The terminology used to describe each framing approach can differ. Those presented here go by a number of names including tools, frameworks, schemes, and techniques all with modest epistemological and definitional differences. In this chapter, approach is used as an encompassing term. Where appropriate, I also use the name that each is most often referred to in academic literature. Schemes are systematic or organized configurations of correlated things; whereas tools are purposive, used as a means of accomplishing some sort of assessment task. Nobel Laureate Elinor Ostrom (Ostrom 2011) provides some differentiation between the different descriptive terms used in a hierarchical manner. She describes a framework
as a meta-language, or metatheoretical map (Ostrom and Cox 2010), denoting a generalized form of theoretical analysis. Theories (e.g. transition theory, rational choice)
, on the other hand, are the working assumptions and hypothesized specifications of the framework variables deemed sufficient to provide adequate explanations or diagnoses of social and/or ecological conditions. Related to the above, models use more targeted assumptions about variables, predictions about the results of combining these variables using a particular theory.
DPSIR is an analysis scheme for describing cause-effect relationships in connection with environmental and natural resource management challenges (Bowen and Riley 2003; EEA 1999; Giupponi 2007)
. DPSIR stands for Driving forces-Pressure-State-Impact-Response; the scheme has been associated significantly with the European Environment Agency in Copenhagen, Denmark. The intention and the strength of the DPSIR scheme are its ability for practitioners to gain an overview of targeted (environmental) policy issues, and to estimate the appropriateness and efficiency of different governance responses (EEA 1999). It also permits the integration of socio-economic and ecological system information into one framework (Bidone and Lacerda 2004)
. The scheme helps to structure information into the five distinct areas, making it possible to identify and structure the important causal relationships. DPSIR conceptualizations can be simplistic or sophisticated dependent on the focus and/or the question(s) they address. The scheme has been used extensively for challenges to water and coastal regions (Gari et al. 2015)
. Figure 3.1 represents a simple depiction of the DPSIR framework for Baltic Sea eutrophication from Swedish agriculture.
The DPSIR approach has evolved from a long line of more simplistic frameworks for environmental issues such as Statistics Canada’s Stress-Response (S-R) framework from the late 1970s (Gari et al. 2015)
, the Pressure-State-Response (P-S-R) scheme launched by the Organization for Economic Cooperation and Development in the 1980s, and the United Nations Commissions on Sustainable Development’s Drivers-Pressure-Response (D-P-R) framework (OECD 1994).
The DPSIR approach has received considerable critique as well; it has often been directed at the mechanistic nature and oversimplification of the scheme, its linearity, and the difficulty in handling parameters that may be a part of multiple DPSIR phases (e.g., driver and state conditions) (Klijn 2014)
. An additional challenge is with its ability in incorporating the multi-dimensional and multi-scalar causal relationships of problems where many sustainability issues are characterized by complex dynamics in time and space are worsened by multiple and interacting anthropogenic and natural driving forces (Kates et al. 2001)
. These issues include, for example, global climate change
, poverty, eutrophication, and biotic diversity. Finally, the DPSIR framework has historically been developed and used for presenting environmental impacts
caused by socio-economic driving forces. Analyses of socio-economic system state conditions and impacts (e.g. HIV/AIDS, malaria, and poverty) have seldom been included in such analyses—thusly not reflecting the broad variety of sustainability challenges (Ness et al. 2010)
. To address many of the deficiencies along with making the scheme more useful for targeted areas, DPSIR has continued to be developed and augmented by scholars and practitioners to include, amongst numerous others, the ‘EBM-DPSER’ concentrating on ecosystem services (Kelble et al. 2013)
, the ‘DPSWR’ on human welfare (O’Higgins et al. 2014)
, the ‘eDPSEEA’ for Health (Reis et al. 2015)
, and the multi-level DPSIR (Ness et al. 2010)
3.3 Causal Loop Diagrams
A causal loop diagram (CLD) is a general approach to the qualitative analysis of systems; CLDs incorporate both human and social parameters into a single, sometimes sophisticated, conceptualization. They are often used as a part of a broader participatory systems analysis approach, including problem and system boundary definition, qualitative conceptualization creation, and quantitative system dynamics modeling. A strength of CLDs is that they are a flexible framework
where creators identify and describe, in increasing levels of complexity, the cause-effect relationships of different sub-components of a larger system. Arrows are used to link cause-effect relationships, connecting the two components.
The diagrams use different symbols to denote different relationships. A positive plus [+] symbol between two variables indicates a parallel behavior of the two, meaning an increase in the causative variable also causes the effect variable to increase; furthermore, a decrease in the causative variable denotes a decrease in the affected variable. Conversely, a negative minus [−] symbol indicates an inverse relationship between the two variables, meaning as the causative variable increases, the affected variable decreases, or vice-versa. Numerous sub-components of a system can form loops, feeding back on one another, either directly or indirectly. A loop that has a reinforcing behavior is often denoted in the diagram with ‘R’; this signifies exponential growth of that subsystem. Loops denoted with ‘B’ indicate a balancing behavior of the subsystem. Temporal aspects in the form of time lags can also be identified in the CLD using two parallel lines through the center of the arrow linking the variables. An example of a simplistic CLD for bush encroachment in southern Africa is shown in Fig. 3.2 (SAPECS 2016). The arrangement shows the causal relationships of two drivers of global climate change
and human populationSeeSeeAging population growth in the region and their ultimate impacts on such factors as woody plant growth, land area and water availability.
