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TRANSITIONS PATHWAYS AND RISK ANALYSIS FOR CLIMATE

CHANGE MITIGATION AND ADAPTATION STRATEGIES

D7.1: Report on the comparisons of transition pathways

Project Coordinator: SPRU, Science Policy Research Unit, (UoS) University of Sussex

Work Package 7

Leader Organization: NTUA, Energy Policy Unit, National Technical University of Athens

Lead Authors: Alexandros Nikas, Haris Doukas

Contributing authors: Aikaterini Forouli, Eleftherios Siskos, Eleni Kanellou, John Psarras,

Aleksander Szpor

October 2017

D.7.1 Report on the comparisons of transition pathways

TRANSrisk

Transitions pathways and risk analysis for climate

change mitigation and adaptation strategies

GA#: 642260

Funding type: RIA

Deliverable number

(relative in WP) 1

Deliverable name: Report on the comparisons of transition pathways

WP / WP number: 7

Delivery due date: Month 26 (October 2017)

Actual date of submission: November 10, 2017

Dissemination level: Public

Lead beneficiary: National Technical University of Athens (NTUA)

Responsible scientist/administrator: Alexandros Nikas, Haris Doukas (NTUA)

Estimated effort (PM): 10

Contributor(s):

Lead Authors: Alexandros Nikas, Haris Doukas (NTUA)

Contributing authors: Aikaterini Forouli, Eleftherios Siskos, Eleni

Kanellou, John Psarras (NTUA), Aleksander Szpor (IBS), Janek

Witajewski (IBS)

Estimated effort contributor(s) (PM): 6

Internal reviewer: Jenny Lieu, Ed Dearnley (editor), Wytze van der Gaast (comments

to be incorporated in planned updated version)


Preface

Both the models concerning the future climate evolution and its impacts, as well as the models

assessing the costs and benefits associated with different mitigation pathways face a high degree

of uncertainty. There is an urgent need to not only understand the costs and benefits associated

with climate change but also the risks, uncertainties and co-effects related to different

mitigation pathways as well as public acceptance (or lack of) of low-carbon (technology)

options. The main aims and objectives of TRANSrisk therefore are to create a novel assessment

framework for analysing costs and benefits of transition pathways that will integrate well-

established approaches to modelling the costs of resilient, low-carbon pathways with a wider

interdisciplinary approach including risk assessments. In addition TRANSrisk aims to design a

decision support tool that should help policy makers to better understand uncertainties and risks

and enable them to include risk assessments into more robust policy design.

PROJECT PARTNERS

No Participant name Short Name Country code Partners’ logos

1 Science Technology Policy Research, University of Sussex

SPRU UK

2 Basque Centre for Climate Change BC3 ES

3 Cambridge Econometrics CE UK

4 Energy Research Centre of the Netherlands ECN NL

5 Swiss Federal Institute of Technology (funded by Swiss Gov’t)

ETH Zurich CH

6 Institute for Structural Research IBS PL

7 Joint Implementation Network JIN NL

8 National Technical University of Athens NTUA GR

9 Stockholm Environment Institute SEI SE, KE

10 University of Graz UniGraz AT

11 University of Piraeus Research Centre UPRC GR

12 Pontifical Catholic University of Chile CLAPESUC CL


Executive Summary

The aim of Work Package 7 is to: (a) compare different transition pathways within the contexts

of (and across) the TRANSrisk case studies, using different perspectives from those employed in

the country case study work and previous work packages; and (b) develop decision support tools

that will help policy makers in the climate policy domain in carrying out analyses. In this

direction, and building on the knowledge acquired in other work packages (namely WP3 and

WP5), this Deliverable seeks to develop and implement an integrated Fuzzy Cognitive Mapping

approach to assessing alternative policy pathways, strategies and mixes against a set of plausible

future socio-economic developments.

Fuzzy Cognitive Mapping (FCM) is a qualitative modelling technique, aiming to model and

represent a stakeholder’s expertise in, and knowledge of, a particular issue in diagrammatic

form, thus allowing for ad-hoc structure and flexibility to add the desired level of detail and

complexity. Elements comprising the Fuzzy Cognitive Map, and essentially the system under

examination, are connected by means of cause-and-effect relationships. Simulations of the

derived model through artificial network techniques capture how the causal propagation across

the system reacts to induced shocks and assumptions formulated by stakeholders and/or data.

The methodology helps experts to assess a complex problem and reach a difficult decision

primarily using their own knowledge. It does this by facilitating the extraction of this knowledge,

and using it to drive semi- quantitative simulations that can draw conclusions that would

otherwise be very challenging for stakeholders to reach on their own. In essence, FCMs are an

expertise-driven decision support tool that has long been used in similar fields (such as

environmental policy or energy planning) and can thus be used in climate policy. They work by

accurately modelling the problem domain and introducing shocks in the form of climate policy

instruments or strategies, as well as external factors representing uncertainties and/or risks.

The methodology described in this Deliverable aims to establish FCMs as a climate policy support

tool. Fuzzy Cognitive Mapping is first evaluated with regard to its applicability in climate policy

making: the FCM literature is thoroughly reviewed and the original methodological framework is

accordingly modified to fit the scope and needs. The proposed TRANSrisk model introduces a

number of modifications to the original framework, by incorporating the capacity to evaluate

policy mixes instead of individual policy instruments, introducing the notion of risk- and

uncertainty-driven scenarios, and determining a strictly defined stakeholder engagement stage.

All of these aspects are new, and are expected to constitute added value for the research

community, as well as for climate policy support.

In line with the proposed FCM approach, a MATLAB-based software application developed under

the framework of the TRANSrisk project is presented in detail. Expertise-driven Semi-

Quantitative Analysis for Policy Evaluation, or ESQAPE, facilitates the creation, visualisation,

editing and simulation of an FCM. It also allows for complete control of the model, through strict


definition of different components (e.g. policies, risks, uncertainties, end goals, etc.), on the fly

editing of structure, link weights, and shock levels, a range of configuration options and easy

visualisation and exportation of results. In addition to this, it enables the user to import/export

a model in both structured and visual format.

Finally, the methodology, proposed model and tool are implemented and validated in two

TRANSrisk country case studies: Greece and Poland. The Greece case study examines the

selection of different policy instrument portfolios for enhancing energy efficiency in the Greek

building sector, over the short- to mid-term. The Poland case study, on the other hand, concerns

the low carbon transition of the Polish power sector, from an almost exclusively coal-dominated

to a renewable energy driven system. It aims to assess the impact of two different policy

pathways on the country’s long-term economic growth.

Although significantly different in terms of scope, model creation approach and evaluation

criteria, both case studies included strictly defined, heavily stakeholder-oriented stages and

quantified scenarios based on the socio-economic factors and descriptions of the Shared Socio-

economic Pathways (SSPs). The rationale behind selecting these two case studies was to validate

the framework in vastly different cases of climate policy support. We did this by stress-testing

its applicability in both near-term and long-term applications, with different stakeholder

engagement processes, for assessing both policy strategies (consisting of individual policy

instruments) and policy pathways, against both climate and socioeconomic criteria, and in

different settings of integrated methodological frameworks.

The model’s application in the Greek building sector drew from results of a portfolio analysis

approach to the same problem as well as risks identified in the context of WP5. It involved

experts from the Greek Ministry of Energy and Environment, engaged via interviews. It showed

that stakeholders perceive that risks appear to have significant impact on the final results. While

single-strategy portfolios appear to be less beneficial to achieving mid-term energy efficiency,

as opposed to portfolios comprising a large number of policy instruments (findings both fully in

line with other TRANSrisk task results), the latter are considered more vulnerable to risk. The

FCM application in the Poland case study included findings from quantitative modelling with the

MEMO model and knowledge elicited in a workshop from various stakeholder groups (including

public administration, the research and development industry, the research and academic

community, and more). It showed that, under all future socio-economic developments and from

the stakeholders’ perspective, a radical transition to a renewable energy sources driven power

sector appears to have a better impact on the long-run growth of the Polish economy, compared

to continued use of coal. The latter (coal) pathway only performs almost as well only in the most

optimistic scenarios.


The Deliverable is expected to be updated in December 2017, in order to include more case

studies (e.g. from Spain and the Netherlands), where significant progress has already been

made.

D.7.1 Report on the comparisons of transition pathways Page 1

Table of Contents

1 EC Summary ........................................................................................ 6

1.1 Changes with respect to the DoA ......................................................... 6

1.2 Dissemination and uptake ................................................................. 6

1.3 Short summary of results (<250 words) ................................................. 8

1.4 Evidence of accomplishment .............................................................. 8

2 Introduction ........................................................................................ 9

2.1 Rationale ...................................................................................... 9

2.2 Research questions........................................................................ 11

2.3 Relation to other tasks ................................................................... 11

3 FCMs as a climate policy support tool....................................................... 13

3.1 Theoretical underpinnings ............................................................... 13

3.2 Literature review .......................................................................... 15

3.2.1 Eliciting stakeholder knowledge ..................................................... 19

3.2.2 Designing the map ..................................................................... 21

3.2.3 Simulating the model .................................................................. 21

3.2.4 Integration with other approaches .................................................. 23

4 The TRANSrisk Model ........................................................................... 26

4.1 An innovative approach .................................................................. 26

4.2 FCM modelling in TRANSrisk ............................................................. 28

4.2.1 Laying the groundwork ................................................................ 29

4.2.2 Capturing causal propagation ........................................................ 31

4.2.3 Simulating the model .................................................................. 35

5 The ESQAPE tool ................................................................................ 36

5.1 Overview .................................................................................... 36

5.2 ESQAPE Model Editor ..................................................................... 38

5.3 File operations ............................................................................. 41

5.4 Map simulation and convergence ....................................................... 45

5.4.1 Simulation parameters ................................................................ 45

5.4.2 Simulation process and results ....................................................... 47

6 Case Study applications ........................................................................ 50

6.1 Near-term policy mix for the Greek building sector ................................ 50

6.1.1 Context of the case study ............................................................ 50

6.1.2 Determining alternative policy mixes and risks ................................... 52

6.1.3 Stakeholder engagement.............................................................. 57


6.1.4 Simulation results ...................................................................... 61

6.2 Long-term policy pathway for the Polish power sector ............................ 63

6.2.1 Context of the case study ............................................................ 64

6.2.2 Determining policy pathways, uncertainties and narratives ..................... 66

6.2.3 Stakeholder engagement.............................................................. 71

6.2.4 Simulation results ...................................................................... 74

7 Conclusions ...................................................................................... 82

8 References ....................................................................................... 84

9 Appendix ......................................................................................... 90


Figures

Figure 1: Example of a system representation in a cognitive map ..................................... 13

Figure 2: Example of system representation in a fuzzy cognitive map ................................ 14

Figure 3: FCM studies per application area ................................................................ 15

Figure 4: Timeline of the publications reviewed, between 2005 and 2016 ........................... 16

Figure 5: Geographic distribution of FCM application case studies in the literature ................ 17

Figure 6: Processes used to extract FCM-related information from the involved stakeholders ... 20

Figure 7: Instances in the reviewed literature where FCMs are integrated with other tools ...... 24

Figure 8: Existing FCM literature compares individual policy instruments against each other .... 26

Figure 9: TRANSrisk approach compares policy mixes against each other ............................ 27

Figure 10: The five shared socioeconomic pathways (O 'Neill et al., 2015) .......................... 28

Figure 11: Partial FCM for information campaigns, based on Tables 5 and 6 ........................ 32

Figure 12: Visual outcome of the example weighted matrix translated into a map ................. 35

Figure 13: Basic use case of the ESQAPE tool ............................................................. 36

Figure 14: Application, logical architecture, boundary and main data flows ........................ 37

Figure 15: ESQAPE Model Editor Pane ...................................................................... 38

Figure 16: FCM Graph visualisation example (with weights, hierarchical layout) ................... 40

Figure 17: Map statistics popup ............................................................................. 41

Figure 18: FCM model spreadsheet weight matrix (partial) ............................................. 42

Figure 19: FCM model statistics table ...................................................................... 43

Figure 20: Editing an ESQAPE FCM graph in the yEd editor ............................................. 44

Figure 21: Example of GML graph formatting ............................................................. 45

Figure 22: Driver and Transfer Function selection ....................................................... 46

Figure 23: Results pane ...................................................................................... 48


Figure 24: Exported simulation result spreadsheet (Concepts, final values and simulation plot) . 49

Figure 25: Integration of different methodologies in this FCM case study of the Greek building

sector in the near-term ...................................................................................... 53

Figure 26: Pareto front of near-optimal policy mixes for the Greek case study ..................... 54

Figure 27: The four policy mixes in the Greek case study .............................................. 56

Figure 28: Part of the final FCM, corresponding to the “Save Energy at Home II” mechanism .... 59

Figure 29: Part of the final FCM, corresponding to the ISO 50001 energy management system

establishment in the public sector ......................................................................... 60

Figure 30: Part of the final FCM, corresponding to the policy instrument regarding energy

managers and NEEAP implementation ..................................................................... 61

Figure 31: Results of the Greek FCM case study (portfolios on the left perform better, according

to stakeholders) ............................................................................................... 62

Figure 32: Prevalence of coal in Total Primary Energy Supply (in MTOE), without electricity;

crude oil and oil products combined. Source: IEA ........................................................ 64

Figure 33: Domination of coal in electricity production in Poland (TWh). Source: IEA ............. 65

Figure 34: CO2 emissions per GDP (PPP) (kg/$) in Poland. Source: World Bank ..................... 65

Figure 35 Lower level of education among miners (%). Source: LFS ................................... 67

Figure 36: Integration of different methodologies and tasks in this FCM case study of the Polish

energy sector in the long-term .............................................................................. 70

Figure 37: Visual presentation of the labour loss story to the Polish stakeholders .................. 71

Figure 38: Example of filling in the stakeholder input matrix .......................................... 73

Figure 39: The global fuzzy cognitive map of the Poland case study .................................. 74

Figure 40: Poland case study results: No external factors assumed ................................... 75

Figure 41: Poland case study results: SSP1-oriented scenario .......................................... 76






Tables

Table 1: Case study regions and application areas of FCM studies in the relevant literature ..... 17

Table 2: Learning approaches used in the reviewed literature ........................................ 22

Table 3: Determining policies and risks for solar power diffusion in the Netherlands .............. 29

Table 4: Developing policy mixes for the Spain case study ............................................. 30

Table 5: Determining causality between a policy and the end goal, in the Dutch case study ..... 31

Table 6: Determining causality between risks and the system, in the Dutch case study ........... 32

Table 7: Example of filling in a stakeholder input matrix towards capturing causal link weights 34

Table 8: National energy savings targets for Greece .................................................... 51

Table 9: Stakeholder input matrix for the Greek case study (next page) ............................ 57


1 EC SUMMARY

1.1 Changes with respect to the DoA

This Deliverable is in accordance with the DoA; there have been no changes to the scope and

aims of Task 7.1 as set out in the DoA, which included:

a) Developing a methodological framework that can support the process of comparing

transition pathways — each one of which inevitably features a range of policy mixes — across

different case studies, sectors and countries;

b) Investigating the cause-and-effect agency in climate policy; and

c) Actively involving engaged stakeholder groups, in a way that not only disseminates the

lessons learned from our case study work and modelling activities but also brings them

closer than ever to the policy making process.

From a methodological point of view, and with regard to the content of Task 7.1, the suggested

approach in the DoA was extended, from crisp causal diagrams and the need to confirm the

assumed causality to Fuzzy Cognitive Maps (FCMs). Indeed, the Fuzzy Cognitive Mapping

methodology, a well-established policy and decision support tool, was selected and customised,

to fit the needs and scope of supporting climate policy making, by enabling the comparison of

alternative transition pathways (including policy instruments, mixes, and strategies), within a

specific sector or country.

