2.1 Conservation–development trade-offs in forest conservation

What is certainly the major question behind the fight against tropical deforestation is the following: can forest conservation and local rural development be compatible? The combination of conservation and development objectives has been investigated for various types of interventions: REDD+ projects (e.g., Delacote et al., Reference Delacote, Le Velly and Simonet2022), payment for ecosystem services (e.g., Bulte et al., Reference Bulte, Lipper, Stringer and Zilberman2008) or protected areas (e.g., Amin et al., Reference Amin, Choumert-Nkolo, Combes, Combes Motel, Kéré, Ongono-Olinga and Schwartz2019).

Groom and Palmer (Reference Groom and Palmer2010) assess how the combination of conservation and poverty alleviation objectives influence the cost-effectiveness of payment for ecosystem services (PES). They show that PES mechanisms may not be the most cost-effective instrument compared to more indirect approaches (e.g., subsidies to capital). Delacote et al. (Reference Delacote, Palmer, Bakkegaard and Thorsen2014) show how the implementer's objective impacts the implementation of REDD+ projects, depending on the type of information that is available on opportunity costs. External private interest may also capture the benefits from forest resources, leading to conflicts with local communities. Engel and Palmer (Reference Engel and Palmer2008) show under what conditions PES mechanisms may help resolve those conflicts. Duchelle et al. (Reference Duchelle, Simonet, Sunderlin and Wunder2018) emphasizes that local participation is key to enhancing REDD+ outcomes, which suggests that local communities have to derive benefit from their participation. Pham et al. (Reference Pham, Roongtawanreongsri, Ho and Tran2023) shows that a payment for forest environmental services increases households’ livelihood quality in several dimensions (income, job satisfaction, expenditures).

Keles et al. (Reference Keles, Delacote, Pfaff, Qin and Mascia2020) and Qin et al. (Reference Qin, Golden Kroner, Cook, Tesfaw, Braybrook, Rodriguez, Poelking and Mascia2019) show that economic pressures are a good predictor of the degazettement and downsizing of protected areas, which suggests strong trade-offs between economic development and forest conservation. Community forest management (CFM) may also be a type of intervention potentially combining conservation and poverty alleviation issues. For example, Oldekop et al. (Reference Oldekop, Sims, Karna, Whittingham and Agrawal2019) show that win-win outcomes in terms of conservation and poverty alleviation have been achieved in the context of community-based management in Nepal. Similar types of results, where conservation does not come at the expense of livelihoods, is found by Mazunda and Shively (Reference Mazunda and Shively2015) in Malawi. In an experimental setting, it has also been shown that intrinsic motivation to poverty reduction of forest-dwelling community members may enhance participation and in turn increase their intrinsic motivation for forest conservation (Palmer et al., Reference Palmer, Souza, Laray, Viana and Hall2020). Those links between deforestation and poverty alleviation are further discussed in Boltz et al. (Reference Boltz, Delacote and Houngbedji2024).

2.2 Siting choices of forest conservation interventions

The conservation science literature considers the siting choice of protected areas, taking into account factors of threats and benefits, such as biodiversity patterns and processes (e.g., Visconti et al., Reference Visconti, Pressey, Segan and Wintle2010). Bringing some economics into the process, Newburn et al. (Reference Newburn, Reed, Berck and Merenlender2005) and Newburn et al. (Reference Newburn, Berck and Merenlender2006) consider the site selection process, where factors of interest are biological benefits of conservation, land costs, and threats to land-use change. They underline a positive link between threats to land-use conversion and protection cost,Footnote ² hence distinguishing high-vulnerability/ suitable land quality/expensive land and low-vulnerability/low cost/ poor quality land. Albers et al. (Reference Albers, Chang, Dissanayake, Helmstedt, Kroetz, Dilkina, Zapata-Morán, Nolte, Ochoa-Ochoa and Spencer2023) underline the importance of jointly considering anthropogenic threats, species richness, and enforcement.

