The Dynamics of Deforestation in the Wet and Dry Tropics: A Comparison with Policy Implications

Abstract

Forests in the dry tropics differ significantly from forests in the humid tropics in their biomass and in their socio-ecological contexts, so it might be reasonable to assume that the dynamics that drive deforestation in these two settings would also differ. Until recently, difficulties in measuring the extent of dry tropical forests have made it difficult to investigate this claim empirically. The release of high resolution LANDSAT satellite imagery in 2013 has removed this impediment, making it possible to identify variations in the extent of wet and dry forests within countries by measuring variations in the canopy cover of their forests. These metrics have in turn made it possible to investigate human differences in the dynamics of deforestation between dry forested and wet forested nations in the tropics. Cross-national analyses suggest that international trade in agricultural commodities plays a more important role in driving deforestation in the wet tropics than it does in the dry tropics. The variable salience of international trade as a driver has important implications, described here, for the success of policies designed to slow deforestation in the dry tropics and the wet tropics. Curbing dry forest losses, in particular, would appear to require locally focused and administered policies.

The United States, Bilateral Debt-for-Nature Swaps, and Forest Loss: A Cross-National Analysis

“…The tree cover density for a nation represents the estimated percentage of a pixel taken from satellite imagery that is covered by tree canopy (World Resources Institute, 2016). The 75 per cent tree cover canopy density level is the level associated with gain or loss of 'wet' forests (Rudel, 2017). We also calculate a second measure of 'dry' forest loss using a 50 per cent tree cover canopy density level.…”

State Spending and Governance: A Cross‐National Analysis of Forest Loss in Developing Nations*

“…I exclude high‐income nations for two reasons. First, it has been noted by scholars that the dynamics of forest loss differ in high‐income nations compared to low‐ and middle‐income nations, and therefore are considered separately from low‐ and middle‐income nations in cross‐national analyses (Rudel ; Shandra, Shircliff, and London ). For example, the theory I am testing concerns state factors in low‐ and middle‐income nations because levels of governance and state spending are hypothesized to impact forest loss in these nations specifically (Crenshaw and Jenkins ).…”

“…These data are available from the World Resources Institute () Global Forest Watch web page. Following Rudel (, ), I calculate the change in forest loss from 2001 to 2014 in the following way. First, I obtain the total number of hectares of forest cleared from 2001 to 2014.…”

Ecologically unequal exchange and national governance: A cross‐national analysis of forest loss

“…The improved data may be obtained online from the World Resources Institute's (2016) Global Forest Watch web page (see Hansen, Stehman, & Potapov, 2010 with the equivalent tree cover canopy density (Hansen et al, 2010). Rudel (2017) that the benefits of these data, which include consistent collection throughout the world to improve cross-nationally comparability and more fine-grained data to make more accurate policy recommendations, outweigh their limitations of including plantations in the measure and the slightly shorter time frame.…”