Environmental Research Methods Essay ⋆ Environment Essay Examples ⋆ EssayEmpire

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The development of methods that allow integrated analysis is central to current research on nature-society relations. Integrated methods combine qualitative and quantitative approaches aimed at understanding human-environment relations. Great challenges are involved in developing such integration due to the strong epistemological barriers that exist between the humanities, social, and natural sciences. Methods are framed by philosophical traditions that reflect key assumptions such as views of the social and natural world (constructivism or realism), positions regarding what is important to know (contextuality or generalizations) and perspectives regarding the nature of knowledge (subjectivity or objectivity).

The common assumption is that natural sciences and certain social sciences such as economics are grounded in logic and that they employ verification and replication, using experimental and quantitative methodologies, whereas most of the social sciences and the humanities emphasize subjectivity and relativism and provide explanations through the use of qualitative methodologies. In order to properly understand how the biophysical environment relates to social processes, however, methods that consider both approaches are necessary. Quantitative and qualitative methods are broad terms that include a huge range of specialized topics and approaches. Quantitative and qualitative methods contribute to research on human-environment relations but have limitations when it comes to understanding naturesociety problems; mixed methods are also employed in human-environment research.

Quantitative Methods

Quantitative methods are based on the ability to measure reality and on the objective representation of reality through such measurements. Quantitative methodologies provide the means to test theory according to logical reasoning and to verify explanations grounded in value-free observations. Quantitative techniques allow for the projection of alternative pathways into the future, and to conduct experiments that test our understanding of key human-environment processes. Variables under these methods are related together empirically and they are analyzed through the extensive use of statistical approaches. Quantitative methods require operational definitions and formal language to interpret patterns between variables. Casual explanation and universal truth are the aspiration of such methods, and the results of empirical testing and statistical analysis are (or are not) backed by explanatory, law-like theories.

A quantitative study first identifies and defines research questions that are theoretically conscious and that can be tested empirically. To be theoretically conscious means that the researcher needs to be familiar with existing research about a specific topic; for instance, if the research question involves the social drivers of landscape change, then the researcher has available a large body of research findings in areas ranging from economic theories of human behavior to studies on the relations of cultural values and the environment. Under such an approach, therefore, knowing where and how to contribute to such a range of theories is critical.

Second, the components of the research question need to be measurable and susceptible to rigorous statistical analysis. Finally, once the research questions are at hand and appropriate data is obtained, it is important to define the way the data is analyzed. Data analysis is mostly of a statistical nature, ranging from descriptive statistics to advanced statistical modeling. In general, statistics are applied to variables that represent a measurement of a social or environmental attribute. Certain attributes are straightforward to quantify within certain limits of certainty like human populations, incomes and most biophysical variables. Measurement of opinion and attitudes is possible through ranking, survey questions and structured observations including, for example, variables such as educational level and socio-economic status.

First of all, variables are analyzed through descriptive or inferential statistics. Descriptive statistics describe and summarize data showing the central tendency of the variables, for instance averages, mode and standard deviations. Inferential statistics provide a way to identify differences between groups, to look for relationships between variables and create models that allow simplification and the ability to make predictions (e.g., multivariate regressions, analysis of variance-ANOVAs). A very important part of statistical analysis is the ability to calculate the likelihood that a difference or a relationship between variables actually exists and that it is not the result of a random process or chance; this is referred to as the statistical significance level of a statistical test.

Quantitative methods aim to simplify the complexity of human-environment systems through the use of mathematical models. Models could be divided in theoretical and empirical models. Methods and data used in theory-based models test social and environmental theories through an evaluation of statistical relations between the explanatory variables and the question in place. These models can also provide predictions, or simulate outcomes of human-environment interactions. For example, when looking at the relation of plant invasions and agricultural practices in rural areas, a theoretical model will try to show the factors that affect the decision of a subsistence farmer to either continue cultivating an invaded agricultural plot or permanently abandon the plot and cultivate elsewhere. Using an agricultural household model of land-use choices and using data from a household survey, the model will test if households maximize utility subject to constraints on land, labor, and income

Empirical models attempt to fit socio-ecological variables to human-environment processes. For example, distance measurements such as distance of land parcels from roads fit spatial patterns of deforestation and provide better predictions from a statistical point of view; however, such variables might be too simplistic in explaining the social processes that lead to deforestation. Another example of empirical models is simulations; the goal of such models is to recreate a process with the use of a smallest number and the most critical variables possible. Sometimes better predictions will fall short in trying to explain the mechanism behind specific human-environment interactions.

Quantitative models have grown in complexity thanks in part to the increasing power of computers. In land change science, major advances have been possible through applying the sophisticated tools of geographic information systems (GIS) and remote sensing analysis. Such tools have been at the forefront of measuring environmental changes and relating them to social processes in a spatially explicit manner.

