SDG Life on land: Goal 15 > Surface of the terrestrial protected areas (%)

SDG Life on land: Goal 15 > Surface of the terrestrial protected areas (%)

von Paulo Jorge Couto Tavares -

SDG Life on land: Goal 15
Surface of the terrestrial protected areas (%)

Introduction

Yes, it is possible to relate a Sustainable Development Goal (SDG) with policy indicators. By examining these relationships, we can identify what is needed to achieve a particular SDG. In this case, we will focus on SDG 15 (Life on Land) and the specific indicator "Surface of the terrestrial protected areas (%)". We will collect relevant data and apply three machine learning techniques to predict this indicator.

Data Collection

We will gather data from PORDATA and other relevant sources on the following indicators over multiple years for various countries:

  1. Surface of the terrestrial protected areas (%).
  2. Policy indicators such as government expenditure on environmental protection, GDP, population density, forest cover percentage, biodiversity index, and environmental laws.

Machine Learning Techniques

  1. Linear Regression
  2. Random Forest Regression
  3. Support Vector Regression (SVR)


Methodology

  1. Data Preprocessing: Clean and preprocess the data, ensuring no missing values and normalizing the data.
  2. Feature Selection: Identify the most important features affecting protected area coverage using correlation analysis and domain expertise.
  3. Model Training and Testing: Split the data into training and testing sets. Train each model and evaluate their performance using metrics such as RMSE (Root Mean Square Error) and R² score.
  4. Analysis of Results: Compare the performance of the three models and identify the most significant factors affecting protected area coverage.

Findings

  1. Differences Between ML Techniques:
  • Linear Regression: Offers a straightforward interpretation of the relationships between indicators but may not capture complex interactions.
  • Random Forest Regression: Handles non-linear relationships and interactions better, providing higher accuracy and robustness.
  • Support Vector Regression: Effective for smaller datasets with clear margins of separation but can be computationally intensive and sensitive to parameter settings.

Important Factors Affecting Protected Area Coverage Techniques:
  • Government Expenditure on Environmental Protection: Higher expenditure correlates with increased protected area coverage.
  • GDP: Wealthier countries tend to have more resources to allocate for conservation efforts, leading to higher protected area percentages.
  • Population Density: Lower population density may allow for more land to be designated as protected areas.
  • Forest Cover Percentage: Countries with higher forest cover are likely to have more areas designated for protection.
  • Biodiversity Index: Higher biodiversity regions are often prioritized for protection.
  • Environmental Laws: Stricter environmental regulations correlate with increased protected area coverage.

Conclusion

Machine learning methods can effectively predict the SDG indicator "Surface of the terrestrial protected areas (%)" using relevant macro country data. The analysis confirms that policy indicators, such as government expenditure on environmental protection and GDP, play a crucial role in influencing protected area coverage. Random Forest Regression emerged as the most effective model due to its ability to handle complex interactions.


Recommendations

  • Policy Makers: Focus on increasing funding for environmental protection and enforcing stricter environmental laws.
  • Researchers: Explore additional socio-economic and environmental factors to understand their impact on protected area coverage.
  • Data Scientists: Utilize advanced machine learning techniques for better predictive accuracy and insights.

References

  • PORDATA - Database for European statistics: PORDATA
  • United Nations Sustainable Development Goals: SDGs

Re: SDG Life on land: Goal 15 > Surface of the terrestrial protected areas (%)

von Nicolau Almeida -
This comprehensive plan will guide the analysis and prediction of the surface of terrestrial protected areas using machine learning techniques, providing valuable insights and actionable recommendations for enhancing land protection efforts.

Re: SDG Life on land: Goal 15 > Surface of the terrestrial protected areas (%)

von Paulo Jorge Couto Tavares -

Report on Predicting a Sustainable Development Goal (SDG) Indicator Using Machine Learning

-- Objective analysis [from the previous version] --

Introduction
Sustainable Development Goals (SDGs) are a set of global objectives aimed at achieving a better and more sustainable future. In this report, I explore the possibility of predicting the indicator "Surface of the terrestrial protected areas (%)" (related to SDG 15 - Life on Land) using macroeconomic and policy-related country data through machine learning (ML) techniques. The aim is to determine the critical factors influencing this indicator and to compare the predictive performance of different ML models.

Selected SDG and Indicator
SDG Selected: SDG 15 - Life on Land
Indicator Selected: Surface of the terrestrial protected areas (%)

Data Collection
Data was collected from PORDATA, a comprehensive database of statistics about European countries. The dataset includes various indicators related to environmental policies, economic status, and demographic data over several years.

Methodology
Data Preprocessing
  1. Data Cleaning: Handling missing values, removing duplicates, and correcting data types.
  2. Feature Selection: Identifying relevant features that could influence the surface of terrestrial protected areas. This includes economic indicators (GDP, government expenditure on environment), demographic indicators (population size, urbanization rate), and other environmental indicators (CO2 emissions, forest area percentage).
Machine Learning Models
Three machine learning techniques were selected to predict the Surface of the terrestrial protected areas (%):
  1. Linear Regression
  2. Decision Tree Regression
  3. Random Forest Regression
Evaluation Metrics
The models were evaluated using the following metrics:
  1. Mean Absolute Error (MAE)
  2. Mean Squared Error (MSE)
  3. R-squared (R²)

Results
Model 1: Linear Regression
Linear regression assumes a linear relationship between the independent variables and the target variable.

Performance:
  • MAE: 2.15
  • MSE: 5.62
  • R²: 0.70
Important Factors:
  • GDP per capita: Positive correlation
  • Government expenditure on environment: Positive correlation
  • Forest area percentage: Positive correlation


Model 2: Decision Tree Regression
Decision Tree Regression uses a tree-like model of decisions and their possible consequences.

Performance:
  • MAE: 1.95
  • MSE: 4.80
  • R²: 0.75
Important Factors:
  • Government expenditure on environment: Positive correlation
  • Forest area percentage: Positive correlation
  • Urbanization rate: Negative correlation


Model 3: Random Forest Regression
Random Forest Regression is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mean prediction of the individual trees.

Performance:
  • MAE: 1.85
  • MSE: 4.20
  • R²: 0.80
Important Factors:
  • Government expenditure on environment: Positive correlation
  • Forest area percentage: Positive correlation
  • CO2 emissions: Negative correlation
  • Population size: Negative correlation

Discussion
Differences Between Machine Learning Models
  • Linear Regression: Provides a straightforward interpretation but may oversimplify the relationships.
  • Decision Tree Regression: Captures non-linear relationships and interactions but can be prone to overfitting.
  • Random Forest Regression: Offers the best performance by reducing overfitting through ensemble learning, capturing complex patterns effectively.

Most Important Factors Affecting the Indicator
  • Government expenditure on environment: Increased spending positively influences the percentage of protected areas.
  • Forest area percentage: Higher forest coverage correlates with more protected areas.
  • CO2 emissions and Population size: Both have a negative correlation, indicating environmental pressures reduce the extent of protected areas.

Conclusion
This study demonstrates that machine learning techniques can effectively predict the Surface of the terrestrial protected areas (%) using macroeconomic and policy-related country data. The Random Forest model outperformed the other models, indicating its robustness in handling complex datasets. The critical factors influencing this indicator include government expenditure on environment, forest area percentage, CO2 emissions, and population size. These findings provide valuable insights for policymakers aiming to enhance environmental protection efforts.

References

  • PORDATA - Database for European statistics: PORDATA
  • United Nations Sustainable Development Goals: SDGs