Policy impacts of ecosystem services knowledge

Abstract

Research about ecosystem services (ES) often aims to generate knowledge that influences policies and institutions for conservation and human development. However, we have limited understanding of how decision-makers use ES knowledge or what factors facilitate use. Here we address this gap and report on, to our knowledge, the first quantitative analysis of the factors and conditions that explain the policy impact of ES knowledge. We analyze a global sample of cases where similar ES knowledge was generated and applied to decision-making. We first test whether attributes of ES knowledge themselves predict different measures of impact on decisions. We find that legitimacy of knowledge is more often associated with impact than either the credibility or salience of the knowledge. We also examine whether predictor variables related to the science-to-policy process and the contextual conditions of a case are significant in predicting impact. Our findings indicate that, although many factors are important, attributes of the knowledge and aspects of the science-to-policy process that enhance legitimacy best explain the impact of ES science on decision-making. Our results are consistent with both theory and previous qualitative assessments in suggesting that the attributes and perceptions of scientific knowledge and process within which knowledge is coproduced are important determinants of whether that knowledge leads to action.

Keywords: boundary organizations; conservation; ecosystem services; knowledge systems; science policy interface.

Fig. 1.

Evaluative framework for how ES knowledge leads to impact. Each column represents a pathway to impacting decisions, with deeper impact going down each pathway and from left to right between pathways. Impacts 2, 3, 4a, and 4b were the basis for our measurement of impact in each of the cases. Impact 3 refers to how ES knowledge can be used to build support for considering ES in decisions.

Fig. S1.

PCA biplots. We reduced the dataset by combining the following highly correlated variables. Represent PC, power and representation (correlation coefficient = 0.74); Interactions PC, in-person interactions and electronic interactions (correlation coefficient = 0.72); Inst. Capacity PC, institutional capacity to measure baseline conditions, monitor changes, and implement policies (paired correlation coefficients of 0.70, 0.79, and 0.80). In subsequent analyses, we used PC1 for predictor variables (or the inverse of Interactions PC1, so that increases in the principal component correlated to increases in the underlying variables).

Fig. 2.

Effects of ES knowledge attributes on policy impact. Bars depict mean (±SE) levels of impact based on five-point expert assessment. For each attribute of knowledge (i.e., salience, credibility, and legitimacy), the 15 cases are divided into low, medium, and high categories for comparison, based on survey scores. No SE bar indicates one case in that category. **P < 0.01; *P < 0.05; ⁺P < 0.1.

Fig. 3.

Effects of multiple attributes on decision impact. Points and whiskers represent model average coefficients with 95% confidence intervals. Predictor variables are described in Table S2. The first principal components were used for three groups of highly correlated predictors (Spearman rank correlation coefficients > 0.80). The resulting variables have a “PC” suffix: interactions PC (interactions in person and by phone/email), represent PC (stakeholder representation and decision-making power), and inst. capacity PC (institutional capacities to measure baseline ES and human activities, monitor changes to ES and human activities, and implement policy).