An Analysis of the Impact of Forest Policy on Rural Areas of Chile
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Methods and Phases of the Study
2.2.1. Selection of Variables and Indicators of Territorial Changes
2.2.2. Descriptive Analysis of Territorial Changes
2.2.3. Relational Analysis of Territorial Changes and Forest Policy
2.2.4. Causal Analysis Framework of Territorial Changes
3. Results
3.1. Implementation of the 1974 Forest Policy in the Study Area
3.2. Demographic, Socioeconomic, and Educational Changes in the Study Area
3.3. Relationship between the Territorial Changes Observed
- (1)
- The results show a significant and inverse correlation between the variation in rural population (VRP) and the variation in the proportion of forest area per county (VPFL). These results are consistent with the descriptive analysis conducted before, which showed an increase in the proportion of forest area and a decrease in rural population (Figure 4 and Figure 7). This relationship can be explained by the forest transition theory [81,82,83,84,85,86,87], which outlines that the economic development of a territory promotes afforestation and therefore rural-urban migration. In fact, Heilmayr et al. [88] would have identified it in Chile driven by the forestry policy analyzed in this study.
- (2)
- There is a positive and significant correlation between indicators related to the increase in poverty (VPR and VPG) and that related to the increase in rural population (VRP). This type of relationship has been previously described in other studies carried out in depressed territories, where the population finally migrates to escape poverty [89,90,91,92,93].
- (3)
- Demographic characteristics are correlated between them. The correlation index that is established between the variation in total population (VTP) and the variation in economically active population (VEAP) is particularly high. In addition, the indicator associated with the increase in aging population (VAI) shows negative correlations with the other two demographic indicators (VTP and VEAP). These results reveal that there is a demographic transition at both regional and country levels, characterized by a decrease in fertility and mortality rates, which results in a lower growth of the total and economically active population and an increase in aging population [72,94].
- (4)
- In terms of educational level, the results show that there is a significant correlation between the variation in elementary education (VEE) and demographic indicators. This correlation is positive in all the indicators, except for the variation in aging population (VAI). These results show that counties in which the proportion of population with elementary education decreases also decrease total (VTP), rural (VRP) and economically active (VEAP) population, but they present an increase in aging population (VAI).
- (5)
- The changes observed in high school education (VHSE) and higher education (VHE) are positively correlated with the variation in total population (VTP), variation in economically active population (VEAP), and also with indicators from other areas. In this sense, the variation in high school education (VHSE) has a positive and significant correlation with the indicators related to the increase in poverty level (VPR and VPG). Similarly, the variation in population with higher education (VHE) is positively correlated with the variation in forest land (VFL). However, these relationships need to interpreted cautiously since, as abovementioned, the trends observed for high school and higher education would be mediated by the political and structural changes of the Chilean educational system.
