Synergies in the Land Use Sector: What Is the Best Policy Approach When Co-benefits and Trade-offs Are Involved?

At an aggregated level, the concept of ES suggests a positive relationship between their provision level and human welfare. However, at a lower scale, and especially when one considers their interactions, this relation becomes complex and often indirect (Daw et al. 2016). In the context of tropical deforestation - the main issue analyzed here - there is a trade-off between provisioning services from agriculture and forestry. Both productive activities compete for land, thus - holding everything else constant - the output in one cannot be increased without reducing the output in the other. In fact, agriculture expansion is recognized as the main driver of deforestation at the global level (Hosonuma et al. 2012).

The relation between regulating and provisioning services is even more difficult to observe. However, there seems to be a co-benefit between forestland and agriculture. Empirical evidence supports that forest in the proximity of croplands have a positive effect in yield (Reed et al., 2017), which suggests a positive relation between regional regulating services and agriculture output. In addition, there are interactions among regulating services at different scales. For instance, carbon storage - an important ES for mitigation - exhibits a mixed effect with respect to different regional regulating services. Soil related services (e.g. soil protection or fertility) tend to increase with higher carbon stock (Locatelli et al., 2014). However, water runoff declines with a denser forest (Duncker et al., 2012; Farley, Jobbágy, & Jackson, 2005; Locatelli et al., 2014). Putting it in another way, carbon storage may be related to both co-benefits and trade-offs.

Taking into consideration the co-benefits and trade-offs related to carbon storage, the impact of deforestation and forest degradation

Forest degradation must be interpreted as a reduction of biomass with respect to a natural forest without involving a land use change.

The conceptual framework developed for this analysis is displayed in Figure 1. It shows the assumed causal chain from land allocation to social welfare. The figure demonstrates that land can be kept as a natural forest (Ln), or reallocated to forestry (Lf), or to agriculture (La). The sign in the link that connects these variables represents their relation. It is negative because increasing land allocation in one productive use is made at the expense of the natural forest, a situation that is representative of what occurs in the tropical region (Hosonuma et al. 2012). The reallocation process also determines the main impacts studied here: deforestation and degradation.

Fig. 1

Land allocation determines the output of environmental goods (Ya, Yf) and carbon stock level (C). In the case of output, land and labor (T) are combined to perform the production process. Naturally, a higher allocation of land to any productive activity leads to a higher output, which explains the positive sign between these variables. It is additionally assumed that output prices (pa, pf) determine the value of output, which is positively related to social welfare.

The reallocation of land, however, has a negative impact on carbon stock. Two main consequences follow from this: firstly, hydrological services (H) increase and, secondly, the state of the environment (E) decreases. Notice that the first effect constitutes a trade-off while the second is a co-benefit. As a consequence, the overall impact of land reallocation on output and ultimately on welfare is ambiguous. The dotted lines in this section of the diagram indicate that these relations are considered as externalities when the economy is unregulated.

Based on the conceptual framework presented in the previous subsection, a 2 × 2 production model augmented with an externality was developed with the purpose of deriving equilibrium allocation of resources and provision level of the ES under consideration. The model derives the equilibrium allocation by solving the optimization problem of the representative firms in the agriculture and forestry subsectors. As it was indicated before, the provision level of ES is determined by land allocation and it has an impact on agriculture output. In what follows, the mathematical representation of these processes is shown. The appendix shows further details regarding equilibrium determination and regulating measures.

To begin with, the representative firm in each subsector (agriculture and forestry) is assumed to be competitive and its goal is to maximize profits subject to a technological constraint. In addition, the profit function considers the policy followed, which takes the form of a Pigouvian tax/subsidy on land. In mathematical terms, the problem of the firm is: (1)maxLj,TjΠj=pjYj-wTj-(r+tj)Ljs.t. Yj=F(Tj,Lj) \eqalign{& \mathop {\max }\limits_{L_j ,T_j } \Pi _j = p_j Y_j - wT_j - (r + t_j )L_j \cr & {\rm{s}}.{\rm{t}}.\,\,\,Y_j = F(T_j ,L_j ) \cr} Where: Π are profits; Y is the output level; T is labor used in the subsector; L is land allocated to that subsector; p is the output price; w is the wage rate; r is the opportunity cost of land; t is the tax/subsidy on land in a subsector (see appendix); and the subscript j represents the subsector (a: agriculture, f: forestry).