Traditional statistical methods are of limited use in a quantitative approach. They rely on replicates, homogeneity, randomness, normal distributions, and controlled experiments. Finding such characteristics in human-environment systems is rare and adequate samples in the real world are very difficult and require enormous resources and ways to prioritize research questions. Human-environment relations are often complex, non-random, non-normal, and non-replicable and-oftentimes-they go against traditional statistical assumptions. To solve such issues, researchers are beginning to consider human environment systems as complex systems and to apply different sets of methods. Studies in complex systems use dynamic modeling as their primary methodological approach. Dynamic modeling includes feedback processes and the adaptation of human environment relations and it looks for common underlying structures despite the apparent differences within a given system. The goal of dynamic models is to define the general structure and behavior of complex systems through mathematically complex relations with the use of high-level computer assistance. There has been quite an increase in the use of dynamic modeling looking at human-environment systems, and most of the techniques had been borrowed from climatic and ecological modeling where dynamic models have been successful at explaining mechanisms of how atmosphere, ocean, and land are related.

A recent and growing methodology that incorporates the dynamics of social, economic and ecological systems is agent-based models. These models are characterized by a combination of a cellular automata model representing the landscape of interest with an agent-based model that represents decision-making entities. Agent-based models claim to be extremely flexible about the representation of heterogeneous decision makers, who are potentially influenced by interaction with other agents and with their natural environment. These models aim to represent the interaction of complex decision making with a complex natural environment. The major weakness of such methods lies in the difficulty of standardizing and finding general mechanisms in social processes, which make it seem, therefore, that there is a particular mathematical dynamic model for each defined human-environment system.

Quantitative approaches tend to look at the dynamics of human-environment relation as the result of the links to various components of the human-environment system. In order to understand such dynamics, the social and natural system must be documented and analyzed. Once the pieces are understood, they should be linked with the use of models that incorporate the complexity of the interactions. For example, in understanding the processes of land use and cover change, a quantitative approach will look at the socioeconomic conditions, based on studies of land-use history and current land-management practices shown in household surveys; spatial landscape conditions, based on remote sensing and GIS analysis of past and current distributions of land covers; and environmental conditions, based on ecological transects and landscape ecology metrics. Once the pieces are understood they are linked in land-change models to explain current land changes and predict scenarios of such changes in different regions. What is critical in this approach is the need to assess the coupled human-environment system as a whole rather than as an assemblage of isolated major components.

Qualitative Methods

Qualitative methods emphasize the interpretive, value-laden, contextual, and contingent nature of knowledge produce by social and natural sciences. Studies through a qualitative approach attempt to make sense of human-environment relations in terms of the meanings people bring to them. These methods reveal the way different practices make the world visible. At the core of qualitative research is the interpretation of the world through observers; information is collected through field notes, interviews, and conversations. Qualitative research, in contrast to quantitative research, stresses the socially-constructed nature of reality, the relations of the subject and object of study, and the limits to knowledge. Examples of such methods are participatory observation, ethnographic methods, and historical analysis. Such methods have been commonly used by anthropologists, historians, and geographers of various social theoretic standpoints (e.g., feminism, post-structuralism, and postmodernism).

Representative qualitative methods looking at human-environment linkages are case studies, participatory observation, and discourse analysis. Case studies have been largely used by researchers interested in understanding human-environment relations. Case studies are defined as depth studies whose goal is to identify and describe the complexity of the linkages between humans and the environment before trying to analyze them and theorize about them. Their objective is to understand the case rather than generalize from it. Case studies can be seen as a methodology that allows for the use of combined methods. The weakness in the use of multiple methods to explore humanenvironment problems is in that it is not possible to generalize statistically from the case study to the population as a whole.

Case studies are common when looking at the relation of rural communities to environmental change such as deforestation, land degradation, or climate change. Through several case studies, deforestation can be shown to be the result of complex interactions of the behavior of farmers, political economic forces, and ecological processes that are intertwined in producing specific patterns of deforestation in a particular region. The ways variables relate are different from one study to another depending on the region; for instance, deforestation in the Brazilian Amazon is a large-scale forest conversion and colonization for livestock-based agriculture, whereas regions in Africa show patterns of deforestation related to cropland expansion by small landholders, and which are the result of changes from pastoralist to sedentary agriculture. Case studies identify the differences and uniqueness that result from the interaction of a set of variables, and explanations derived from case studies are often criticized for their inability to generalize beyond the particular case.

Methodologies involved in participatory observation generally lack a specific design of how the information is going to be collected. The researcher, in this case, has to go with the flow of social action that unfolds as the study progresses. This type of methodology requires a significant amount of time spent in the field becoming familiar with the subjects of the study, collecting data, understanding social and cultural meanings for people in situ, and representing the social world in which people live and interpret their lives. Information is collected through field diaries and open ended questionnaires.

