CC-VISAGES Project (2013-2015)
Basic project information
The project is located at the Environmental Justice Institute (EJI). It is also associated with the Environmental Research Centre (FFU) of the Freie Universität Berlin / Germany, and the Institute for Technique Assessment and System Analysis at Karlsruhe Institute for Technology / Germany. The CC-VISAGES was originated by Dr. Jason Samson (McGill University in Montréal / Canada) and Dr. Götz Kaufmann (Freie Universität Berlin) in Germany.
The Canadian field research will be conducted by Mari Justine Galloway as her master thesis between September 2015 and January 2016 in Nainamo, British Columbia. For detailed information please click here.
The Brazilian and German field researches are still in planning, but will be done with reference to the q sample created in the Canadian field research.
- Stiftung Deutsch-Amerikanische Wissenschaftsbeziehungen (SDAW) 2013-2015
- Deutscher Akademischer Austausch Dienst (DAAD) - University Alliance for Sustainability 2015-2016
The CC-VISAGES project (Climate Change Inferred through Social Analysis, Geography and Environmental Systems) is the postdoc / Habilitation project at Environmental Policy Research Centre (FFU).
Its goal is to investigate a scheme for climate change policy analysis for multi-level governance. Considering findings that climate change mitigation covers a broad range of negotiations on the international scale including development, migration, and securities issues too (Vlassopoulos, 2012), the different perceptions between national governments and their federal states, and between governmental bodies and communities, the different demands from different perspectives must be considered in the policy making process. The failure of the debates was identified as been caused in the lack (or ignorance) of knowledge regarding the different demands from the various stakeholders in the multi-level policy-making process. Perceived justice (Kaufmann, 2012; Maguire & Lind, 2003; Steelman & Maguire, 1999) and the degree of marginalization frame antagonistic demands towards successful climate change governance. In order to provide such a frame, a critical policy analysis (Dryzek, 2009) frame was applied to describe the vulnerability to the climate change related distribution of environmental burdens and environmental goods.
The project developed a comparable human stress index (HSI) on the community level in the three chosen case countries of Brazil, Canada, and Germany. Using six (6) social vulnerabilities (income, education, age, gender, migration, population density) und the Temperature Humidity Index (THI), a Climatological Environmental Justice Index (CEJI) was developed. By the HIS, THI, and CEJI three geographical representation of climate change vulnerabilities for each of the three countries was created through a geographical information system (GIS).
The comparable result is a listing of vulnerable communities for each of the countries. Top vulnerable communities are now analysed at local with help of a comparable, mixed-method approach called Q Oracle (for detailed information click here). The findings will be displayed in community based Public Participation Geographic Information System (PPGIS) that complement to the macro GIS models.
The CC-VISAGES project provides a much needed multi-level policy frame to deal with the multifaceted issue of climate change.
Download the full text here.
After the funding period of the
The first presentation of the findings at
The selected CEJI variables are as follows:
Climate Stress Index (CSI)
Narrowing it down to a simple formula, the calculation appears to be like this:
THIs=T-1.0799 · e0.03755 · T · [1-e0.0801 · (D-14)]
Another formula was used to consider both the magnus paramter and the relative humidity.
D = λ·(ln(RH/100)+β·T/λ+T)/β+(ln(RH/100)+β·T/λ+T)
Human Stress Index (HSI)
We reduced the number of variables by combining two redundant variables (3 & 4) - as a person can be either young or old. The weightening of each variables has not yet probably been reviewed, but is under discussion in our research team. This results in a formula that reads:
HSI = PD + FG + ((YA + OA)/2) + LE + PoC + LI / 6
Where are the hotspots?
Based on the measuring and with help of geographical representation, areas of highest vulnerability could have been measured. These are imaged as follows on three country maps for the Climatological Environmental Justice Index, for the Human Stress Index, and for the Climate Stress Index.
As stated in the presentation of our findings, the weightening of the variables (interdependencies of social variables, perceived climate change impacts at local, ratio if climatological and social variables etc. pp.) are under ongoing discussion in the team. Additionally, field researches will follow now in one of the five "top" vulnerable areas. Our new colleague in the team, Mari Galloway, will make the start with the research in the city of Nanaimo, British Columbia where her research will centre on perceptions and patters regarding sustainable food systems, food security, and environmental justice in the context of climate change. Comparable research using Q Oracle will follow in Brazil and Germany.
