Reducing tropical deforestation through local collective action in the Brazilian Amazon
Policies inducing positive outcomes in one place may fail drastically when local settings differ, particularly when collaboration among diverse and competing social groups is required to overcome collective problems. Anti deforestation policies for the Brazilian Amazon are traditionally implemented at the level of the biome, disregarding context specific realities and the role of local actors and institutions mediating land use decisions. My dissertation project approaches the deforestation blacklist, an innovative instrument centering monitoring and sanctioning initiatives at the county level. Previous studies confirm the positive effect of this instrument, highlighting the role of bottom up, collective arrangements among local stakeholders towards controlling deforestation rates to remove their counties from the blacklist. Despite that, only 11 out of 52 counties were removed from the blacklist to date. I will draw on case studies in a sample of counties to examine how the interplay among diverse social groups has driven either the success or failure of blacklisted counties to reduce deforestation rates and exit the blacklist. 1 Paulo Massoca is also affiliated with the Center for the Analysis of Social Ecological Landscapes (CASEL) His work is funded by the Brazilian National Council for Scientific and Technological Development (CNPq) through the Sciences without Border scholarship program. Ostrom Workshop Research Series Paulo Massoca 2 Statement of the problem The Brazilian Amazon has experienced rapid and intense social and environmental changes since the 1960s. At the regional scale, government development projects and market incentives have prompted population growth, infrastructure development, agricultural expansion, and urbanization. Besides resulting in marked land use and land cover changes, these macro scale processes have also promoted the rise of diverse social groups struggling for identity, land, and resources' access in the region (Lima and Pozzobon 2005) Local actors and institutional arrangements embedded in particular biophysical, socioeconomic, and political settings have mediated varied responses to regional and global forces upon the region, and the interplay of processes across different spatial and temporal scales has shaped the Amazon as a complex mosaic of juxtaposed social and environmental realities (Brondizio 2006; Becker 2005) As the largest continuous tropical forested landscape, the Amazon biome provides economic goods and non market ecosystem services which support livelihoods from local to global scales. Holding 60% of the biome in its territory, Brazil has significant responsibility for its conservation and development. However, degradation and loss of forestlands have been salient outcomes from the accelerated changes in the region. Brazil had the highest rate of tropical forest loss in the 1990 2015 period (Keenan et al. 2015) and alternative land uses replaced some 780,000 sq.km of forests in the Amazon to date (Almeida et al. 2016) This scenario has challenged stakeholders negotiating strategies whereby Brazil can achieve its voluntary commitments to zero deforestation by 2030 (Brasil 2016) Environmental policies regulating land use and cover change in the Amazon have frequently been designed by the federal government and implemented at the level of the biome. This top down ecological approach is insensitive to intraregional variability, disregarding the role and diversity of local actors mediating land use decisions impacting forestlands (Gibson, McKean, and Ostrom 2000) Since rates and patterns of deforestation relate to particular social groups (e.g. large landholders, small farmers, traditional and indigenous communities, private firms) competing for land and resources (Godar et al. 2014) comprehending context specific interactions among these actors becomes crucial to inform analysts and policy makers. Depending on the size and diversity of social groups settled in a given area, the interplay of local actors withinand across different groups will likely result in different responses to regional antideforestation policies (Ostrom 2005) My dissertation project will approach that puzzle by framing the deforestation blacklist, an instrument which centers monitoring and sanctioning initiatives on 52 counties considered deforestation hotspots in the Amazon. Because removal criteria were set at the county level, the blacklist requires collective action among local stakeholders towards controlling deforestation rates. I will draw on cross sectional analysis Ostrom Workshop Research Series Paulo Massoca 3 and case studies to examine how intraregional variability and the interplay among local social groups have driven either the success or failure of blacklisted counties to reduce deforestation and exit the blacklist. Project rationale and literature review The List of Priority Counties (hereafter, "deforestation