Identifying Determinants of Nations' Wetland ... - Springer Link

DOI: 10.1007/s002670010194

Identifying Determinants of Nations’ Wetland Management Programs Using Structural Equation Modeling: An Exploratory Analysis MEGAN K. LA PEYRE* I. A. MENDELSSOHN Wetland Biogeochemistry Institute Department of Oceanography and Coastal Sciences Louisiana State University Baton Rouge, Louisiana 70803, USA M. A. REAMS P. H. TEMPLET Environmental Studies Institute Center for Coastal Energy and Environmental Resources Louisiana State University Baton Rouge, Louisiana 70803, USA J. B. GRACE USGS, National Wetlands Research Center 700 Cajundome Blvd. Lafayette, Louisiana 70506, USA ABSTRACT / Integrated management and policy models suggest that solutions to environmental issues may be linked to the socioeconomic and political characteristics of a nation. In this study, we empirically explore these sugges-

A recurring theme in environmental literature is the suggestion that there exists a relationship between the environment and the socioeconomic and political characteristics of a nation. The majority of models suggest that the wealth of a nation is central to both the state of the environment and the extent of government policies and activities related to the environment (WCED 1987, Barrett 1996, Williams 1997). Other researchers argue that the social and political environments are key determinants of environmental impacts, protection, and actions (GroomKEY WORDS: Wetland management; Wetland protection; Economic– environment relationship; Social capital– environment relationship; Structural equation modeling; Ramsar Convention *Author to whom correspondence should be sent at current address: Fish and Wildlife Cooperative Research Unit, School of Forestry, Wildlife and Fisheries, Louisiana State University, Baton Rouge, Louisiana, 70803, USA; email: [email protected]

Environmental Management Vol. 27, No. 6, pp. 859 – 868

tions by applying them to the wetland management activities of nations. Structural equation modeling was used to evaluate a model of national wetland management effort and one of national wetland protection. Using five predictor variables of social capital, economic capital, environmental and political characteristics, and land-use pressure, the multivariate models were able to explain 60% of the variation in nations’ wetland protection efforts based on data from 90 nations, as defined by level of participation in the international wetland convention. Social capital had the largest direct effect on wetland protection efforts, suggesting that increased social development may eventually lead to better wetland protection. In contrast, increasing economic development had a negative linear relationship with wetland protection efforts, suggesting the need for explicit wetland protection programs as nations continue to focus on economic development. Government, environmental characteristics, and land-use pressure also had a positive direct effect on wetland protection, and mediated the effect of social capital on wetland protection. Explicit wetland protection policies, combined with a focus on social development, would lead to better wetland protection at the national level.

bridge 1992, Redclift 1992, Panayatou 1997, Hukkinen 1998). Still others have suggested that the relationship between socioeconomic and political characteristics and the environment may vary over time (Templet 1996). Identification of the determinants of environmental protection activities would enable a better understanding of the capacity of nations to deal with environmental issues as well as suggest practical solutions to environmental problems. As our awareness of the global nature of the environment increases, identifying the determinants of nations’ policies for environmental protection becomes increasingly important. Explicit understanding of the underlying determinants of environmental policies may lead to increased opportunities for policy intervention and indicate when action is necessary (Andreson and Ostreng 1989, Panayotou 1997). Using wetland protection programs as a model system, we explore determinants of national wetland protection efforts using structural equation modeling. ©