The siting of forest conservation interventions is likely to have strong influence on their implementation and effectiveness. The effectiveness of protected areas has been shown to depend on an optimal location, taking into account distance between forest patches (Albers et al., Reference Albers, White, Robinson and Sterner2020b).Footnote ³

Those links between economic threat to ecosystems and conservation costs lead to the mostly empirical location bias concept (Joppa and Pfaff, Reference Joppa and Pfaff2009; Pfaff and Robalino, Reference Pfaff and Robalino2012; Sims, Reference Sims2014; Pfaff et al., Reference Pfaff, Robalino, Herrera and Sandoval2015), according to which protected areas are implemented in most remote areas, where forests are less threatened. This concept suggests that strong trade-offs take place between forest conservation and rural development: effective forest conservation would imply strong constraints on local development; conversely, implementing effective protected areas is challenged in places with high economic pressure. Hence, conservation is implemented further away from the most active areas. In a spatially explicit setting applied to marine protected areas, Albers et al. (Reference Albers, White, Robinson and Sterner2020b) also considers this point, linking the siting choice of protected areas to enforcement effort and response of fishermen.

This question of the siting choice appears to be less investigated in REDD+ projects. At the macro level, Cerbu et al. (Reference Cerbu, Swallow and Thompson2011) assess which countries’ characteristics better explain early REDD actions, emphasizing a strong bias toward South America and against Africa. Lin et al. (Reference Lin, Sills and Cheshire2014) identifies potential areas for REDD+ projects, mapping both forest carbon, deforestation risk and opportunity costs. For Pasgaard and Mertz (Reference Pasgaard and Mertz2016), the location of REDD+ interventions can be explained by previous engagements of the project implementers. More recently, Delacote et al. (Reference Delacote, Le Velly and Simonet2022) assess this choice for six REDD+ projects in the Brazilian Amazon. They show that projects combining conservation and development objectives are more likely to be implemented in areas with stronger opportunity costs, while projects focusing on the conservation objective are more likely to be implemented in more remote areas.

2.3 Effectiveness and additionality

Both theoretical and empirical work focuses on the effectiveness of forest conservation interventions (Engel et al., Reference Engel, Pagiola and Wunder2008; Alix-Garcia and Wolff, Reference Alix-Garcia and Wolff2014).

From a theoretical standpoint, contract theory has been used to assess factors influencing the effectiveness of REDD+ projects. Chiroleu-Assouline et al. (Reference Chiroleu-Assouline, Poudou and Roussel2018) and Salas et al. (Reference Salas, Roe and Sohngen2018), among others, focus on asymmetric information on opportunity costs of deforestation, analyzing how those asymmetries affect the efficiency of REDD+ policies. Other papers (Albers and Robinson, Reference Albers and Robinson2013; Delacote and Angelsen, Reference Delacote and Angelsen2015; Delacote et al., Reference Delacote, Robinson and Roussel2016) assess how project implementation produces some spatial or sectoral displacement of activities, leading to leakage of deforestation and forest degradation. Another branch of the literature analyzes the effectiveness of collective PES (see Hayes et al., Reference Hayes, Grillos, Bremer, Murtinho and Shapiro2019; Segerson, Reference Segerson2022 for reviews and Nguyen et al., Reference Nguyen, McElwee, Le, Nghiem and Vu2022 for case studies).