The CC-VISAGES project relies on the underlying assumption that the most important reason for the failure stems from an incomplete social portrait where many perspectives on the climate change issue are excluded. From the international negotiations on climate change, we can clearly see that the stakeholders’ discourses are discordant and that the definition of climate change by one stakeholder focuses not even on similar priorities than the definition of other stakeholders. As Vlassopoulos (2012) suggested, the concept of climate change was initially characterized as an environmental degradation problem to which other competing definitions were gradually added. In consequence, climate change was framed by polyphonic discourses related to developmental, migratory or security issues. This project perceives the vagueness in the definition of the climate change issue as a reason for the lack of “new institutional equilibriums” (Vlassopoulos 2012: 116) and, as a consequence, the impossibility of successful policies. While Vlassopoulos (2012) provided a much needed plurality in this debate, the project suggests that it is necessary to also take into consideration the academic sphere and the non-institutional level of civil society in order to provide the full array of climate change definitions, which is a necessary condition for successful policies to arise.
For the academic sphere, the different perspective of nature in social and natural science will be considered from its historical divergence since the beginning of the 20th century (Grundmann 1997). The concept of climate change is approached differently from the two sciences in terms of concepts, theories and methods. Such dichotomy is well reflected in this project by the collaboration with the expert in macroecology from McGill University in Montréal. CC-VISAGES will take advantage of this diversity by creating a research design in which different perspectives will inform both the methods (GIS and field research) and the spatial scale of the research (global and local). This collaboration is expected to be highly synergistic and that it will provide an opportunity to address the research question in a way that would not be possible for either field of expertise alone.
As for the importance of the non-institutional community level, we consider the concept of environmental justice (cf. Gosine/Teelucksingh 2008, Kloepfer 2006, Elvers 2007) with its focus on community identities as legal entities (Kameri-Mbote et al 1996) with idiosyncratic “perceived justice” (Maguire et al 2003) as undeniable factors for successful policy-making. Environmental justice incorporates the ethical and social question from a behavioural viewpoint and provides an important alternative perspective to climate change, which is too often defined by simplistic abstract broad measurements and forecasts. Communities have different climate change adaptation capacities arising from different customs and, more importantly, different interests. The lack of appreciation of these community perceptions and interests can only increase the resistance against new policies, however well-intended they might be. The reason for listening to those that are not institutionalized, without an organized lobby and/or without (educational, economic, political) capacity to raise their voice is less ethically driven than one may think. While the reasons for acting against climate change are numerous, and often deceiving as when the industry wants to sell new green technologies, or politicians want to be labelled (and re-elected) for foreseeing future challenges, or when environmental groups and NGOs want popular interests for financial support, the environmental justice concept suggests that marginalized people’s reactions may be counter-intuitive. The “weapons of the weak” (Sharman 2003) are in fact their reluctance to environmental regulations if they cannot see their share in it. No policy can be successfully established against the will of the people
In order to develop successful policies, particularly in times shortening time and space dimensions of globalisation (Sachs 2002), it is imperative to recognize that climate change impacts must be perceived both from the macro level and from local insights and that a failure to consider one side of the issue is a failure to recognize the whole issue. In consequence, the project will seek the global environmental justice pattern of climate change impacts. Under such framework, CC-VISAGES assumes a distribution of environmental goods and bads in each of the countries. The revealed congruence and difference in the structure of that distribution should help understanding the similar challenges German, Brazilian and Canadian societies are facing. We expect to find that the same patterns will influence the likeliness of certain people to be affected by climate change burdens.
Here is proposed to create a geographical representation of climate change vulnerabilities for Brazil, Canada and Germany based on ecological, social, and climate variables through a geographical information system (GIS) in cooperation with the experts from McGill University. Areas in each country with high climate change vulnerabilities will be selected in order to perform comparative field research to understand the local perspectives on climate change impacts. From these field analyses of stakeholders’ perceptions, the global environmental justice pattern of climate change will be developed. The project is assumed to be highly relevant for the German, Brazilian and Canadian societies (civil society, politics, economy, etc.) as well as for the many disciplines in both natural and social sciences interested in climate change. Furthermore, CC-VISAGES is planned to deepen the European-North-South American scientific cooperation by developing further projects in the context of CC-VISAGES.
An overview of the project’s planned structure can be found here.
Global Environmental Justice Pattern of Climate Change Impact
Using fuzzy set Qualitative Comparative Analysis (QCAfs), available variables of the social impacts of climate will be listed in possible truth tables (cf. Schneider et al 2007). The existing truth tables will then be tested by the following techniques at the macro and at the local level. “During this process the researcher regularly refers back to the cases with all their richness and specificity. This back-and-forth ‘dialogue with the cases’ (…) is unquestionably a virtue of QCA techniques.” (Rihoux et al 2008: 14).