As a result, stakeholders are not only presented and informed of our research output but also

constitute the core element of this exercise. This approach further validates our case study and

modelling work, and encourage policy makers to trust policy recommendations emerging from

complex research and climate-economy modelling procedures.

For the purposes of establishing FCMs as a decision support tool in the climate policy domain,

significant effort has been put to (a) reviewing the methodology’s position in the respective

literature, (b) developing a dedicated software application (beyond the contractual

agreements), and (c) implementing the methodological framework in case studies. All of these

activities indicate that the efforts put into Task 7.1 are in line with the DoA.

1.2 Dissemination and uptake

This Deliverable has potential for widespread usage, given that the developed methodological

framework and its validation through case studies have implications both for the research

community and for policy makers. In this respect, we envisage dissemination and uptake of the

contents of this Deliverable and the research carried out in this context in a number of areas.


Firstly, this methodology has been disseminated across the TRANSrisk consortium, so that all

project partners can utilise it in their country case studies; this Deliverable already presents in

detail applications from two case studies and is expected to be updated by December 2017 with

applications from more case studies.

Secondly, the thorough literature review, the dedicated software application and the innovative

methodological work have been (and will be) disseminated to the broader academic community,

through a series of conference presentations, scientific book chapters and journal articles. In

particular, a preliminary assessment of the method’s applicability for the comparison of

alternative pathways for the transition of EU countries to low carbon economies was presented

in the 4th Student Conference of the Hellenic Operational Research Society in Athens, Greece

(Nikas et al., 2015). A detailed presentation of the original framework elaborated in the context

of TRANSrisk was published in a book chapter in Springer’s “Robustness Analysis in Decision

Aiding, Optimization, and Analytics” (Nikas and Doukas, 2016); the dedicated software tool.

ESQAPE, along with an initial pilot appraisal, were presented at Elsevier’s 1st International

Conference on Energy Research and Social Science in Sitges, Spain (Nikas et al., 2017a). The

presentation of another TRANSrisk outcome, the MATISE tool for evaluating technological

innovation systems in the context of climate change, on the visualisation capacity of which the

development of ESQAPE drew, was published in the Journal of Knowledge Management (Nikas et

al., 2017b), in which potential links with the FCM methodology are explored.

A thorough literature review of FCMs in the climate policy-related literature, some core findings

of which are presented in Section 3, as well as of other decision support tools that are central to

the TRANSrisk project, has been submitted to another scientific journal as an invited review

paper. This has already been reviewed, and is currently pending revision. A detailed

presentation of the ESQAPE tool, as well as findings of its validation in the Greek case study will

be submitted in another journal article. More scientific publications with direct policy

implications are also expected to emerge from other case studies that will be added to the

Deliverable in its subsequent update.

Thirdly, the methodological framework used in TRANSrisk and its capacity to enable decision

making in climate policy has been presented to Greek policy makers in the Ministry of

Environment and Energy as well as Polish stakeholders in a workshop. Lessons learned from using

the methodological approach for energy efficiency policy in the Greek building sector have been

disseminated to the participating stakeholders in the Ministry. Results from the case study

concerning transition in the Polish power sector will be disseminated to the workshop

participants shortly after the submission of this Deliverable. Finally, in close collaboration with

UPRC, results from all case studies will be presented in a dedicated section of the TRANSrisk

website.


1.3 Short summary of results (<250 words)

Despite having widely and increasingly been used in similar applications (e.g. environmental

planning and management, energy policy, etc.), Fuzzy Cognitive Maps have been significantly

underexploited in climate policy making. FCMs are a primarily stakeholder-driven tool that

provides unbound freedom of structure, and features flexibility to include even the most hard-

to-model aspects of policy assessment. Consequently, they can serve as an effective decision

support tool for this problem domain.

The methodological framework was modified to fit the needs of climate policy making. We did

this by limiting stakeholder engagement needed for carrying out the FCM analysis in order to

base the exercise on previous (both modelling and otherwise) case study work and not to confuse

stakeholders with exhaustive communication steps. This enabled the assessment of climate

policy mixes instead of individual instruments, and incorporated the notion of risk- and

uncertainty-driven scenarios. The proposed approach also proves that the FCM component of the

TRANSrisk work flow can be integrated with other tasks and methodologies.

Finally, results from the Greek building sector case study show that stakeholders back financial

support to energy upgrading initiatives in the residential sector and programs aimed at SMEs, as

well as favouring multiple instrument over single instrument policy portfolios when little to no

risk is assumed. If larger socioeconomic risks are assumed, single instrument portfolios perform

more poorly. Regarding the Polish power sector, stakeholders seem to value the impact of a

renewable energy transition to the economy more than the insistence on fossil fuels.

1.4 Evidence of accomplishment

Beyond this report, evidence of accomplishment of Task 7.1 comes from a number of sources. In

the academic community (aside from TRANSrisk dissemination activities), from conference

presentations (Nikas et al., 2015; and Nikas et al., 2017a) to scientific publications (Nikas and

Doukas, 2016; Nikas et al., 2017b; with more, currently pending submission, review or

publication). Secondly, in the broader research community, with the publicly available, open

source software application that was developed in the context of this Task1.

Finally, results from the case studies are soon expected to be included and documented in

respective reports and policy briefs. At the time of writing, the methodological framework and

part of the results have been disseminated via interviews to Greek policy makers at the Greek

Ministry of Environment and Energy, as well as to Polish stakeholders in a workshop in Warsaw,

on October 12, 2017.

1 www.transrisk-project.eu/esqape.zip

http://www.transrisk-project.eu/esqape.zip


2 INTRODUCTION

2.1 Rationale

Achieving low-carbon transitions is a complex, multi-disciplinary process that, not only involves

developing long-term concentration (of GHG emissions) and socio-economic pathways, but also

requires assessing the policy instruments, strategies and mixes that can promote these pathways

in a robust, socially acceptable and adaptable manner. Most climate-economy modelling

frameworks, or what we call Integrated Assessment Models, cannot directly incorporate all kinds

of policy instruments, are driven by formalised assumptions, and are too complex for

policymakers to understand and trust their results (Kelly and Kolstad, 1999); otherwise, they

would blindly guide us through one of the most challenging tasks of the century. At the same

time, it would be both meaningful and beneficial to directly consider the interests and expertise

of stakeholder groups, bringing experts and their knowledge as close to the modelling process as

possible in order to bridge that gap. These constitute the background to our work, explaining

why we are so committed to integrating quantitative models with several other quantitative and

qualitative methodologies.

Aside from climate-economy models, which are heavily used in the project, there already exist a

number of policy support tools and methodologies in the literature that fit the aforementioned

needs. These have already been, or can potentially be used, in the climate policy domain. From

the onset of TRANSrisk, project partners have identified, employed or reframed qualitative or

quantitative methodologies in the context of climate policy making, including frameworks from

the Systems of Innovation literature such as:

Technological Innovation Systems (D3.2)

The Multi-Level Perspective (D6.2) framework

Multiple-Criteria Decision Making (upcoming D5.5)

System or Market Mapping (D3.2), and

Portfolio Analysis (upcoming D7.2).

In this report, the Fuzzy Cognitive Mapping methodological approach is explored as a decision

support tool. It can help policy makers select policy pathways that support on-time mitigation of

climate change, and the desired low carbon transition of the European and global community.

From the very beginning of the project, however, (and following a thorough literature review,

the core findings of which are presented in the next Section) the team leading Task 7.1

acknowledged that the original FCM framework features certain limitations that potentially

hinder its application in the climate policy domain. These limitations primarily concern the

definition of the methodology as a stand-alone, exclusively stakeholder-driven process and,

subsequently, the numerous and long stakeholder engagement processes that are required for

the creation of the FCM model. Additionally, common practice in the literature revolved around


a usually vague definition of simulation drivers (policies, external factors and other shocks) in

respect to the mathematical foundations of the simulation process.

Especially with regard to the aforementioned key limitations, stakeholders are a core element of

TRANSrisk activities and, as such, they are actively involved in many project activities. As a

result, stakeholder engagement for the purposes of comparing transition pathways in Task 7.1

should be focused down onto fuzzy cognitive mapping, which could potentially confuse and

alienate stakeholders. At the same time, the aims and scope of the task clearly dictated that we

make use of results from our previous work throughout the TRANSrisk project, and synthesise

this to inform stakeholders about the outcomes of our research.

On this basis, we aimed to develop a methodological approach that makes use of both the

findings of previous work carried out in TRANSrisk and stakeholder knowledge and expertise.

This can be well integrated with other methodologies employed in the project, in an innovative

way. Furthermore, we drew conclusions from the results of this research in respect to the

context of, and the computational processes employed in, the FCM methodological framework.

Finally, we also customised the FCM framework in a way that serves the purposes of climate

policy making, not only for use in TRANSrisk but also to be disseminated across the research and

broader academic community. In this direction, an innovative way of assessing policy mixes as

sets of policy instruments activated in different levels was elaborated. Additionally, simulations

comprised a number of different configuration combinations, in order not only to evaluate and

compare alternative policy strategies and pathways but also to stress-test the results against

several risks and uncertainties.

Especially with regard to the latter, we have collectively defined the terms of uncertainty and

risk, drawing from the literature, as well as the need to provide a concrete and consistent

terminology that suits our purposes. In this respect, ‘uncertainty’ expresses a broad concept

that refers to a general lack of knowledge of possible outcomes and states of the world. ‘Risk’

refers to a specific possible outcome that is perceived to be negative, may stem from an

uncertainty and depends on the perspective in which we examine a given system. Risks are,

then, classified into implementation (or exogenous) and consequential risks: the former refer to

risks that may hinder the successful design and successful implementation of a policy, while the

latter refer to risks that may emerge because of the implementation of a policy. For example,

there is the uncertainty of how economic growth may evolve in the future, and the

implementation risk of not being able to fund a policy instrument or incentivise transition due to

potentially poor economic growth. Therefore, instead of excluding uncertainties and

implementation risks, when modelling a system with Fuzzy Cognitive Mapping, we assume that

the model construction begins from both the policy instruments/strategies to be evaluated and

exogenous risks and uncertainties, while also allowing experts themselves to identify or validate

potential consequential risks. These risks and uncertainties have already been identified in the

context of Task 5.2 and TRANSrisk D5.2 for each case study. Uncertainty, in FCMs, was treated

deterministically, by designing five scenarios based on the story factors describing the five

Shared Socio-economic Pathways (O’ Neill et al., 2015; O’ Neill et al., 2017).


2.2 Research questions

In Task 7.1 we aimed to develop a methodological approach that can support the process of

comparing transition pathways within and potentially across case studies, by investigating the

cause-and-effect agency in climate policy. We also aimed to actively involve key stakeholders, in

a way that brings them close to the modelling activities and policy making processes, as well as

informs them of our research outcomes. We anticipate that the implementation of the proposed

integrated approach can help answer the following set of broad questions:

a) How can Fuzzy Cognitive Mapping successfully support decision making in climate policy?

b) How can we capture and combine stakeholder- and research-driven narratives, in order

to synthesise what we have learned from our case study work?

c) How can we productively/effectively inform policy makers and other stakeholder groups

of our research results without guiding them through every detail of employed climate-

economy modelling and other activities?

Depending on the case study, more research questions emerge and are anticipated to be

addressed in this task. In the Greece case study, we also sought to help policy makers select an

optimal policy portfolio, consisting of investments in a large number of policy instruments, from

a set of near-optimal alternatives (as already defined in a previously carried out portfolio

analysis approach). The overarching goal was to identify a strategy that, according to the policy

makers’ perspective, outranks the other alternatives in terms of impact on energy efficiency in

the short- to medium-term, taking into consideration their adaptability to different

socioeconomic developments, as expressed in a set of policy-related implementation risks.

In the Poland case study, the discussion was shifted towards comparing a low carbon policy

pathway against a fossil fuel-dependent policy pathway using different evaluation criteria from

those assumed in other TRANSrisk case studies. In particular, it sought to answer the following

additional research questions:

a) What impact do Polish stakeholders think policies and uncertainties have on the Polish

economy?

b) Which of the two policy pathways, one driven by the deployment of intermittent

renewables and one oriented on supporting coal-based power, is more beneficial to

Poland’s long-term economic growth?

2.3 Relation to other tasks

One of the core aims of Work Package 7 and, consequently, of Task 7.1 has been to develop

decision support tools for climate policy making while also enhancing integration with other

tools and methodologies. The proposed methodological approach, in particular, seeks to make


use of the research carried out for our case study work. It uses this research in the process of

developing the structure of the FCM and determining the policy instruments, strategies/mixes

and implementation risks and uncertainties, before asking stakeholders to define the

relationships and essentially drive the simulation processes.

In this respect, different types of integrated approaches are proposed. The common ground

among all case studies lies in the relation of Task 7.1 to the case study Work Package (WP3) as

well as Task 5.2, which aims to identify the key risks and uncertainties associated with the

policy strategies of each case study.

The Greece case study made use of data deriving from quantitative modelling frameworks

employed by the Ministry of Environment and Energy, risks identified and assessed by means of

multiple-criteria decision making in TRANSrisk Tasks 5.4 and 5.5, and a set of near-optimal

policy portfolios determined by means of portfolio analysis in TRANSrisk Task 7.2.

The Poland case study, on the other hand, made use of uncertainties identified in TRANSrisk

Task 5.2, as well as narratives associated with policy instruments and strategies emerging from

the literature, and climate-economy modelling activities with the MEMO Integrated Assessment

Model.

As results from more TRANSrisk tasks are available, Task 7.1 case studies will continue to draw

from findings of our case study work.


3 FCMS AS A CLIMATE POLICY SUPPORT TOOL

3.1 Theoretical underpinnings

Fuzzy Cognitive Mapping is a semi-quantitative modelling technique, which represents the

assumptions concerning a particular issue in diagrammatic format (Eden and Ackermann, 1998),

thus allowing for ad-hoc structure (Brown, 1992) and unbound freedom. It employs

computational processes used in artificial neural networks, and has its roots in cognitive

mapping. Cognitive maps can be seen as a graphical representation of a system, with every node

representing a concept in the system and every arc representing the perceived interconnections

between the concepts. These maps can work, among others, as a tool for experts to express and

enhance their knowledge on a specific problem domain, by assessing the influence, causality and

dynamics within the system (Huff, 1990).

Figure 1: Example of a system representation in a cognitive map

Taking the above mentioned a step further, it was realised that causal relations between two

concepts come with obscurity (fuzziness), thus the notion of Fuzzy Cognitive Maps was

introduced. FCMs quantified these fuzzy causal relations by adding a causal weight on the

connecting arc (Kosko, 1986). These weighted values comprise the weight matrix of

the FCM. The entries of this matrix can be of any numerical value within the interval . A

link weight between concepts Ci and Cj takes a value in the interval , if there is a causal

connection from concept to concept and a positive change in concept Ci leads to an increase

in the value of concept Cj. Otherwise, the link weight takes a value , if a positive


change in concept leads to a decrease in concept . If there is no connection between the

two concepts, then .

Figure 2: Example of system representation in a fuzzy cognitive map

In order to develop an FCM, stakeholder engagement is required; in fact, according to the

original framework, FCM creation is exclusively stakeholder-driven. Stakeholder input is

translated into nodes and arcs. After the design of the FCM, causality, or propagation (Kosko,

1986), is traced through simulations (Papageorgiou and Kontogianni, 2012), driven by different

scenarios as shocks to the system. In order to capture this causal propagation, a simulation

driver function and a transfer function are employed. These simulations can converge to a fixed

point (or lead to an undesired outcome) (Dickerson and Kosko, 1994). That depends on the three

dimensions of the FCM, namely the structure, the link weights and the initial state vector. The

analysis then stress-tests the system under multiple what-if scenarios by changing one of the

above-mentioned dimensions at a time. The results of the comparisons between the different

scenarios can support the decision-making process (Stach et al., 2010).