Empirical assessment of the effectiveness of PES (especially REDD+) has been widely performed for the past few years. A first systematic review (Samii et al., Reference Samii, Lisiecki, Kulkarni, Paler, Chavis, Snilstveit, Vojtkova and Gallagher2014) suggests that projects tend to fail to achieve the common objective of forest conservation and poverty alleviation. Duchelle et al. (Reference Duchelle, Simonet, Sunderlin and Wunder2018) noted that few studies were focusing on the carbon outcomes of REDD+ projects at the time. Since then, the additionality of REDD+ projects has been widely questioned and challenged. Evaluating 40 REDD+ projects in nine countries, Guizar-Coutiño et al. (Reference Guizar-Coutiño, Jones, Balmford, Carmenta and Coomes2022) underlines the relatively low levels of deforestation reduction achieved by those projects. West et al. (Reference West, Börner, Sills and Kontoleon2020) considers that the over-estimation of emission reductions from Brazilian REDD+ projects is related to the over-estimation of the crediting baseline compared to their own control. West et al. (Reference West, Wunder, Sills, Börner, Rifai, Neidermeier, Frey and Kontoleon2023) also notice this lack of additionality in REDD+ projects, attributed to inaccurate baselines by carbon credit organisms. Montoya-Zumaeta et al. (Reference Montoya-Zumaeta, Wunder and Tacconi2021) evaluate the impact of six Peruvian incentive-based conservation projects and find sub-optimal environmental outcomes. A meta-analysis of the effectiveness of forest conservation interventions is currently being performed (Chabé-Ferret et al., Reference Chabé-Ferret, Delacote, Missirian and Voia2024), and is being updated as new results are published in peer-reviewed journals. So far, the project underlines the heterogeneous additionality of forest conservation programs. However, Wunder et al. (Reference Wunder, Börner, Ezzine-de-Blas, Feder and Pagiola2020) argue that PES can be as effective as other types of interventions, but issues of self-selection, inadequate targeting and poor enforcement can undermine the effectiveness of those schemes.

The effectiveness of protected areas has also been widely investigated. Most recently, Duncanson et al. (Reference Duncanson, Liang, Leitold, Armston, Krishna Moorthy, Dubayah, Costedoat, Enquist, Fatoyinbo, Goetz, Gonzalez-Roglich, Merow, Roehrdanz, Tabor and Zvoleff2023) has shown that protected areas were globally effective as a climate mitigation tool. Focusing on the Brazilian Amazon, Amin et al. (Reference Amin, Choumert-Nkolo, Combes, Combes Motel, Kéré, Ongono-Olinga and Schwartz2019) shows that integral protected areas and indigenous lands do reduce deforestation. In contrast, they do not find evidence of deforestation reduction in sustainable use areas. This result suggests that strong protection can be effective, while the combination of conservation and development objectives may be difficult to achieve. Keles et al. (Reference Keles, Pfaff and Mascia2023) find similar results when it comes to the degazettement and downsizing of protected areas in the Brazilian Amazon: protected areas may be withdrawn in remote or high economic pressure areas, and they can be effective or ineffective before their withdrawal. It is shown that reducing forest protection increases deforestation in cases where (1) development pressure is high, and (2) protection was effective.

The literature on community-based forest management is scarcer when it comes to deforestation outcomes. Yet, Oldekop et al. (Reference Oldekop, Sims, Karna, Whittingham and Agrawal2019) show that, in the Nepalese context, the impact of CFM on deforestation decreases when poverty baseline levels are higher, and increases with the length and size of forest management. Deforestation has also been found by Mazunda and Shively (Reference Mazunda and Shively2015) to be lower due to CFM in Malawi.

Generally, papers underline the heterogeneity of impacts (Ezzine-de-Blas et al., Reference Ezzine-de-Blas, Wunder, Ruiz-Pérez and Moreno-Sanchez2016; Chervier and Costedoat, Reference Chervier and Costedoat2017; Ruggiero et al., Reference Ruggiero, Metzger, Reverberi Tambosi and Nichols2019), which suggests that the sources of this failure and success should be more carefully investigated (Börner et al., Reference Börner, Baylis, Corbera, Ezzine-de-Blas, Honey-Rosés, Persson and Wunder2017). Among empirical studies, Delacote et al. (Reference Delacote, Le Velly and Simonet2022) is the one to which our theoretical analysis strongly relates. The paper shows that the additionality of REDD+ projects in Brazil strongly depends on the objective of the project implementer and the siting of the project: projects combining environment and development objectives were found ineffective, while one project with a strong focus on forest conservation was additional.