A small open economy setting is followed, which implies that output prices are exogenously determined. Thus, the overall value of the production of the given economy is: (2)GDP=paF(E)Ya+pfYf+rLn {\rm{GDP}} = p_a F(E)Y_a + p_f Y_f + rL_n Where: F(E) is a concave function in terms of the state of the environment (E), defined later in equation (5); and the subscript n represents natural forest standing.

In this analysis the GDP is used as the social welfare measure and changes in this variable are interpreted as an adaptation indicator as a proxy to regional welfare. In addition, the last term in equation (2) represents a compensation scheme dependent on policy configuration. In the unregulated setting, this term is zero as no land is left as a natural forest.

At this point, it is important to clarify that the use of GDP as a measure of welfare is subject of debate and alternative measures have been proposed to better assess this dimension of sustainable development (Giannetti et al. 2015; Kubiszewski et al. 2013). However, as the model presented here is formulated, the GDP formulation simply captures the behavior of output value, leaving aside any further complexity that in reality might take place. This implies that a weak sustainability approach is followed, or in other words, a non-declining consumption is considered the indicator of sustainability (Ang and Van Passel 2012; Davies 2018).

Land allocation directly affects carbon stock through deforestation and degradation. These impacts affect C as follows: 3C=Cmax-C¯La-dLf {\rm{C}} = C_{max} - \bar CL_a - dL_f Where: C is the carbon stock contained in biomass; Cmax is the maximum level of carbon stock; C¯ \bar C is the average carbon storage per unit of land; d is the degradation parameter.

The previous equation can be interpreted as follows: if no production process is performed, carbon stock is at its maximum capacity (the first term on the right hand side of equation (3)). However, if land is reallocated to agriculture, C is affected by deforestation. In particular, every unit of forestland lost implies a reduction equivalent to the average carbon storage (second term in the right hand side of the equation). Similarly, when land is allocated to forestry, C is reduced by degradation. In this case, biomass extraction reduces the carbon stock in a fixed proportion (third term on the right hand side).

Changes in the carbon stock affect the provision level hydrological services and the state of the environment. More specifically, it is assumed that the provision of H negatively and non-linearly relates to C. To capture this relation, a Hill function was followed: (4)H(C)=11+(c/k)n {\rm{H}}({\rm{C}}) = {1 \over {1 + \left( {c/k} \right)^n }} Where: H is the provision level of hydrological services; k is a parameter that determines the inflection on the horizontal axis; n is a parameter that determines the steepness around the inflection.

This formulation implies that when carbon stock is reduced, either by deforestation or degradation, the provision of hydrological services increases (e.g. due to lower evapotranspiration).

The state of the environment is specified by a normalized CES function (Barro and Sala-i-Martin 2004) that depends on C and H as follows: (5)E(C,H(C))=[s(CCmax)ψ+(1-s)(H(C)H(Cmax))ψ]1/ψ {\rm{E}}({\rm{C}},{\rm{H}}({\rm{C}})) = \left[ {s\left( {{C \over {C_{max} }}} \right)^\psi + (1 - s)\left( {{{H(C)} \over {H(C_{max} )}}} \right)^\psi } \right]^{1/\psi } Where: H is the provision level of hydrological services; s ∈ [0,1] is the carbon share parameter; Ψ is σ-1σ {{\sigma - 1} \over \sigma } ; α is the elasticity of substitution between C and H.

This formulation captures the key idea that ES at aggregated level increase welfare but, at a lower level, their effect on the indicator mentioned is ambiguous and indirect. For example, a decrease in C means a lower value in the first term of the equation but a higher one in the second term. The parameters of equations (4) and (5) determine the net effect of the change mentioned.

Figure 2 illustrates the effect of land use change on the state of the environment at aggregated level, which results from the combination of equations (4), (5) and different sets of parameters. It is precisely this relation, the one treated under deep uncertainty conditions, as it will be detailed later. The dotted line shows the unregulated situation: it is inaccurately perceived that land use change has no effect on the state of the environment. However, the reduction of the carbon stock through land reallocation has an initial ambiguous effect on the state of the environment. For instance, a combination of parameters might immediately deteriorate it, as the black line shows. Other combination of parameters might exhibit an initial smoother response, as indicated by the blue curve. There is even the possibility of initially observing a relatively neutral response in the state of the environment, as the red line illustrates. Excessive land use change, nevertheless, leads to a deterioration of the environment and lower productivity in all cases. Situations like the one exemplified in by the gray curve are considered unrealistic, as excessive land use change would lead to an improvement in the state of the environment.

