost. We must continue with aiming to create schemes that share these qualities, while supporting and fostering autonomous motivation, even if participants are paid.
We would like to thank the faculty, students, research assistants, and alumni of Quest University Canada, particularly T. Veen, T. Trafton, C. Tomlinson, D. Francis, P.A. Higgins, and E. Cetas for supporting this project and reading previous drafts of this manuscript.
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[Table 1], [Table 2], [Table 3]