Our modeling approach in the next section comes at the intersection of those three sides of the literature: we show that the siting of forest conservation intervention can be the result of conservation/development trade-offs, and that what is generally considered a location bias does not necessarily lead to lack of additionality.

3.1 The implementer of the forest conservation intervention

Our aim is to describe a wide range of possible forest conservation interventions over the conservation/development spectrum. For that purpose, we consider that the implementer's objective may encompass two components:

1. 1. Conservation additionality: a weight $\alpha$ is given to the intervention outcome in terms of avoided deforestation $AD$;

2. 2. Development impacts: a weight $\beta$ is given to the livelihoods improvements $\Delta$ of the intervention.

Two choices made by the implementer are considered: (1) site selection: a site is chosen on its development potential $b$, that is also an indicator of threat on forests; and (2) effort allocation: between conservation $(e)$ and development objectives $(1 - e)$.

Several modeling choices have been made to take into account the wide variety of interventions. First, the weight given to conservation and development objectives can describe a large spectrum of conservation/development objectives.

Second, conservation interventions may consist of direct PES to households,Footnote ⁴ but they can also consist of constraints put on access to land (guards and control) or direct investment or measures (e.g., providing advice on agricultural techniques, improving agricultural resilience, creating new economic opportunities) that contribute to development. In order to take into account this wide variety, the implementer's intervention is modeled as an effort-allocation model: effort $e$ is allocated to conservation, while effort $(1-e)$ is allocated to poverty alleviation.

In order to have interior solutions, we consider that the implementer's utility from conservation additionality ($E$) and from development impacts ($L$) are increasing and concave.Footnote ⁵

The intervention with site type $b$ and effort allocation $e$ provides the following payoff to the implementer:

(1)\begin{equation} v(b, e) = \alpha E(AD(b, e)) + \beta L(\Delta(b, e)). \end{equation}

3.2 Site and community

3.2.1 Business-as-usual case

We consider a continuum of potential sites where the intervention could be implemented. Each site is represented by a potential benefit $b$ for each unit of deforestation $d$, which can be considered as an indicator of opportunity costs for the agents living onsite. This simplification states that deforestation leads to short-term economic development. In the long run, the accumulation of deforestation may become detrimental to development, for instance when the loss of ecosystem services puts agricultural activities at risk. This negative feedback has been investigated in a forest transition setting, including REDD+, by Ollivier (Reference Ollivier2012).

We assume convex costs of deforestation, including non-market benefits from forest conservation, with a quadratic specification. The site community chooses its level of deforestation to maximize its livelihood:

(2)\begin{equation} \max_{d} u = b d - \frac{d^2}{2}. \end{equation}

Under no intervention, the optimal level of deforestation is: $\overline {d} = b$. The level of development is $\overline {u} = {b^2}/{2}$. Those levels are considered to be the business-as-usual scenario. This baseline is considered common knowledge and with no uncertainty, in order to focus on our matter of interest.Footnote ⁶

3.2.2 Reaction to the conservation intervention

The implementer allocates her effort between reducing deforestation ($e$) and improving livelihood of agents living on the site ($1-e$). We focus on a case in which effort for reducing deforestation and effort for improving livelihoods are not complementary, meaning that we focus on environment–development trade-off situations, such as the ones described in section 2.1. Indeed our main interest is to consider how development pressure and site selection affect intervention additionality. Note, however, that the intervention can achieve both conservation and development objectives.

3.2.2.1 Conservation effort effectiveness and institutional context:

conservation effort may not always have the same effectiveness, depending on the site and context where the intervention is implemented.

