DEFORESTATION DATA

By Lucio Muñoz

munoz@interchange.ubc.ca or at http://www.truesustainability.com

 

Deforestation Data Tables

The information collected with the help of deforestation data collection tables(See a sample copy here), in this case quantitative, is organized in deforestation data tables. These tables show specific information relevant to each country and to the whole region, which is the raw quantitative data used to develop the qualitative information that provides the basis for qualitative comparative approach introduced in this section.

See the deforestation data tables here

 Deforestation Data Change Tables

The determination of data changes through time leads to the generation of data change information, in this case, quantitative data changes, which are organized in deforestation data change tables.

See the deforestation data change tables here

Quantitative changes are usually classified in trichotomy forms such as neutral(0), positive(+), and negative(-), but can also be classified in dichotomy form as positive(+) and non-positive(-), where the non-positive entries include also de neutral entry.

See trichotomy deforestation data change tables here

See dichotomy deforestation data change tables here

Generating qualitative information from deforestation data tables

Qualitative approaches can be used to produce qualitative rankings, which can be a powerful and simple tool to appreciate specific conditions within countries, similarities and differences between countries, and similarities and differences between specific country conditions and related regional situations. These different appreciations can be done at a particular point in time or through time. Two specific qualitative tools are presented here, a country-country ranking tool and the country-region tool.

*Country-Country Ranking

By placing countries in ascending or descending order in terms of the state of specific factor at a specific point in time we can generate qualitative ranking information that is simple, and easy to communicate.

Once the orderings are done, these rankings provide an insight on how specific factors in one country compare to similar factors in other countries. It facilitate the visualization of similarities and differences, in terms of specific conditions and policy options available as it is easy to identify who is first or who is last or who is at about the middle. Qualitative ranking based on ascending order has been used to handle data from the five Central American Countries presented here, which leads to five different types of rankings, 1,2,3,4,and 5, where type 1 = the one with highest value or first place and type 5 = the one with the lowest value or last place.

See the country-country ranked data tables here

*Country-Region Ranking

By using regional values such as actual or average states as benchmarks, individual countries can be ranked into three qualitative groups: below regional(BR) or below average(BA) values; regional(R) or average(A) values; and above regional(AR) or above average(AA) values. For example, an entry AA1 means that this factor beside being above average(AA) it is the one of the highest value(type 1) or an entry of BR5 indicates that this factor besides being below regional values(BR) it is the one with the lowest value(type 5). Once the classification is done, these rankings provide an insight on how specific factors in one country compare to similar regional factors. It permits the appreciation of similarities and differences, in terms of specific conditions and policy options as it is easy to identify particular countries within groups. Qualitative ranking procedures based on grouping has been used to codify data for country-region comparisons.

See the country-region ranked data tables here

Generating Qualitative Information from Deforestation Data Change Tables

Qualitative procedures can be used to transform the information in the trichotomy and dichotomy deforestation data change tables into qualitative information. Two procedures are introduced here, qualitative trend coding and ascending qualitative ranking. The qualitative trend coding leads to the generation of trichotomy and dichotomy qualitative trends, both country specific trends and regional trends. which are organized in deforestation data trend tables. The ascending ranking procedure classifies countries according to the intensiveness of absolute trends as compared to similar trends in other countries or similar regional trends. All these procedures are described below

*Trichotomy Data Trend Tables

Using the qualitative coding procedure of neutral pressures(0) when changes are zero; of increasing pressures(1) when trends are expected to have a positive impact on deforestation; and decreasing pressures(2) when trends are expected to have a negative impact on deforestation, then we can transform the quantitative changes in the trichotomy deforestation data change tables into qualitative data, and eliminate this way the illusion of precision attached to quantitative data. This approach is consistent with the trichotomy notion that changes in factors believed to be associated with deforestation may lead to increasing pressures, decreasing pressures or neutral pressures. Notice that increasing pressures on deforestation, type 1 trend can result from either a positive trend or a negative trend depending on the factor in question. For example, a positive change in the levels of land area dedicated to agriculture is expected to indicate increasing pressure on deforestation, but also a negative change on the levels of natural forest areas remaining would indicate increasing pressures on deforestation too. See the rules followed when classifying deforestation pressures.

See the trichotomy data trend tables here

*Dichotomy Data Trend Tables

Using the qualitative coding procedure of non-increasing pressures(2) when changes are zero or are expected to have a negative impact on deforestation and increasing pressures(1) when changes are expected to have a positive impact on deforestation, then we can transform the quantitative changes in the dichotomy deforestation data change tables into qualitative data too, and eliminate this way again the illusion of precision attached to quantitative data. This approach is consistent with the dichotomy view that changes in factors believed to be associated with deforestation may lead to inceasing pressures or non-increasing pressures. In a similar way as explained above, increasing pressures on deforestation may come from positive or negative changes depending on the factor in question. See the rules followed when classifying deforestation pressures.

See the dichotomy data trend tables here

 *Country-Country Ranked Data Change Tables

Based on the magnitude of the absolute change of specific factors at specific points in time, countries can be ordered in ascending form from type 1,…..to type "n", where 1 = the highest or more intensive absolute change and n = the lowest or the less intensive absolute change. Since there is information about five countries in the trichotomy deforestation data change tables, each piece of information of each country is assigned a rank from 1 to 5 depending on its intensiveness with respect to similar factors in other countries.

See the country-country ranked deforestation data change tables here

*Country-Region Ranked Data Change Tables

Using the regional absolute change as a benchmark, absolute changes in specific aspects of each country can be classified as belonging to one of three groups: below regional(BR) or below average(BA) values; at regional (R) or average(A) values; and above regional(AR) or above average(AA) values. Using this procedure to codify the information in the trichotomy deforestation data change tables, then country-region ranked data change tables can be generated.

See the country-region ranked deforestation data change tables here

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