Deforestation of Functional Programs through Type Inference

Deforestation optimises a functional program by transforming it into another one that does not create certain intermediate data structures. Short cut deforestation is a deforestation method which is based on a single, local transformation rule. In return, short cut deforestation expects both producer and consumer of the intermediate structure in a certain form. Starting from the fact that short cut deforestation is based on a parametricity theorem of the second-order typed lambda-calculus, we show how the required form of a list producer can be derived through the use of type inference. Type inference can also indicates which function definitions need to be inlined. Because only limited inlining across module boundaries is practically feasible, we develop a scheme for splitting a function definition into a worker definition and a wrapper definition. For deforestation we only need to inline the small wrapper definition

Deforestation for food production

Deforestation contributes to carbon emissions and therefore to climate change. Within food systems, agricultural production is the stage which plays the largest role in deforestation and forest degradation, and it is therefore the focus of this chapter. There is a critical link between food systems and deforestation. Arable lands most often have a forested past. It might be ancestral, with deforestation having happened in the early occupation of land by humans or be very recent on current forest frontiers. Over the past two decades, commercial agriculture has overtaken subsistence agriculture as the main driver of deforestation in LI and LMI countries, especially in tropical areas

Is small-scale agriculture really the main driver of deforestation in the Peruvian Amazon? Moving beyond the prevailing narrative

A key premise underlying discussion about deforestation in Amazonian Peru is that small-scale or so-called migratory agriculture is the main driver of deforestation. This premise has been expressed in government documents and public outreach events. How the Peruvian government understands drivers of deforestation in the Amazon has profound implications for how it will confront the problem. It is therefore important to critically revisit assumptions under-lying this narrative. We find that the narrative is based on remote sensing of deforestation patch sizes but not on field data, potentially conflating distinct drivers of deforestation under the umbrella of “migratory,” “small-scale,” or “subsistence” agriculture. In fact, small patches of deforested land may indicate any number of processes, including sustainable fallow management and agroforestry. Moreover, the data underlying the narrative tell us little about the actors driving these processes or their motivations. Different pro-cesses have distinct implications for environmental sustainability and require targeted policy responses. We unpack these diverse actors, geographies, and motivations of small-patch deforestation in the Peruvian Amazon and argue that differentiating among these drivers is necessary to develop appropriate policy responses. We call for researchers to revisit assumptions and critically assess the motivations of observed deforestation to appropriately target policy action

Type-Inference Based Short Cut Deforestation (nearly) without Inlining

Deforestation optimises a functional program by transforming it into another one that does not create certain intermediate data structures. In [ICFP'99] we presented a type-inference based deforestation algorithm which performs extensive inlining. However, across module boundaries only limited inlining is practically feasible. Furthermore, inlining is a non-trivial transformation which is therefore best implemented as a separate optimisation pass. To perform short cut deforestation (nearly) without inlining, Gill suggested to split definitions into workers and wrappers and inline only the small wrappers, which transfer the information needed for deforestation. We show that Gill's use of a function build limits deforestation and note that his reasons for using build do not apply to our approach. Hence we develop a more general worker/wrapper scheme without build. We give a type-inference based algorithm which splits definitions into workers and wrappers. Finally, we show that we can deforest more expressions with the worker/wrapper scheme than the algorithm with inlining

The Effect of Trade Openness on Deforestation: Empirical Analysis for 142 Countries

This study explores the effect of trade openness on deforestation. Previous studies do not find a clear effect of trade openness on deforestation. We use updated data on the annual rate of deforestation for 142 countries from 1990 to 2003, treat trade and income as endogenous, and take into consideration an adjustment process by applying a dynamic model. We find that an increase in trade openness increases deforestation for non-OECD countries while slowing down deforestation for OECD countries. There is a possibility that both capital-labor and environmental-regulation effects have a negative impact on deforestation in developing countries, whereas the opposite holds in developed countries.Trade Openness; Environment; Comparative Advantage; Deforestation

Disentangling the rationale of deforestation to understand better the partial effectiveness of protected areas. A case study for Madagascar's eastern rainforest corridor (2001-12)