2001 Springer-Verlag New York Inc.

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M. K. La Peyre and others

Wetland Systems: International Resources

Methods

One of the earliest environmental issues to receive international attention was the loss of wetland systems: the first international treaty to focus on conservation of a single ecosystem centered on wetlands (UNESCO 1971). Ironically, despite this relatively early international attention and the fact that wetlands are of immense ecological and socioeconomic importance to humans, wetland systems remain among the most threatened habitats in the world with less than half of the world’s wetlands left. Draining, filling, and ultimate destruction of wetlands, largely under the guise of economic gain and human health improvements, have been especially detrimental to wetland systems over the last few decades (Dugan 1994, Mitsch and Gosselink 1995). Fortunately, recent recognition and understanding of the natural services provided by wetlands, including flood and storm protection, water quality maintenance, and breeding and feeding grounds for many aquatic and land-based animals, have resulted in concerted efforts to protect, restore, and preserve wetland systems. While many nations have taken unilateral and international action to protect wetlands, there appears to be a marked disparity between the protection efforts and policies of nations. There is no clear evidence or theory explaining why certain nations are more or less likely to protect wetland resources, although it has been suggested that economics, local social norms, politics, and land-use patterns all play a role in wetland management (Turner 1991, Barbier 1994, Barbier and others 1997). Exploring these variables as potential determinants of wetland protection in different nations would enable identification and greater understanding of a nation’s capacity to deal with wetland protection, as well as point to potential solutions to increasing wetland protection in nations. The major objective of this study was to develop and explore a general model of determinants of wetland protection. This study empirically investigated the relative influences of political and socioeconomic variables on national wetland protection. For exploratory purposes, a general model of influence on wetland protection effort and action is hypothesized in which wetland protection is a function of the direct and indirect effects of surrounding political, environmental, and socioeconomic conditions. Using structural equation modeling, we developed and tested a model of determinants of wetland protection.

No general model of influences on national wetland protection effort or action exists. A model of determinants of wetland management and protection was developed in which it was hypothesized that variation in national wetland programs can be explained by the direct and indirect effects of surrounding social, economic, political, and environmental characteristics (Figure 1). The hypotheses were based on the literature relating directly to wetland management, as well as more general literature related to environmental management issues (integrated management) and determinants of public policy (e.g., Dye 1972, Ringquist 1993, 1994, Crooks and Turner 1999, Nichols 1999, Turner and others, 1999). In order to explore the hypothesis that variation in national wetland management may be determined by surrounding context, data were collected to characterize national wetland management efforts. A second set of data, consisting of independent variables chosen to represent various socioeconomic, political, and environmental characteristics of each nation, was then collected from a variety of sources, as outlined below. Using this data, structural equation modeling using LISREL (Joreskog and Sorbom 1996) was used to explore the hypothesis and assess the relative importance of these socioeconomic, political and environmental characteristics as determinants of wetland management. National Wetland Management Data concerning the status of wetland management for nations were explored through national, regional, and international databases. The structure and philosophy behind wetland protection programs varies by nation, with some nations focusing more on developing national protection regulations/programs (i.e., United States), while other nations see international programs as the main path to wetland protection (i.e., United Kingdom). Despite these differences in approach, we have decided to use nation participation in the Ramsar Convention as a measure of wetland protection. While this approach may underrepresent the efforts of some nations to protect their wetland systems, based on our review of national and regional programs, those nations with the stronger national programs tend to also be nations that are more active in international conventions, while nations lacking national wetland protection programs entirely tended to have very limited if any participation international agreements. This analysis will be an evaluation of factors influencing the willingness and effort of nations to act to protect wetland systems. The general model (Figure 1) was tested

Determinants of Wetland Management

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Figure 1. Initial hypothesized structural equation model for determinants of wetland management. Variables enclosed by ellipses are latent (conceptual) variables. Variables in boxes are the observed (measured) variables used to represent the latent variables. The structural model to be fit measures the relationships among the latent variables. Two uncorrelated indicators of “wetland program” were used. “Effort” represented the participation of nations in the Ramsar Convention on wetlands, as an indicator of programmatic effort, or strength, of the program. “Protection” was used to represent the actual on-the-ground effort of nations to protect wetlands, as the percent of wetland area given protected status. Information on the measured variables can be found in the text (Methods) and in Table 1.

