Gold-rush in a forested El Dorado: deforestation leakages and the need for regional cooperation

Our study area includes Guyana, Suriname, French Guiana and the Brazilian State of Amapá. These political entities cover an extent of approximately 600 000 sq. km of which more than 500 000 sq. km are dense tropical rainforests. Gold-mining is closely associated with the presence of a Greenstone formation Hammond et al (2007) over the region (figure 2).

Zoom In Zoom Out Reset image size

Large scale operations coexist with artisanal small-scale gold-mining which is rarely restricted to a few workers using a gold pan. Typically most operations involve the use of excavators and motor-pumps. The introduction of these modern techniques by Brazilian 'garimpeiros' radically modified mining operations (Veiga 1997). Brazilian gold-mine workers spread in the Guiana Shield in the 1990's, when Brazil started to strictly regulate the mining sector (Veiga 1997, WWF Guianas 2012). They constitute a great majority of the gold-mining labour force in the Guianas, with estimates of 75% in Suriname and French Guiana (Heemskerk et al 2004).

Annual deforestation data was derived from deforestation maps by Hansen et al (2013) for 2001–2014. Expert-based assessments of areas impacted by gold-mining at the regional scale and at three points in time in 2000, 2008 and 2014 (Debarros and Joubert 2010, Rahm et al 2015), in the form of GIS layers, were used to identify deforestation due to gold-mining and exclude other types of activities. Such studies were based on medium to high resolution images, ranging from 5 to 30 meters, consistent with the 30 meters resolution of the maps produced by Hansen et al (2013). A previous study by Alvarez-Berríos and Mitchell Aide (2015) used MODIS images at 250 meters resolution and might identify small-scale gold-mining, which is dominant in the Guiana Shield, less accurately (Rahm et al 2015).

To avoid excessive inter-annual variability in deforestation due to a potential lag between actual deforestation and its detection by satellites because of clouds, a moving average over three years was calculated and used in following models.

As it is impossible to distinguish between industrial and artisanal mining from remote sensing data (Rahm et al 2015), we weren't able to make such distinction. Using deforested patches size to differentiate large from small mining sites is biased because several neighbouring small mining sites may merge in time into a unique large deforested patch, while the fragmented pattern of gold-mining might represent a large mining site as a succession of small deforested patches. Another way of making this distinction would be to separate open mining pits, which often characterize large-scale mining operations run by multinational companies, from alluvial gold-mining more associated with artisanal activities. Using previously mentioned assessments of areas impacted by gold-mining over the Guiana Shield, we estimated that only about 1% of areas impacted by gold-mining corresponded to open pits likely to be associated with multinationals projects. Given this scarcity, potential biases caused by the contribution of large multinationals' projects are limited, in our opinion. As such, our study de facto mainly focuses on alluvial gold-mining, often designated as small-scale gold-mining even when covering several hectares, which we assumed to be operated by a homogeneous set of agents. Such a generalization is of course questionable but preferable under an unclear context. Even in French Guiana, where the level of enforcement is high and the distinction between illegal and legal activities is clear a priori, the existence of illegal mining sites very close to legal exploitation permits (Gond and Brognoli 2005) raises the question of potential interactions between legal and illegal spheres.

Hammond et al (2007) noted that gold production in the Guiana Shield during 1941–1990 was strongly and positively correlated with gold prices. Assuming that this correlation is still valid in the current context and that deforestation due to gold-mining is proportional to gold production, we modelled deforestation due to gold-mining over 2001–2014 using annual nominal gold-price as the unique explanatory variable. Gold-price was expected to be a strong driver of gold-mining activity, as small-scale gold-miners are usually paid in gold and use it as a means of payment (Heemskerk 2010). The log-normal regional model was formulated as follows:

$$\begin{equation} Def_t \~\text{ln}\;N(\alpha _0 \times Value_t^{\alpha _V } ;\sigma _{\text{reg}}^\text{2}) \end{equation}$$

where Def_t is the deforestation due to gold-mining over the Guiana Shield at year t in thousands hectares. α₀ and α_V are the model's parameters which we estimated. Value_t = GoldPrice_t−min(GoldPrice), where GoldPrice_t is the mean annual international nominal gold price on global markets at year t, obtained from internet⁸, and min(GoldPrice) is the minimum gold price observed over the period of interest, here corresponding to gold price in 2000. We assumed no time lag between changes in gold prices and associated deforestation at a yearly temporal resolution. This is because gold miners are likely to adapt quickly to changing prices. Moreover, as previously mentioned, a moving average over three years was calculated for annual deforestation due to gold-mining. For a better modelling consistency, this averaging was also applied to gold prices. This may also tend to hide any time lag between increasing gold prices and increasing deforestation.