Madagascar's notoriously high level of biodiversity is currently threaten by deforestation. Protected Areas (hereafter “PAs”) remain until now the central instrument to protect it whilst little is known about their environmental effectiveness in the country. With a matching approach in a quasi-natural experiment setting, we demonstrate for the entire island's rainforest that PAs' additionality has been limited from 2001 to 2012. PAs have made it possible for deforestation to be stabilized in a trend and has restricted the upsurge of deforestation resulting from the country's late political instability. Nonetheless, post-matching analyzes reveal that PAs have only contained some of the causes of deforestation. Effectively stopping the latter will require further ambitious policies to trigger the necessary agricultural transition for the country

Towards a low carbon economy in the Amazon: the role of land-use policies

Climate change, rising oil prices and the global financial crisis has put sustainability and ‘green growth’ of the economy on the political agenda. While the transition towards a “low carbon” economy in developed countries like in the European Union should mainly be found in renewable energy production, developing countries like Brazil face with high land use emissions which will further rise in the coming decades without proper policy instruments. Deforestation and cattle production are the main sources of land use emissions in Brazil and we expect that these emissions will further rise with liberalisation of agricultural trade. A transition towards a “low carbon” economy in Brazil thus calls for appropriate, and effective land-use policies. Agricultural intensification on one hand can meet the world demand for soy and beef. For example we calculate that increasing the meat content of cattle can reduce emissions from deforestation up to 30%, but intensification may also accelerate further deforestation of Cerrado and Amazon forests. In order to avoid such additional deforestation, large areas of degraded lands have to be taken back into production, which requires large agricultural investments. In addition, (new) economic instruments, monitoring, law enforcement and appropriate conservation policies are also needed to halt further deforestation and biodiversity loss. The recently amended change of the Forest Code policy, for example, is expected to accelerate deforestation further, thus making more difficult to reach mitigation targets for the Brazilian State

Deforestation and climate change: acting on the causes. What the (carbon) market cannot do...

With an estimated average loss of around 13 million hectares per year between 2000 and 2005 – 7.3 million hectares if reforestation is taken into account, according to FAO –, tropical deforestation is a major source of greenhouse gas emissions. At around 4.4 to 5.5 GtCO2 per year (the latter including peat forest degradation) according to the latest estimates, these emissions account for about 12 to 15% of annual anthropogenic CO2 emissions (from 8 to 20% taking into account the considerable uncertainties in the deforestation and degradation estimates). Moreover, tropical deforestation has a devastating impact on biological diversity, since tropical forests contain over two thirds of the 250 000 higher plants known to scientists. At present, emissions caused by deforestation in developing countries are regulated neither by the Framework Convention on Climate Change nor by the Kyoto Protocol. However, the issue of “avoided deforestation” is expected to be one of the difficult areas of the 15th Conference of the Parties to the UNFCCC (Copenhagen, December 2009), which will propose a post-Kyoto “climate” regime. Is the solution a market mechanism to “reward” actors or a fund to finance reforms that tackle the causes? The debate is open

Environmental Costs of Government-Sponsored Agrarian Settlements in Brazilian Amazonia

Brazil has presided over the most comprehensive agrarian reform frontier colonization program on Earth, in which ~1.2 million settlers have been translocated by successive governments since the 1970's, mostly into forested hinterlands of Brazilian Amazonia. These settlements encompass 5.3% of this ~5 million km2 region, but have contributed with 13.5% of all land conversion into agropastoral land uses. The Brazilian Federal Agrarian Agency (INCRA) has repeatedly claimed that deforestation in these areas largely predates the sanctioned arrival of new settlers. Here, we quantify rates of natural vegetation conversion across 1911 agrarian settlements allocated to 568 Amazonian counties and compare fire incidence and deforestation rates before and after the official occupation of settlements by migrant farmers. The timing and spatial distribution of deforestation and fires in our analysis provides irrefutable chronological and spatially explicit evidence of agropastoral conversion both inside and immediately outside agrarian settlements over the last decade. Deforestation rates are strongly related to local human population density and road access to regional markets. Agrarian settlements consistently accelerated rates of deforestation and fires, compared to neighboring areas outside settlements, but within the same counties. Relocated smallholders allocated to forest areas undoubtedly operate as pivotal agents of deforestation, and most of the forest clearance occurs in the aftermath of government-induced migration