Climate change vulnerability
The project will create an index of climate change vulnerability (CCVI) based on a suite of variables. Even though many indicators have been suggested (USGCRP, 2011; EEA, 2012), the lack of a unified framework that can be applied internationally requires the selection of specific indicators. The indicators will be chosen based on the availability of data in each selected country. CC-VISAGES seeks to gather all the available indicators and perform a principal component analysis to obtain composite metrics of CCV that represent both the most important and least correlated aspects of climate change. This step is essential because it is impossible to evaluate the relative importance of each indicator in terms of relevance to social vulnerability to climate change. The results will be transferred to a geographic information system (GIS) where the identification of regions with high and similar CCVI in the countries will then be possible.
In order to locate the areas to perform our field research, a suite of social variables will be assembled that represents different facets of social marginalization (e.g. income, ethnicity, public services, etc.). These variables will be spatially projected and linked to the CCVI through GIS. For each country, one region of high CCVI and similar social marginalization will be chosen to perform field research.
The survey data will be obtained at the community level in the regions previously targeted and will be similarly analysed with qualitative-quantitative methods. In a nutshell, we will gather qualitative data and analyze them by factor analysis in order to reveal ideal type discourses. We avail ourselves of Q Methodology (Barry/Proops 1999, Previte et al 2008) for the first step. Then we use the analyzed discourses to conduct a Delphi technique expert evaluation. In appliance of the Delphi (Linstone 1975) and considering the critiques on the method (Sackman 1975) we will reveal the possibility or impossibility of a consensus in each community. Furthermore, we will be able to show, how consensus could most likely be achieved, or why not. The joint method is called Q Oracle (Kaufmann 2012).
Following the results of the macro and micro (community) analyses, falsified truth tables (QCA) will be removed from the list. If more than one truth table can still be possibly true, Baysian inference of perceived climate change impacts derived from media analysis will be used to select the truth table that best describes the pattern.
Although this project is still in its infancy, we have collated relevant data in order to provide an example of our methodology to select field research sites. Here we show an example using a univariate analysis for simplicity even though the final product will be based on multivariate analysis (see methodology section). We have used the climate demographic vulnerability index of Samson et al. (2011) to evaluate regions with high climate change impacts. In Canada, there are a few regions with high climate change impacts, such as Toronto, Vancouver and Calgary while similar regions in Germany are located near Munich and Berlin (Fig. 1, panel A and B). Given that this example is purely to visually describe the methods, we then show the analysis for Canada because of space constraints of the proposal. The final analysis will be of course performed at the three selected sites and with many impact and marginalization variables. The marginalization variable used in this example is the average income within census districts (Fig. 1, panel C). By searching for lowest income within the regions of high climate change impacts, we were able to locate two suitable areas to conduct field research, namely Toronto (Fig. 1, panel D).
Figure 1. Example of the procedure for selecting field research sites. Regions of high climate change impacts are shown for Canada (A) and Germany (B). Average income for Canada (C) is used as the marginalization variable. Selected regions for field research are based on high climate change impacts and high marginalization factors at the local level (panel D). In this example, Toronto is the chosen cities for the field research in Canada. The final analysis will be of course performed at the two selected sites and with many impact and marginalization variables.
First finding is assumed to be the global environmental justice pattern of climate change impacts from the field analyses of stakeholders’ perceptions. The issue of climate change cannot be understood under a single perspective given its relevance to environmental health, social security, ecosystem services, social equality, economic prosperity, etc. It is thus necessary to develop a multi-disciplinary framework that speaks to the underlying complexity of climate change impacts and adaptation. If the impacts of climate change are strongly dependent on the sociological reality of the communities, then our results will provide a robust foundation for stakeholders of all kind (political and economic decision-makers, NGOs, civil society) to make appropriate climate change adaptation plans informed by both ecological and social realities. The expected results of the CC-VISAGES project should provide such multi-disciplinary perspective to European nations and nations of the Americas, which can further the collaboration and synergy between national strategies regarding climate change.
Actual state of affairs (10/2014)
The suite of seven (7) social variables that represents different facets of social marginalization (e. g. income, population, ethnicity, etc.) and three (3) climate conditions (temperature, vapor pressure, etc.) have been assembled and processed for the countries Canada and Germany. The indicators are chosen based on the availability of data for all three countries (thus including Brazil) and with reference to their importance within the climate change debate in both social and nature sciences.
Based on the variables, a climate stress index (CSI) and a human stress index (HIS) were created, which resulted in a final climatological environmental (in)justice index (CEJI) that then was calculated by combining these.
Until now, comparable data sets for the three countries have been selected and bundled as representation of the Climatological Environmental Justice Index (CEJI). Here, Johanna Seidel was responsible for the data collection in Germany and Canada and also processed the development of the CSI, HIS, and CEJI. Dr. Jason Samson on the other side analyzed the Brazilian data set and provided these for including them into the final analysis.
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