From an economic point of view, miners might be more interested in the actual gold price instead of the difference between average gold price at year t and gold price at a reference point in time, as we used here. On the contrary, one could argue that economic agents reacts more to differences than to absolute values since differences partially indicate changes in the cost-benefit ratio of gold extraction. From a modelling point of view, using such a difference is more robust; the intercept α₀ corresponds to the amount of deforestation due to gold-mining when prices reached their lowest value during 2001–2014. On the contrary, directly using GoldPrice_t as the explanatory variable would make α₀ uninterpretable, corresponding to the value of deforestation due to gold-mining when gold prices reach a hypothetical null value.

In the previous section, we assumed a regional determination of deforestation due to gold-mining, with no focus on where miners choose to settle. While they seek to maximize profit, a null hypothesis might assume no difference in production costs between the countries considered, implying that gold miners would spread randomly over the Greenstone Belt. This is supported by the homogeneity in the origin of the labor force mostly coming from a few poor Brazilian States (Heemskerk et al 2004, de Theije 2007) and a lack of border controls (de Rohan et al 2011). Although such a hypothesis is naïve given the higher level of control of gold-mining in French Guiana and Amapá, as mentioned in our introduction, this simple model allows to test for significant differences between countries with respect to the link between gold price and deforestation.

2.5.1. Estimating national intrinsic gold-mining potential.

2.5.2. National model of deforestation due to gold-mining.

The model of deforestation due to gold-mining considering country effect was nested into the regional model and formulated as follows:

$$\begin{equation} Def_{t,c} \~\text{ln}\;N(\beta _{0,c} \times Value_t^{\beta _{V,c} } \times GMPotential_c ;\sigma _{\text{nat}}^2 ) \end{equation}$$

where β are the estimated model's parameters, with an additional index c corresponding to each individual country or political entity: Guyana, Suriname, French Guiana or Amapá, and GMPotential_c is the extent of Greenstone in country c in million hectares.

2.6.1. The contribution of law enforcement against illegal mining.

2.6.2. Leakages model.

The model of deforestation due to gold-mining and leakages between Suriname and French Guiana was formulated as follows:

$$\begin{equation} Def_{t,c} \~\text{ln}\;N(\gamma _{0,c} \times Value_t^{\gamma _V } \times I_c \times Repression_t^{\gamma _R } ;\sigma _{\text{leak}}^2 ) \end{equation}$$

where γ are the model's parameters. Repression_t, the scaled number of illegal miners' shelters destroyed by the army in French Guiana (see SM2 for more details on the construction of this variable) was used as a proxy for the intensity of the repressive policy. In order to take into account the inertia of the repressive effect, i.e. the fact that an intense repression a given year might deter new gold-mining activities for some years later, Repression_t was averaged over a three-year period, between t − 2 and t. This three-year period was chosen based on preliminary models' results, giving better statistical significance than shorter or longer periods. I_c is a dummy variable taking value −1 for French Guiana and +1 for Suriname. This variable considers the negative expected effect of repressive policies on deforestation due to gold-mining in French Guiana, and positive in Suriname.

Gold-mining potential was similar in both countries and was excluded from the model. In the absence of military repression, we hypothesized a similar relationship between prices and deforestation in both countries, which explains the unique parameter γ_V used for both French Guiana and Suriname.

This model assumes a multiplicative effect of the French repressive policy, as shown by the use of multiplication signs. In concrete terms, we assume that changing policies are not displacing a certain amount of gold-mining activity from a country to another symmetrically, which would be expressed in an additive model, but rather translocating the whole process from French Guiana to Suriname, provoking an amplification of gold-mining in the later. Although questionable, such a hypothesis is credible considering that more gold-miners emigrating might create opportunities for economies of scale and more intense logistic flows of production factors, provoking non-linear changes in gold-mining activity.

After setting Repression_t to 0 for each year, we could estimate a counter-factual scenario, in a similar way as a reference scenarios could be built to estimate avoided deforestation under a REDD+ like mechanism.