blacklist" is an environmental policy designed by the Brazilian Ministry of the Environment (MMA) and disclosed in the period between 2008 2013 listing 52 counties considered deforestation hotspots in the Amazon. This policy instrument is part of a broader environmental program launched in 2004 by the Brazilian government (Brasil 2004) and represents an innovative strategy to curb deforestation in the Amazon. First, it shifted the focus of surveillance activities from the entire biome to concentrate it in a few counties. Second, the deforestation blacklist set its compliance criteria at the county level, which has promoted vertical decentralization among government levels, and fostered horizontal co responsibility at the local level, requiring collective rather than individual action among stakeholders (Mello and Artaxo 2017) Finally, it mandated farmers to register georeferenced data about their landholdings in a public cadaster, therefore resulting in a database linking agents to land cover changes. By 2013, only 11 counties had been removed from the blacklist after accomplishing its removal criteria. These criteria are (i) the control of annual deforestation rates below 40 sq.km and (ii) the register of georeferenced information about at least 80% of the eligible area (i.e. private landholdings) in a public cadaster (CAR, Portuguese acronym) In spite of the novelties implemented by this environmental policy, the deforestation blacklist still illustrates the usual approach of federal anti deforestation policies designed for the Amazon: a set of standard targets applied to a heterogeneous region. Impacts arising from the deforestation blacklist The monitoring efforts centered upon only a few counties allowed the government to raid stronger operations in blacklisted counties. This resulted in the sanction of actors and embargo of facilities and properties engaged in illegal activities, impacting a substantial number of individuals associated with illegal logging (Assuncao and Rocha 2014; Arima et al. 2014) The strategy of "naming and shaming" also harmed the reputation of counties, hindering the trade of agricultural products in regional markets. Parallel initiatives such as the moratoria in the soybean and beef supply chains worsened the scenario since retailers stopped trading with farmers from blacklisted counties (Massoca, Delaroche, and Lui 2017; Sills et al. 2015) Additionally, the concession of agricultural credit to farmers became conditioned upon the registry of private landholdings in the public cadastral system (Banco Central do Brasil 2008) further complicating Ostrom Workshop Research Series Paulo Massoca 4 local agrarian activities. In synthesis, the literature has evidenced that these cumulative problems impacted local economies, increased unemployment rates, and in some counties have resulted in violent outcries (Zwick and Calderon 2016; Viana et al. 2012; Abranches 2016; Marconato and Queiroz 2012) Outcomes arising from the deforestation blacklist At the regional scale, econometric models have confirmed the role played by this environmental policy in curbing deforestation among blacklisted counties (Assuncao and Rocha 2014; Arima et al. 2014; Cisneros, Zhou, and Borner 2015; Sills et al. 2015) However, the channels by which this result was achieved remain unclear. Assuncao and Rocha (2014) confirmed that the policy increased monitoring and law enforcement mechanisms, but did not affect economic variables such as the price of commodities, local agricultural production, and agricultural credit loans. Cisneros, Zhou, and Borner (2015) on the other hand, found that all these mechanisms had only a marginal role in explaining why blacklisted counties experienced higher reductions in deforestation rates than non blacklisted counties. The authors suggest that potential channels explaining the success of this policy are context specific, mainly related to the ability of local stakeholders to organize themselves towards complying with the blacklist removal criteria. Indeed, because the removal criteria are set at the county level, actions taken individually by farmers and other stakeholders are not enough to overcome the collective problem faced by the county. The persistence of uncoordinated action among local actors replacing forests by alternative land uses makes everyone worse off. The solution requires landholders to both reduce and coordinate forest clearings, besides agreeing in providing sensitive information about their properties a polemic issue in places where land tenure is usually unclear and compliance with environmental laws is the exception. Moreover, decision makers and governmental agencies must collaborate to support monitoring activities and provide the technology and information for the registry of landholdings a task eventually requiring the expertise and support from third party actors. Finally, all such activities demand financial resources. Therefore, the deforestation blacklist demands the emergence of botto