Elsevier

Journal of Environmental Management

Volume 145, 1 December 2014, Pages 268-276
Journal of Environmental Management

Goal specificity: A proxy measure for improvements in environmental outcomes in collaborative governance

https://doi.org/10.1016/j.jenvman.2014.06.029Get rights and content

Highlights

  • We compare watershed partnerships to link collaborative processes to environmental outcomes.

  • We use a paired-watershed experimental design to control for confounding factors.

  • Goal specificity is positively correlated with environmental outcomes.

  • Collaborative outputs are a useful proxy measure for environmental outcomes.

  • Logic models are useful in analyzing environmental planning and management.

Abstract

Collaborative governance critics continually call for evidence to support its prevalent use. As is often the case in environmental policy, environmental outcomes occur at a rate incompatible with political agendas. In addition, a multitude of possibly confounding variables makes it difficult to correlate collaborative governance processes with environmental outcomes. The findings of this study offer empirical evidence that collaborative processes have a measurable, beneficial effect on environmental outcomes. Through the use of a unique paired-waterbody design, our dataset reduced the potential for confounding variables to impact our environmental outcome measurements. The results of a path analysis indicate that the output of setting specific pollutant reduction goals is significantly related to watershed partnerships' level of attainment of their environmental improvement goals. The action of setting specific goals (e.g. percentage of load reductions in pollutant levels) is fostered by sustained participation from partnership members throughout the lifecycle of the collaborative. In addition, this study demonstrates the utility of logic modeling for environmental planning and management, and suggests that the process of setting specific pollutant reduction goals is a useful proxy measure for reporting progress towards improvements in environmental outcomes when long-term environmental data are not available.

Introduction

Proponents of collaborative management approaches have argued that collaborative governance can lead to effective solutions through increasing partnerships' capacity to achieve environmental outcomes. However, many of the criticisms of collaborative governance revolve around the lack of clear indicators of improved environmental conditions resulting from collaboration (Kenney, 2001, Koontz and Thomas, 2006). While existing research has measured and compared collaborative outputs, to date, few studies have empirically linked collaborative processes to end outcomes. This gap in the collaborative governance literature exists for three primary reasons. First, in order to determine whether environmental improvements occurred, environmental data must be collected over relatively long time periods (Koontz and Thomas, 2006, Sabatier et al., 2005). For example, the lag time between implemented best management practices (BMPs) to control pollution and measurable improvements in environmental outcomes often occurs at rates incompatible with political agendas. As a result, policy makers frequently have to make decisions without complete data. Second, collecting these data is often cost prohibitive. Monitoring environmental conditions is expensive and requires technical expertise, and is often the first line item cut in environmental management budgets. The lack of monitoring resources exacerbates the issue of incomplete data sets. Third, it is difficult to empirically control for confounding influences on environmental conditions, which limits analysts' ability to attribute environmental changes to particular processes (Born and Genskow, 2006). These confounding influences may affect environmental conditions and yet have little to do with the efficacy of collaborative governance. For example, changes in land use within the watershed may result in water quality improvements in the absence of collaborative governance.

At present, literature on collaborative governance is mostly explanatory, not evaluative, explaining the antecedents to collaborative governance partnerships, but not linking processes to outcomes directly. Research has examined the role institutions play (Koontz et al., 2004, Ostrom, 1990, Leach et al., 2002), the importance of leadership (Thomas, 2003, Ansell and Gash, 2007; Emerson et al., 2012), financial and technical resources (Koontz et al., 2004, Bidwell and Ryan, 2006), member diversity (Koontz and Johnson, 2004; Weber, 2003), stakeholder perceptions (Sabatier et al., 2005), mutual trust (Leach and Sabatier, 2005, Ansell and Gash, 2007; Emerson et al., 2012), scientific understanding (Thomas, 2003), and collaborative outputs (e.g. plans) (Mandarano, 2008, Wilkinson, 2007); however, little research has empirically linked collaborative outputs with environmental outcomes. Outcome literature begins to unpack this “black box” with regards to governance outcomes (Rogers and Weber, 2010) and social outcomes (Leach and Sabatier, 2005, Lubell et al., 2002), but not to environmental outcomes.

This study investigates whether collaborative processes have a beneficial effect on environmental outcomes. To do so, we use a logic model to examine the degree to which collaborative partnerships attain their environmental improvement goals. In addition, this study addresses the constraints facing public managers of natural resources when trying to determine environmental outcomes in the absence of complete data. The findings of this study contribute to outcome and collaboration literature through the evaluation of relationships between collaborative governance processes, outputs and outcomes.

Section snippets

Collaborative governance logic model

This study investigates the linkages between planning and implementation processes and the corresponding outputs for improving environmental conditions utilizing a collaborative governance logic model. Collaborative outputs and outcomes are affected by the inputs and processes executed by the participants in the collaborative governance effort. Therefore assessing the relationship between collaborative governance processes and outputs and their capacity to achieve environmental improvement

Data

To empirically assess the linkages in the logic model, we relied on data collected from a survey questionnaire of participants in collaborative governance partnerships. These data were coupled with longitudinal water quality data from EPA's National Nonpoint Source Monitoring Program (NNPSMP). The program began in 1992 and consists of 28 partnerships that were established primarily to evaluate the effectiveness of watershed technologies that control nonpoint source pollution (Lombardo et al.,

Results

Nine survey items, measuring governance processes fell out in PCA into the following three factors: 1) sustained participation, 2) information sharing, 3) collective documentation. Table 1 lists the factor loadings and alpha coefficients for three factors. These factors are described in further detail below.

We present the results of path analysis3 (Fig. 2). This analysis examined the relationships between processes performed by collaborative partnerships, corresponding intermediate outputs, and

Pathway to goal attainment

The results of the path analysis illustrate the pathways linking elements of collaborative governance processes with outputs and outcomes. Standardized coefficients indicated that partnerships that set specific pollutant reduction goals (63% of the partnerships did) were more likely to attain their environmental improvement goals than those that set broader BMP assessment goals. There was no significant correlation between setting BMP assessment goals (95% of the partnerships did) and

Conclusions

The findings of this study offer empirical evidence suggesting that collaborative processes have a measurable, beneficial effect on environmental outcomes by linking elements of collaborative processes with outputs and outcomes. Given the inherent difficulties in relating environmental improvement to collaborative governance elements and processes, and the lengthy time horizon required for establishing such a causal link, this study provides a useful analysis of the collaborative governance

Acknowledgments

We would like to thank the U.S. Environmental Protection Agency's National Nonpoint Source Monitoring Program for assistance in obtaining the unique dataset used in this study. Specifically we would like to thank Tom Davenport for providing detailed reports on individual watershed partnerships. We also would like to thank Jean Spooner, Steve Dressing and Don Meals for their expertise and assistance qualifying the environmental outcomes and deciphering the data. Additionally, we thank the

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