The FORESCENE Scenario Modelling Workshop

Transcription

1 The FORESCENE Scenario Modelling Workshop Brussels, 8 Sept Stefan Bringezu, Mathieu Saurat Roy Haines-Young, Alison Rollett Mats Svensson

2 Contents The FORESCENE project Short introduction to Bayesian networks Structure of the FORESCENE Meta Model Scenario assumptions First results Unfolding of the workshop

3 The FORESCENE project

4 Initial points Need of a framework for creating sustainability scenarios integrating different environmental topics (water, soil, resource use etc.) Need for access to scenarios that can be used for strategic policy preparation in the context of the Sustainable Development Strategy. Need to understand the key driving forces and their cross-cutting linkages, which lead to increased pressure on the environment.

5 Objectives FORESCENE is developping an analytical framework for consistent environmental sustainability scenario building (forecasting, backcasting, simulation) in areas such as water, soil, biodiversity, waste and natural resources. There is a focus on backcasting, to identify different scenarios leading to the achievement of future targets.

6 Overview of project tasks describe the chosen environmental problems, review policy objectives and indicators, and determine the cross-cutting driving forces; develop core elements of integrated sustainability scenarios (goal definition); determine cross-sectoral measures and processes to be considered for change (pre-backcasting); address quantitative and qualitative parameters for measurement (parametrization); develop a Business-As-Usual (BAU) scenario framework and example projections (forecasting); develop alternative scenarios (incl. backcasting); check the options for modelling, and work out conclusions.

7 Workpackages in a nutshell Participatory processes WP1 Cross-cutting drivers WP2 Sustainability goals Sustainability strategies Assembling the «puzzle pieces» WP 3 Preliminary narratives Checking possibilities for modelling Modelling framework WP4 Baseline scenario WP5 Alternative scenarios Forecasting Backcasting Conclusions WP6 Policy recommendations

8 Participatory input FORESCENE has organized and conducted a series of workshops in order to involve DGs and stakeholders, to integrate knowledge on cross-cutting drivers of various environmental problems and priority policy fields where these drivers should be controlled, and to define essentials for integrated sustainability scenarios in terms of goals and cross-cutting policy measures.

9 Basis of WP1 and WP2 Socio-industrial metabolism and DPSIR framework

10 Results of WP1 Scoring card for crosscutting drivers

11 Results of WP2 Sustainability goal references Sustainability targets Resource use Water Biodiversity, soils and landscape Reduce TMR by 80% Ratio (foreign TMR) / (domestic TMR) should not increase Net import of land should not increase Net agricultural land use per capita in Europe should not increase world average land availability Water supply and water abstraction should be balanced Overall biodiversity status: favourable Terrestrial biodiversity status: favourable Aquatic biodiversity status: favourable Soil carbon: high Soil erosion: low Soil quality: high

12 Results of WP2 Cross-sectoral, multi-beneficial sustainability strategies

13 Results of WP3 Preliminary narratives Increased resource productivity Changed consumption pattern towards service economy Changed diet Climate change mitigation: increased use of biofuels Liberalisation of commercial agriculture Options for modelling The review of relevant scenario studies and simulation models indicated the need for a meta-model.

14 Short introduction to Bayesian networks

15 Short description Used in Artificial Intelligence, medical and forensic research More recently applied to natural resource management Bayesian networks are a graphical tool for building decision support systems to make decisions under uncertain conditions Represent systems in terms of a set of relationships that have probabilities associated with them

16 Short description Used in Artificial Intelligence, medical and forensic research More recently applied to natural resource management Bayesian networks are a graphical tool for building decision support systems to make decisions under uncertain conditions Represent systems in terms of a set of relationships that have probabilities associated with them Directed acyclic graph made up of nodes that interact with each other Nodes are parent, child or, most of the time, both Interactions expressed as links (edges or arcs) Links expressed as probabilistic dependencies quantified in a conditional probability table (CPT)

17 Probabilistic relationships example using discrete variables Decision support tool: Given the season and the weather observed in the morning, is it worth taking an umbrella when leaving home?

18 Conditional Probability Table (CPT) can be filled in by using: Statistical analysis of historical data Elicitation of expert judgement

19 Conditional Probability Table (CPT) can be filled in by using: Statistical analysis of historical data Elicitation of expert judgement

20 Probabilistic relationships example using discrete variables A cloudy morning in winter Question for the decision-maker using the BN: Is it worth taking an umbrella when leaving home?

21 Probabilistic relationships example using discrete variables A rainy morning in summer Question for the decision-maker using the BN: Is it worth taking an umbrella when leaving home?

22 Deterministic relationships example using continuous variables Simulation based modelling - Samples drawn by Netica (Monte Carlo like) - Plot of resulting probability distributions Driver Economic situation Mean 90% CI GDP growth rate Recession -0.5% / year -1% 0% Low growth +0.5% / year 0% +1% High growth +2% / year +1% +3% GDP in year Y+1 GDP(Y+1) = GDP(Y) * (1+Growth rate)

23 Structure of the FORESCENE Meta Model

24 Disclaimer Important: The Bayesian network approach is not seen in FORESCENE as a replacement of other models in current use but rather as a means of integrating different forms of knowledge, whether from existing models, reported data or expert judgement. In other words, the Bayesian network approach is used to construct a meta-model.

