Format

Send to

Choose Destination
Ambio. 2018 Sep;47(5):595-607. doi: 10.1007/s13280-017-0998-3. Epub 2017 Dec 16.

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

Author information

1
Institute for the Oceans and Fisheries, The University of British Columbia, 2202, Main Mall, Vancouver, BC, V6T 1Z4, Canada. a.cisneros@oceans.ubc.ca.
2
Institute for the Oceans and Fisheries, The University of British Columbia, 2202, Main Mall, Vancouver, BC, V6T 1Z4, Canada.

Abstract

Evaluating progress towards environmental sustainability goals can be difficult due to a lack of measurable benchmarks and insufficient or uncertain data. Marine settings are particularly challenging, as stakeholders and objectives tend to be less well defined and ecosystem components have high natural variability and are difficult to observe directly. Fuzzy logic expert systems are useful analytical frameworks to evaluate such systems, and we develop such a model here to formally evaluate progress towards sustainability targets based on diverse sets of indicators. Evaluation criteria include recent (since policy enactment) and historical (from earliest known state) change, type of indicators (state, benefit, pressure, response), time span and spatial scope, and the suitability of an indicator in reflecting progress toward a specific objective. A key aspect of the framework is that all assumptions are transparent and modifiable to fit different social and ecological contexts. We test the method by evaluating progress towards four Aichi Biodiversity Targets in Canadian oceans, including quantitative progress scores, information gaps, and the sensitivity of results to model and data assumptions. For Canadian marine systems, national protection plans and biodiversity awareness show good progress, but species and ecosystem states overall do not show strong improvement. Well-defined goals are vital for successful policy implementation, as ambiguity allows for conflicting potential indicators, which in natural systems increases uncertainty in progress evaluations. Importantly, our framework can be easily adapted to assess progress towards policy goals with different themes, globally or in specific regions.

KEYWORDS:

Adaptive management; Aichi Biodiversity Targets; Convention on Biological Diversity; Fuzzy logic; Policy evaluation

PMID:
29249050
PMCID:
PMC6072633
[Available on 2019-09-01]
DOI:
10.1007/s13280-017-0998-3
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center