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System Analysis for Environmental Policy

System Analysis for Environmental Policy

System thinking through system dynamic modelling and policy mixing as used in the SimRess project

Systems analysis could be an essential approach to shape resource efficiency policy in a sustainable long term perspective. In the SimRess project, we tested systems thinking to develop a system dynamic resource use simulation model and ii) to investigate policy mixes for resource conservation. The report, which is available for download, documents and summarizes the various results of the workshops and the systems analysis. The study was carried out by the SimRess project partners, with Martin Hirschnitz-Garbers from Ecologic Institute as lead-author.

Diverse and complex interactions as well as multi-actor systems characterise resource use and resource policy. This makes system analysis a relevant tool to orient resource policy towards the long term. Analysing such complex systems requires systemic thinking, consideration of causal loops as well as time-lags and delays in system responses.

In the SimRess project, system analysis encompassed participatory conceptual system modelling via involving external stakholders into identifying system boundaries and elements via causal loop diagrams (CLDs). The CLDs were than reflected in the parametrisation of simulation models and the development of policy mixes.

Only a limited number of stakeholders participated in two of the five workshops needed for a fully-fledged group modelling process. Therefore, the project team finalised internally the conceptual system model. Although this reduced ownership and transparency of the system model, the two workshops provided relevant system knowledge for further modelling work and policy mix development.

During policy mix development in SimRess, we needed to deviate from the theoretical concept of policy mixing based on available project capacities and stakeholder decisions. On the one hand, understanding and assessing cumulative effects of policy mixes challenged conceptual policy mix development and simulation capacities. On the other hand, stakeholder decisions impacted on the depth at which system analysis via simulation models could be undertaken.


Citation

Hirschnitz-Garbers, Martin et. al. 2018: System analysis for environmental policy – System thinking through system dynamic modelling and policy mixing as used in the SimRess project. Models, potential and long-term scenarios for resource efficiency (SimRess) – Report 1. UBA Texte 49/2018. Umweltbundesamt: Dessau-Roßlau.

Language
English
Author(s)
Deniz Koca (Lund University)
Harald Sverdrup (Iceland University)
Mark Meyer (Gesellschaft für wirtschaftliche Strukturforschung (GWS))
Martin Distelkamp (Gesellschaft für wirtschaftliche Strukturforschung (GWS))
Publisher
Year
2018
Published In
TEXTE 49/2018
ISSN
1862- 4804
Dimension
65 pp.
Project ID
2518
Table of Contents

List of Abbreviations
Zusammenfassung
Summary
1 Systems thinking approach used in the SimRess modelling work
1.1 Conceptual modelling and systems analysis
1.1.1 Causal loop diagrams and group modelling process in SimRess project
1.1.2 Potentials and challenges of CLDs
1.2 System dynamics modelling and integrated scenario analysis
1.3 Dynamic modelling of the structures of complex system interdependencies – annotations from an applied econometrician’s perspective
1.4 How do two modelling approaches complement each other in terms of system analysis?
2 Policy mixing as a concept for systemic resource
2.1 The need for more systemic responses in resource policy
2.2 The concept of policy mixing for resource policy
2.3 Promises and challenges of policy mixing
2.4 Policy mixing for systemic resource policy in the SimRess project – approach, challenges and lessons learnt
2.4.1 A systemic resource policy mix approach tackling key drivers and trends
2.4.1.1 Setting objectives and targets
2.4.1.2 Underlying conceptual causal system model
2.4.1.3 Selecting promising policy instruments
2.4.1.4 Undertaking ex-ante assessments
2.4.2 A resource policy mix approach based on selected ProgRess II policy instruments
2.4.2.1 Setting objectives and targets
2.4.2.2 Underlying conceptual causal system model
2.4.2.3 Selecting promising instruments
2.4.2.4 Undertaking ex-ante assessments
2.4.3 A systemic resource policy mix approach aimed at contributing to more ambitious, longer-term resource policy targets
2.4.3.1 Setting objectives and targets
2.4.3.2 Underlying conceptual causal system model
2.4.3.3 Selecting promising instruments
2.4.3.4 Undertaking ex-ante assessments
2.5 Lessons learnt on policy mixing for systemic resource policy
2.5.1 Conceptual development of the policy mix approaches
2.5.2 Scientific assessment of the policy mix approaches
3 Main conclusions
4 References used
5 Appendix
List of figures and tables
Figure 1: Integrative systems science
Figure 2: A sample Causal Loop Diagram (CLD)
Figure 3: Two phases and six steps of the group modelling process
Figure 4: Causal Loop Diagram with the theme of private household consumption
Figure 5: With mining industry, various metal ores are provided to different metal industries to be processed into basic metals. Different fabricated metal industries then turn these basic metals into fabricated metals.
Figure 6: Flow chart showing main services/industries using metal ore and basic metals
Figure 7: Causal loop diagram showing cause effect relations, feedbacks and time delays in the metal sector
Figure 8: Causal loop diagram showing the demand for cars and the production, and the causal linkages between these factors
Figure 9: Heuristic concept for policy mix development
Figure 10: CLD for the consumption area of food (Koca and Sverdrup 2014a, 24)
Figure 11: Screenshot of the SimRess consistency matrix in EIDOS
Table 1: Stakeholder categorisation
Table 2: List of selected policy approaches from ProgRess strategic approaches and action areas
Table 3: Snapshot of the option space created for the systemic resource policy mix tackling key drivers and trends (cf. section 2.4.1)

Keywords
environmental policy, system thinking, policy mixing, resource efficiency, , causal loop diagrams, development models, system analysis, system dynamic modelling