Interaction effects between economic development and forest cover determine deforestation rates

Ewers, Robert M.

doi:10.1016/j.gloenvcha.2005.12.001

Cited by 134 publications

(75 citation statements)

References 71 publications

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“…We investigated the potential effect of the domestic economy on deforestation because previous analyses have either shown or suggested that (a) deforestation should decrease with increasing levels of economic development Ewers 2006); (b) high levels of inflation can lead to high discount rates that promote short-term resource exploitation (Fearnside 1997(Fearnside , 2005Laurance 1999); (c) depreciation of exchange rates can increase deforestation rates in developing countries (Arcand et al 2008) and exchange rates alter the relative value of export commodities such as soy and cattle (Kaimowitz et al 2004); (d) credit availability in the form of government subsidies underpins much of the expansion of cattle ranching across the Brazilian Amazon (Barreto et al 2006a); and (e) the need to service international debts can result in elevated rates of natural resource use and forest conversion to agriculture to facilitate economic growth (Kahn & McDonald 1995;Marquart-Pyatt 2004).…”

Section: Methodsmentioning

confidence: 99%

Temporal fluctuations in Amazonian deforestation rates

2008

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SUMMARYTropical deforestation is one of the most important components of global change. Rates of deforestation in Brazil, the nation with the single largest concentration of tropical forest on Earth, have fluctuated widely over the last twenty years. Based on local knowledge, such fluctuations have been variously attributed to a wide range of factors such as the expansion of cattle ranching and soybean farming, infrastructural expansion and the proliferation of paved and unpaved roads, macroeconomic shocks to the Brazilian economy and international exchange rates. Many, if not all, of these arguments are plausible explanations for temporal variation in deforestation rates, but have to date not been subjected to rigorous statistical testing; this study investigates the potential impact of these variables on Brazilian tropical deforestation over the period [1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005]. When analysed at the basin-wide scale, nearly all variables were highly inter-correlated through time and were also closely correlated with deforestation rate, but appropriate time-series analysis found no statistical evidence that any of the variables have systematically caused variation in deforestation rates. Power analysis showed that the variables may exert small or medium influences on deforestation rates, but the impacts, if present, are not strong. Future analyses of time series data at finer spatial scales that exploit spatiotemporal variation in deforestation rates and in the hypothesized predictor variables may find significant causal processes that are overlooked when analysed at the basin-wide scale.

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Section: Methodsmentioning

confidence: 99%

Temporal fluctuations in Amazonian deforestation rates

2008

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“…Drawing on examples from the Neotropics, Sloan [20] further argues that even sparse or declining rural populations can still cause heavy and sustained deforestation, contrary to the views of Wright and Muller-Landau. Indeed, a recent study demonstrates that the poorest and most poorly governed nations, which unfortunately include many tropical countries, are less likely than other nations to make the transition from deforestation to afforestation [42].…”

Section: Box 2 Environmental Impacts Of Burgeoning Biofuelsmentioning

confidence: 99%

Have we overstated the tropical biodiversity crisis?

Laurance¹

2007

Trends in Ecology & Evolution

259

207

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“…Throughout the tropics, between 1980 and 2000 more than 80 % of new agricultural land came at the expense of intact and disturbed forests . Other studies (Rudel et al 2005;Ewers 2006) highlighted a strong interaction between land cover and economic development. The notion that the economic pressure for land conversion radiates in concentric circles from markets and diminishes in an inverse relation to distance, dates from the dawn of economic theory (von Thunen 1826).…”

Section: Introductionmentioning

confidence: 98%

“…The desire to better describe drivers and patterns of land-cover change resulted in the development of several computational models representing a variety of approaches and underlying concepts (Rindfuss et al 2004;Verburg et al 2006;Smith et al 2010). Briefly, among a multitude of classifications, models can be divided into spatial (Pontius et al 2001;Verburg et al 2002;Goldstein et al 2004;Lepers et al 2005;Bouwman et al 2006) and nonspatial (Evans et al 2001;Stephenne and Lambin 2001;Tilman et al 2001;Ewers 2006), dynamic (GEOMOD; CLUE; SLEUTH) and static (Chomitz and Thomas 2003;Overmars and Verburg 2005), descriptive (Verburg et al 2006) and prescriptive (Lambin et al 2000;van Ittersum et al 2004), global (Rosegrant et al 2002;Hsin et al 2004;Lepers et al 2005;van Velthuizen et al 2007) and regional (Soares et al 2006). There is no single superior approach to model land-cover change (Verburg et al 2006), as no single model is capable of answering all questions and the choice of approach depends on the research or policy questions and data availability.…”

Section: Introductionmentioning

confidence: 99%

Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

Strassburg

Latawiec

Creed³

et al. 2013

Sustain Sci

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There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of ''Economic Pressure on Land'' (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future landcover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a ''business as usual'' and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD ? debate: if restricted to forests, ''cross-biome leakage'' would severely reduce REDD ? effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD ? would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD ? remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage.

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Interaction effects between economic development and forest cover determine deforestation rates

Cited by 134 publications

References 71 publications

Temporal fluctuations in Amazonian deforestation rates

Temporal fluctuations in Amazonian deforestation rates

Have we overstated the tropical biodiversity crisis?

Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

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