A fuzzy logic expert system for evaluating policy progress towards sustainability goals

Area of Science:

• Environmental science
• Marine ecology
• Conservation policy

Background:

• Assessing environmental sustainability is hindered by data limitations and undefined objectives, especially in complex marine ecosystems.
• Marine environments present unique challenges due to natural variability and difficulties in direct observation.
• Existing frameworks often lack the flexibility to adapt to diverse social and ecological contexts.

Purpose of the Study:

• To develop and test a fuzzy logic expert system for formally evaluating progress toward environmental sustainability targets.
• To provide a transparent and modifiable analytical framework for assessing progress in data-scarce and variable systems.
• To evaluate progress towards Aichi Biodiversity Targets in Canadian marine environments.

Main Methods:

• Development of a fuzzy logic expert system incorporating criteria such as indicator type, time span, spatial scope, and suitability.
• Evaluation of diverse indicators reflecting state, benefit, pressure, and response.
• Testing the model's sensitivity to assumptions and data limitations.

Main Results:

• Canadian marine systems show progress in national protection plans and biodiversity awareness.
• Overall species and ecosystem states in Canadian oceans do not demonstrate significant improvement.
• The framework identified quantitative progress scores and information gaps for specific biodiversity targets.

Conclusions:

• A well-defined goals are crucial for effective policy implementation and reducing uncertainty in progress evaluations.
• The developed fuzzy logic framework offers a adaptable tool for assessing progress towards diverse policy goals globally.
• The study highlights the need for improved data and clearer objectives for effective marine conservation.