The widespread use of fuzzy cognitive mapping as a decision support tool in policy making

(Section 3.2) can be attributed to the strengths that inherently come with the methodology. The

main advantage lies in the fact that their development does not depend on the availability of

data. They are flexible and come without constraints, built on human expertise and knowledge

alone. Apart from the research interest they present, they have attracted interest from

modellers and other experts alike, as FCMs reportedly bring those two groups together in the

process of decision making, thus creating greater trust in the process and results amongst both

groups (van Vliet et al., 2010).

In the following section, a thorough literature review of FCM studies in the climate policy-

related domain is presented, broken down into the various stages of constructing and simulating

the FCMs described in detail.


3.2 Literature review

Aside from a limited number of applications (e.g. in climate adaptation policy or mitigation

policy in sectoral studies), FCMs have not been used extensively in studies aiming to support

climate policy making. However, the methodology has been increasingly used both in application

areas and fields that are of interest to climate policy (such as environmental policy) and in

studies with climate policy implications (e.g. energy policy and planning).

The figure below (Figure 3) depicts the number of studies per application area found in the

literature. These feature a rather diverse set of application areas, mainly comprising climate

change scenario analyses and policy strategy evaluation, for the purposes of supporting

environmental, climate change mitigation and climate change adaptation policy and decision

making. Scenario analysis applications mainly study alternative climate change scenarios or

alternative future system developments against these scenarios without necessarily targeting

specific policies; environmental policy covers ecosystem conservation and environmental

decision making; climate change adaptation policy refers to FCM applications that explicitly

study system resilience and evaluate actions in the aim of responding to climate change; while

all other applications revolve around policy choices towards mitigation mainly with regard to the

development of agriculture and land use, renewable energy sources and electricity planning, and

the transition of the transportation sector.

Figure 3: FCM studies per application area

Figure 4 presents the number of studies per application area and year of publication. As we can

observe, since 2009 fuzzy cognitive mapping in these areas have been enjoying increasing

attention from the research community.


Figure 4: Timeline of the publications reviewed, between 2005 and 2016

Climate change and the involvement of stakeholders in the decision-making process is a

phenomenon that needs to be addressed at a global level. However, FCM applications have so far

been carried out at local or national scales. This is an expected outcome of this review, since

the level of detail, in combination with the number of concepts to be represented and modelled

(30-35), present a significant obstacle to implementing FCMs at regional or global scales.

Additionally, environmental policy (the most common application area of FCMs within the

literature reviewed) includes a variety of issues and subdomains that are usually assessed at a

local or national level. These include environmental impacts of climate change (e.g. Gray et al.,

2014), land use (e.g. Wildenberg et al., 2010; Mallampalli et al., 2016), water management (e.g.

Kafetzis et al., 2010; Ceccato, 2012), industrial development (e.g. Lopolito et al., 2011; Zhang

et al., 2013), natural phenomena (e.g. Giordano et al., 2010; Samarasinghe and Strickert, 2013)

and ecosystem management (e.g. Özesmi and Özesmi, 2003; Vassilides and Jensen, 2016; Peng

et al., 2016). Figure 5 shows the geographic scale at which studies in the reviewed literature

carried out the FCM approach.


Figure 5: Geographic distribution of FCM application case studies in the literature

The majority of the case studies appear to have taken place in European and Asian regions.

Outside of Europe and Asia, environmental policy applications have been mainly carried out,

except for a land cover scenario analysis in the Brazilian Amazon (Solera et al., 2010).

Interestingly, Asia features the largest share of FCM applications.

Table 1 summarises the findings of our literature review and categorises them by region and

application area of the case study.

Table 1: Case study regions and application areas of FCM studies in the relevant literature

Application area

Region of case study

Africa N. America S. America Asia Europe Oceania

Adaptation

Policy

Reckien

(2014)

Gray et al.

(2014)


Agriculture Solera et al.

(2010)

Rajaram and

Das (2010)

Nair and Singh

(2012)

Ortolani et

al. (2010)

Lopolito et

al. (2011)

Papageorgiou

et al. (2011)

Vanwindekens

et al. (2013)

Sacchelli

(2014)

Christen et

al. (2015)

Electricity

Planning

Ghaderi et al.

(2012)

Olazabal and

Pascual

(2016)

Karavas et al.

(2015)

Environmental

Policy

Gray et al

(2015)

Mourhir et

al. (2016)

Hobbs et al.

(2002)

Vassilides and

Jensen (2016)

Samarasinghe

and Strickert

(2013)

Ceccato

(2012)

Özesmi and

Özesmi (2003)

Celik et al.

(2005)

Özesmi

(2006a)

Özesmi

(2006b)

Rajaram and

Das (2010)

Kontogianni

et al. (2012)

Meliadou et

al. (2012)

Papageorgiou

and

Kontogianni

(2012)

Zhang et al.

(2013)

Hsueh (2015)

Peng et al.

(2016)

Giordano et

al. (2010)

Kafetzis et al.

(2010)

Ortolani et

al. (2010)

van Vliet et

al. (2010)

Wildenberg et

al. (2010)

Papageorgiou

and

Kontogianni

(2012)

Gray et al.

(2013)

Vanwindekens

et al. (2013)

Gray et al.

(2014)

Kontogianni

et al. (2012)

(Samarasinghe

and Strickert

(2013)


Renewable

Energy Sources

Amer et al.

(2011)

Zhao et al.

(2014)

Hsueh (2015)

Amer et al.

(2016)

Lopolito et

al. (2011)

Kyriakarakos

et al. (2014)

Sacchelli

(2014)

Scenario

Analysis

Solera et al.

(2010)

Amer et al.

(2011)

Reckien

(2014)

Singh and Nair

(2014)

Amer et al.

(2016)

van Vliet et

al. (2010)

Wildenberg et

al. (2010)

Kyriakarakos

et al. (2014)

Gray et al.

(2014)

Anezakis et

al. (2016)

Transport Shiaua and

Liu (2013)

Kontogianni

et al. (2013)

3.2.1 Eliciting stakeholder knowledge

In this section, a concise overview of how researchers in the existing literature elicited

stakeholders’ perceptions and expertise is provided. The method used to extract information

from stakeholders is of extreme importance. Different methods require different efforts from

analysts, facilitators and experts alike, while allowing stakeholders to understand the framework

to different extents and share their valuable knowledge more or less willingly. The stakeholder

engagement methods employed in each study of the reviewed literature are presented in the bar

figure below (Figure 6); note that some studies included more than one methods of eliciting

information.


Figure 6: Processes used to extract FCM-related information from the involved stakeholders

It appears that face-to-face interviews are the most commonly employed approach (e.g. Hobs et

al., 2002; and Lopolito et al., 2013). Interviews are a more personal medium, since experts feel

more confident to express their views and opinions; also, there is more dedicated one-on-one

time to explain the method. In the reviewed literature, the studies based on interviews are

followed by those employing workshops and surveys. Workshops (e.g. van Vliet et al., 2010; and

Shiaua and Liu, 2013) are a time-consuming process that requires skilled facilitators and

preparatory processes (like pre-workshop sessions) regarding the communication of the FCM

methodology itself. Although they constitute a longer process, they can more effectively

mobilise participants’ cumulative knowledge, enabling analysts to develop one social cognitive

map at once, without having to put extra effort in aggregating the provided input before

proceeding with the simulation process. Additionally, workshops may reduce researchers’ bias,

since researchers do not need to make assumptions that may be made when aggregating

individual results from interviews.

Another commonly used medium is semi-structured questionnaires, as in Ghaderi et al. (2016)

and Mallampalli et al. (2016). These may be considered impersonal and still require a lot of work

from the analysts when designing both the personal FCMs based on the input and aggregating

those into the collective map through condensation techniques. This is why surveys are usually

used in combination with other approaches (e.g. Biloslavo and Grebenc, 2012; and Gray et al.,

2014). Finally, there have been instances in the literature where modellers built their model

based on historical data, without the participation of experts (e.g. Anezakis et al., 2016).


3.2.2 Designing the map

After designing the model with the help of experts, and regardless of the stakeholder

engagement approach employed, the final step in constructing the model lies in the

quantification of the causal links. Weights are the main driver of causal propagation; however,

as weights are quantitative values, they are hard to directly elicit from stakeholders. In most

cases, the experts (in the interviews, workshops, etc.) are asked to describe each causal

relationship among identified concepts by determining the influence of one concept on another

by means of a sign. In other words, whether the perceived influence is positive or negative, and

an indication of the level of influence expressed in a linguistic scale, for example

(Kyriakarakos et al., 2014). These linguistic values and

signs are then quantified in the interval , either by directly translating linguistic values

into numerical values (see Nikas and Doukas, 2016) or by means of fuzzy rules and

‘defuzzification’ methods (e.g. Papageorgiou and Kontogianni, 2012). In the literature, only a

small number of publications translated stakeholder input into the fuzzy set and then defuzzified

(translated) it into numerical values. The most frequently used defuzzification method is the

Centre of Gravity or Centre of Area method (e.g. Natarajan et al., 2016; Mourhir et al., 2016),

followed by scarce instances of the Max Criterion Method (Kottas et al., 2006) and the Weighted

Area method (Rajaram and Das, 2010).

3.2.3 Simulating the model

3.2.3.1 Activating the policy impact

After the map has been constructed, the model is simulated by using simulation techniques from

artificial neural networks. This is done to allow causal propagation to take place and examine

the attitude of the system, given its unique combination of structure, weights and initial values

of the concepts. A simulation driver function is thus selected to calculate the value of each

concept at the end of an iteration. One particular activation function has almost exclusively

been used in FCM applications (e.g. Kyriakarakos et al., 2012; Gray et al., 2014; and Olazabal

and Pascual, 2016) is the following:

Another form of this simulation driver function can be found in other studies (e.g. Zhao et al.,

2014; Peng et al., 2016):


The difference between the two forms lies in the liberty of stakeholders to define the level of

autocorrelation for each concept (Nikas and Doukas, 2016), i.e. the extent to which the value of

a concept is dependent on its previous value, instead of the values of concepts affecting it.

Instead of the commonly used activation functions described above, other approaches have also

been used, including learning algorithms (Table 2) and time-defining activation functions.

Table 2: Learning approaches used in the reviewed literature

Learning Category Learning Approach Applications

Hebbian-based Learning Nonlinear Hebbian Learning (NHL) (Natarajan et al., 2016)

(Peng et al., 2016)

Petri Nets (PN) (Kyriakarakos et al., 2012)

Population-based Learning Genetic Algorithms (GA) (Natarajan et al., 2016)

Particle Swarm Optimization (PSO) (Karavas et al., 2015)

(Kyriakarakos et al., 2012)

Social Cognitive Optimisation (SCO) (Sacchelli, 2014)

Other Decision Tree Learning (Papageorgiou et al., 2011)

Self-Organizing Maps (SOM) (Samarasinghe and Strickert, 2013)

Due to the nature of some problem domains and the need to determine actions that respond to

said domains both efficiently and in time, the ill-defined time dimension is sometimes also taken

into consideration, but not in the vast majority of studies (van Vliet et al., 2010). Some studies

straightforwardly translated each iteration into a specific time period and assigned a time delay

(or lag) to each causal relationship (Nikas and Doukas, 2016), while others transformed the


weight matrix into a time function (Biloslavo and Dolinšek, 2010) or adapted the weights

dynamically during simulations (Mouhrir et al., 2016).

Outside the reviewed literature, however, other approaches have been used towards

incorporating the time aspect into the Fuzzy Cognitive Mapping methodological framework.

These include the expression of the implicit time delay of every relation and the selection of a

base time in Rule-Based Fuzzy Cognitive Maps (Carvalho et. al.,2001); the use of Fuzzy Time

Cognitive Maps for analysing trust dynamics in virtual enterprises (Wei et. al., 2008); the agent-

based FCM methodological framework developed by Lee et al. (2013), to better address the

drawbacks identified by Hagiwara (1992) and further analysed by Schneider et al. (1998), which

was applied in industrial marketing planning.

3.2.3.2 Normalising values

In order to normalise simulation results into a bound interval in which concepts are allowed to

take values, a threshold function is usually employed at the end of each iteration. In the

reviewed literature, the most frequently-used function is the sigmoid function (e.g. Lopolito et

al., 2011; Papageorgiou et al., 2011; Olazabal and Pascual, 2016), which squashes values in the

interval (-1, 1). When concept values are negative, the hyperbolic tangent function is used

instead (Amer et al., 2016). Other functions used in this research area include the bivalent (Peng

et al., 2016), trivalent (Biloslavo and Grebenc, 2012; Zhao et al., 2014), and a ramp (Hobbs et

al., 2002) function. In an attempt to address the weaknesses of sigmoid function, in cases where

the initial state vector is hard to define, a modified activation function was used (Papageorgiou

et al., 2011; Papageorgiou and Kontogianni, 2012).

Regardless of the configuration parameters of the FCM simulations, the calculated output of the

model shows how the system reacts under the assumptions provided by the stakeholders.

Comparisons between the final state vectors of the examined alternatives should be drawn to

assess to what extent the desired transition has been promoted, by activating each option. A

specific set of concepts is selected as evaluation criteria; in policy making, the larger the value

of the goal concept is at the end of the simulation, the better stakeholders appear to judge the

selected policy. In most cases, system dynamics are evaluated by looking at the value of a

specific concept, but given the multidisciplinary nature of climate policy problem domains,

many researchers evaluated their systems by looking at the final state vector or a set of

concepts (e.g. Lopolito et al., 2011). Nevertheless, it should be noted that no study in the

literature explicitly evaluates different policy mixes, but rather examines specific policy

instruments.

3.2.4 Integration with other approaches

As already discussed in the introductory section, we are interested in integrating the FCM

methodology with other methods and tools. In the literature of interest, fuzzy cognitive mapping

has been used as a standalone tool or as part of another methodology, framework or tool.

Studies in which FCMs are part of other methodologies are presented in Figure 7.


Figure 7: Instances in the reviewed literature where FCMs are integrated with other tools

Regarding communication strategies, most studies use the Delphi method (e.g. Kayikci and Stix,

2014; Hsueh, 2015; and Amer et al., 2016), while Ceccato (2012) uses the Building Block

Methodology and Samarasinghe and Strickert (2013) employ the Geomorphic Assessments

approach. Multiple-Criteria Decision Making (which is very close to the TRANSrisk overall scope

and methodological integration, as part of Task 5.5) has also been integrated with FCMs,

primarily by means of the Analytical Hierarchy Process (Biloslavo and Dolinšek, 2010; Biloslavo

and Grebenc, 2012); Shiaua and Liu, 2013) and TOPSIS (Mourhir et al., 2016). Computational

models have been built upon FCM approaches, including Agent-Based Modelling (Ortolani et al.,

2010) and Multi-Agent Systems (Karavas et al., 2015); while climate (Anezakis et al., 2016) and

environmental (van Vliet et al., 2010) modelling frameworks have also used input from, or

provided output to, Fuzzy Cognitive Mapping exercises. Other methodological frameworks used

along with FCMs in the reviewed literature include statistical analysis approaches, like Principal

Component Analysis (Hobbs et al., 2002; Shiaua and Liu, 2013); Zhao et al., 2014) and Structural

Equation Modelling (Huang et al., 2013); Technological Roadmapping (Amer et al., 2011; Amer et

al., 2016) and other frameworks.


As we can deduct from the review of the studies, only a few of them actually integrate FCMs in

quantitative modelling frameworks and methodologies. Only two studies, namely Nikas and

Doukas (2016) and Mallampalli et al. (2016) actually refer to links between FCMs and

quantitative models that aim at policy evaluation, simulation or optimisation (integrated

assessment models), but are limited in the description of the methodological framework.


