1,720,984 research outputs found
An accuracy assessment of three forest cover databases in Colombia
No full textAccurate forest assessment is essential to detect and tackle deforestation, especially in emerging economies. In Colombia, three different geo-spatial data sources are available for forest monitoring: the European Space Agency (ESA), the Institute for Hydrology, Meteorology and Environmental Studies (IDEAM), and the Global Forest Change Data (GFCD) from the University of Maryland. These information sources have distinct characteristics, purposes, and coverage, and their peculiarities can lead to marked differences in the results when they are used to produce forest cover maps. In this study, we determine the optimal forest threshold for GFCD and assess the accuracy of the three data sources in mapping forests, on the basis of a stratified sample of sites, with Colombian ecoregions used as strata. At each site, the classification into forest or non-forest, according to one of the sources, is compared with reference data collected through Google Earth imagery and landscape photographs. Accuracy measures are produced at both the ecoregion and national level. IDEAM and GFCD prove to be quite accurate in most cases, and each of them turns out to be the best forest map in about half of the ecoregions. GFCD's optimal threshold is found to be equal to 90% in almost all those ecoregions for which it represents the best performing data set
Willingness to pay confidence interval estimation methods: comparisons and extensions
Full text linkThis paper systematically compares methods to build confidence intervals for willingness to
pay measures in a discrete choice context. It contributes to the literature by including methods
developed in other research fields. Monte Carlo simulations are used to assess the performance
of all the methods considered. The various scenarios evaluated reveal a certain skewness in the
estimated willingness to pay distribution. This should be reflected in the confidence intervals.
Results show that the commonly used Delta method, producing symmetric intervals around
the point estimate, often fails to account for such a skewness. Both the Fieller method and
the likelihood ratio test inversion method produce more realistic confidence intervals. Some
bootstrap methods also perform reasonably well. Finally, empirical data are used to illustrate
an application of the methods considere
Coca cultivation and deforestation in Colombia: an example of unsustainable (local) development
No full textThe Amazon rainforest plays a pivotal role in promoting environmental
sustainability on a global scale. Often referred to as the “lungs
of the Earth”, it absorbs significant carbon dioxide, mitigating climate
change impacts and maintaining global ecological balance. Its rich biodiversity,
unique flora, and fauna contribute to the well-being of indigenous
communities and the entire planet. Its preservation is crucial for
a sustainable future. Unfortunately, deforestation and unsustainable
practices severely threaten this invaluable natural treasure. In this paper,
we explore in depth the case of Amazon deforestation in Colombia. We
first discuss the availability of reliable data for measuring this phenomenon
by comparing different sources. Results show that, among the three
major datasets available for assessing deforestation in Colombia, IDEAM
is the preferred choice for national-level analyses due to its comparable
overall accuracy to GFCD but superior user’s accuracy, while ESA
generally performs worse. We then analyze the drivers of deforestation,
with particular emphasis on the effects of illegal activities. In particular,
coca cultivation is found to increase significantly the extent of deforestation
in Colombia, particularly in the Andes and the Pacific Coast, two
regions encompassing key biodiversity hubs. Finally, we illustrate principal
conservation policies, as well as the unintended effects of some of
them. We provide evidence that glyphosate aerial aspersion of coca crops
has the unforeseen consequence of increasing coca cultivation, rather
than reducing it, and leads to a series of negative outcomes, such as the
destruction of legal crops, increased health risks for local populations,
and significant harm to vital natural ecosystems
Size-Dependent Enforcement, Tax Evasion and Dimensional Trap
No full textSize-dependent tax enforcement is quite widespread worldwide, but the literature on its effects over firms’ behaviour is very scarce. By assuming different audit probabilities for small and large firms, we propose a dynamic model to study the consequences of size-dependent monitoring level on a firm’ fiscal compliance and its decision to invest and grow in a single-firm perspective. By combining analytical findings and simulation results, we show that: (1) under certain conditions, a dimensional trap may emerge, as
small firms have no advantage in growing and prefer to remain small to avoid stronger enforcement; (2) audit activity and fine levels are important tools available to the State to fight evasion, but a careful calibration is required not to incur undesired effects
Industrial structure and evasion dynamics, is there any link?
No full textWe propose a model to describe the link between industrial structure and evasion dynamics in a heterogeneous setting, in which the monitoring efforts put in place by the State to fight evasion of firms depend on the market share of each firm. More precisely, while the convenience to evade taxes is determined as a Nash solution of an incomplete information game, a differentiated monitoring activity related to market concentration represents the original contribution of this work w.r.t. previous studies on the topic. As empirical evidence shows, high dimensional firms are more likely to be monitored, hence, when dynamics are considered, evasion and industrial structure evolution are strictly related. In fact, by combining analytical findings and simulation results, our work shows that: (1) firms with a sufficiently large market share, that is, larger than a certain dimensional threshold, comply with fiscal rules, thus confirming empirical evidence; (2) this dimensional threshold changes with the competition level between firms in a nonlinear way; (3) evasion affects the market structure, so that markets with high (low) competition tend to increase (decrease) their concentration over time; (4) evasion is affected by market structure and is minimum in moderately concentrated markets
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