A spatial regression analysis of Colombia's narcodeforestation with factor decomposition of multiple predictors

Area of Science:

• Environmental Science
• Ecology
• Conservation Biology

Background:

• Global warming intensifies challenges in forest conservation, particularly in developing nations where illegal economies exploit woodlands.
• Narcodéforestation in Colombia, driven by activities like agribusiness and cattle ranching for money laundering, poses a significant threat to forest ecosystems.

Purpose of the Study:

• To investigate the impact of coca cultivation on deforestation at both national and local scales in Colombia.
• To analyze the direct and indirect effects of narcoactivities on forest loss using spatially explicit models.

Main Methods:

• Spatially explicit regressions were employed to analyze deforestation patterns.
• Principal component analysis was used for factor decomposition of predictor variables.

Main Results:

• A statistically significant positive correlation was found between coca cultivation and deforestation at the national level.
• Deforestation linked to coca cultivation was identified in specific regions, notably Northern Santander and the Pacific coast.
• Significant direct and spillover effects of narcodeforestation were revealed, impacting adjacent municipalities.

Conclusions:

• Coca cultivation is a significant driver of deforestation in Colombia, with both direct and indirect impacts.
• The findings support the hypothesis that narcodeforestation extends beyond direct crop expansion to include money laundering and infrastructure development for illicit activities.