A myriad of methods and approaches exist to support the policy design, implementation and review phases of the policy cycle. Here, they are put into three groups:
Below is an example of the use of scenarios and models for policy design and implementation:
Figure SPM4: This figure shows an example of the use of scenarios and models in support of policy design and implementation. This case is in the Thadee watershed in southern Thailand, where the water supply for farmers and household consumption has been degraded by the conversion of natural forests to rubber plantations. Policy-screening scenarios (step 1) based on local datasets and knowledge were developed by stakeholders and scientists to explore plausible future land uses (step 2). Models were then used to evaluate the effects of three plausible rainfall levels on sediment load in rivers as a result of soil erosion and on other ecosystem services (step 3). The conservation scenario was foreseen to produce substantially less sedimentation than the development scenario with rapid expansion of rubber plantations and crops. The economics component of the Resource Investment Optimization System (RIOS) tool was then used to translate these effects into economic costs and benefits (step 4). A decision-support component of the RIOS tool was used by scientists and local decision makers to identify areas where forest protection, reforestation or mixed cropping could best be implemented. The municipality has agreed to find means of collecting a conservation fee based on payments for watershed services to fund these activities (step 5).
Decision-making to tackle real world challenges is a highly complex process. Consequences are seldom restricted to impacts that can naturally or readily be described by a single criterion (e.g. monetary). Multiple values imply multiple objectives each requiring estimates of consequence.
Most decisions involve alternatives and cause-and-effect predictions of expected consequence, providing a natural role for scenarios (to characterise alternatives) and models (to predict consequences). When predictions are made over multiple objectives, an additional element is required to resolve the decision problem: the articulation of preferences or trade-offs reflecting the relative importance of the different objectives. Most environmental policy, planning and management decisions involve trade-offs.
Examples of tools:
- Consequence tables
- Multi-Criteria Decision Analysis
- Multi-Attribute Value Theory
- Analytic Hierarchy Process
- Outranking techniques
- Multi-Attribute Utility Theory
There are potentially thousands of alternative options in most real-world planning and management decision problems. Various mathematical programming techniques from the field of operations research are available to help identify better (or best) candidates from a large set. Optimisation approaches can be viewed as providing the analytical machinery to assist in the generation and analysis of ‘target-seeking’ or ‘backcasting’ scenarios.
Examples of tools:
This last family of decision support tools includes a large number of frameworks, approaches and methods. Multiple variants exist for every approach described, often with similar structures and underpinnings but with different names arising from their application in different sectors (e.g. forestry, fisheries, transport) or regions.
Examples of tools and case studies:
- Structured decision making
- Management strategy evaluation
- Integrated territorial planning
- The Delphi technique
- Strategic Environmental Assessment
- Environmental Impact Assessment
- The Open Standards for the Practice of Conservation