Although the research tools provided by the ISO 14001 framework are both qualitative and quantitative, this approach is consistent with the guidance provided by Neuman (2003) who points out that, "Both qualitative and quantitative research use several specific research techniques (e.g., survey, interview, and historical analysis), yet there is much overlap between the type of data and the style of research. Most qualitative-style researchers examine qualitative data and vice versa" (p. 16). Indeed, researchers have used qualitative and quantitative surveys to assess consumer reactions to proposed environmental initiatives at the local level (Neuman, 2003). In fact, quantitative and qualitative research methods are characterized by a number of similarities that lend themselves to environmental systems analyses and development (as well as some differences) (Neuman, 2003). The distinct differences in the qualitative and quantitative research suggest that the use of quantitative data for environmental system development is highly appropriate, but that such data must be interpreted by taking into account a wide range of potentially qualitative factors that will not be possible using one approach to the exclusion of the other research approach (Neuman, 2003).

A summary of the foregoing research methods for environmental system development is provided in Table 1 below.

Table 1

Summary of Research Methods Used for Environmental System Development: Past Five Years to Date

Research Method

Description

Operation

Quantitative risk-assessment methods

1. Risk assessment for potential risk to human health;

2. Risk assessment for potential damage to the environment from manmade activities;

3. Formulating timely and efficient responses to environmental disasters such as hazardous waste spills and their management.

4. Biomonitoring and exposure assessments of environmental threats to human health (Vandenberg.et al., 2010).

Two types are quantitative risk assessment methods are available that make it valuable for environmental system analyses:

1. Risk analysis. The first type of quantitative risk assessment involves computation of the risk corresponding to a given level of exposure or dose; for example expressed in terms of excess risk or the number of extra disease cases.

2. Hazard analysis. The second type involves calculation of the exposure or dose corresponding to a given level of risk; for example the exposures estimated to cause adverse health outcomes in a certain percentage of exposed subjects (Carvalan et al., 2009).

Geospatial data

Geospatial technologies is an umbrella term that includes:

1. Remote sensing,

2. GIS,

3. GPS,

4. Computer mapping,

5. Spatial modeling,

6. Data visualization (Yao & Zou, 2008); and,

7. Wind farm siting (Dincer & Rosen, 2007).

.In addition, Internet-based GIS is becoming increasingly accessible to business, governments and consumers around the world as well (Yao & Zou, 2008).

These technologies rely on global information and positioning systems to create maps and three-dimensional visualizations, among other applications (Yao & Zou, 2008). The use of geospatial data by geostatistics for environmental system development include the following:

1. Prediction;

2. Determination of the scale of spatial variation;

3. Design of sampling for primary data collection;

4. Smoothing of noisy maps;

5. Region identification;

6. Multivariate analysis; and,

7. Probability mapping (Haining et al., 2010)

ISO 14001 framework

The standards and guidelines in the ISO 14001 framework that address specific environmental aspects, include the following:

1. Labeling,

2. Performance evaluation,

3. Life cycle analysis, and

4. Communication and auditing (ISO 14000 essentials, 2012, para 1).

The two standards, ISO 14001:2004 and ISO 14004:2004 deal with environmental management systems (EMS). ISO 14001:2004 provides the requirements for an EMS and ISO 14004:2004 provides general EMS guidelines (ISO 14000 essentials, 2012).

In sum, then, the research methods used during the past 5-year period have included conventional risk-assessment methods using quantitative data as well as biomonitoring techniques, geospatial data analytical methods such as geostatistics, and the qualitative and quantitative methods provided by the ISO 14000 family of environmental management evaluation and auditing tools. Each of these systems has its respective advantages and drawbacks, though, and these issues are discussed further below as they apply to their application in developing environmental systems.

Assumptions and Limitations Encountered in Developing Environmental Systems

Assumptions. Any type of research enterprise will require some fundamental assumptions about the phenomenon being modeled, and the enormous array of variables that affect environmental systems makes such research problematic from the outset. Indeed, identifying what should be measured and how it should be measured requires a comprehensive knowledge of the most salient environmental factors that are involved, but even the most careful approach cannot predict every possible...

