A Portfolio Approach to Analyzing Complex Human-Environment Interactions: Institutions and Land Change

• Main Authors: Oran R. Young, Eric F. Lambin, Frank Alcock, Helmut Haberl, Sylvia I. Karlsson, William J. McConnell, Tun Myint, Claudia Pahl-Wostl, Colin Polsky, P. S. Ramakrishnan, Heike Schroeder, Marie Scouvart, Peter H. Verburg
• Format: Article
• Language: English
• Published: Resilience Alliance 2006-12-01
• Series: Ecology and Society
• Subjects: land change; institutions; methodology; analysis; socio-ecological systems; statistical techniques
• Online Access: http://www.ecologyandsociety.org/vol11/iss2/art31/

The challenge confronting those seeking to understand the institutional dimensions of global environmental change and patterns of land-use and land-cover change is to find effective methods for analyzing the dynamics of socio-ecological systems. Such systems exhibit a number of characteristics that pose problems for the most commonly used statistical techniques and may require additional and innovative analytic tools. This article explores options available to researchers working in this field and recommends a strategy for achieving scientific progress. Statistical procedures developed in other fields of study are often helpful in addressing challenges arising in research into global change. Accordingly, we start with an assessment of some of the enhanced statistical techniques that are available for the study of socio-ecological systems. By themselves, however, even the most advanced statistical models cannot solve all the problems that arise in efforts to explain institutional effectiveness and patterns of land-use and land-cover change. We therefore proceed to an exploration of additional analytic techniques, including configurational comparisons and meta-analyses; case studies, counterfactuals, and narratives; and systems analysis and simulations. Our goal is to create a portfolio of complementary methods or, in other words, a tool kit for understanding complex human-environment interactions. When the results obtained through the use of two or more techniques converge, confidence in the robustness of key findings rises. Contradictory results, on the other hand, signal a need for additional analysis.