4 THE TRANSRISK MODEL

As already described in Sections 1 and 2, the TRANSrisk FCM approach features innovations with

regard to both the original framework and the common practice in the literature. The following

sub-sections present these features and describe the TRANSrisk model in detail.

4.1 An innovative approach

After reviewing the existing literature, it was acknowledged that the vast majority of

applications aim to evaluate and assess individual policy instruments. Furthermore, existing

applications do not focus on risks and uncertainties, but rather try to include any risk that may

come up through stakeholder engagement (which is the sole source of data when developing the

structure of the model) as a direct or indirect consequence of a certain policy (Figure 8). That is

very similar to what we have defined as consequential risks.

Figure 8: Existing FCM literature compares individual policy instruments against each other

The proposed methodological framework, in contrast, aims to address the need to evaluate

policy mixes or pathways towards a low carbon future, as sets of numerous policy instruments

activated at different levels. By doing so, we essentially consider policy mixes and/or pathways

as policy portfolios (Figure 9).


Figure 9: TRANSrisk approach compares policy mixes against each other

Additionally, our approach deals with the necessity to directly incorporate the dimension of

policy implementation risks and uncertainties, as a starting point for drawing and simulating a

system. This way, transition pathways are not only compared against each other but can also be

stress-tested against different risk- and uncertainty-driven scenarios, in a deterministic

approach. These scenarios consist of the implementation risks and uncertainties identified in

TRANSrisk Task 5.2 and selected by case study leaders (or the key implementation risks and

uncertainties resulting from the multiple-criteria decision making approaches employed in

TRANSrisk Tasks 5.4 and 5.5), activated at different levels based on the story factors describing

the five Shared Socioeconomic Pathways or SSPs (O’ Neill et al., 2015).

The pathways constitute one of the three components of the long-term scenarios integrating

future changes in climate and society, which are used in climate-economy modelling in order to

investigate both climate impacts and options for mitigation and adaptation (O’ Neill et al.,

2017). The other components are the Reference Concentration Pathways (van Vuuren et al.,

2011) and the Shared Climate Policy Assumptions (Kriegler et al., 2014). The five SSPs represent

reference future socioeconomic developments, assuming no climate change occurs and no

climate policy is implemented, corresponding to different levels of challenges for climate

mitigation and adaptation (Figure 10). These story factors are presented in detail in the

Appendix, as synthesised in tables by ETHZ in the context of Task 5.2.


Figure 10: The five shared socioeconomic pathways (O 'Neill et al., 2015)

Finally, and as described in the following section, the TRANSrisk project requires limited

stakeholder engagement for the purposes of building the FCM for each case study, when

compared to the original framework. Most importantly, the complexity revolving around the

design and quantification of the map is acknowledged. Case study leaders construct the map by

drawing from their expertise and the lessons learnt throughout the TRANSrisk project, before

quantifying the causal relations featured in each FCM with the help of stakeholders. Example of

such settings, where different tasks and methodologies within the TRANSrisk project provide

input to the FCM building process, are displayed in the featured case studies (Section 6).

4.2 FCM modelling in TRANSrisk

Having already worked on their case studies, and supervised or taken part in the respective

modelling activities, case study leaders have formulated a set of the most prominent policies

and risks with regard to their case studies. In this respect, they are asked to design their

cognitive maps in a structured manner, drawing from the acquired knowledge and results from

methodologies employed across the project, i.e. modelling activities to identify policy strategies

and multiple-criteria decision making processes as well as key risks and uncertainties.

Subsequently and by means of appropriate stakeholder engagement options (i.e. interviews and

workshops), experts’ knowledge is incorporated into the researchers’ perspective-driven models,

in order to connect the dots and narrate the pathways primarily from the stakeholders'

perspective. Following the completion of the FCM, and the quantification of the featured causal

relations, the model is simulated to produce results with climate policy implications.

SSP 5

Fossil-fueled

development

SSP 1

Sustainability

SSP 3

Regional

rivalry

SSP 4

Inequality

SSP 2

Middle of the

road

Challenges for adaptation

Chall

enges

for

mitig

ation


This approach differs from the commonly employed methodological framework, according to

which stakeholders are invited to drive the whole FCM building process. The TRANSrisk model

draws from lessons learned in the modelling activities and case study research carried out for

the project, before including stakeholders in the process. In this respect, quantitative modelling

(i.e. IAMs and other models) is directly linked to stakeholder knowledge and expertise, and the

FCM methodological framework is modified in a manner that significantly simplifies the

otherwise highly intensive stakeholder engagement part. This process is presented in detail in

the steps below.

4.2.1 Laying the groundwork

Step 1: Determination of policies and risks

Initially, the potential policy framework of each case study is drawn from Work Package 3. Key

implementation risks are identified based on the outcomes of Tasks 5.2 or 5.5. Table 3 displays

an example from the Dutch solar power case study (currently in progress).

Table 3: Determining policies and risks for solar power diffusion in the Netherlands

Policy strategies Key implementation risks Key consequential risks

1. “Energy Agreement”

2. Energy planning for

municipalities

3. Information campaigns

to raise awareness within

the public

4. Installation of smart

metering systems

5. Net-metering and its

predecessor from 2023

(for the residential sector)

6. SDE+ subsidy scheme

for larger solar

installations

7. Stricter building codes

8. Education: increasing

the number of energy

professionals and aiding

system integration

a) Public distrust

b) Crisis (lack of funding)

c) Land use issues

a) Grid balancing issues

b) Overstimulation and tax

losses

c) Incorrect data

monitoring


Step 2: Developing policy mixes

For each case study, different policy mixes are formulated and differentiated based on the

selected policy strategies. These policy mixes must be carefully formulated and meaningful, to

enable simulations in an innovative approach, as opposed to the traditional way of carrying out

fuzzy cognitive mapping. Table 4 presents an example based on the Spain renewable energy

diffusion case study (currently in progress).

Table 4: Developing policy mixes for the Spain case study

Scenario 1.

Winter

Package

Scenario 2.

Thermal

phase out

discussion

Scenari

o 3.

Nuclear

Phase

out

discussi

on

Scenario 4.

Carrier

Switch

Scenario 5.

Dealing

with

Intermitten

cy and

Storage

Scenario 6.

RE options

(Native

Options and

Repowering

)

Scenario 7. Democratization of energy

and participation of society in the new

energy model

Scenario 8.

Recom. the

Grid

(Public)

Energy

Coops

Promoting

Distributed

Systems

Off-grid

/micro-

grid

(Removal of)

solar tax

Low High high Moderate High Very high High High High Low

(Removal of)

Capacity

Payment

(thermal subs)

Moderate High Low Moderate Moderate Very high High Very High Moderate Low

(Reduce

Access to) grid

costs

High Low Low Low Very High Very high Moderate High High Very high

Improve

interconn.

facilities

Very high High Low Low High Moderate Low Moderate Low High

R&D Low Low Low Moderate Very High Moderate Low Low Moderate/

High

Low

Reduce

regulatory

costs and

actors

High Low Low Low Moderate High High Low High High

Finance

mechanisms

(w/o

regressive

High Low High High Low High Moderate High High Low


effects)

Interaction

between local,

regional and

national actors

High High High High High High Low Moderate Low High

RES Job

promotion

Low Very High Very

High

Low Moderate High High High Low Low

4.2.2 Capturing causal propagation

Step 3: Identifying causality

In Step 3, causal propagation must be captured. This constitutes the hardest part of the original

FCM framework, since it requires organising a workshop or carrying out a sufficient number of

interviews. Traditionally, stakeholders are asked to help the case study leaders draw the FCM,

by identifying the key concepts and the interactions between them. The simplified TRANSrisk

approach includes the identification of causality by case study leaders, drawing from their desk

research and the knowledge gained through stakeholder engagement. For each policy

instrument/strategy, chains of linear cause-and-effect relationships are determined by means of

single-row tables. The purpose is to identify what lies between the policy strategy and the end

goal, i.e. what exactly takes place when implementing the project and until the objective (e.g.

low carbon transition) is achieved. Table 5 presents an example from the Dutch case study.

Table 5: Determining causality between a policy and the end goal, in the Dutch case study

Information

campaigns to

raise public

awareness

Governmental,

local/regional

campaigns

Awareness for

energy saving Acceptance

and social

compliance

Behavioural

change Low carbon

transition

Information

campaigns to

raise public

awareness

Public

knowledge on

renewable

energy

technologies

Upgrading the

existing

building stock

Low carbon

transition


For each implementation risk the concepts that were identified in the above process, and are

perceived to be affected by the risk, are listed in single-row tables (e.g. in Table 6).

Table 6: Determining causality between risks and the system, in the Dutch case study

Distrust in

governmental bodies Awareness for energy

saving Public knowledge on

renewable energy

technologies

Lack of funding to

invest in solar panels

Upgrading the existing

building stock

Behavioural change

In the tables above, the case study leaders deem that information campaigns will promote the

transition to a low carbon power sector, by raising awareness and encouraging behavioural

change, as well as by enhancing knowledge of available options and thereby persuading the

public to proceed with building renovations. However, existing distrust in governmental bodies

and inadequate capital to invest in solar power production in buildings may have an adverse

impact on this strategy. These tables would be translated in the partial map displayed in Figure

11.

Figure 11: Partial FCM for information campaigns, based on Tables 5 and 6


Step 4: Stakeholder engagement

The tables are then synthesised into a square matrix, which is filled in with the help of

stakeholders. The weights are provided in a given, strictly defined scale. In other words, the

case study leaders ask the stakeholders: “given the constructed FCM and your knowledge of the

national context and the case study, how strongly do you think that the identified concepts

affect each other?”.

The scale used in most TRANSrisk case studies includes the following linguistic terms:

{negatively very very strong, negatively very strong, negatively strong, negatively medium,

negatively weak, negatively very weak, zero, positively very weak, positively weak, positively

medium, positively strong, positively very strong, positively very very strong}.

An example based on the Greek building sector case study can be found in Table 7. The map

corresponding to this information is drawn and presented in Figure 12.


Table 7: Example of filling in a stakeholder input matrix towards capturing causal link weights

Poor

economic

growth

Installation

of smart

meters

Lack of

financial

capacity

Public

acceptance

Social

compliance

and

behavioural

change

Energy

saving and

efficiency

Low carbon

transition

of the

building

sector

Poor

economic

growth

Negatively

strong

Installation

of smart

meters

Positively

very weak

Lack of

financial

capacity

Negatively

moderate

Public

acceptance

Positively

very strong

Social

compliance

and

behavioural

change

Positively

moderate

Energy

saving and

efficiency

Positively

strong

Low carbon

transition

of the

building

sector


Figure 12: Visual outcome of the example weighted matrix translated into a map

Stakeholder engagement can be carried out in a workshop, or via a number of individual

interviews. Differences between the two options are discussed in Section 3. It should, however,

be noted that, although they require more coordination than individual interviews, workshops

directly result in one social FCM, ready to be simulated. Interviews, on the other hand, result in

numerous individual maps which must then be aggregated in a social FCM.

4.2.3 Simulating the model

The model is finally simulated by means of the ESQAPE software application, developed by NTUA

in the framework of TRANSrisk and presented in Section 5. For the purposes of TRANSrisk,

simulations assume that the value of a concept at the end of each iteration depends on both the

values of the concepts leading to it (and the weights of the corresponding links) and the value of

said concept at the beginning of the iteration. This is described in the follow formula:

Additionally, values are reduced by means of the hyperbolic tangent threshold function, allowing

for negative values to appear. Both functions are discussed in Section 3.


5 THE ESQAPE TOOL

5.1 Overview

Expertise-driven Semi-Quantitative Analysis for Policy Evaluation, or ESQAPE, is a MATLAB-based

application for the creation, editing, visualising and iterative convergence of Fuzzy Cognitive

Maps. Among the advantages of the MATLAB platform are its capability for rapid prototyping of

applications, and its rich set of I/O, Graphical User Interface, computation and visualisation

libraries.

The purpose of this Section is to present the basic functionality of ESQAPE in respect to the

needs of the TRANSrisk project. Detailed documentation can be found on the TRANSrisk website,

so that it can serve as reference for members of the research and broader academic community

to exploit and/or modify this outcome of the project.

Parts of the code and the user interface are based on the Mapping Tool for Innovation Systems

Evaluation (MATISE) software application developed by Nikas et al. (2017), and the FCM tool

developed by Papaioannou et al. (2010). The ESQAPE tool is open source software, available

under a permissive BSD license from http://transrisk-project.eu/esqape.zip.

The basic use case of the application involves three core elements: the FCM model input, the

simulation process, and the results output. First, the FCM model input, where the user creates

or imports an FCM model to the application and edits its structure and parameters. Second, the

Simulation of the model and network convergence, where the application simulates the

interaction between FCM concept nodes iteratively; the simulation ends when the network is

stabilised, a loop state appears (an oscillating “cycle”), or the maximum number of iterations is

reached. Third, the Results output, where the results are presented in the GUI and exported in a

data file or in graphical form.

The graphic representation is depicted in Figure 13:

Figure 13: Basic use case of the ESQAPE tool

http://transrisk-project.eu/esqape.zip


In the following figure (Figure 14), the three main functions in the logical architecture of the

application, along with its interactions with the user, are graphically presented.

Figure 14: Application, logical architecture, boundary and main data flows

On the input side, the user can import specially formatted .xslx or .gml files (generated from

editing applications, such as Microsoft Excel and the yEd graph editor (yWorks GmbH, 2017) into

the FCM Model Editor. This flow is bi-directional: any (valid) model in the Model Editor can be

exported as a compatible spreadsheet or graph file. The Model Editor can also be used to create

new models entirely within the ESQAPE application. The model parameters can be saved in a

formatted MATLAB file (.mat), and restored at a later time.

The model is run through the FCM Simulation Engine, which—if successful—provides a set of

“Convergence Results”, i.e. the concept values of a stable or oscillating FCM. The results of the

simulation process can then be visualised in the application and exported to a new spreadsheet

file. The user can also choose to transfer some (or all) of the final concept values to the starting

model, e.g. to re-run the simulation.

The application provides its functionality via a Graphical User Interface. The main interface

consists of two main panes (the Model Editor and Results panes), complemented by a drop-down

menu bar and a set of specialised popups.

The application elements and their functionality are described in detail in the following

sections.


5.2 ESQAPE Model Editor

The ESQAPE Model Editor pane contains tools and interface elements for editing the model (i.e.

either a new model created within ESQAPE or for one imported from a file). Users can add or

remove concept groupings, and add, remove or rename concepts as well as assign concepts to

groupings. For each concept, the application allows the user to edit the initial concept values.

Once the concepts are in place, the users can edit the weight and the time delay (based on and

discussed in detail in Nikas and Doukas, 2016) matrix directly in the application.

The “ESQAPE model editor” pane is illustrated in Figure 15.

Figure 15: ESQAPE Model Editor Pane

The pane is subdivided in four segments: Groupings, where the concepts of the FCM model can

be assigned either to specific groups or to no group at all; Concepts, where the Model Editor

displays a list of the concepts in the FCM model, according to the selection in the “Groupings”

list; Starting Concept Values (which essentially describe the policy mixes and risk-/uncertainty-

driven scenarios), where the list displays the starting values of the concepts used for the


convergence of the FCM network; and Weights/Delays, where the application displays the two

types of correlations between the concepts in the FCM map, namely the weights and time delays

of the influences between concepts.

In the “Starting Concept Values”, the user can assign initial values, and choose whether the

value will remain constant or vary during the simulation. This is a core element of ESQAPE, as

well as FCM implementation in the climate policy support domain. It should be noted that sender

or simulation driving concepts, i.e. concepts representing climate policy instruments, risks or

uncertainties, should be modelled as constants and all other concepts should be configured to be

variables. For instance, assuming an FCM model of a system comprising one policy instrument,

one risk and five other concepts, the policy instrument’s starting value will determine the level

at which this policy, modelled as a constant, has been activated; correspondingly, the risk’s

starting value will determine the level at which the scenario has manifested said risk, also

modelled as a constant; all other concepts must be assigned zero values and modelled as

variables.

