Lessons learnt from a participatory integrated assessment of greenhouse gas emission reduction options in firms

This article discusses an approach for identification and evaluation of short-term greenhouse gas (GHG) emission reduction options in firms. The approach is based on lessons learnt from a project using Participatory Integrated Assessment (PIA) and builds on the idea that effective public climate policy for firms requires options that have support from stakeholders and are practically feasible. Scenarios are used to provide a link with short-term policy developments and a model assists to communicate quantitative effects of options to participating stakeholders. Our approach can be seen as a first step towards a framework that meets the need for more systematic approaches to PIAs identifying effective public policies for short-term GHG emission reduction options in firms. In order to identify effective options for non-carbon dioxide GHG emission reductions, our approach has been applied to Dutch dairy farms, after which it has been refined. The case study suggests that our approach can provide balance between practical, context specific issues and scientific-theoretical aspects, thereby avoiding common pitfalls of participatory research projects to focus too much on either theory or practical issues.

Uncertainty analysis in integrated assessment: the users’ perspective

Integrated Assessment (IA) models aim at providing information- and decision-support to complex problems. This paper argues that uncertainty analysis in IA models should be user-driven in order to strengthen science–policy interaction. We suggest an approach to uncertainty analysis that starts with investigating model users’ demands for uncertainty information. These demands are called “uncertainty information needs”. Identifying model users’ uncertainty information needs allows focusing the analysis on those uncertainties which users consider relevant and meaningful. As an illustrative example, we discuss the case of examining users’ uncertainty information needs in the SEAMLESS Integrated Framework (SEAMLESS-IF), an IA model chain for assessing and comparing alternative agricultural and environmental policy options. The most important user group of SEAMLESS-IF are policy experts at the European and national level. Uncertainty information needs of this user group were examined in an interactive process during the development of SEAMLESS-IF and by using a questionnaire. Results indicate that users’ information requirements differed from the uncertainty categories considered most relevant by model developers. In particular, policy experts called for addressing a broader set of uncertainty sources (e.g. model structure and technical model setup). The findings highlight that investigating users’ uncertainty information needs is an essential step towards creating confidence in an IA model and its outcomes. This alone, however, may not be sufficient for effectively implementing a user-oriented uncertainty analysis in such models. As the case study illustrates, it requires to include uncertainty analysis into user participation from the outset of the IA modelling process.     

Reconciling model results with user needs to improve climate policy

The aim of this study is to understand the ‘reconciliation process’ between model results and user needs in Participatory Integrated Assessments. This process is analyzed for the Delft Dialogue, a project in which scientists and UNFCCC delegates used the IMAGE model to assess the consequences of different climate policy proposals. The Delft Dialogue consisted of five iterations in which model results were presented and requests for new analyses were prioritized. These requests were diverse, changed over time and linked long-term model projections with short-term policy targets. We conclude that two factors played an essential role in the reconciliation process in the Delft Dialogue: (1) user requests were identified in iterative interactions and guided the selection of model analyses and (2) model analyses were co-produced by participants and modelers. This Dialogue shows that reconciliation processes can be facilitated through a transparent, interactive and iterative process where user needs are elicited to guide the choice for model analyses.