First, we consider that economic pressures, described by the site type $b$, may reduce the effectiveness of effort allocated to forest conservation. Indeed, forest conservation implies increasing the cost of deforestation, which can be in conflict with private economic interests (especially if opportunity costs are high), which may try to overcome the effort made to decrease deforestation.Footnote ⁷

Second, other local factors can also impact the conservation effort effectiveness. In particular, factors related to institutions, mainly the ones related to ecosystem management, can influence the links between conservation effort and outcome.Footnote ⁸ At the national scale, institutions (democratization, rule of law) have been shown to influence the implementation of protected areas (Bareille et al., Reference Bareille, Wolfersberger and Zavalloni2023) and deforestation (Burgess et al., Reference Burgess, Hansen, Olken, Potapov and Sieber2012). At the more local level, institutions can be referred to as the capacity to enforce the ecosystem management rules. Robinson et al. (Reference Robinson, Albers, Lokina and Meshack2015) underlines the importance of village level institutions to increase the compliance to REDD+ projects, while Albers and Robinson (Reference Albers and Robinson2013) reviews the importance of property rights enforcement for non-timber forest products extraction. Robinson et al. (Reference Robinson, Somerville and Albers2019) notice that REDD+ should be implemented in areas where property rights are well-defined. Robinson et al. (Reference Robinson, Albers, Ngeleza and Lokina2014) distinguish resource extraction from insiders and outsiders. In our framework, one can consider that institutions encompass the capacity both to prevent resource extraction by outsiders and to limit unsustainable extraction by insiders.

$\delta (b) \in [0,\,1]$ is our indicator of this conservation effort effectiveness, relative to effort allocated to livelihood improvement: when $\delta (b) = 1$, effort is equally efficient for conservation and livelihood objectives; when $\delta (b) < 1$, effort allocated to conservation is relatively less efficient than effort allocated to development. It is totally ineffective for $\delta (b) = 0$.Footnote ⁹

In our framework, both local institutions and development influence the effectiveness of effort allocated to the conservation objective ($\delta (b)$).Footnote ¹⁰ In the case of weak institutions, larger opportunity costs from deforestation $b$ makes more difficult the implementation of an efficient conservation effort $e$: $\delta '_b << 0$. For example, if property rights are not well enforced, deforestation by outsiders is more difficult to contain in areas with higher development potential. In the case of strong institutions, the effectiveness of conservation effort is less sensitive to the development potential: $\delta '_b \rightarrow 0$. Thus, $\delta (b) \rightarrow 1$ and $\delta '_b \rightarrow 0$ relate to more reliable local institutions and strong conservation enforcement.Footnote ¹¹ Effort allocated to the conservation objective increases the cost of deforestation for the community (equivalently increases the benefit from forest conservation), becoming: ${(1 + \delta (b) e) d_i^2}/{2}$. Effort allocated to development improvement increases the net benefit from the community's activities : $(1 + (1-\delta (b) e)) (b d - {(1 + \delta (b) e) d_i^2}/{2})$.

3.2.2.2 Voluntary or coercive intervention:

forest conservation interventions may be voluntary (e.g., REDD+ projects) or coercive (e.g., integral protected areas). If participation is voluntary, the community accepts to participate if and only if the following participation constraint is satisfied: $e \leq {1}/{2 \delta (b)} \equiv \overline {e}$, implying that effort allocated to livelihood improvement has to be large enough to make the community better off.Footnote ¹²

Figure 1. Influence of development potential $b$ and institutional quality $a$ on community's participation constraint $\overline {e}$.

