Those making environmental decisions must not only characterize the present, they must also forecast the future. They must do so for at least two reasons. First, if a no-action alternative is pursued, they must consider whether current trends will be favorable or unfavorable in the future. Second, if an intervention is pursued instead,they must evaluate both its probable success given future trends and its impacts on the human and natural environment. Forecasting, by which I mean explicit processes for determining what is likely to happen in the future, can help address each of these areas. Certain characteristics affect the selection and use of forecasting methods. First, the concerns of environmental forecasting are often long term, which means that large changes are likely. Second, environmental trends sometimes interact with one another and lead to new concerns. And third. interventions can also lead to unintended changes. This chapter discusses forecasting methods that are relevant to environmental decision making, suggests when they are useful, describes evidence on the efficacy of each method, and provides references so readers can get details about the methods. A key consideration is whether or not the forecasting methods are designed to assess the outcomes of interventions. The chapter then examines issues related to presenting forecasts effectively. Finally, it describes an audit procedure for determining whether the most appropriate forecasting tools are being used.
Problem How to help practitioners, academics, and decision makers use experimental research findings to substantially reduce forecast errors for all types of forecasting problems. Methods Findings from our review of forecasting experiments were used to identify methods and principles that lead to accurate forecasts. Cited authors were contacted to verify that summaries of their research were correct. Checklists to help forecasters and their clients undertake and commission studies that adhere to principles and use valid methods were developed. Leading researchers were asked to identify errors of omission or commission in the analyses and summaries of research findings. Findings Forecast accuracy can be improved by using one of 15 relatively simple evidence-based forecasting methods. One of those methods, knowledge models, provides substantial improvements in accuracy when causal knowledge is good. On the other hand, data models – developed using multiple regression, data mining, neural nets, and “big data analytics” – are unsuited for forecasting. Originality Three new checklists for choosing validated methods, developing knowledge models, and assessing uncertainty are presented. A fourth checklist, based on the Golden Rule of Forecasting, was improved. Usefulness Combining forecasts within individual methods and across different methods can reduce forecast errors by as much as 50%. Forecasts errors from currently used methods can be reduced by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity (Occam’s Razor). Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions. Scientists can use the checklists to devise tests of the predictive validity of their findings.