The direction of the influence is determined by the position of the corresponding value. The

rows correspond to the “source” concepts (i.e. the originators of the influence) and the columns

to the “target” concepts (i.e. the receivers of the influence). The weights indicate the

magnitude of the positive or negative influence of the concept values on each other, as a

decimal value ranging from -1 to 1. On the other hand, the time delays correspond to the delay

of the influence in terms of simulation iterations. For example, a value of “1” means that the

influence is exerted in the next simulation iteration, a value of “2” corresponds to a two-

iteration delay etc. (Nikas and Doukas, 2016). For instance, assuming a policy instrument

promoting renovations in residential buildings, one might define a cause-and-effect relationship

with the mobilisation of the construction market as well as a slower (in terms of time) impact on

the achieved energy efficiency; this can be more accurately depicted by using different time

delays in the corresponding matrix.

The application can also provide a visualisation of the FCM graph within the Model Editor, by

clicking on the “View Cognitive Map” button, with or without weight information. The graph is

displayed on a separate popup, as illustrated in Figure 16.


Figure 16: FCM Graph visualisation example (with weights, hierarchical layout)

The ESQAPE tool uses a set of graphical conventions to facilitate the comprehension of the

cognitive map. “Sender” concept nodes, with outgoing but no incoming influences, correspond

to green trapezia and can be used to represent policy instruments and risks/uncertainties, since

these are assumed to induce shocks to the system. “Ordinary” concept nodes, with both

outgoing and incoming influences, are represented by red rectangles and comprise ordinary

system components (neither policies nor risks). “Receiver” concept nodes, with incoming but no

outgoing influences, are blue inverted trapezia and represent end consequences that analysts

attempt to assess. Positive influences are orange lines, while negative influences are blue lines.

The line weight corresponds to the magnitude of the influence between nodes. The shapes,

colours and line styles were selected to maximise clarity and accessibility. This feature allows

for easy and effective supervision of the model.

In addition to the visualisation function, the application can calculate a set of statistics based on

the properties of the graph structure (see Özesmi and Özesmi, 2003). For each node, the

application can calculate the “indegree” of a concept, as the sum of the absolute weights of the

incoming FCM graph links; the “outdegree” of a concept, as the sum of the absolute weights of

the outgoing FCM graph links; and the “centrality” of a concept as the sum of the “outdegree”

and “indegree” of the concept. Also, for the entire FCM, the application can display the number

of concepts (N) and the number of links (L), the density of the map (D = L/N²), the number of

“Sender” concepts, the number of “Ordinary” concepts, the number of “Receiver” concepts,

and the map complexity as the ratio of Receiver to Sender concepts (assuming there is at least

one “Sender”). The statistics can be displayed by clicking on the “Map Stats” button, and a

relevant popup will appear, as illustrated in Figure 17.


Figure 17: Map statistics popup

5.3 File operations

The user can save a model as a spreadsheet or a graph file, and import an already saved model.

The ESQAPE tool provides a set of file operations, accessible via the “File” menu item.

It should be noted that in all the file operations involving Excel and .gml files, the application

checks the model parameters and the format of the file data. Specifically, the application

checks: whether the worksheets are appropriately numbered and named (importing Excel files),

whether the weight and time delay matrices are square (importing Excel files), whether the

correct formatting of weight and time delays is applied (importing. gml files), whether the

correct type and range of the initial values is given (real numbers in [-1,1]), whether the correct

type and range of relationship weight values (real numbers in [-1,1] or blanks) is applied and

whether the correct type and range of time delays (integers equal or greater than 1) is given.

Finally, it checks if the proper correspondence of links and time delays is given, as in: if existing

cause-and-effect links are not assigned a time delay (in which case, they are assigned the

default time delay), and if non-existing causal links are falsely assigned time delays (in which

case, an error window pops up).


ESQAPE-compatible MS Excel files can be built in a suitable spreadsheet application, or exported

directly from the ESQAPE tool. They contain three worksheets: the weight matrix, the time lag

matrix and, if exported from the ESQAPE tool, a set of map statistics. The weight matrix sheet

contains a square table corresponding to the weights of the interactions between the concepts

in the cognitive map. Each concept can be given an initial value and (optionally) be assigned to a

specific group of concepts. Weight values are real numbers in [-1,1], as illustrated in Figure 18.

Figure 18: FCM model spreadsheet weight matrix (partial)

Finally, in spreadsheet files exported from the ESQAPE tool, a third worksheet is included,

containing a set of map statistics. These are automatically generated by the ESQAPE tool, each

time the map is saved as an Excel worksheet, and are not used for user input. They are identical

to the map statistics calculated by the corresponding functionality of the Model Editor. It is

depicted in Figure 19.


Concept Indegree Outdegree Centrality Number of Concepts (N) 30

1. P1 Integrated energy planning for municipalities 0 1,032 1,032 Number of Links (L) 46

2. P2 District heating network design 0 1,024 1,024 Density (D = L / N²) 0,051111111

3. P3 Installation of smart meters and ICT tools 0 0,278 0,278 Number of Sender Nodes (S) 13

4. P4 Incentives for NZEB constructions 0 0,884 0,884 Number of Ordinary Nodes (O) 14

5. P5 Educational programmes on climate change 0 0,644 0,644 Number of Receiver Nodes (R) 3

6. P6 Funds for RnD in energy efficiency technology 0 0,215 0,215 Map Complexity (C = R / S) 0,230769231

7. P7 Exoikonomo kat oikon 0 1,357 1,357

8. P8 Tariffs for building integrated PV systems 0 0,808 0,808

9. Ρ9 Incentives for green business and energy efficiency in

commercial buildings 0 1,085 1,085

10. U Economic growth 0 0,685 0,685

11. ER Real estate market collapse 0 0,784 0,784

12. U Perception of policy framework instability 0 0,463 0,463

13. U Energy consumption 0 0,683 0,683

14. CR Conflict between local and national government 0,383 0 0,383

15. CR LAGIE deficit 0,276 0 0,276

16. PV diffusion in buildings 0,648 0,319 0,967

17. Upgrading existing buildings 2,714 0,777 3,491

18. Environmental concerns 0,746 0,999 1,745

19. Need for green energy 1,093 0,477 1,57

20. Attractiveness of Renewables 1,036 0,247 1,283

21. Depletion of fussil fuels 0,341 0,411 0,752

22. Energy saving and efficiency 1,88 0,881 2,761

23. Awareness of the importance of energy efficiency in

buildings 0,477 0,578 1,055

24. Construction of new energy-efficient buildings 2,157 0,394 2,551

25. Public acceptance of energy saving measures 0,608 0,217 0,825

26. Costs of energy efficiency for citizens 0,561 0,389 0,95

27. Reduction of final energy consumption 1,121 0,661 1,782

28. Social compliance 1,073 0,119 1,192

29. Overall change of habits 0,317 0,063 0,38

30. GHG mitigation 1,043 0 1,043

Figure 19: FCM model statistics table

The Graph Modelling Language (GML) is a human-readable ASCII representation of graphs. GML

files are compatible with a range of commonly used graph editors. GML files represent graphs as

a hierarchy of nodes, edges and groups, and include further information such as node shapes,

edge weights, colours and others (Himsolt, 1997).

In ESQAPE, GML files can be used both as an output and an input format. In theory, GML files can

be created in any text editor, by following the GML specification. However, this is a tedious,

non-intuitive and error-prone method. In practice, it is advisable to initially construct the

model, and define its parameters, in a GML-compatible application, as seen in Figure 20.


Figure 20: Editing an ESQAPE FCM graph in the yEd editor

Regular GML graphs can be made ESQAPE-compatible by adding weight and time-lag information

in the edge labels. ESQAPE edge labels use the format [W;T], where W is the weight of the

interaction and T is the associated delay. For example, the “-0.4;2” label on an edge signifies

that the interaction has a weight of -0.4 and occurs with a time delay of 2 iterations. In the

example in Figure 21, the “Social Compliance” concept has a positive influence on “Energy

Saving and Efficiency” with a weight of 0.119 and a time delay of 1 iteration.


Figure 21: Example of GML graph formatting

Finally, the application enables the user to save and load the model in the form of a MATLAB

formatted data file (.mat). In this file, the application records matrices containing the

groupings, concepts, group assignments, weights, and time delays. The file can then be

imported to the application, and the operation will automatically populate the elements of the

user interface. There is no parameter checking in this case, as this functionality is only used to

keep a faithful copy of the model’s editing workspace, and not to keep the valid FCM models.

5.4 Map simulation and convergence

5.4.1 Simulation parameters

Once the users have completed editing the model, they can start the simulation process. The

users select the appropriate driver and transfer functions, i.e. the functions that are used to

drive the simulation and to normalize results respectively (see Section 3.2.3), from the

application menus, as it can be seen in Figure 22.


Figure 22: Driver and Transfer Function selection

Driver function options include “A = A*W” and “A=A+A*W”, while transfer function options

include “Sigmoid”,” Tanh”, “Bivalent”, “Trivalent” and “None”. The values of the concepts are

calculated in two stages, the “Driver” stage and the “Transfer” stage.

In the “Driver” stage (i.e. the mechanism with which changes occur in the system), the value of

the concept is calculated, taking into account the previous value (the “activation level”) and the

influence of other linked concepts, depending on the Driver function selected:

“A = A*W”: the value of a concept is the sum of the values of concepts from which there

is an influence, multiplied by the weight of each influence (as declared in the weights

matrix).

“A = A + A*W”: the value of a concept is the previous concept value plus the sum of the

values of concepts from which there is an influence, multiplied by the weight of each

influence (as declared in the weights matrix). This is the one used in TRANSrisk case

studies.

In the “Transfer” stage (i.e. the mechanism with which resulting values are reduced in the right

intervals, as necessitated by the process), the calculated value of the concept is “normalised”

depending on the transfer function selected:

“Sigmoid”: an “S” shaped function is used to compress values from the interval (-∞,∞) to

the interval [0,1].

“Tanh”: The new value is the hyperbolic tangent of the previous value. This is the one

used in TRANSrisk case studies.

“Bivalent”: If the calculated concept value is equal or less than zero, it is assigned a

value of 0. If the value is greater than 0, it is assigned a value of 1.

“Trivalent”: If the calculated concept value is:

o equal or less than -0.5, it is assigned a value of -1.

o between -0.5 and 0.5, it is assigned a value of 0.

o equal or greater than 0.5, it is assigned a value of 1.

“None”: No normalisation is applied.


5.4.2 Simulation process and results

Once the user presses “Run”, the application checks the validity of the model parameters:

The correct type and range of the initial values (real numbers in [-1,1])

The correct type and range of relationship weight values (real numbers in [-1,1] or

blanks)

The correct type and range of relationship weight values (real numbers in [-1,1] or

blanks)

The correct type and range of time delays (integers equal or greater than 1)

The correspondence of links and time delays

If any model errors are found the process stops and a popup informs the user about the type and

location of the errors. Otherwise, if the model is correct, the simulation starts.

The application applies the “Driver” and “Transfer” stages iteratively, until one of three

conditions are met:

The system has converged and reached a steady state, meaning that there is no change in

concept values between iterations.

The system has entered an oscillating feedback “cycle”. This is detected by checking if

the current state of the network (i.e. the concept values) is the same as one encountered

previously. In this case the network is expected to keep returning, deterministically, to

this particular state by the same path. Therefore, further simulation is meaningless.

The maximum number of iterations has been reached.

Once the process has ended, the application displays the “Results” pane as in Figure 23.


Figure 23: Results pane

The “Results” pane displays a report on the outcome of the simulation, a comments field where

users can enter arbitrary comments regarding one specific run, a plot of the values of all

concepts in the FCM, and a list of the concepts and their final values, i.e. the simulation results.

These results indicate how the system has reacted to the policy and risk shocks configured, each

time, and helps identify how of these shocks perform against each other.

Finally, the application can generate and save an Excel spreadsheet (Figure 24).


1. P1 Integrated energy planning for municipalities 0 variable 0,659046068

2. P2 District heating network design 0 variable 0,659046068

3. P3 Installation of smart meters and ICT tools 0 variable 0,659046068

4. P4 Incentives for NZEB constructions 0 variable 0,659046068

5. P5 Educational programmes on climate change 0 variable 0,659046068

6. P6 Funds for RnD in energy efficiency technology 1 variable 0,659046068

7. P7 Exoikonomo kat oikon 0 variable 0,659046068

8. P8 Tariffs for building integrated PV systems 0 variable 0,659046068

9. Ρ9 Incentives for green business and energy efficiency in commercial buildings 0 variable 0,659046068

10. U Economic growth 0 variable 0,659046068

11. ER Real estate market collapse 0 variable 0,659046068

12. U Perception of policy framework instability 0 variable 0,659046068

13. U Energy consumption 0 variable 0,659046068

14. CR Conflict between local and national government 0 variable 0,726990086

15. CR LAGIE deficit 0 variable 0,681109953

16. PV diffusion in buildings 0 variable 0,77154988

17. Upgrading existing buildings 0 variable 0,938616599

18. Environmental concerns 0 variable 0,781236126

19. Need for green energy 0 variable 0,841626149

20. Attractiveness of Renewables 0 variable 0,733830261

21. Depletion of fussil fuels 0 variable 0,720068953

22. Energy saving and efficiency 0 variable 0,918476285

23. Awareness of the importance of energy efficiency in buildings 0 variable 0,761951212

24. Construction of new energy-efficient buildings 0 variable 0,75899402

25. Public acceptance of energy saving measures 0 variable 0,533035733

26. Costs of energy efficiency for citizens

0 variable 0,593772381

27. Reduction of final energy consumption 0 variable 0,811990663

28. Social compliance 0 variable 0,724032959

29. Overall change of habits 0 variable 0,725808727

30. GHG mitigation 0 variable 0,841599204

5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure 24: Exported simulation result spreadsheet (Concepts, final values and simulation plot)


6 CASE STUDY APPLICATIONS

This section presents the implementation of the TRANSrisk FCM approach in the project’s case

studies, and discusses the findings.

6.1 Near-term policy mix for the Greek building

sector

This case study in Greece (one of the three case studies in the country) is unique among the

TRANSrisk pool of case studies for two reasons. The first is that it is the only EU case study to

concern the building sector, and the second is that it focuses on mitigation and development

pathways in the near to mid-term, rather than the long-term transition. It is acknowledged that

mitigation efforts in the context of national action plans and policies are crucial in promoting

the desired transition pathways in the long-term.

6.1.1 Context of the case study

The European Union faces significant challenges regarding the need to address both climate

change and an increasing dependence on energy imports. Enhancing energy efficiency is

therefore of vital importance: energy efficiency policies can benefit end-users in terms of utility

bill costs, as well as contribute to the mitigation GHG emissions and enhance security of energy

supply, competitiveness, economic sustainability and job creation. In this context, and at an

early stage, the EU set the near-term goal of achieving a 20% savings in primary energy

consumption by 2020, as well as laying the groundwork for more savings beyond 2020

(2012/27/EC). In November 2016, the European Commission suggested that this policy direction

be reinforced and upgraded the goal to 30% by 2030.

In order to better supervise and secure progress in achieving said targets, it was deemed that

national commitments and respective mechanisms must be updated. In this direction, Member

States were requested to submit national energy efficiency action plans, covering significant

energy efficiency measures and estimates for expected and achieved energy savings. In Greece,

the 3rd National Energy Efficiency Action Plan (NEEAP) was submitted in December 2014

(following those in 2008 and 2011), as the first national action plan in respect to this Directive.

