Interrelationships between economic policy and agri-environmental indicators: an investigative framework with examples from South Africa

A number of methodological approaches to understanding and quantifying the potential impacts of changes in macroeconomic and sectoral policies on the natural resource environment have been developed in recent years. However each has its limitations, resulting in policy change still being implemented without due attention to environmental impacts. Two key drawbacks of those methodologies that do attempt to model these impacts are that they are generally static in their approach, thus may not alert the decision maker to the often quite different long-term implications, and that they attempt to generate rather specific sets of indicators, making them difficult to use and/or interpret outside case study applications. In this paper we expound a framework for addressing these limitations in the context of the agriculture sector. In developing countries in particular the dynamic dimension is critical given the twin pressures of population growth and rising incomes associated with economic growth. In light of the second drawback, it is the propensity of policy to impact upon the natural resource environment via its effect on the type of farming practice adopted that forms the focus of the paper. A methodology is first developed to facilitate the tracing of likely impacts of both price and non-price reforms, via both the incentives and constraints to increased food production. By separating out the impacts on environmental indicators associated with extensification and intensification of agriculture, it is possible to determine which of these indicators are most likely to be affected by policy changes, and to what degree in both the short and longer term. The framework is then applied to case study data from the South African agriculture sector to demonstrate how consideration of the risk of natural resource degradation earlier in the policy dialogue process could result in the implementation of more effective complementary measures. © 2000 Elsevier Science B.V. All rights reserved.