Fig. 2

The EMA approach is a research methodology designed to analyze complex systems under deep uncertainty conditions. These are situations where it is reasonable to incompletely enumerate multiple possibilities without being able to rank how likely these are judged to be. In the context of EMA, a single model run is considered an experiment and it reveals how the system would behave if various guesses were correct. By conducting multiple experiments, it is possible to explore the implications of those guesses (Kwakkel and Pruyt 2013).

In this exercise, the model shown in the previous section was simulated using different policy configurations considering deep uncertainty in parameters specification. Specific details about model quantification and simulation settings are shown in the next subsections.

Figure 3 displays the classification of outcomes of each policy scenario analyzed. As it can be seen from the figure, the scenarios based on current policy approaches (reserve and compensation) perform poorly compared to the scenarios implementing alternative policy schemes, based on the internalization of externalities (adaptation, mitigation and policy integration). For example, a disjunction was observed in 22% of the times in the case of reserve and compensation scenarios. Likewise, a mitigation trade off was observed 74% of the times in the case of reserve and 16% of the times in the case of compensation scenario. And a synergy was observed 4 and 62% of the times respectively. These last numbers show that the main effect of a compensation scheme is to switch some of the instances from a mitigation trade off to a synergy, which reflects the relevance of the mechanism mentioned in the design of current environmental policy in the land use sector.

Fig. 3

A drastically different behavior was observed when alternative policies were implemented. As the figure shows, only a small fraction of instances corresponded to disjunction (2%) or mitigation trade off (16%) when adaptation policy was implemented. Moreover, synergies were observed in all cases under mitigation and policy integration scenarios.

It is worth noticing that synergies associated to each policy have varying quantitative properties, which were not possible to distinguish in the qualitative analysis presented previously. In other words, it was not possible to elucidate whether improvements in mitigation and adaptation indicators were substantially higher in a specific policy intervention. Figure 4 shows a notched box-plot of synergic outcomes in environmental (C in left panel) and welfare terms (GDP in right panel), no overlapping notches indicate an statistically different median in different groups (Wickham 2016). As the figure reveals, the overall performance of current policy approaches is weak compared to alternative ones in both indicators. The median equilibrium carbon stock is considerably lower in the reserve and compensation scenario (0.13 and 0.15 respectively) than in the alternative policies (A:0.28, M:0.38, PI:0.42). It follows that forestland conservation tended to be higher in the alternative policies.

Fig. 4

A similar behavior is observed in GDP with the difference that this indicator shows a higher variability in reserve and compensation scenarios, as it is shown by the width of the boxes and the outliers. This reflects that only under very specific conditions current policy approaches are comparable to alternative ones in adaptation terms.

The results obtained in reserve and compensation scenarios can be anticipated using the Rybczynski theorem (Mas-Colell, Whinston, and Green 1995). Specifically, the net effect of decreasing the endowment of land is an increase in the amount of this factor allocated to agriculture, the relatively less land intensive sector. The magnitude of this effect is fully explained by technologies and output prices. As the parameters that defined those relations remained fixed, the resulting allocation of land was the same for all experiments in these configurations. It follows that synergies are determined by extraction level in forestry sector (degradation parameter) and compensation level (see equation 2, in particular the third element in the right hand side). In the case in which these effects overcome GDP loss due to land endowment reduction and extraction in forestry sector allows a net carbon gain, a synergy is realized. However, as land allocation remains fixed, only relatively weak synergies were expected under these schemes.

The mechanism described so far also takes place in the other three configurations. However, it is complemented by the internalization of the relevant externality. Depending on policy configuration, the opportunity cost of land is modified to account for the effect that the economic subsector has on a particular environmental service. As a consequence, the allocation of factors is determined not only by the parameters mentioned before but also by the impact that land reallocation has on the provision of ES. At this point it is convenient to recall that ES determining the state of the environment (see equation (5)) were treated under deep uncertainty conditions. Thus, variability was involved and only general patterns could have been identified.

Figure 5 summarizes the results obtained for the alternative policy scenarios. The scatterplot plot in this figure shows two distinctive patters: mitigation and PI tend to group on a cloud located in the top right corner while adaptation exhibited a positively sloped shape. As the carbon density plot reveals (top panel), the main characteristics of the clouds formed by M and PI is that, in general, these configurations conserve a higher fraction of carbon compared to adaptation. In addition, these configurations delivered a higher GDP (see the right panel). The figure explains why synergies are stronger in M and PI scenarios. However, PI exhibited an even better performance, as carbon storage tended to be higher under this configuration without sacrificing GDP.