The 2020 national energy efficiency target (for a 20% savings) was set at 18.4 MTOE of final

energy consumption, at 24.7 MTOE of primary energy consumption, and at 0.081 and 0.109

kTOE/€ of energy intensity respectively. The yearly and final energy savings targets for 2020 are

displayed in Table 8.


Table 8: National energy savings targets for Greece

Year Annual savings (kTOE) Cumulative

savings

(kTOE)

2014 100.2 100.2

2015 100.2 100.2 200.5

2016 100.2 100.2 125.3 325.8

2017 100.2 100.2 125.3 125.3 451.0

2018 100.2 100.2 125.3 125.3 150.3 601.4

2019 100.2 100.2 125.3 125.3 150.3 150.3 751.7

2020 100.2 100.2 125.3 125.3 150.3 150.3 150.3 902.1

Total 3,332.7

The intermediate periods established for supervising progress and adjusting the policy

framework were: (a) 2014-2015, with the intermediate cumulative target of 300.7 kTOE (3.5

TWh) saved, and (b) 2016-2018, with the intermediate cumulative target of 1768.9 kTOE (19.5

TWh) saved (NEEAP, 2014).

In order to achieve these targets, the National Plan designed eighteen policy instruments for

achieving energy efficiency exclusively in end-users, without forcing obligatory measures for

electricity retailers and distributors. According to the results submitted in the annual progress

report for 2015, regarding the achievement of the National Energy Efficiency Target, a negative

divergence of 36% (or 108.4 kTOE) from the 2015 target was identified. As a result, additional

mitigation efforts were needed to realigning progress. These required reconstructing the policy

instruments and determining the optimal policy portfolio, without disregarding the available

budget constraints and taking into consideration the associated implementation risks and

uncertainties.


6.1.2 Determining alternative policy mixes and risks

In this context, NTUA used data from the Greek Ministry of Energy and Environment regarding

the cost effectiveness of different policy measures (costs, expected savings, technical and

physical constraints), almost all of which regarded the building sector. These included the

following:

P1. The “Save Energy at Home II” financing mechanism

P3. Energy efficiency and demonstration projects in SMEs

P4. Implementation of the “ISO 500001” energy management system in the public sector

P5. Energy upgrade of commercial buildings through ESCOs

P6. Deployment of smart metering systems

P8. Offset of fines on illegal buildings with energy upgrades

P9. Energy managers in the broader public sector

P11. Replacement of old public and private light trucks

P13. Improvements in road lighting

P14. Pump stations

P15. Energy Performance Certificates (EPCs)

A multi-criteria decision analysis approach was implemented, with the help of policy makers

from the Ministry. Policy instruments were assessed against risks identified in TRANSrisk Task

5.2, thereby calculating a risk index for each policy by means of the TRANSrisk Task 5.5

methodological framework. The study and results of this exercise will be presented in detail in

the upcoming TRANSrisk Deliverable D5.5.

Next, using this index, along with the cost effectiveness data deriving from quantitative

modelling frameworks utilised by the Ministry, a portfolio analysis approach was carried out.

This forms part of TRANSrisk Task 7.2, towards building a set (Pareto front) of near-optimal

policy portfolios.

The integrated methodological approach employed in Task 7.1 for this case study is presented in

Figure 25.


Quantitative Models

Stakeholder

engagement

Risk assessment

Narratives for

transitions

Front of near-optimal

policy mixes

Risk indices

Risk evaluation

Perceived chain of causal propagation

Ranking of

policy mixes

Policy mixes

Task 5.5Case Study Task 7.2 Task 7.1

Data for costs and savings

Figure 25: Integration of different methodologies in this FCM case study of the Greek building sector in the near-term

The results of this portfolio analysis study are presented in detail in the upcoming TRANSrisk

Deliverable D7.2. However, the Pareto front comprising all of the near-optimal policy portfolios,

given the available budget of the Ministry, is presented in Figure 26.


Figure 26: Pareto front of near-optimal policy mixes for the Greek case study2

Instead of presenting these results to the Ministry and letting officials directly select a policy

portfolio located on the resulting Pareto front, the TRANSrisk FCM approach (as presented in

Section 4 of this report) was employed to support policy makers translate their knowledge and

expertise into a ranking of alternative near-optimal policy portfolios. Four policy mixes along the

Pareto front were selected. These consisted of up to eight policy instruments, which are briefly

described below:

P1. The “Save Energy at Home II” financing mechanism

The “Save Energy at Home II” programme’s main target is to provide support on energy upgrade

actions for residencies, including both houses and blocks of flats or apartments. This specific

mechanism provides its beneficiaries with financial support through fractional subsidisation

combined with loans from a contracting financial institution. The selection criteria regard both

the initial energy category of the residency and the beneficiary’s income. The supported actions

must lead to specific measurable energy improvements of the residency, and usually include

both improvements to the fabric of the residency and to the heating/cooling and hot water

systems.

P3. Energy efficiency and demonstration projects in SMEs and supporting measures

The programme’s final beneficiaries are SMEs, with the main aim to achieve the energy upgrade

of the buildings hosting SMEs. In particular, the proposed actions include upgrading the fabric of

the building, electric/mechanical equipment and installations, and lights, as well as

2 resulting from the portfolio analysis approach employed in Task 7.2 for a fixed available budget; horizontal axis represents energy savings, vertical axis represents risk


implementing energy management systems. This specific programme provides financing to its

beneficiaries through fractional subsidisation, the percentage of which varies with the

geographical location of the business of the beneficiary and the required actions.

P4. Implementing an energy management system in broader public-sector agencies, in

accordance with the ISΟ 50001 standard

This programme concerns financing the broader public-sector entities for the purposes of

implementing an energy management system on their buildings, based on the international ISO

50001 standard.

P5. Energy upgrade of commercial buildings through Energy Service Companies (ESCOs)

This programme aims at further developing the market of energy service companies through

contracts for energy efficiency. Specifically, it provides an advantageous context for lending

through subsidised interest rates or through the provision of collateral, specifically for

companies that provide energy services. It also aims for the implementation of actions of energy

improvement for buildings that are for professional use. In this case, the loan is gradually paid

off through the achieved energy savings and according to the specifications of the energy

performance contract.


This programme concerns the wide-range replacement of the existing metering systems in the

electricity distribution network. It aims to achieve active participation of consumers in the

energy market as well as at better, cheaper and more efficiency energy management.

P8. Offset of fines on illegal buildings with energy upgrades

This measure refers to the achieved saved energy from actions implemented according to Article

20 of Law 4178/2013 (“Dealing with illegal building”) of the Greek legislation. Specifically, there

is the possibility of offsetting costs for services, tasks and materials used for the energy upgrade

of residential buildings, with up to 50% of the amount corresponding to fines that are predicted

in Article 20.

P9. Energy managers in the public sector and implementation of NEEAP

This measure refers to the achieved energy savings from defining energy managers’ duties in

public buildings, as defined in Ministerial Decision No. D6/B/14826/2008, as well as through the

implementation of the energy efficiency plans for municipal buildings according to Article 7,

paragraph 12 of Law 4342/2015.


P11. Replacing old public and private light trucks

This policy is the only one not concerning the building sector, and falls into the wider scope of

actions regarding providing motives for the replacement of old or technologically outdated

vehicles. It has a main target of saving energy and renewing light trucks of both the public and

the private sector; motives vary based on the category of the vehicle that is being replaced.

Policy instruments were quantified, for the purposes of Task 7.1, in the interval [0, 1]. This was

based on the amounts invested in these instruments in relation to the total available budget,

and took into consideration that the budget for the deployment of smart metering systems is

fixed and provided by a different public entity, outside the Ministry’s budget. These are

presented in Figure 27.

Figure 27: The four policy mixes in the Greek case study

Key implementation risks identified and used in the portfolio analysis approach included:

R1. Difficulties in aligning local authorities with obligations of the central government

R2. Political instability

R3. Bureaucracy

R4. Demanding regulatory framework in relation to market maturity

R5. Inadequate banking sector

R6. Social opposition


R7. Inexperienced personnel – poor technical skills

R8. Poor market conditions (economic crisis)

These risks were quantified in five distinct SSP-matching scenarios, based on the descriptions of

the closest SSP story factors, namely Policies & Institutions (Institutions, Policy Orientation,

Equity, and Societal Participation), Economy & Lifestyle (Economic growth per capita), Human

Development (Education) and Technology (Development).

Finally, one consequential risk was identified and incorporated in the FCM context, namely the

rebound effect.

6.1.3 Stakeholder engagement

Based on the TRANSrisk framework described in Section 4 of this report, NTUA identified

causality between the policy instruments and the goal of enhancing energy efficiency. Then,

causality between the implementation risks and the emerging model was identified and a

stakeholder input matrix was created. This matrix (Table 9) was provided to seven policy makers

of the Greek Ministry, in individual interviews, who were asked to fill in blank cells (representing

causal links). Stakeholders were provided with necessary explanations and facilitated in

providing their knowledge in linguistic variables (corresponding to predefined numerical values).

Since policy instruments and risks are only “sender” concepts, i.e. affect the system but are not

affected by it, they are only included as rows and not columns. Respectively, “receiver”

concepts (jobs, rebound effect, and energy saving and efficiency) are only included as columns

and not rows, since they can only be affected by the system and bear no impact on any of its

components.

Table 9: Stakeholder input matrix for the Greek case study (next page)


Applic

ations

for

financi

ng

Renovations

in e

xis

ting r

esi

dential build

ing s

tock

EPC iss

uin

g

Public

acc

epta

nce

of

energ

y s

avin

g m

easu

res

Cost

s fo

r end-u

sers

, ow

ners

Renovations

in e

xis

ting c

om

merc

ial build

ing s

tock

Soci

al co

mplia

nce

and b

ehavio

ura

l ch

ange

Inst

alla

tion o

f BEM

S

Bett

er

energ

y m

onitoring &

contr

ol of

utilit

y b

ills

Inst

alla

tion o

f sm

art

mete

rs

Off

set

of

fines

with e

nerg

y e

ffic

iency

renovations

Energ

y m

anagers

in t

he p

ublic

sect

or

Upgra

de in t

he e

xis

ting lig

ht

truck

fle

et

Engin

eering, co

nst

ruct

ion, co

nsu

ltin

g jobs

Rebound e

ffect

Energ

y s

avin

g a

nd e

ffic

iency

R1. Difficulties in aligning local and central gvmt.

R2. Political instability

R3. Bureaucracy

R4. Demanding regulatory framework

R5. Inadequate banking sector

R6. Social opposition

R7. Inexperienced personnel

R8. Poor market conditions (economic crisis)

P1. “Save Energy at Home II”

P3. Energy efficiency and demo projects in SMEs

P4. ISO 50001 system in the public sector

P5. Upgrade of commercial buildings (w/ ESCOs)


P8. Offset of fines

P9. Energy managers and NEEAP

P11. Replacing old public and private light trucks

Applications for financing

Renovations in existing residential building stock

EPC issuing

Public acceptance of energy saving measures

Costs for end-users, owners

Renovations in existing commercial building stock

Social compliance and behavioural change

Installation of BEMS

Better energy monitoring & control of utility bills

Installation of smart meters

Offset of fines with energy efficiency renovations

Energy managers in the public sector

Upgrade in the existing light truck fleet


The seven stakeholder input matrices were then aggregated into one, with each value of the

latter being equal to the arithmetic average of the seven corresponding values. For presentation

reasons, the values of the final map were then retransformed into linguistic variables, using the

same scale. The resulting fuzzy cognitive map was broken down into eight maps, one for each

policy instrument. Some examples can be seen in Figures 28-30.

Figure 28: Part of the final FCM, corresponding to the “Save Energy at Home II” mechanism

Figure 28 Part of the final FCM, corresponding to the “Save Energy at Home II” mechanism shows

that, according to our stakeholders, the “Save Energy at Home II” financial support mechanism

will result in both energy saving and efficiency and the increase of engineering, construction and

consulting jobs. Initially, stakeholders believe that the programme will of course lead to

residents applying for financing; some of those applications will be successful, and will therefore

spark renovations in the existing residential stock. These renovations will not only enhance

energy saving but also have a positive impact on employment in this industry, both directly (in

the framework of carrying out the necessary renovations) and indirectly (through issuing energy

performance certificates). However, it is also evident that, according to the involved

stakeholders’ perception, this policy instrument is also significantly vulnerable to six of the

identified risks.


Figure 29: Part of the final FCM, corresponding to the ISO 50001 energy management system establishment in the public sector

According to Figure 29, stakeholders deem that implementation of the ISO 50001 energy

management system in buildings of the broader public sector, which requires the installation of

Building Energy Management Systems, will positively affect jobs in the industry and, to a larger

extent, improve energy consumption monitoring in the public sector. The latter will directly

lead to energy savings, but will also foster public acceptance of the framework and lead to

behavioural changes. However, and in line with the literature, better control of utility bills may

lead to the so-called rebound effect. Again, we can see how risks may affect the system.

Successfully deploying building energy management systems in municipal buildings depends

highly on how easily local authorities will align their policy with the national obligations. It also

depends on the extent to which the responsible personnel are qualified to implement this

system, and, to a lesser extent, on what financial capacity local authorities have to support such

changes and how stable the political scene proves to be in the country.


Figure 30: Part of the final FCM, corresponding to the policy instrument regarding energy managers and NEEAP implementation

Finally, Figure 30 shows how stakeholders perceived that appointing energy managers in the

broader public sector and implementing the action plans in municipalities will lead to enhancing

energy saving and efficiency. It is noteworthy that, although better energy monitoring might

cultivate rebound behaviour, energy managers are perceived to negate that effect. Again,

difficulties in aligning local authorities with national obligations as well as other, relatively

minor risks (political instability, bureaucracy and demanding regulatory framework in respect to

market maturity) may hinder the successful implementation of this policy measure.

6.1.4 Simulation results

Using ESQAPE, the four policy mixes were stress-tested against the five risk-driven scenarios and

one no-risk scenario. Their performances were evaluated and compared against each other. The

results of these 24 simulation runs are presented in Figure 31.


Figure 31: Results of the Greek FCM case study (portfolios on the left perform better, according to stakeholders)

Greek stakeholders appear to deem that risks significantly affect the policy strategies towards

achieving better energy savings. In a world where no (“No SSP”) or little (“SSP 1”) risk

manifests, strategies investing exclusively in energy efficiency and demonstration projects in

SMEs prove to be less beneficial to achieving mid-term energy efficiency than portfolios

comprising a large number of policy instruments. These findings are completely in line with the

portfolio analysis results, since this ranking is aligned with the Pareto front, from most cost-

effective and riskiest portfolios to less risky portfolios of worse performance in terms of energy

savings.

However, as future socioeconomic developments shifted towards less optimistic scenarios along

the SSP axes (see Figure 30), the ranking of policy mixes changed drastically. Results show that

in all riskier SSPs (SSP2 to SSP5), from the stakeholders’ perspective, the 2nd policy mix

(comprising large investments in the “Save Energy at Home II” financing mechanism, energy

efficiency and demonstration projects in SMEs, and smart metering systems as well as small

investments in replacing old light trucks in the public and private sectors) outranks all other

policy mixes. It is closely followed by the 3rd policy mix (which slightly reduces the amount

invested in SMEs and allocates it evenly in commercial building upgrades and the

implementation of the ISO 50001 energy management system). The riskier the scenario

becomes, the worse the all-instrument portfolio (4th policy mix) performs. This is an expected

outcome: as more risks manifest, all policy instruments are affected, making the portfolio


significantly vulnerable to poor socioeconomic developments. In a similar manner, the 1st policy

mix gains ground, as risks become greater. This is also another noteworthy observation, and

indicates that stakeholders realise that an individual policy instrument can only be affected by a

number of implementation risks, whereas a set of policy measures is prone to even more risks,

and as these risks manifest at more severe levels the latter becomes more likely to fail.