Discourse analysis is concerned with the investigation of language and the way knowledge is produced and communicated, particularly what is regarded as truth in relation to power relations in society. It focuses primarily, but not exclusively, on language processing (linguistics), and criticizes the notion that language is transparent or neutral. Discourse analysis is particularly critical of the claim of scientific knowledge to universality, objectivity, and neutrality. The objective of discourse analysis is to reveal the socio-historical situation of how knowledge is constructed and how texts or other social representations (e.g., mass media) are produced. Discourse analysis looks at how different realities, representations and imaginations relate to each other in producing or changing conceptions of human-environment relations. For instance, the analysis of the discourses on global environmental change evaluates how scientific knowledge is produced and analyzes the relation of dominant forms of knowledge to environmental politics that construct different regions in terms of their importance in global climate change, like the Arctic or the Amazon basin.

Mixed Methodologies

The use of mixed methods has become increasingly popular due to the ability to gather and represent human-environment phenomena with numbers as well as with words. These methods combine different kinds of data collection and analysis and sometimes different types of research design within the same study. If quantitative methods and qualitative methods involved a wide range of approaches, with mixed methods the possibilities increase. Mixed methods aim to illuminate statistical findings with case studies, or generalize from case studies using quantitative methods while representing graphically elements in terms of their heterogeneity and context.

Combining numerical and tabular data with some sort of narrative provides a valuable contribution to the understanding of human-environment relations. For example, land degradation from a natural sciences perspective will show the decrease in soil fertility and its effects on microclimate, but interviews and participatory observation will show the way farmers adapt through changes in land management practices and how such changes in fertility really represent degradation or not.

The Resilience Alliance (a multidisciplinary research group that explores the dynamics of complex socialecological systems in order to discover foundations for sustainability) provides an interesting example in terms of integrating methodologies when addressing human-environment linkages. The proposed methodologies to be used by a recent research program looking at the role of water in sustaining resilience in social and ecological systems that are characterized by agricultural land use and that are vulnerable to water changes consist of formal mathematical models looking at water cycles; participatory approaches to stakeholder-driven analysis of particular regions (case studies); informal group analyses; agent-based models; Bayesian Belief Networks; historical profile analysis and scenario development. The goal is to compare the case studies and develop controlled experiments in the laboratory and the field on interactions between individuals, institutions, and their common resources.

Among the examples of how combined methods provide a complete understanding of human-environment relations is research looking at land change, which has been a pioneer in the use of combined methodologies. Landscape is seen as a coupled human-environment system, created by and with consequences for the interactions among human and biophysical subsystems creating it. People-pixel methods combine social data (people) with biophysical and remotely sensed data (pixels). The approach could be either to understand the biophysical process and the spectral signature to the use of social science data, or to understand social processes through the resulting patterns from remote sensing analysis. Difficulties, however, still arise when social, biophysical and geographical data are subject to differences in cartographic structures, space and time scales, and units of measurement. This challenge seems to be more conspicuous when linking remote sensed data and social data at the local or micro level. There is not an overarching theory or formulae in how people and land should be linked; on the contrary, such links should be designed differently in different settings and should be responsive to the needs and conditions of such variations.

Another example of combined methods comes from recent research in political ecology that produced methodologies that reconcile concerns with the subjectivities of human actors with quantitative methods in order to produce an objective understanding of environmental and social relations.

Specific examples exist in integrating conventional positivist methods and traditional, local environmental knowledge arrived at usually through participatory techniques and oral histories. Rocheleau, for instance, provides an example of how mixed methods illuminate a better understanding of how gender relationships within households in the Dominican Republic shape environmental and economic change locally and nationally. Using a feminist approach, the study questions the objectivity of forestry programs and provides an explanation of how women are key participants in such programs. It invokes quantification in order to gain legitimacy through an analysis that shows the magnitude and distribution of gender differences, the understanding of landscapes, livelihood systems, and ecologies. Still, most nature-society research is approached in terms of either social/qualitative methods or scientific/quantitative methods.

Differences in methodological approaches from social and natural sciences could be considered one of the barriers to addressing human-environment problems. It is important to recognize the conflicts that exist within the social sciences, in similar fashion to those existing between natural scientists and humanities. For example, the economists’ use of optimization models that reflect a profit maximizing behavior contrast with narratives explaining how culture and value systems affect human choices used by anthropologists and sociologists. Methods in economics then seem to be closer to those of the natural sciences, a reason for why we see integrated methods develop through ecological economics differently from integrated methods in political ecology or ecological anthropology. Overall, epistemological barriers need to be resolved among the disciplines to approach environment-society questions in a more holistic manner.

Bibliography:

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C. Parker, T. Berger, and S.M. Manson, “Agent-based Models of Land-Use and Land-Cover Change,” LUCC Report Series No. 6 (v.124, 2001);
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