using two independent measures of wetland management activity/effort: one measures the effort of wetland protection, as indicated by activity in the international wetland convention, while the second measures wetland protection, as indicated by percent of wetlands given protected status. The first dependent variable, effort, involves two indicator variables: the number of sites designated as protected areas, and the number of years that nations have participated in the wetland convention. The second model, measuring wetland protection, uses the percent of total nation wetland area protected through the wetland convention as the dependent variable. Percent of wetland area protected was calculated by dividing the number of hectares protected, as indicated by the Ramsar database (UNEP 1998), by the area of wetlands in the nation, as reported by the World Resources Institute (WRI 1994) (mangrove plus wetland areas). Both participants and nonparticipants in the convention were included in the analysis. National Context Characteristics (Independent Variables) A number of independent variables were chosen to represent local conditions that might be influencing

the effort of nations to protect wetlands. The choice of variables was based on past analysis and theory of what factors might influence environmental resource protection by governments (Boyce 1994, Grossman and Krueger 1995, Torras and Boyce 1998). Variables were chosen to capture differences in socioeconomics, politics, local pressures, and the potential importance of wetland systems within each nation. In many cases, a number of potential variables could be used to represent a given influence. Final selection of variables was based on availability, reliability of data, and correlations among other potential variables. Indicators of a nation’s characteristics are undoubtedly less than entirely objective or accurate in all cases; however, they serve an important purpose as relative measures of national characteristics. A summary of the independent variables selected is given in Table 1. Below is a discussion of the variables chosen for this analysis. While admittedly these cover a limited number of variables, our study is the first to try to address this question for wetland protection at the international level. Economic capital. Poverty has often been cited as the world’s biggest environmental problem, and many studies have examined various aspects of the link between

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Table 1.


Conceptual variables and indicators used for structural equation modelinga

Concept represented Dependent variables Program effort Protection Independent variables Social capital Economic capital Environment Government Land use pressure Environmental minister Wetland type

Indicator variables (code)

Source

years in wetland convention (YEAR) number of wetland sites designated (SITE) protected wetland area/total wetland area (PROT)

UNEP (1998) UNEP (1998) UNEP (1998), WRI (1994)

human development index (HDI) economic performance index (ECON) environmental index (ENV) political system index (GOV) percent agricultural land (FARM) presence or absence of position (MIN) length of coastline (COAST)

UNDP (1995) Yeung and Mathieson (1998) Yeung and Mathieson (1998) Yeung and Mathieson (1998) WRI (1994) UN (1990) CIA (1997)

a

More detailed description of the variables is in the Methods section of the text.

economic wealth and environmental quality/action (Goodland and others 1991, World Bank 1992, 1996, Peet and Watts 1995, Deavenport 1998). The main focus of the debate concerning the link between economic wealth and environmental issues centers around the proposed inverted U-shaped environmental Kuznets curve (EKC). The EKC hypothesis suggests that initially, environmental quality will decrease with increasing per capita income, but eventually increase as per capita income continues to rise. At a certain threshold level, increasing economic capital brings more environmental improvements only if appropriate policy responses are made (Grossman and Krueger 1993, Selden and Song 1994). Evidence suggests that the EKC relationship may exist only for certain types of environmental degradation (e.g., short-term and local impacts) rather than for environmental degradation that is more global, indirect, and has long-term impacts (Shafik 1994, Arrow and others 1995, Barbier 1997, Cole and others 1997, McConnell 1997). These long-term impacts are hypothesized to have linear relationships with economic wealth. Thus, we expect a positive linear relationship between economic capital and wetland protection. Numerous indicators exist to measure economic capital: the economic performance index developed by Yeung and Mathieson (1998) was selected for this study as it provides a summary score of a number of conventional indicators. Economic capital captures the overall performance of the nation in economic growth, per capita income, investment growth and external trade and finance. Social capital. The social culture or norms of a nation may also be important in determining environmental policies and their outcomes (UNEP 1996, IUCN 1999). While economic capital may set some boundaries on possible environmental actions, allocation of funds and