This ranking also appears to be quite robust among the four riskier SSPs, although it should be

noted that the single-policy portfolio would keep gaining ground if simulations shift towards

even riskier scenarios than the “Regional Rivalry”-oriented socioeconomic pathway (SSP 3),

which presents the highest challenges for both mitigation and adaptation, potentially leading to

different rankings.

Overall, it appears that the involved stakeholders were neither too risk-taking nor too risk-

averse. They sought to maximise energy savings, while taking into consideration the

implementation risks potentially affecting the alternative policy instruments. In other words,

based on the results of our approach, policy makers who took part in this process would rather

the government achieved an intermediate level of energy savings in an uncertain world, instead

of trying to maximise energy efficiency in hopes that “all goes well”. Similar insights can be

gained with regard to the policy framework’s impact on employment and the rebound effect.

This remains detached to the achieved energy efficiency in the residential sector across

simulations and, if linked, could potentially change the results.

It should also be pointed out that this case study indicatively assessed four policy mixes along

the Pareto front calculated in Task 7.2. However, the identification of policy mixes and the

quantification of risks are two independent processes that do not affect the stakeholder

engagement part of the TRANSrisk FCM framework. This means that, after having elicited

stakeholder knowledge regarding the map, the analysts have unbound freedom in creating

scenarios and policy mixes to evaluate and compare against each other.

6.2 Long-term policy pathway for the Polish power

sector

In line with most TRANSrisk case studies, the Poland case study evaluates mitigation pathways

for a low carbon transition in the long term. However, instead of evaluating alternative policy

mixes comprising the same policy instruments at different levels, it seeks to compare two

completely distinct policy pathways for the Polish power sector. One scenario is based on coal

and another one is based on intermittent renewable energy sources. Most importantly, it does

not seek to assess which pathway leads to better climate mitigation results, but rather to

explore how the long-term economic growth of Poland can benefit most. This acknowledges the

different national dynamics, in terms of regime, niche and landscape, from other EU Member

States and captures the current political debate.


6.2.1 Context of the case study

Poland is a coal-dependent economy. For over a century, coal guaranteed economic wealth and

energy independence. Its role has declined since the beginning of 1990s; however, even in 2014,

it represented around half of the Total Primary Energy Supply (Figure 32).

Figure 32: Prevalence of coal in Total Primary Energy Supply (in MTOE), without electricity; crude oil and oil products combined. Source: IEA

In electricity generation, coal plays an even more important role: until the late 1990s, it was

responsible for almost all electricity produced in the country. After joining the EU certain

renewable energy sources (such as biomass and wind) were introduced on a larger scale.

However, all RES combined represent less than 15% of the power generation mix (Figure 33).


Figure 33: Domination of coal in electricity production in Poland (TWh). Source: IEA

Poland aims to permanently decoupling economic growth and CO2 emissions (both generally in

the economy and in the energy sector), which explains the fast pace of technological

convergence with other European economies. Although the country performs at around the

average of EU countries in terms of tonnes of CO2 emitted per capita, it remains among the top

emitters per unit of GDP. In fact, with almost 0.29 kg of CO2 emissions per 1$ of GDP (PPP), it is

(after Estonia and Bulgaria) the third most carbon-intensive economy within the European Union

(Figure 34). This is one of the main reasons behind the coal sector facing strong pressure from

EU climate and energy policy to lower its emissions.

Figure 34: CO2 emissions per GDP (PPP) (kg/$) in Poland. Source: World Bank

In order to simplify the narrative, the transformation of the coal-based energy system in Poland

could be reflected in the debate on two approaches to energy transformation. In the


conservative policy pathway, represented and supported by the current government, the energy

sector will be transformed in an evolutionary process. In this policy pathway, coal will remain

the main source of energy, although the modernisation agenda (including, for example, the

development and diffusion of Clean Coal Technologies) will lead to some reduction of CO2

emissions in the long run. In the more ambitious policy pathway, coal will be rapidly replaced by

other technologies, namely intermittent renewable energy sources and natural gas, thereby

emitting substantially less greenhouse gases than in the conservative scenario.

Assessing which of the two pathways is most beneficial to the low carbon transition of the power

sector is a straightforward task, and of little interest to Task 7.1. Both policy pathways assume

that the low-carbon transformation of the energy system needs to ensure long-run economic

growth. Therefore, the aim of this study is to assess which of the two policy pathways most

benefits economic growth in the long term, from the stakeholders’ perspective.

6.2.2 Determining policy pathways, uncertainties and

narratives

We, therefore, define two policy pathways, “deployment of intermittent renewable energy

sources” and “support for coal-based power”. Drawing from a literature review as well as

modelling activities based on the MEMO integrated assessment model (as part of work carried

out in Task 7.4; more in the upcoming TRANSrisk deliverable D7.3), potential policy shocks were

condensed into seven generic policy strategies. The first policy pathway comprises market

mechanisms for intermittent renewables (e.g. auctions, tenders, tariffs and premiums, etc.);

stability of RES support policies; subsidies for research and development in the RES industry; and

changes in education to orient it away from dirty (e.g. mining) jobs towards other (for instance

green) jobs. The second pathway includes political support for investments in coal power

generation; subsidies for research and development in the coal industry; and dedicated market

design for domestic coal. The seven policy strategies are:

P1. Market mechanism for intermittent RES

P2. Stability of support policies

P3. Subsidies for RES R&D

P4. Schooling in mining regions oriented on new jobs

P5. Political support for investments in coal power plants

P6. Subsidies for coal technologies R&D

P7. Market design for domestic coal

Drawing from work carried out in TRANSrisk Task 5.2, the following uncertainties associated with

the aforementioned policy strategies were selected for the development of the FCM:

R1. Availability of foreign and domestic capital

R2. Barriers of entry for domestic firms/competitiveness of foreign firms

R3. Exogenous technological progress

R4. Costs of gas and nuclear electricity


R5. Non-adaptability of miners

R6. Price of gas

R7. International relations

R8. European attitude towards climate change mitigation

R9. International coal prices

R10. Costs of domestic coal extraction

R11. Price of emission permits

A key uncertainty that we want to assess is the capacity of miners to adapt to a new reality

(R5), since this will have a significant impact on the long-term economic growth. Impacts of

changes in the power generation (coal) sector have already been seen in Poland: the sector was

struggling with overproduction, overemployment and decapitalisation. Most Polish governments

since the democratic transformation in 1989 have tried to improve the sector’s efficiency.

Faster declines in employment than production substantially increased the sector’s productivity.

Radical downsizing of the coal sector did not strongly affect the structure of employment. With

relatively low education levels (Figure 35), miners earn above average salary. This is mostly due

to the risks and health problems related to work in mines, which despite improvement of

occupational health and safety remains an issue. Together with strong position of trade unions,

the perspective of moving away from coal is unattractive to most miners.

Figure 35 Lower level of education among miners (%). Source: LFS

The extent to which coal miners will be able to adapt is of particular importance to “the loss of

labour” narrative (described below) associated with the RES pathway.

Based on the results of the literature review and the modelling activities in the context of

TRANSrisk, the following narratives were captured.


Policy pathway 1: Deployment of Intermittent Renewable Energy Sources

1. The labour loss story: Support for intermittent renewable energy deployment in Poland

(through the current version of the market system or any other) will lead to a rapidly

decreasing demand for coal. In result, the number of jobs in the coal sector, particularly

requiring low skills, will decrease. This will require active labour policies (in schooling or

creation of new, potentially green jobs). There is a risk, however, that these policies will

be rejected by the miners. High earnings historically enjoyed by miners, a relatively low

level of education, as well as the social and cultural factors will lead a proportion of

miners to leaving the labour market and becoming inactive. This loss of the labour force

will negatively affect long run economic growth. This argument is supported by the

recent evidence on surprisingly slow flow of labour between sectors after major

structural changes (Autor et al., 2016; and Tyrowicz and van der Velde, 2014).

Explanation of dependency culture is provided by Hudson (1989) and other—urban and

socio-economic—aspects are described in Gwosdz (2016).

2. Energy security: Deployment of intermittent RES will require substantially greater use of

natural gas. In case of failure to diversify its supplies from countries other than Russia

(enlarging LNG terminals or building new gas routes from Norway), or in case of high

prices of alternative supplies of gas from other countries, Poland will be vulnerable to

political and economic pressure from Russia, which has previously reportedly used that

leverage in Central and Eastern European countries. Higher gas prices and/or insecurity

of supplies will decrease the competitiveness of Polish firms and consequently lead to a

slower long-run economic growth.

3. The barriers of entry: Increased demand for intermittent RES resulting from more

generous support from the state may be consumed mostly by foreign companies. This will

primarily be the case because of the long-term investments, which have been made in

previous decades (particularly in some western economies, as well as China for on-shore

wind and solar power technologies) at levels not achievable for the Polish economy.

Therefore, even with an effort to invest in R&D and develop capabilities, the positive

result for employment will be insignificant. This will be against the preferred pathway of

increasing the position in the global value chain of production. The climate policy

framework would simply require that the Polish economy replace domestic technologies

with expensive ones imported from abroad. In effect, this will have a relatively negative

impact on long-run economic growth.

4. Development of competences: Deployment of intermittent RES will positively affect

demand among domestic producers. When accompanied with support for R&D, this

branch of economy will grow rapidly, also incentivising faster development of

capabilities. Both trends will increase the absorption capacity of external technological

progress which will lead to lower costs of domestic RES. In effect, domestic energy prices

will fall and ensure faster economic growth.

5. Low EU-ETS prices: Stagnation of the EU-ETS prices will negatively affect the

development of both domestic and foreign intermittent RES (Klima and Poznańska, 2013).


Investments in that segment of energy in Poland will be wasted and lead to increased

costs for the energy system. This will negatively affect the long-run economic growth.

Policy pathway 2: Support for a coal-based power system

1. International reputation: Sustaining the coal sector will lead to slower reduction of CO2

emissions. With the consistent EU climate policy, this process will lead to gradual

alienation of Poland, seen as a pariah at the negotiation tables. In effect, EU Member

States will refrain from consulting with Poland on policy initiatives, marginalising its role

in the EU. This will also have an impact on international finance, which will look for

alternative investment opportunities away from Poland. Lower investments will

negatively affect the long run-economic growth.

2. Maintaining competitiveness in coal technologies: Further support for the coal sector will

positively affect demand for domestic coal (even if we assume that part of domestic

consumption will come from import). Demand for domestic coal will provide resources

for the sector to invest in R&D and reduce the extraction costs in the long-run (Acemoglu

et al., 2012). In view of the closure of mines in many other countries, Poland might

strengthen its role as a technological leader in this market. Competitive coal in the

energy sector will be able to provide low energy prices, positively affecting the long-run

economic growth. The potential of clean coal technologies for Poland has been described

by Stańczyk and Bieniecki (2007).

3. The lock-in and the waste of coal R&D effort: Investments in support for the coal sector

described in the previous narrative will turn out to be sub-optimal and lead to increasing

inefficiencies in both technological and human capital terms. The inefficiency would

arise due to two mechanisms. First, fast technological development of RES globally will

lead to substantial reduction in their costs, making them competitive with coal-based

energy in Poland. This would make investments in coal (and respective R&D) a waste of

money and time, negatively affecting the long-run economic growth. Second, this will

slow down the development of RES in Poland, which by that time will benefit from global

scientific collaboration. Lack of experience in manufacturing and installation of RES will

limit the capacity to absorb the technological advancement at the global frontier. This

will lead to additional costs in energy transformation. The role of absorption capacity has

been recognised, for instance, by Goulder and Scheider (1999).

4. Dependence on imported coal: Support for the coal sector in Poland will lead to new

investments in coal-based energy. This support however, in view of the global trends at

the coal market and increasing costs of coal extraction in Poland (due to an insufficient

investment framework for exploiting coal resources, deteriorating operating mining

conditions, trade unions’ negotiations, etc.), will turn to be insufficient to maintain the

competitive price of domestic coal. As a result, the import of coal to Poland will lead to

decline of the Polish coal sector and increase of dependency on imports.

5. High EU-ETS prices: Support for the coal sector will sustain the current level of coal use

in Poland and negatively affect emission reductions. This will preserve a high demand for


carbon emission permits. As a result of reduced exemptions and either faster economic

growth in the EU or political decisions, the price of permits will drastically increase. The

rising costs of the coal-based energy system will negatively affect long-term economic

growth.

The integrated methodological approach employed in Task 7.1 for the Polish case study is

presented in Figure 36.

Figure 36: Integration of different methodologies and tasks in this FCM case study of the Polish energy sector in the long-term


6.2.3 Stakeholder engagement

Unlike the Greek case study (Section 6.1) in which policymakers participated in individual

interviews, Polish stakeholders were engaged in a workshop entitled “Risks of low carbon

transition in Poland” that took place in October 12, 2017, in Warsaw. This, as discussed in

Section 2, required significantly more effort from the consortium’s side (IBS and NTUA), in order

to introduce the TRANSrisk project and design a workshop session, in which stakeholders are

sufficiently informed on the scope and aims of the FCM methodology, effectively guided

throughout the process and successfully facilitated into providing their input in a timely and

structured manner.

To this end, in the dedicated FCM session, the two pathways were presented to the stakeholders

in detail and the aforementioned narratives emerging from the literature and modelling

activities were discussed. This process was supported by crisp system maps, so as to introduce

stakeholders to the nature of our approach. An example of such a map can be seen in Figure 37.

Figure 37: Visual presentation of the labour loss story to the Polish stakeholders

The ten maps corresponding to the five narratives for each policy pathway had been condensed

into one global FCM, which had also been translated into a stakeholder input matrix like the one

presented in Table 9. The global matrix (included in the Appendix) had been broken down into

three parts, each one of which was printed in several copies for the workshop.


At the workshop, one of the three parts was randomly handed out and instructions on the

knowledge elicitation process were provided to each stakeholder, both written (included in the

printed copies) and in slides (including an example provided in Figure 38), as follows:

“Please fill in each white (blank) cell of the table, by indicating the type and level of

impact that the row concept (on the right) has on the column concept (on the top), and

disregarding all other cells.

A positive impact means that a positive change on the row concept will have a positive

effect on the column concept, whereas a negative impact means that a positive change

on the row concept will have a negative effect on the column concept.

Also, please use the following set of variables:

+ = positive, very weak impact

++ = positive, weak impact

+++ = positive, strong impact

++++ = positive, very strong impact

- = negative, very weak impact

-- = negative, weak impact

--- = negative, strong impact

---- = negative, very strong impact

Leave blank if you deem there is no connection between the two concepts."


Figure 38: Example of filling in the stakeholder input matrix

Stakeholders were given 30 minutes to fill in their tables, while the slide presenting the global

map of the Polish FCM was constantly on display (Figure 39). In the meantime, stakeholders’

questions were addressed either in person, or by briefly presenting slides displaying example

stakeholder input matrices from the Greek case study (presented in the previous section) filled

in by the Greek policymakers. We also displayed how these matrices would translate in the map,

in visual format.

After 30 minutes had passed and all stakeholders had completed their assigned task, all

stakeholder input matrices were gathered. In total, six of each of the three parts were filled in

by (a) representatives of private Research and Development firms in the power sector industry,

(b) stakeholders from Public Administration offices, and (c) researchers and members of the

academic community, 18 in total.

The session was carried out in English, as were most of the sessions of the stakeholder workshop.


Figure 39: The global fuzzy cognitive map of the Poland case study

6.2.4 Simulation results

Using ESQAPE, the two policy pathways were stress-tested against the five uncertainty-driven

scenarios and one no-externality scenario (assuming zero values for all eleven uncertainties).

Their performances were evaluated and compared against each other. Following 12 simulation

runs, the results of this case study are presented in Figures 40-45.