2.6.3. Extending the dataset from 1996

The models parameters described above were estimated using Bayesian inference in a MCMC scheme. Compared to frequentist inference, this allowed us to restrict the range and distribution of values of each parameter to realistic intervals. Priors are given in table 1. 50 000 iterations were computed for each model. 10 000 runs were excluded as burning periods.

All data processing was accomplished via the free and open-source softwares GRASS GIS (GRASS Development Team 2015), QGIS (QGIS Development Team 2009) and R (R Core Team 2015).

Gold price is a very strong predictor of deforestation due to gold-mining (table 1). Deforestation at the regional scale was close to 2000 hectares per year when gold prices were below 400 USD/ounce, and reached values higher than 8000 hectares annually when prices peaked around 2012. The relationship between both variables is dynamic and valid not only when prices were increasing but also when they sharply decreased at the end of the period (figure 3). Larger variability in deforestation is observed at the end of the period of interest when gold prices reached 1300–1400 USD/ounce. However, this result remained within the 95% confidence interval of our model.

Zoom In Zoom Out Reset image size

Country effects are statistically significant and different between each other, except between Suriname and Guyana, showing a great heterogeneity in the relationship between gold price and national deforestation due to gold-mining (table 1). This relationship is particularly strong in both Suriname (β_V,S = 2.21 [1.95; 2.55]) and Guyana (β_V,G = 2.47 [2.16; 2.76]), weak in Amapá (β_V,A = 0.32 [0.03; 0.61]) and negative in French Guiana (β_V,FG = −0.78 [−0.94; −0.38]) (figure 4). In Suriname and Guyana, deforestation increased massively until 2011, boosted by gold-prices, and suddenly dropped when prices sharply decreased.

Zoom In Zoom Out Reset image size

γ_R, the parameter associated with the military repression, is statistically significant and positive (table 1), provoking a decrease in deforestation due to gold-mining in French Guiana and an increase in Suriname.

After removing the part of the model associated with the repressive policy, we could estimate deforestation due to gold-mining in both countries under a hypothetical scenario in which no military repressive policy was launched. Under our modelling hypothesis, policy changes avoided the deforestation of approx. 4300 hectares in French Guiana over 1996–2014 and increased it by approx. 12 100 hectares in Suriname (figure 5).

Zoom In Zoom Out Reset image size

Our leakages model assumed a multiplicative effect of the repressive policy on deforestation. This choice has crucial consequences, meaning that repression is not displacing a fraction of gold-mining activity from French Guiana to Suriname, but exporting the whole process. This explains why the estimated decrease in deforestation in French Guiana and increased deforestation in Suriname are not symmetrical; thus contributing to an amplification of gold-mining in Suriname. This choice may overestimate the effect of repression and underestimate the effect of gold prices. Moreover, multiple leakages are likely to occur in the absence of any border controls, not only to Suriname.

Explanatory variables choice is also critical. Characterizing the intensity of a repressive policy only by counting the number of shelters destroyed is a simplification of the occurring processes. Indeed, this number is not only a function of the intensity of the repressive effort, but also of the number of illegal mining sites under activity or their accessibility.

Finally, in French Guiana, illegal gold-miners tend to exploit smaller alluvial sites or convert to primary gold production (Parc Amazonien de Guyane 2014) which are more difficult to detect by the authorities and provoke less deforestation. Under such repressive context, the link between deforestation and the level of gold-mining activity gets unclear, and deforestation could decrease even if the same number of gold miners are present, provoking biases in deforestation leakages estimation.

For those reasons, we emphasize the need to carefully build leakages models and to interpret the results of our model with care. Field studies could contribute to more precise assessment of the magnitude of deforestation leakages, especially regarding the origin of gold miners in Suriname, while the repressive context in French Guiana de facto limits access to illegal mining sites.

All the countries considered in the present study are interested in REDD+ projects. Guyana signed a commitment with Norway to limit its deforestation rates below 0.275% annually (Karsenty and Ongolo 2012). Suriname's Readiness Preparation Proposal was recently approved by the World Bank (van Kanten et al 2013). French Guiana, as part of an Annex I country, is not eligible for REDD+, but a domestic project could be an alternative approach (Wemaëre 2014). Amapá is also in a specific situation as part of Brazil, but sub-national projects are under construction (Guadalupe Gallardo 2014).