3.4. Determinants of Territorial Changes
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Dimension | Variable | Indicator | Acronym | Definition |
---|---|---|---|---|
Productive | Forest land | Inter-census variation in forest land | VFL | Percentage variation of forest land between Census 1977 and Census 2007 |
Inter-census variation in the proportion of forest land | VPFL | Variation in percentage point changes in forest land proportion between Census 1977 and Census 2007. The forest land proportion corresponds to the county’s forest land based on the total surveyed area. | ||
Demographic | Total population | Inter-census variation in total population | VTP | Percentage variation in total population between Census 1982 and Census 2017 |
Economically active population (EAP) | Inter-census variation in economically active population (EAP) | VEAP | Percentage variation in EAP between Census 1982 and Census 2017. The EAP corresponds to population aged 15 to 64. | |
Rural population | Inter-census variation in rural population | VRP | Percentage variation in rural population between Census 1982 and Census 2017 | |
Aged population | Inter-census variation in Aging Index | VAI | Percentage variation in Aging Index between Census 1982 and Census 2017. The Aging Index is the proportion of people older than 60 to 100 people younger than 15. | |
Socio-economical | Poverty | Variation in poverty rate | VPR | Variation in percentage point changes in poverty rate between 1992 and 2013. The poverty rate is the percentage of poor people with respect to the total population in a given area. |
Variation in poverty gap | VPG | Variation in percentage point changes in poverty gap between 1992 and 2013. Poverty gap measures the difference in percentage points between the county’s and the country’s poverty rate | ||
Educational | Educational Level | Variation in Elementary Education | VEE | Percentage variation in population with Elementary Education between 1992 and 2017 |
Variation in High School Education | VHSE | Percentage variation in population with High School Education between 1992 and 2017 | ||
Variation in Higher Education | VHE | Percentage variation in population with Higher Education between 1992 and 2017 |
Productive | Demographic | Socioeconomic | Educational | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
VFL | VPFL | VTP | VRP | VEAP | VAI | VPR | VPG | VEE | VHSE | VHE | |
VFL | 0.40 * | 0.46 ** | |||||||||
VPFL | 0.40 * | −0.31 * | |||||||||
VPT | 0.66 ** | 0.99 ** | −0.66 ** | 0.88 ** | 0.33 * | 0.37 * | |||||
VRP | −0.31 * | 0.66 ** | 0.66 ** | 0.32 * | 0.37 * | 0.48 ** | |||||
VEAP | 0.99 ** | 0.66 ** | −0.68 ** | 0.89 ** | 0.39 * | 0.40 * | |||||
RAI | −0.66 ** | −0.68 ** | 0.17 * | −0.60 ** | |||||||
VPR | 0.32 * | 0.95 ** | 0.35 * | ||||||||
VPG | 0.37 * | 0.17 * | 0.95 ** | 0.40 * | |||||||
VEE | 0.88 ** | 0.48 ** | 0.89 ** | −0.60 ** | 0.49 ** | 0.54 ** | |||||
VHSE | 0.33 * | 0.39 * | 0.35 * | 0.40 * | 0.49 ** | 0.67 ** | |||||
VHE | 0.46 ** | 0.37 * | 0.40 * | 0.54 ** | 0.67 ** |
Dimension | Variables | Multiple Linear Regression Models Dependent Variables | ||
---|---|---|---|---|
VTP (1) | VTP (2) | VPR (3) | ||
All the territory | Coastal Mountain Range | |||
Standardized regression coefficients (B) | ||||
Constant | 75.39 ** | 96.5 ** | −38.82 ** | |
Productive | VPFL | −0.89 * | 0.99 ** | |
Socioeconomic | VRP | 0.3 ** | 0.3 ** | |
Demographic | VAI | −0.28 * | ||
Educational | VEE | 1.78 ** | 2.47 ** | |
Model diagnostics | ||||
R2 adjusted | 0.88 | 0.86 | 0.62 | |
F ** | 78.99 ** | 41.82 ** | 1.31 * | |
N | 37 | 17 | 17 |
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Cerda, R.; Gallardo-Cobos, R.; Sánchez-Zamora, P. An Analysis of the Impact of Forest Policy on Rural Areas of Chile. Forests 2020, 11, 1105. https://doi.org/10.3390/f11101105
Cerda R, Gallardo-Cobos R, Sánchez-Zamora P. An Analysis of the Impact of Forest Policy on Rural Areas of Chile. Forests. 2020; 11(10):1105. https://doi.org/10.3390/f11101105
Chicago/Turabian StyleCerda, Raúl, Rosa Gallardo-Cobos, and Pedro Sánchez-Zamora. 2020. "An Analysis of the Impact of Forest Policy on Rural Areas of Chile" Forests 11, no. 10: 1105. https://doi.org/10.3390/f11101105
APA StyleCerda, R., Gallardo-Cobos, R., & Sánchez-Zamora, P. (2020). An Analysis of the Impact of Forest Policy on Rural Areas of Chile. Forests, 11(10), 1105. https://doi.org/10.3390/f11101105