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:
- Surface of the terrestrial protected areas (%).
- Policy indicators such as government expenditure on environmental protection, GDP, population density, forest cover percentage, biodiversity index, and environmental laws.
Machine Learning Techniques
- Linear Regression
- Random Forest Regression
- Support Vector Regression (SVR)
Methodology
- Data Preprocessing: Clean and preprocess the data, ensuring no missing values and normalizing the data.
- Feature Selection: Identify the most important features affecting protected area coverage using correlation analysis and domain expertise.
- 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.
- Analysis of Results: Compare the performance of the three models and identify the most significant factors affecting protected area coverage.
Findings
- 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