However, our conclusions question three corner-stones of REDD+: additionality, i.e. the proof that avoided deforestation would not have occurred in the absence of any deforestation reduction mechanism, permanence or the guarantee that lands where deforestation was avoided will not be deforested later, and leakages (van Oosterzee et al 2012).

Reference scenarios formulation, from which avoided deforestation is estimated, is a major challenge in implementing REDD+ (Huettner et al 2009). But formulating a baseline when deforestation is dependent on volatile gold prices is risky. As gold-prices sharply decreased after 2012, deforestation due to gold-mining adjusted downward quasi-instantaneously in Guyana and Suriname: lower deforestation can stem from a less profitable financial context in the absence of efforts, thus contradicting the additionality principle. On the contrary, extremely high prices can limit the efficiency of policies that aim to controlling gold-mining and provoke non-permanence of carbon credits. Indeed, the current repressive policy in French Guiana seems weak by being very careful about protecting the human rights of illegal miners, and thus represents a low threat to them (de Rohan et al 2011). It is probably unable to ensure long-term forest protection in case of decreased political willingness to eradicate illegal gold-mining or ever increasing gold prices which would increase the opportunity cost of abandoning gold-mining. On the contrary, the repression of gold-mining in Brazil in the 90's seems still to be effective in deterring miners to exploit gold in Amapá. Little is known about this Brazilian policy, but violent military interventions by the military police were reported, such as in the Serra Pelada mining pit in Pará State, where several small-scale miners were killed (Hoogbergen and Kruijt 2004, Memorial da Democracia 2016). Besides its long-term efficiency, it is thus very unlikely and undesirable that such a policy would be applied elsewhere. Finally, leakages make baseline even more unreliable, due to the multiple interactions between national environmental policies.

Our national model of deforestation due to gold-mining showed a negative relationship between deforestation due to gold-mining and gold prices in French Guiana. This is likely explained by the repressive policy against illegal gold-mining. In Amapá, the corresponding coefficient is close to zero. As such, the gold-rush observed in the Guiana Shield is mainly driven by increasing mining pressures in Guyana and Suriname in response to increasing gold prices.

Due to the difficulty of controlling the borders within the Guiana Shield in remote and forested areas, the existence of refuge for gold-miners in countries where no strict law exists impedes local policy to have an impact at a supra-national level, contributing to a permanence of logistic flows, i.e. illegal transportation of gasoline, mercury or other production factors, to countries where law enforcement is stronger, such as observed between Suriname and French Guiana (de Rohan et al 2011). In an opposite direction, massive gold smuggling from Guyana and French Guiana to Suriname are suspected (Heemskerk 2010) due to more favourable taxations in the latter. A displacement of the activity to a neighbouring country is costly in response to stricter law enforcement, but this increased cost is probably too small to completely deter gold-miners from working. A regional policy is the only efficient option for a long-term solution to avoid the devastating consequences of illegal gold-mining, which forms part of the official political agenda of all the countries in our study area (de Rohan et al 2011).

Whether it is undertaken for forest carbon preservation within centralized REDD+ projects or to limit the broader environmental, health and social impacts of gold-mining, this policy should embody the complexity of the network of economic agents involved and existing cross-sectoral linkages between gold-mining and other forest uses (Hirons 2013). Police cooperation agreements were signed between Suriname and French Guiana, and French Guiana and Brazil in 2008, but differences in the legal frameworks and the opposition of local politicians as in Amapá are slowing down cooperation (de Rohan et al 2011, WWF 2014). Although control and repressive policies are necessary and urgently needed to avoid more environmental disasters, a focus should be given to the preservation and enhancement of the livelihoods of local populations, taking into account their preferences while ensuring sustainable alternative jobs creation (Hirons 2011). Such a policy should also encompass neighbouring Brazilian States. Indeed, if local Maroon communities are involved in gold-mining directly or indirectly (Heemskerk et al 2004), a large majority of gold-miners are Brazilians, with estimates between 65% and 75% (Heemskerk et al 2004, Legg et al 2015), 90% of them coming from the rural States of Pará and Maranhão in Northern Brazil (de Theije 2007). This deagrarianisation situation and seasonal migrations adds even more complexity to the gold-mining issue in the Guianas and elsewhere, but must be dealt with by governments and the international community in order to provide efficient ways of limiting environmental degradation and resulting carbon emissions.