development of programs reflects a nation’s interest and commitment to environmental protection (social and cultural norms). Social capital refers to the standards of living and is often represented by education, health and quality of life. Social capital has many links to economic capital (World Bank 1993, 1995, Biswanger and Landell-Mills 1995), and thus this model allows the two variables to covary. It is hypothesized that as social capital increases, wetland protection effort increases. Social capital represents the quality of life, such as levels of education and health of citizens. It is represented by the Human Development Index (HDI) developed for the Human Development Reports (UNDP 1995). It is composed of a factor score of education, quality of life, and health indices. Political characteristics. Research has shown that both the structure and type of political systems are influential factors in determining policy (e.g., Rosenau 1994, Barbier 1997, Panayotou 1997, Hukkinen 1998). The characteristics of government are likely to be very important in mediating the influence of social and economic capital. In a study of sulfur dioxide pollution across countries, Panayotou (1997) found that effective environmental regulation changed the economic– environmental quality relationship. It is hypothesized that the political environment is influenced by the social and economic context, and that it may be an important determinant of protection policies. Specific characteristics of governments are represented by the democracy and freedom index (Yeung and Mathieson 1998). This index reflects the type of government structure and the level of democracy and stability in the nation. It is probably the most difficult and perhaps most politically sensitive measure for scoring nations and is based on the civil liberties, political


rights, and social equality enjoyed by the citizens of a nation. Environmental commitment. The resources that a government commits to an issue can determine how much is actually done. Thus, the presence of an environmental minister was hypothesized to increase the chances of more wetlands being given protected status. For the measure of wetland area protected, the presence of an environmental minister (UN 1990) was added. Environmental characteristics. While it is generally accepted on one level that many environmental problems are global issues, national decisions still determine the actual level of protection (Sand 1990, Keohane and others, 1993). Political pressure in many nations is believed to be one of the most important variables that account for policy changes and environmental actions (Keohane and others, 1993). The environmentalism of a nation is hypothesized to be a mediating variable as it is likely influenced by the social and economic capital of the country, and the current state of the environment is likely to interact with decisions regarding the amount of protection of wetland systems. Yeung and Mathieson’s (1998) environmental index provides a summary score of measures of air and water quality, government action in protection treaties, and citizen action and participation in environmental groups. Nations with higher environmental scores are hypothesized to be more likely to have better wetland protection programs. Pressures. Conversion to agricultural lands is overall the biggest threat worldwide to wetland systems (Dugan 1994, Mitsch and Gosselink 1995). Thus, agricultural land-use pressure, as measured by the percent of agricultural land in the nation (WRI 1994), is hypothesized to negatively influence wetland programs. Importance. One last influence that might increase the likelihood of a nation protecting its wetlands might be captured by the relative importance or the value of resources derived from wetlands in the nation. No consistent and reliable data exist to capture the value of resources derived from wetlands for many nations (e.g., fish landings). In general, coastal wetlands have been valued to a much greater extent than inland wetlands, largely due to their role as important nursery habitat for many commercial fish and shellfish species. While inland wetlands are acknowledged to be equally important, the pressure to protect coastal wetlands has generally been greater, and more research has focused on the role of coastal wetland systems as nursery habitat, and storm and flood protection (Mitsch and Gosselink 1995). It is hypothesized that nations with greater coastal wetland areas will have stronger wetland protection programs.

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Length of coastline (CIA 1997) will be used as a surrogate indicator of area of coastal wetlands for nations as coastal wetland areas are not available for most nations. Length of coastline was divided by area of the nation in order to provide a variable that captured more closely the potential relative importance of coastal areas to a nation. Statistical Methods Structural equation modeling (SEM) was used for hypothesis development to analyze a potential model of wetland effort and protection (Bollen 1989, Hair and others 1992, Joreskog and Sorbom 1996). Combining multiple regression and factor analysis, SEM is valuable in several ways: (1) it provides a method for statistically testing multiple and overlapping regressions, (2) it allows the use of latent variables (unobserved concepts) when multiple indicators of the latent variable exist, and (3) it partitions the direct and indirect effects of variables (Hair and others 1992). Furthermore, SEM is valuable in exploratory analyses including, as in this case, hypothesis development. Bivariate plots of the data were examined for correlations and nonlinearity among variables. SEM was run using LISREL, in which the observed covariance matrix was compared to the expected covariance matrix derived from the hypothesized model using maximum likelihood methods (Joreskog and Sorbom 1996). Model fit was analyzed using the normed-fit and goodness-of-fit indices as well as the Satorra-Bentler chisquare statistic. The Satorra-Bentler chi-square statistic was used as it has been found to perform better for smaller sample sizes and to handle nonnormality of data (Satorra and Bentler 1986). Pathways were retained in the model if they were significant at P ⫽ 0.05 using a one-tail test. Complete data sets were collected for 90 nations, and these were all used in the final analysis.