When reading these figures, it is evident that, from the involved Polish stakeholders’

perspective, the pathway associated with diffusing intermittent renewable energy sources

outperforms the pathway of insisting on and further supporting a coal-fuelled power sector, in

terms of long-term economic pathway. The same results can be gained for all five Shared

Socioeconomic Pathways, across the axes of challenges for climate change mitigation and

adaptation.


Figure 40: Poland case study results: No external factors assumed


Figure 41: Poland case study results: SSP1-oriented scenario


SSP 3




As seen in Figures 40-45, the more catastrophic the scenario is, in terms of mitigation and

adaptation challenges, the worse the coal-oriented policy pathway performs in relation to the

RES-oriented policy pathway. In the two scenarios driven by the story factors of Shared

Socioeconomic Pathways 3 and 4, the gap between the impacts of the two policy pathways on

the long-run economic growth of Poland appears to grow. On the other hand, in the scenarios

respectively corresponding to SSPs 1 and 5 (low challenges for adaptation in conditions of

economic growth), the two policy pathways perform very close to each other, but with the RES

pathway again slightly outranking the coal-oriented one.

Another significant finding is that, among the seven policy strategies, the only one always

affecting economic growth adversely is political support for investments in coal-fired power

plants. All other policy strategies, when assessed individually (i.e. modelled as the only strategy

activated), appear to have positive impacts on national economic growth, in most scenarios.

Finally, it was interesting to also note that almost all stories and narratives resulting from the

literature review or the MEMO model runs and presented to the stakeholders were to some

extent verified in the FCM exercise. Regarding employment, all scenarios showed that the RES

pathway would result in a large increase in new (green) jobs creation and a proportionate

decrease in traditional jobs. The coal pathway, on the other hand, would always lead to positive

changes in both types of jobs, although the impact on the traditional workforce would be larger,

except for the 3rd SSP scenario. In the latter scenario, adverse socioeconomic developments


(including sustainability, technological development and international relations), coupled with

insistence on a coal-powered energy system, appear to result in significant losses in new jobs.

This potentially shows the result of poor education, as described in the SSP’s story factors.

It should be highlighted that, in this case study, other scenarios could have been used instead of

the Shared Socioeconomic Pathways, since the latter—despite including factors on economy and

lifestyle—are primarily oriented on mitigation and adaptation challenges instead of challenges

for economic growth, which is of particular importance in the Polish context.

The FCM prediction on economic benefits associated with low-carbon transition contrasts with

the rhetoric of policy-makers supporting the presence of coal in Polish energy mix (see D3.2, the

Poland case study), as well as the predictions of general equilibrium economic models (see D5.3,

the Poland case study). However, one should keep in mind an important difference between the

FCM result and the cost-benefit evaluations. During the FCM workshop, the stakeholders were

not asked directly which pathway has higher economic costs, and thus FCM results should not be

interpreted as support for the transition by the sample of stakeholders. The primary purpose of

the exercise was to identify crucial interdependences between policies and factors determining

economic growth. In this light, the most appropriate conclusion which could be derived from the

FCM results is that, according to the stakeholders there exist important channels through which

low-carbon transition could increase economic growth.

Some of these channels are not considered in many economic models (such as the experience of

domestic firms allowing them to absorb and adapt green innovations from other countries),

which means there are two further lessons from the study. First, the evaluations which use

standard economic models should be taken with caution. Second, there is an urgent need to

improve the structure of the models in order to account for the channels recognised by the

stakeholders.


7 CONCLUSIONS

In this report, a decision support tool based on the Fuzzy Cognitive Mapping methodology is

developed and presented. First, the contribution of the original FCM methodological framework

in relevant studies is reviewed and explored. This review showed that, as a decision support

tool, it has been used for supporting policy making but underexploited in the climate policy

domain. Following this, an innovative approach is introduced, aiming at better framing the

method in the context, challenges and specifications of the climate policy domain, as well as in

the scope and needs of the TRANSrisk project. For the purposes of implementing the proposed

approach, a MATLAB-based software application tailored to fit the needs of climate policy

support by means of FCMs, ESQAPE, was developed and is also presented in the report. Finally,

the TRANSrisk FCM model and tool are validated through two case study pilots. They are also

expected to be further utilised in other case studies, to be integrated in a planned update of

this Deliverable.

From a methodological point of view, several conclusions can be drawn. First, drawing from its

successful implementation, the FCM approach developed and employed has been well

established as a climate policy support tool. The case studies show that it can successfully

support climate policy making with the aim of assessing and comparing both alternative policy

pathways and policy mixes or strategies, both in the short- to medium-term and in the long-

term, against both climate- (e.g. energy savings and efficiency or GHG mitigation) and economy-

related (e.g. economic growth) criteria. Second, our experience showed that, in line with the

overall scope of TRANSrisk, the FCM model can be well integrated with other quantitative and

qualitative methodologies. In fact, the Greece case study was based on an integrated approach,

the components of which included quantitative models (the Greek Ministry’s energy systems

models), multiple-criteria decision support tools (TRANSrisk Task 5.5) and portfolio analysis

(TRANSrisk Task 7.2).

The Poland case study was based on a different setting of integrated methodologies, including

integrated assessment modelling with the MEMO modelling framework (TRANSrisk Task 7.4) and

literature review. In other words, the proposed method appears to bridge the gap between

climate-economy modelling and stakeholders (including policymakers), as well as integrating

with both IAMs and other climate policy support tools. Finally, in terms of stakeholder

engagement, the proposed model features significant modifications in relation to commonly

used method, in that it limits stakeholder engagement so that the FCM process is driven both by

stakeholders and by findings of different methods and tools.

However, certain limitations must be highlighted as well. The number of concepts (or

components) into which a system can be broken down must be limited to 30-35, according to the

literature, so that they remain meaningful for the stakeholders. This limitation significantly

impacts the level of detail and complexity of an FCM. Additionally, despite having developed a

software application that attempts to incorporate the notion of time, the latter remains

underexploited in the context of the case study applications. Finally, given that the maps are

constructed a priori and not exclusively based on stakeholder input (as is the common practice),


during the implementation phase certain stakeholders appeared to disagree on, or not

understand, all assumed statements.

Regarding the application of the proposed policy support tool (and software application) in the

case studies per se, empirical findings suggest that (a) Greek policymakers appear to be risk-

neutral and to prefer robust, diverse policy portfolios comprising a small number of policy

instruments and primarily involving financial support for upgrades in residencies and SMEs as

well as deployment of smart metering systems; and (b) from the Polish stakeholders’

perspective, a pathway revolving around diffusion of renewable energy sources would be more

beneficial to Poland’s economic growth in the long-term, compared to a pathway insisting on

supporting coal.

These are discussed in detail in Sections 6.1.4 and 6.2.4 respectively.


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9 APPENDIX

Drawing from O’Neill et al. (2015), the following table presents the story factors describing the

five Shared Socioeconomic Pathways in a concise manner, as synthesised for the purposes of

TRANSrisk Work Package 5.

Category Factor SSP 1 SSP 2 SSP 3 SSP 4 SSP 5

Possible SRES analogues: B1, A1T None,

intermediate

between SSP1 and

SSP3

A2 No analogue A1F1

Demographics Population

growth

Relatively low Medium Poor countries:

high

Rich countries:

low

Poor countries:

rel. high

Rich countries:

low

Relatively low

Population

fertility

Poor countries:

low

Rich countries:

medium

Medium Poor countries:

high

Rich countries:

low

Poor countries:

high

Rich countries:

low

Poor countries:

low

Rich countries:

high

Population

mortality

Low Medium High Poor countries:

high

Rich countries:

medium

Low

Migration Medium Medium High Medium High

Urbanisation

level

High Medium Low High High

Urbanisation

type

Well-managed Continuation of

historical

patterns

Poorly managed Mixed across

and within

cities

Better

management

over time,

some sprawl

Human

Development

Education High Medium Low Poor countries:

very unequal

Rich countries:

unequal

High

Health

investments

High Medium Low Unequal within

regions

Poor countries:

low

Rich countries:

high

High


Access to

health

facilities,

water,

sanitation


regions

Poor countries:

low

Rich countries:

high

High

Gender

equality


regions

Poor countries:

low

Rich countries:

high

High

Equity High Medium Low Medium High

Social cohesion High Medium Low Low, stratified High

Societal

participation

High Medium Low Low High

Economy &

lifestyle

Economic

growth (per

capita)

Poor countries:

high

Rich countries:

medium

Medium, uneven Slow Poor countries:

low

Rich countries:

medium

High

Inequality Reduced across

and within

countries

Uneven moderate

reduction across

and within

countries

High, especially

across countries

High, especially

within countries

Strongly

reduced,

especially

across countries

International

trade

Moderate Moderate Strongly

constrained

Moderate High, with

regional

specialisation

in production

Globalisation Connected

markets,

regional

production

Semi-open

globalised

economy

De-globalising,

regional security

Globally

connected

elites

Strongly

globalised,

increasingly

connected

Consumption

and diet

Low growth in

material

consumption,

low meat diets

starting with

rich

Material-intensive

consumption,

medium meat

consumption

Material-intensive

consumption

Elites: high

consumption

lifestyles

Rest: low

consumption &

mobility

Materialism,

status

consumption,

tourism,

mobility, meat-

rich diets

Policies &

Institutions

International

cooperation

Effective Relatively weak Weak Effective for

globally

connected

Effective in

pursuit of

development


economy, not

for vulnerable

populations

goals, limited

for env. goals

Environmental

policy

Improved

management of

local and global

issues

Tighter

regulation of

pollutants

Concern for local

pollutants but

only moderate

success in

implementation

Low priority for

environmental

issues

Focus on local

environment in

medium to rich

countries

Little attention

to vulnerable

areas or global

issue

Focus on local

environment

with obvious

benefits to

well-being

Little concern

with global

problems

Policy

orientation

Towards

sustainable

development

Weak focus on

sustainability

Oriented toward

security

Towards the

benefit of

political and

business elite

Towards

development,

free markets,

human capital

Institutions Effective at

national and

international

levels

Uneven, modest

effectiveness

Weak global

institutions

National

governments

dominate societal

decision-making

Effective for

political and

business elite,

not for rest of

society

Increasingly

effective,

oriented

toward

fostering

competitive

markets

Technology Development Rapid Medium, uneven Slow Rapid in high-

tech economies

and sectors

Slow in other

sectors

Rapid

Transfer Rapid Slow Slow Little transfer

within countries

to poorer

populations

Rapid

Energy tech

change

Directed away

from fossil

fuels, to

efficiency &

renewables

Some investment

in renewables but

continued

reliance on fossil

fuels

Slow tech change,

directed toward

domestic energy

sources

Diversified

investments

including

efficiency &

low-carbon

sources

Directed

toward fossil

fuels

Alternative

energy sources

not actively

pursued

Carbon

intensity

Low Medium High in regions

with large

domestic fossil

fuel resources

Low to medium High

Energy

Intensity

Low Uneven, poor

countries: higher

High Low to medium High


Environment

& natural

resources

Fossil

constraints

Preferences

shift away from

fossil fuels

No reluctance to

use

unconventional

resources

Unconventional

resources for

domestic supply

Anticipation of

constraints

drives up prices

with high

volatility

None

Environment Improving

conditions over

time

Continued

degradation

Serious

degradation

Highly managed

and improved

near high/

middle-income

living areas,

degraded

otherwise

Highly

engineered

approaches,

successful

management of

local issues

Land use Strong

regulations to

avoid

environmental

tradeoffs

Medium

regulations lead

to slow decline in

the rate of

deforestation

Hardly any

regulation

Continued

deforestation due

to competition

over land & rapid

expansion of

agriculture

Highly regulated

in richer

countries

Largely

unmanaged in

poor countries

leading to

tropical

deforestation

Medium

regulations lead

to slow decline

in the rate of

deforestation

Agriculture Improvements in

agr.

productivity

Rapid diffusion

of best

practices

Medium pace of

tech change in

agr. sector

Entry barriers to

agr. markets

reduced slowly

Low technology

development,

restricted trade

Agr.

productivity

high for large

scale industrial

farming, low for

small-scale

farming

Highly

managed,

resource-

intensive

Rapid increase

in productivity


The stakeholder input matrix, which Polish stakeholders were asked to fill in, is presented below:

Inte

rmit

tent

RES

deplo

ym

ent

Suff

icie

nt

finance

Insi

stence o

n c

oal

Dem

and f

or

RES

inst

allati

ons

Dem

and f

or

RES

inst

allati

ons

by

dom

est

ic p

roducers

New

(gre

en)

jobs

Tra

dit

ional jo

bs

Dem

and f

or

gas

Energ

y s

ecuri

ty

Energ

y p

rices

(=energ

y s

yst

em

cost

s)

Fore

ign p

rogre

ss

abso

rpti

on c

apacit

y

Long-r

un R

educti

on

in R

ES inst

allati

on

cost

s

GH

G e

mis

sions

and

polluti

on

Import

of

coal

Inte

rnati

onal

Reputa

tion a

nd

Fin

ance

Com

peti

tiveness

of

coal ele

ctr

icit

y

Long-r

un e

conom

ic

gro

wth

R1. Availability of foreign and domestic capital

R2. Barriers of entry for domestic firms/competitiveness of foreign firms

R3. Exogenous Technological Progress

R4. Costs of gas and nuclear

R5. Non-adaptability of miners

R6. Price of gas

R7. International relations

R8. European attitude towards mitigations

R9. International coal prices

R10. Costs of domestic extraction

R11. Price of permits

P1. Market mechanism for intermittent RES

P2. Stability of support policies


In

term

itte

nt

RES

deplo

ym

ent

Suff

icie

nt

finance

Insi

stence o

n c

oal

Dem

and f

or

RES

inst

allati

ons

Dem

and f

or

RES

inst

allati

ons

by

dom

est

ic p

roducers

New

(gre

en)

jobs

Tra

dit

ional jo

bs

Dem

and f

or

gas

Energ

y s

ecuri

ty

Energ

y p

rices

(=energ

y s

yst

em

cost

s)

Fore

ign p

rogre

ss

abso

rpti

on c

apacit

y

Long-r

un R

educti

on

in R

ES inst

allati

on

cost

s

GH

G e

mis

sions

and

polluti

on

Import

of

coal

Inte

rnati

onal

Reputa

tion a

nd

Fin

ance

Com

peti

tiveness

of

coal ele

ctr

icit

y

Long-r

un e

conom

ic

gro

wth

P3. Subsidies for RES R&D

P4. Switch in schooling oriented on new (green) jobs

P5. Political Support for investment in coal power plants

P6. Subsidies for coal technologies R&D

P7. Market design for domestic coal

Intermittent RES deployment

Sufficient finance

Insistence on coal

Demand for RES installations

Demand for RES installations by domestic producers

New (green) jobs

Traditional jobs

Demand for gas

Energy security


In

term

itte

nt

RES

deplo

ym

ent

Suff

icie

nt

finance

Insi

stence o

n c

oal

Dem

and f

or

RES

inst

allati

ons

Dem

and f

or

RES

inst

allati

ons

by

dom

est

ic p

roducers

New

(gre

en)

jobs

Tra

dit

ional jo

bs

Dem

and f

or

gas

Energ

y s

ecuri

ty

Energ

y p

rices

(=energ

y s

yst

em

cost

s)

Fore

ign p

rogre

ss

abso

rpti

on c

apacit

y

Long-r

un R

educti

on

in R

ES inst

allati

on

cost

s

GH

G e

mis

sions

and

polluti

on

Import

of

coal

Inte

rnati

onal

Reputa

tion a

nd

Fin

ance

Com

peti

tiveness

of

coal ele

ctr

icit

y

Long-r

un e

conom

ic

gro

wth

Energy prices (=energy system costs)

Foreign progress absorption capacity

Long-run Reduction in RES installation costs

GHG emissions and pollution

Import of coal

International Reputation and Finance

Competitiveness of coal electricity

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