In the case of a coercive intervention, such a participation constraint may not take place and the effort allocation can be set to $e > \overline {e}$, meaning that the intervention is implemented at the expense of the local community.Footnote ¹³

3.2.2.3 Reaction to the intervention:

under the forest conservation implementation, the community's objective becomes:

(3)\begin{equation} \max_{d} u = (2 - \delta(b) e) (b d - \frac{(1 + \delta(b) e) d_i^2}{2}), \end{equation}

leading to the following reaction:

(4)\begin{align} d^*(b, e) & = \frac{b}{(1 + \delta(b) e)} \end{align}

(5)\begin{align} u^*(b, e) & = \frac{ (2 - \delta(b) e)}{(1 + \delta(b) e)} \frac{b^2}{2}. \end{align}

Avoided deforestation is

(6)\begin{equation} AD^*(b, e) =\overline{d}(b) - d^*(b, e)= \frac{b \delta(b) e}{(1 + \delta(b) e)}.\end{equation}

Avoided deforestation is unambiguously increasing in $e$:

(7)\begin{equation} AD{^*}'_{e} = \frac{b \delta(b)}{(1 + \delta(b) e)} > 0.\end{equation}

3.2.2.4 Potential and effective additionality:

the impact of type $b$ on avoided deforestation is:

(8)\begin{equation} AD{^*}'_{b} = \underbrace{\overbrace{ \frac{\delta(b)}{(1 + \delta(b) e)}}^{Potential \ Additionality} + \frac{b \delta'_b e }{(1 + \delta(b) e)^2}}_{Effective \ Additionality}. \end{equation}

Result 1 Potential and effective additionality. Choosing a site with high development potential $b$ suggests high potential additionality, as the baseline deforestation is large if no forest conservation intervention is implemented; the first part of equation (8) is positive.

Figure 2. Influence of development potential $b$ on avoided deforestation $AD^*$, for diverse levels of institutional quality $a$.

The level of effective additionality depends on the quality of institutions; the second part of equation (8) is negative. If institutions are strong, ($\delta (b) \rightarrow 1$, $\delta '_b \rightarrow 0$), effective additionality is close (possibly equal) to its potential. In this case, avoided deforestation is larger in communities with high development potential $b$. If institutions are weak, ($\delta (b) \rightarrow 0$, $\delta '_b << 0$), the difference between potential and effective additionality is larger. In that case, avoided deforestation is smaller in communities with high development potential $b$.

Figure 2 illustrates this result with a numerical example.

Overall this distinction between potential and effective additionality implies that baseline deforestation is not a good indicator of additionality when institutions are weak. Indeed, in that case, the level of avoided deforestation may be higher in sites where the baseline deforestation is low. It follows that site selection focusing mainly on threat to ecosystems that do not take into account socioeconomic contexts (including institutions) is likely not to achieve its conservation objectives.

Development impact of the intervention is

(9)\begin{equation} \Delta(b, e) = u^*(b, e) - \overline{u}(b)= \frac{b^2}{2} \frac{(1 - 2 \delta(b) e)}{(1 + \delta(b) e)}.\end{equation}

Increasing $e$ decreases the intervention benefits in terms of livelihoods, while selecting a site with strong development potential $b$ increases it,

(10)\begin{align} \Delta{^*}'_{e} & = \frac{- b^2}{2} \frac{3 \delta(b)}{(1 + \delta(b) e)^2} < 0 \end{align}

(11)\begin{align} \Delta{^*}'_b & =\frac{b (1 - 2 \delta(b) e)}{(1 + \delta(b) e)} - \frac{3 b^2 \delta'_b e}{(1 + \delta(b) e)^2} > 0. \end{align}

3.3 Intervention implementation

In this section, the effort allocation and the site selection are described. First, in order to give some intuition about our results, we focus on two extreme cases of implementer objectives: when the implementer focuses only on conservation ($\alpha = 1$) or only on development ($\beta = 1$). We combine them with two corner cases of local institutions: $\delta '_b = 0$ and $\delta '_b$ strongly negative. The results are presented in table 1.