Results Wetland Effort Results from the SEM analysis for the hypothesized model (Figure 1) indicated that an alternative model provided the best fit and parsimony (Figure 2). Several paths were found to be nonsignificant. The paths from economic capital to government and environment were dropped, as was the path from social capital to environment. Furthermore, both the coastal and minister variables did not contribute to the fit of the model or the explanation of variation in effort, and thus they were left out of the final model. The resultant model (Figure 2) was found to have a good fit and was accepted.

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Figure 2. Results for the final model of wetland protection “effort.” All path coefficients shown are completely standardized partial regression coefficients and are statistically significant at P ⬍ 0.05. The variables government, environment, and effort have 40%, 11%, and 60% of their variance explained by the model.

Table 2.

Completely standardized regression coefficients for wetland protection effort modela

Independent variable

Government characteristics

Environmental characteristics

Economic capital Social capital

0.63 (8.11)

Government characteristics

0.34 (3.77)

Environmental characteristics Land-use pressure Adjusted R2

0.40

0.11

Effort ⫺0.25 (⫺2.06) 0.53 (2.31) 0.30 (2.36) 0.19 (1.84) 0.20 (1.85) 0.60

a

T values are in parentheses.

The Satorra-Bentler chi-square value was 10.22 with 11 degrees of freedom (df ) (P ⫽ 0.51), indicating a very good fit for the accepted model (Figure 2). The accepted model explains 60% of the variance in wetland effort and fit the data well, with a root mean square error of approximation (RMSEA) of 0.0 (df ⫽ 11, P ⫽ 0.6854). For RMSEA, P ⬎ 0.05 indicates no significant deviation between expected and observed covariances. The normed-fit-index (NFI) value was 0.94, and the goodness-of-fit (GFI) Index value was 0.97. For both GFI and NFI, a value of greater than 0.90 generally corresponds to a high degree of fit for the structural model. The standardized prediction equation for the accepted model of nation wetland protection effort was:

Effort ⫽ 0.76 ⴱ social ⫹ 0.30 ⴱ government ⫹ 0.19 ⴱ environment ⫺ 0.24 ⴱ economic ⫹ 0.20 ⴱ land pressure

(1)

The standardized direct and indirect pathways describe the precision of the relationship between variables (Tables 2 and 3). In this case, social capital had the greatest total effect of 0.76 (total effects are the sum of all pathways, with indirect effects being the product of all connecting paths), economic capital had a total effect of ⫺0.25, and government, environment, and land pressure had total effects of 0.36, 0.19, and 0.20, respectively. For both social and economic capital, the predominant effect was direct (0.53 and ⫺0.25 respec-


Table 3. Completely standardized effects coefficients for model of protection efforta

Environmental characteristics Government characteristics Economic capital Social capital Land use pressure

Direct

Indirect

Total

0.19 0.30 ⫺0.25 0.53 0.20

— 0.06 — 0.23 —

0.19 0.36 ⫺0.25 0.76 0.20

a

Indirect effects are equal to the product of the pathways between the independent and the final dependent variable. Total effects are the sum of the direct and indirect effects.

tively) rather than indirect (0.23 and 0, respectively). Environmental characteristics were affected by government (0.34), with R2 ⫽ 0.11. Government characteristics were found to be influenced only by social capital (0.63) with R2 ⫽ 0.40. Wetland Protection The hypothesized model for wetland protection did not provide a good fit. No alternative models based on the variables used were found to be acceptable.

