PDE - Population, Development and Environment

Introduction

During the late 1980s in-house discussions between various IIASA projects, as well as extensive discussions with external experts, resulted in the conclusion that the highly complex issues of long-term population-environment interactions can be meaningfully analyzed through a series of case studies. An important prerequisite for the success of this approach is, however, that the studies follow a common approach including a certain minimum set of parameters so they can form a basis for generalizations. So far studies on Mauritius, Cape Verde and the Yucatan peninsula (Mexico) have been conducted (Other Case Studies).

During 1997-2000 population-development-environment (PDE) case studies were conducted for Botswana, Namibia and Mozambique with funding from the European Commission, Directorate General for Development. Preliminary working versions of the models were used in Namibia, Botswana and Mozambique in several informal meetings in the year 1999. These draft versions were also the basis of the southern Africa training workshop held at the University of Pretoria in January 1999 and of the two-week intensive training workshop at Chulalongkorn University in Bangkok in November 2000.

The PDE models are disseminated and implemented at institutions in Namibia, Botswana and Mozambique. The PDE models are designed to help policy makers, stakeholders, NGOs, researchers, and others to look at possible future development paths which are based on alternative and various development assumptions. Potential users with scientific and political backgrounds were taught to use the PDE country models and to run alternative scenarios in small workshops guided by the IIASA research team. These workshops took place after the International Science-Policy Dialogue Meeting on Sustainable Development Challenges in the Context of the HIV/AIDS Pandemic. Results of an EU-sponsored Research Project on Namibia, Botswana and Mozambique, held in Windhoek, Namibia, March 14, 2001.

The PDE Approach

The IIASA Population Project's general PDE approach, which was developed by demographers and shown in the figure below, is organized in three concentric circles, with population and development embedded in the environment.

(Click on the picture to obtain a bigger size)

This concept differs from many other conceptualizations where the three factors population, development and environment are shown as boxes and are interconnected with causal arrows. But since, for instance, the human population is not independent of, but part of the biosphere, the concentric circles are considered a more adequate representation. The fundamental understanding is that population and environment are not separate entities that can be seen independently or even in opposition to each other. The population is seen as an integral part of nature.

Human population and its quality of life, our main point of interest, is in the center (P). Emanating from and driven by population, through their activities, we can interpret and analyze the economic developments and environmental changes. The population is therefore surrounded in the Figure by the man-made environment; we call it development (D). As explained above, all human life is embedded in the environment at every point, and is affected by environmental changes (E). The environment is grouped into four categories (four elements): air, water, land (earth), and energy (fire).

The Aim of the PDE Approach

The approach aims to develop easy-to-use tools for decision makers, stakeholders and scientists. These tools, integrative dynamic systems models, offer the opportunity to involve the major stockholders in the discussion platform.

Based on these tools, the PDE approach explores a set of alternative sustainable development paths based on different assumptions (scenarios) up to 2020 or 2050. The results can be utilized for the choice among competing policy options. Further such simulation models, as past experiences show, can be used for awareness creation. These models can also be used as an "effective translation tool" to close the gap between scientific and political language.

Moreover, local environmental and socioeconomic conditions, and local priorities and interests of local researchers and decision makers are taken seriously into account. Notwithstanding, the approach aims to compromise between comparable research designs and the adoption of research based on local conditions. The comparable part of the comprehensive studies is the population model (or model module). The population part considers the population by age groups, sex, and educational status, and uses multi-state cohort component models to project the population. The environmental models will only be comparable within groups (for instance for Botswana, Namibia and Mozambique); the economic models can be classified, in terms of comparability, as in between.

For instance, basic PDE questions are: How may human activities change the environment and vice versa, and to what degree? What policies can improve certain aspects of population-development-environment interactions?

Methodology

The PDE approach can be compared to the integrated assessment approach. It has the aim not only to analyze the pieces in an isolated way, but to investigate how the various pieces together reveal a picture. The PDE approach shows clearly the strong interrelations and feedback flows between all the sectors.

"Integrated" means that the information is assembled from a set of various disciplines, and not only from a single discipline. "Assessment" in this context means making knowledge on complex issues relevant and helpful to decision makers. The policy dimension of the PDE research, i.e., science-policy dialogues and management of knowledge, is a main part of the PDE approach and has a long tradition at IIASA.

The analytical method (system dynamics modeling) provides a basis for multidisciplinary and interdisciplinary work and discussion platforms. System dynamics models try to quantitatively describe the cause-effect relationships of a specific issue, and the inter-linkages and interactions among different issues. To represent complex systems the model must be reduced and simplified to key variables. The "simplification" is not only necessary because of the dependency and availability of data, but also to make the model per se more understandable and comprehensible.

In addition, the model has to be designed in a way that is transparent and user friendly, so that users will be able to run different scenarios without long-term software training. Scenarios are descriptions of alternative images of the future, created from mental models that reflect different perspectives on future developments. Integrative scenarios do not predict the future; they design different pictures of possible futures and explore the different outcomes. It is between a narrative story and a quantitative description. It tries to keep the balance. This is of importance for testing several assumptions by politicians, decision makers, other scientists and stakeholders. Because of a complex model design, the change of one single parameter could change the results in a stronger way than assumed and/or expected. The incorporation of qualitative information is also possible with certain limitations, for instance, when collected with participatory methods and traditional interviews.

Click here to receive more information on system dynamics.

Strengths and Weaknesses

In summary, even a simplified but integrated model can provide a helpful guide to complex issues and complement highly detailed models that cover only some parts of complex phenomena. The major strengths of integrated models are, for instance, in exploring interactions and feedback; helping to identify uncertainties and lacking knowledge; and being a helpful tool for the communication of complex issues. On the other hand, the model has obvious weaknesses. For instance, the accumulation of uncertainties and the high aggregation of data make some practical applications difficult. Also, the models depend on many preconceptions, since the modelers determine which variables are reported and how, and which objectives are optimized and how. The computer dynamic simulation model used as a communication tool can be a helpful "translator" for closing the gap between the scientific and political language. IIASA PDE projects have always had this policy component. Experience shows that for many policy-related questions, the short-term effects are of much more importance than the long-term ones. Hence, especially user-friendly computer models can be a helpful tool for assessing different assumptions and alternative policies by incorporating easily actualized data, running different scenarios, and changing time horizons (limited, the behavior of the model may change). Hereby, possible short-term effects can be shown without losing the long-term horizon. As a chart or as line diagrams, the output is easy to understand and interpret. As explained earlier, one of the weaknesses of computer models is the limitation in using qualitative data in a quantitative systems approach. Besides other helpful tools (for instance, participatory methods, such as focus groups and gaming, and visibility analysis were not used in the PDE projects), contributions written by local scholars, by NGO staff and by policy makers are important. Experience from earlier studies shows that the addition of a descriptive part complements and improves the model in a qualitative way. It has also become apparent that bringing together scientists, politicians and stakeholders with different backgrounds and working experiences may lead to the establishment of interdisciplinary discussion platforms which probably might not have happened otherwise and is useful beyond the scope of this modeling exercise.