Table 1. Effort allocation and site selection

Second, we generalize those results and consider the maximization problem presented in equation (1). The first-order conditions implicitly describe this set of choices:

(12)\begin{align} v'_b& = \alpha E' AD'_b + \beta L' \Delta'_b = 0 \end{align}

(13)\begin{align} v'_e& = \alpha E' AD'_e + \beta L' \Delta'_e = 0. \end{align}

3.3.1 Effort allocation

Looking at table 1, effort allocation is straightforward. The implementer allocates all her effort to development if it is her unique objective: $e^* = 0$ if $\beta = 1$ (e.g., a project of community forest management with a strong poverty alleviation objective). If avoided deforestation is her unique objective and participation is voluntary (e.g., REDD+ project), the implementer selects the effort allocation that satisfies the community participation constraint if avoided: $e^* = \overline {e}$ if $\alpha = 1$. If the intervention is coercive (e.g., integral protected area), she can allocate all her effort to conservation: $e^* = 1$.

This intuition is generalized in equation (13): the implementer allocates her effort between her conservation and development objectives in order to balance marginal benefit from avoided deforestation and marginal benefit from livelihood improvement.

Result 2 The implementer allocates her effort according to her objectives: larger conservation objective ($\alpha$) implies larger effort allocated to avoided deforestation (larger $e$); while larger development objective ($\beta$) implies larger effort allocated to improving livelihoods (smaller $e$). Figure 3 illustrates this result with a numerical example.

Figure 3. Implementer objective $\alpha$ and optimal effort allocation $e^*$, for diverse levels of institutions $a$.

3.3.2 Site selection

The trade-off behind site selection is described in table 1. If development is the unique objective of the implementer ($\beta = 1$), she selects the site with the highest development potential whatever is the local institutional quality. In contrast, when considering an implementer only focusing on conservation outcomes ($\alpha = 1$), institutions matter and the difference between potential and effective additionality described in result 1 is crucial. If institutions are strong and effort allocated to conservation is effective, then effective additionality is close to potential additionality and the implementer selects the site with the highest development potential (hence the highest potential and effective additionality). If institutions are weak, the effectiveness of effort allocated to conservation is strongly negatively affected by the development potential. The implementer then selects a site with the lowest development potential in order to maximize effective additionality.

This intuition is generalized in equation (12). The implementer balances the trade-off between the impact of the development potential on avoided deforestation, and the impact on livelihoods. This trade-off depends on two interconnected factors: first, on the relative weight between conservation ($\alpha$) and development ($\beta$) objectives; and second, on the link between development potential $b$ and conservation effort effectiveness $\delta (b)$ (as shown in result 1).

Result 3

• • Development first: a development-first implementer (high $\beta$) selects a site with high development potential $b$, whatever is the level of local institutions.

• • Conservation first: a conservation-first implementer (high $\alpha$) selects a site

  • • with high development potential $b$, if local institutions are strong ($\delta '_b \rightarrow 0$, small $a$)

    $\rightarrow$ the best strategy is to target places where potential additionality is high and close to effective additionality.

  • • with low development potential $b$, if local institutions are weak ($\delta '_b << 0$, small $a$)

    $\rightarrow$ the best strategy is to target places where potential additionality is lower, but effective additionality easier to achieve.

Figure 4 illustrates this result with a numerical example.

Figure 4. Influence of institutional quality $a$ on site choice $b^*$, for diverse levels of implementer objective $\alpha,\, \beta$.

Institutions are shown to strongly affect the site choice of the implementer if she gives strong importance to the conservation objective. If they are strong, the implementer can be ambitious about the intervention outcome, and target sites where development pressure is high; if they are weak, it is preferable for the implementer to focus on sites where development pressure is lower, in order to more easily capture conservation benefits.

This result underlines that an implementer with strong conservation objectives may have an interest in selecting a site with low development potential, which is frequently considered as a location bias in the literature. Hence it gives new insight into this concept, which generally considers that interventions implemented in remote areas are ineffective. In our framework, an implementer with a focus on conservation may pick a site with low development potential, in a remote area, simply because weak institutions reduce the effectiveness of conservation effort.