Discussion No study has previously empirically investigated the influence of socioeconomic, political, and environmental variables on the protection of wetland systems by nations. Our results provide empirical evidence of the magnitude and direction of effects that surrounding social, economic, and political factors have on the level of effort of wetland protection by nations. Specifically, social capital had a dominant, positive linear relationship with wetland protection, while economic capital had a strong, negative linear relationship with wetland protection. Other variables, such as government, environmental history, and land-use pressure also had significant, positive linear relationships with wetland protection. The implications of these findings are crucial to understanding the underlying determinants of wetland protection, highlighting the range of policy intervention possible. While these findings can only provide evidence for determinants specific to wetland protection, the larger questions to be addressed focus on whether social and economic development can continue to be a main priority of nations with protection of natural resources assumed to occur as development continues, or whether explicit environmental protection policies are required (Panayotou 1997). Research results on the relationship between environmental quality/actions, in general, and economic development, in particular, have not provided clear

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guidance; it appears unlikely that all environmental issues conform to one dominant pattern (i.e., EKC) (Barbier 1997, List and Gallet 1998). In a global arena with multiplying environmental issues, identifying which issues need explicit action and which issues will be resolved as economic growth occurs is important for policy-makers. The literature on economics and wetland management is far more developed than for any other contextual factor explored. In fact, wetland loss and destruction are often linked to economic development pressure, as well as market and intervention failure (Turner 1991, Barbier and others, 1997). While our model demonstrated a link between economic factors and wetland protection, economic wealth had the opposite influence of what was expected: a nation’s economic wealth negatively influenced wetland protection efforts. This suggests that wetland protection will not occur as a matter of course as economic development proceeds, and in fact, the evidence indicates just the opposite. We need to develop explicit wetland protection programs in order to protect wetland systems in light of most nations’ focus on economic development. This trend of increasing wealth with decreasing wetland protection is similar to findings for municipal waste (Shafik 1994, Cole and others 1997), suggesting that there are certain types of environmental issues that are likely to continue to worsen with economic development. While environmental degradation from certain resources may follow the EKC [e.g., sulfur dioxide (Grossman and Krueger 1995, Selden and Song 1994, Cole et al. 1997); nitrogen oxide (Selden and Song 1994, Panayotou 1995); carbon dioxide (Cole and others, 1997); fecal coliform, BOD, COD (Grossman and Kreuger 1995); deforestation (Panayotou 1997)] and may decrease as economic development occurs, other environmental issues, such as wetland loss and degradation and municipal waste issues may only worsen with economic development. These issues following this trend need to be identified and specific policies to prevent the continued degradation of these resources developed. Similarly, the precise role and relationship of social, political, and environmental variables as determinants of environmental quality/action remain ill-defined. Whether a national focus on social development can lead to improved environmental quality/action without explicit environmental protection is also an important policy issue that needs to be explored for more environmental issues. In the case of wetland protection, the evidence overwhelmingly supports the idea that as quality of life and education increase, wetland protection also increases. One aspect of social capital that has

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often been discussed in the context of environmental protection is education. Increased education in general and increased education specific to natural resources is often suggested and used by international organizations as a tool for achieving better environmental quality and sustainable use of resources (UNESCO 1992, 1995, Huckle and Sterling 1996). China’s National Environmental Policy recognizes this with environmental education as a key component (Lee and Tilbury 1998). The importance of political/government institutions in identifying and implementing appropriate policies is evident from our model through its role in mediating the effects of social capital, but also through its direct influence on wetland protection policy. While both social and economic development are important determinants of environmental quality, to what degree as well as when and how better environmental protection occurs is also dependent on government policies and institutions. The case for the importance of institutional factors in achieving better air quality was explicitly demonstrated for ambient sulfur dioxide levels (Panayotou 1997). The adoption of better policies by government was shown to lower and flatten the EKC. While this example is not directly comparable to our model, as there was no EKC pattern, it demonstrates how influential government policies may be in achieving better environmental protection/quality. Similar to the first law of ecology suggested by Commoner (1972) that “everything is connected to everything else,” it appears that connectivity is also crucial to environmental problem-solving. To date, the results have been mixed on the relationship between economic and social development and environmental protection. The results for the wetland model indicate that while economic development showed a negative linear relationship with wetland protection, social capital had a positive linear relationship with wetland protection. At the same time, government appears to be an important determinant of wetland protection. Overall, the evidence from this model would indicate that at the national level, wetland protection is not likely to occur as a matter of course. Explicit wetland protection policies, combined with continued focus on social development, are necessary to promote national protection efforts.

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