Gus Greenstein (staff) - Jul 26th, 2012
Explanation of the Variables and How They Were Chosen
The 17 variables used in the filter were selected based on the availability of reliable secondary information that could be compared across the five countries.
(1) Investment Cost ($US/km):
The higher the unit investment costs, the lower the probability that the road will prove economically efficient.
(2) Topography (a proxy for maintenance costs):
Roads in mountainous regions are more expensive to build and maintain than roads in flatter areas. Since the investment cost is included explicitly, this topography is used as a proxy for maintenance costs. Click here for an explanation of topographic relief.
(3) Regional GDP (municipalities crossed by the road):
Roads passing through regions with higher gross domestic product values will tend to have more users and thus generate more economic benefits. Roads passing through areas with limited economic activity are therefore assigned a high level of economic risk due to the weaker justification for the investment.
(4) Population Density (inhabitants/Ha):
As a road crosses regions with higher population densities or connects a greater number of population centers, the probability of the road reaching economic efficiency is higher.
(5) Gross Agricultural Revenue ($US/Ha):
Regions with greater agricultural revenues are more likely to merit the investment in road infrastructure. Therefore, the filter assigns high values (greater economic risk) to the roads that cross through regions with lower agricultural revenues.
(6) Tree Cover (% cover):
Roads in forested areas pose a greater risk of habitat conversion and subsequent loss in biodiversity and ecosystem services; therefore, the environmental risks, as well as the environmental-based arguments against building such roads, will also be greater.
(7) Presence of Wetlands (present/not present):
This variable is included in order to recognize the ecological importance of wetlands. The model therefore assigns higher risk to roads that cross through wetlands.
(8) Average Hydrological Balance:
This variable is factored into the Roads Filter largely because roads diminish soil infiltration capacity. Consequences of a reduced ability for water to permeate the ground include reduction of the availability of water in aquifers (thus affecting availability of this resource during the dry season) and increased flood risk during the wet season, which, when coupled with densely vegetated areas, has GHG emissions implications, on top of the likelihood of physical damages.
(9) State of Nature Conservation:
The values assigned to this variable come from global scale geographic information for various factors that indicate ecosystems alteration: distribution of human population, urban areas, roads, navigable rivers and agricultural uses. The human intervention index is derived from the combined influences of these factors. The least affected areas according to this index have the highest risk in our Filter, given that the construction of a road implies higher environmental risks for areas that have so far been subject to a smaller degree of intervention.
(10) Proximity to Conservation Areas/Indigenous Territories:
The Roads Filter allocates a higher score to roads crossing closer to conservation areas or indigenous territories, as such construction threatens these areas’ well-being.
(11) Length of Route (km):
A road’s total length influences the magnitude of potential investment. This variable is considered because large projects with an extensive area of influence have a greater probability of generating major cumulative environmental and/or transformative impacts per road kilometer constructed.
(12) Type of Investment (improvement/new road):
This variable attempts to distinguish between new road projects or improvement of existing ones. The assumption is that building a new road has environmental impacts substantially greater than those of improving an existing road. Opening a new road, even if it is only a dirt road, is the decisive step in opening an area for exploitation and settlement.
(13) Level of objection to road projects by the affected population (1-5 scale):
The higher the degree to which a population objects to a road, the more significant the risk of social conflict there exists and the higher risk ranking in the filter.
(14) Violation of legal norms (does not/does violate):
Similar to (13), if a road proposal violates any norm (national or regional), the risk of potential social conflicts also increases.
(15) Existence of external pressure favoring a road project (1-5) scale:
Projects financed or promoted by external governments or international financing agencies are assumed to be more likely to stir social conflict.
(16) Existence of indigenous population in voluntary isolation (yes/no):
A road that crosses through the territory of a population in voluntary isolation presents greater risks of generating negative cultural impacts.
(17) Possibility of archeological damages (yes/no):
If there are archaeological sites in the road’s zone of influence, the risk of generating negative cultural impacts is greater.
*The social variables are subjective in nature, based on local data collectors’ judgments, while the cultural variables are objective, based purely on the existence or nonexistence of archeological sites.
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