Many of the limitations that have been encountered in developing environmental systems using geospatial data have been associated with a paucity of timely and accessible data, as well as expert interpretation of this data (Satapathy et al., 2008). Although there are a growing number of uses for geospatial data, Satapathy and his colleagues (2008) cite a lack of access to such timely data, at least in India. This view is countered by the report from Sui (2007) that found geospatial data is readily accessible, at least in the United States, and that these technologies will continue to redefine how environmental systems are developed in the future. Furthermore, Goodchild and Janelle (2004) report that a number of other research tools have become available in recent years that provide geospatial data directly to researchers and consumers alike, including Space Imaging Inc. IKONOS (launched in 1999) and Digital Globe's Quickbird (launched in 2001). Both of these systems provide commercial satellite imagery products that are offered to the general public and the research community (Goodchild & Janelle, 2004). Despite its increasing availability, there remains a lack of expert interpretation of geospatial data that takes into account the specialized needs of local communities (Brinegar & Popick, 2010).
According to Brinegar and Popick, the world's 200 or so countries are defined by arbitrary geopolitical lines drawn on the globe, and these boundaries continue to shift, making the analysis and interpretation of geospatial data especially challenging. In this regard, Brinegar and Popick report that the need for defined areas is an essential requirement for developing environmental systems that take into account population growth trends and other human factors, but these analyses are complicated by the fact that such human-created boundaries tend to change over time. According to these analysts, "Unfortunately, human-defined boundaries vary over time and among data sources, complicating geographic inquiries. Spatial data issues occur, for example, when municipal boundaries expand and diverge from static census blocks, or when variables of interest are recorded in noncontiguous geographic units" (Bringer & Popick, 2010, p. 273).

These are increasingly salient issues in the application of geospatial data to environmental systems that focus on anthropomorphic impacts over time. This type of research has become possible in recent years through sophisticated modeling applications that include both geospatial as well as temporal data in formulating a variety of potential scenarios (Goodchild, 2008). Indeed, Goodchild even goes so far as to characterize this type of environmental system research as a "paradigm shift" and emphasizes that this trend is driven in large part by the introduction of specialized software for this purpose, among other compelling reasons. According to Goodchild, this trend is "driven in part by a new abundance of spatiotemporal data, in part by the development of improved methods of analysis and improved software tools, and in part by the realization that the dynamic aspects of the Earth's surface are in many ways more interesting and important than the static aspects" (2008, p. 312).

Moreover, although several approaches have been developed to help resolve these constraints, there remains a need for comparative studies to determine the accuracy of these models in real-world settings (Bringer & Popick, 2010). These arbitrary geopolitical national boundaries have also adversely affected the ability of geospatial data to be used for environmental system development for more effective disease control methods (Leyk et al., 2011). .In this regard, Leyk and his associates (2011) emphasize that recent attempts to analyze geospatial data over time have been impacted by the influence of regional risk factors include climate and the prevailing socioeconomic conditions. According to these researchers, "Often the unit of analysis for such studies are administrative reporting units (states or larger) used in disease reporting, resulting in highly aggregated outcomes with limited representation of the underlying environmental phenomenon that might be more realistically reflected by analytical units defined by natural barriers, ecological systems, and other important factors in pathogen occurrence, exposure, and transmission" (Leyk et al., 2011, p. 224).

Similarly, a recent study by Goovaerts (2010) confirmed the challenges of using geostatistics for epidemiological applications based on regional or national boundaries that can shift. While there are some indications that geostatistics can be used for a wide range of environmental systems analyses, there have been some problems in applying the methods that were originally developed for analyzing earth properties to health-related research precisely because of these arbitrary boundaries. In this regard, Goovaerts emphasizes that, "Transferring methods originally developed for the analysis of earth properties to health science presents several methodological and technical challenges. These arise because health data are typically aggregated over irregular spatial supports (e.g., counties) and consist of a numerator and a denominator (i.e., rates)" (p. 32).

Potential for Error and Resolution Techniques

According to Benz and Newman (1998), the potential for error in quantitative analysis can be minimized by the use of computer-based applications, but humans…