CLDs are a useful approach for grasping the casual interactions of defined systems and like the DPSIR scheme, allow the practitioner to experiment with solutions to the particular challenge area. However, CLDs possess a number of shortcomings that can influence their usefulness in framing sustainability challenges. First, the labeling of the different sub-components can appear problematic. The parameters must always be labeled as more or less of something (e.g., human population, greenhouse gas releases, biodiversity loss). This can lead to difficulties in understanding the respective sub-components of a system. In addition, critique has been lodged against a CLD’s spaghetti-like appearance, and related inability in understanding sophisticated conceptualizations of a problem area. Related, the aim of a CLD is to create causal relationships in as few steps as possible. Gross oversimplifications in processes also can often cause difficulties in interpreting a CLD therefore creating opportunities for creating false conclusions to be drawn about the system in question.
3.4 Multi-scale & -level Perspective (Including Transitions)
Another approach for understanding and structuring sustainability challenges is through the multi-scale and -level perspective. This form of assessment has been promoted and used for decades, and has been used for a variety of socio-ecological systems including sustainable tourism (Crnogaj et al. 2014)
, wastewater treatment systems (Molinos-Senante et al. 2014)
, water resources management (Daniell et al. 2014)
, climate change
(Bulkeley and Betsill 2013)
, and renewable energy transformations (Di Lucia and Ericsson 2014)
, to name a few. Scale refers to the analytical dimensions for measuring and studying objects and processes. Examples of different scales can be spatial, administrative, jurisdictional, managerial, or temporal. Levels refer to locations along those scales (Gibson et al. 2000)
. Related to these is hierarchy. A hierarchy is a conceptually linked system for grouping phenomena along a particular scale.
The strength of the approach is not based on causal relationships between phenomena as with the initial two approaches; instead, applying the perspective creates the ability to match usually distinct bio-geo-physical systems scales with social system
scales such as management systems (Cash and Moser 2000)
where the practitioner gains a robust understanding of a problem constellation. Like the first two approaches described, conceptualizations can be simple or sophisticated depending on the phenomena assessed. Additionally, an important intention with this approach is to detect where disconnects or mismatches can lie between different scales or levels (Cash and Moser 2000)
Scales can be predominantly inclusive or exclusive (Gibson et al. 2000)
. An inclusive (or nested) hierarchy is a group of objects or processes that is contained in subdivisions of groups of higher systems such as the modern taxonomic classification. An exclusive hierarchy is where groups of objects (or processes) in a lower ranked hierarchy are not included or as subdivisions of higher ranked groups such as the military ranking system (Gibson et al. 2000).
3.5 Multi-level Perspective in Transition Theory
A particular type of approach for understanding processes of sustainable change, often over time, is the multi-level perspective (MLP) in transition theory
and management. Broadly, transitions are deliberate processes of societal change in culture, practices and structure (e.g., agroecology in Uganda, renewable energy development in Sweden) (Nevens et al. 2013; Geels 2011)
. This mid-level theory is an extension of socio-technical systems rooted in sociology, institutional theory, and innovation studies (Geels 2004)
. Studies in this research field examine complex adaptive systems from the perspectives of long-term processes and non-linearity (Avelino and Rotmans 2009)
. The objects of focus of transitions are not abrupt, fast societal (sustainable) change; rather, a transition is an incremental and constant process of change where the fundamental character of society—or a sub-system of society—transforms (Rotmans et al. 2001)
. The field has extended to sustainability over the past decade-plus with a number of “experiments,” especially in urban areas throughout Europe. A conceptualization of the three levels with more specific divisions of different socio-technical regimes and how a niche can emerge over time is displayed in Fig. 3.3.
The MLP in transitions consists of three unique levels to encapsulate the social dynamics: landscape, regime, and niche. Landscape development (macro-level) refers to the broad societal material and immaterial elements. These landscape are the important elements that “surround” the particular system of study (Avelino and Rotmans 2009)
. Examples can include public infrastructure or concepts that dominate societal discourses (e.g. sustainable development, resilience, free-market economy). Regimes are patchworks of institutions and actors that support the societal status quo (Avelino and Rotmans 2009)
; they represent the rules that set the boundaries private action and public policies (Rotmans et al. 2001; Hägerstrand 2001)
Finally, niches are small areas of experimentation, innovation, and learning that challenge the stability of socio-technical regimes. They are often protected spaces
to deviate from the regime, and, if successful, eventually become a regime themselves (Geels 2004)