25 General model structure Inputs (drivers, strategies) are considered at EU level Outputs (environmental impacts associated with EU) are considered at world level (except for water)

26 Detailed model structure Different modules model target indicators of environmental impact under the influence of drivers and control parameters whose values can be modified to test the efficiency of sustainability strategies.

27 Detailed model structure Different modules model target indicators of environmental impact under the influence of drivers and control parameters whose values can be modified to test the efficiency of sustainability strategies.

28 Detailed model structure Different modules model target indicators of environmental impact under the influence of drivers and control parameters whose values can be modified to test the efficiency of sustainability strategies.

29 Detailed model structure Different modules model target indicators of environmental impact under the influence of drivers and control parameters whose values can be modified to test the efficiency of sustainability strategies.

30 Model structure: main modules The modules are separately developed Bayesian networks. Modules can evolve independently, due to different levels of knowledge.

31 Model structure: main modules Mineral materials module Economy module Fossil energy module Biofuel module Water module Biomass and agri land use module GHG module Biodiversity and soils module The modules are separately developed Bayesian networks. Modules can evolve independently, due to different levels of knowledge.

32 Time dimension Bayesian networks do not allow for intrinsic modelling of time dynamics in a satisfactory way.

33 Scenario assumptions

34 Baseline scenario Initial conditions - Initial year is 2000 (or 2005 in when data available) - Initial values are directly taken or estimated from reported or modelled data (e.g. Eurostat), or derived from expert judgement Assumptions for forecasting: - Time frame: Assumptions for growth rates are directly taken or estimated from existing studies and models (e.g. EEA environment outlook) Uncertainties - They are implemented as normal distributions - For example: when two different sources give the values x and y for a given variable A, the representation of A in the BN can be a normal distribution with mean = (x+y)/2 and 90% CI = x-y /2

35 Alternative scenarios Method for backcasting: - Consider a preliminary narrative (from WP3): - Identify the input nodes (drivers) and target nodes (goals) involved in the meta-model - Quantify the sustainability goals for milestone years in the future - Starting today, determine the combinations of driver values (the value change when it is assumed that a strategy is applied) that allow reaching the milestone targets up to the furthest goal - The backcasting results are the pathways of adequate driver values that lead to the sustainability goals Uncertainties - Modelling results are obtained as full probability distributions: it allows the decision maker to choose the degree of confidence within which the sustainability goal is expected to be reached.

36 First results

37 Mineral materials module Overview of the structure

38 Mineral materials module Overview of the structure TMRminerals

39 Mineral materials module Baseline scenario

40 Mineral materials module Baseline scenario

41 Mineral materials module Baseline scenario

42 Mineral materials module Backcasting: sustainability target and pathways Target: reduction of TMR by 80% in 2050 compared to 2000

43 Mineral materials module Backcasting: following a pathway At each time step: look for a driver combination that could fit the pathway

44 Mineral materials module Backcasting: possible alternative scenario Scenario: - Service share in domestic demand: 65% -> 90% -Service share in exports: 20% -> 80% - Decrease of Material intensity in manufacturing: 0% -> 70% Target line

45 Unfolding of the workshop

46 Schedule 8h45 9h00 Registration 9h00 10h30 10h30 11h00 Welcome Presentation of the FORESCENE Meta-Model Background, structure, scenario modelling Coffee break 11h00 12h15 12h15 13h15 Optional: Organisation in working groups Session 1: First tier of questions Lunch break 13h15 15h15 Session 2: Second tier of questions 15h15 15h45 Break 15h45 17h00 Plenary session, presentation of the results Closing

47 Questions for the discussion Suitability and relevance of the scenario modelling approach - With the given goal references, key strategies and drivers is the backcasting approach approproiate for assessing/designing sustainability scenarios? - Are the preliminary narratives developed by the project relevant? Plausibility of the meta-model: - How would you assess the plausibility of the basic assumptions and first modelling results? - How do you evaluate the representation of uncertainty, probabilistic relationships, combination of different types of knowledge in terms of plausibility and validity?

48 Questions for the discussion Possibilities of extension - Which existing models or data bases could be used to improve the relevance of the (present and future) meta-model results? - Could existing models make use (e.g. as a plug-in, after modification of the meta-model if needed) of the meta-model specificities (handling of uncertainties, probabilistic relationships etc)? - How could those nodes that depend on more qualitative infomation be made more robust? Policy use - Is the idea of a meta-model useful? - How might the meta model be extended for - concrete policy design, and - ex ante impact assessment of EU policies. - Is the notion of probabilities of outcomes useful in a policy design context?

49 Contact Wuppertal Institute for Climate, Environment and Energy Mathieu Saurat phone: fax: mathieu.saurat@wupperinst.org