Sh Shap aping ing so soci cial al ac acce ceptance ptance of - - PowerPoint PPT Presentation

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Sh Shap aping ing so soci cial al ac acce ceptance ptance of of ene nergy rgy pr proj ojects cts Mathilde TESSIER, Sandrine SELOSSE, Nadia MAIZI Intr trodu oduction ction (1/3) 3) Energy modeling Climate crisis Long

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Sh Shap aping ing so soci cial al ac acce ceptance ptance of

  • f

ene nergy rgy pr proj

  • jects

cts

Mathilde TESSIER, Sandrine SELOSSE, Nadia MAIZI

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SLIDE 2

Intr trodu

  • duction

ction (1/3) 3)

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Energy modeling

  • Long service lifes of the

technologies

  • Design long-term scenarios
  • Looking for robust and

reliable scenarios (that could be actually used)

Centre de MathématiquesAppliquées - MINES ParisTech

Climate crisis

  • Limiting GHG to limit the

global rise in temperature to 1,5/2°C

  • Need to design low-carbon

solutions and rethink energy systems

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Introduction (2/3)

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  • Energy projects have been hindered by local opposition
  • Start research on the phenomenon and how public

perception is formed

  • Wide phenomenon studied mostly by social scientists

Centre de MathématiquesAppliquées – MINES ParisTech

Social issues

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Intr trodu

  • duction

ction (2/2) 2)

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  • Two very diverse fields
  • No literature linking the two topics
  • Lack of social aspects in current long-term scenarios -> less realistic

scenarios

Centre de MathématiquesAppliquées – MINES ParisTech

Goal of the PhD :

Connect nect the dot

  • ts bet

etween een energ ergy model elling ling and d social al issue ues

First t step of the PhD : Under ersta stand nd social al acceptance nce

  • f ene

nerg rgy pro rojec ects ts

Resear search question stion :

What are the impacts of social acceptance of energy projects on long-term modelling ?

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Plan an

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I. Concepts involved II. Analysis of the literature III. Key issues

Centre de MathématiquesAppliquées – MINES ParisTech

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Concept ncepts s at at stak ake an and points ints of vi view

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  • Acceptance : a posteriori evaluation
  • f a project
  • Acceptability : a priori evaluation of

a project

  • Support : active engagement for a

project

  • Opposition : active engagement

against a project

  • NIMBY (Not In My Back

Yard) : Opposition between a general positive opinion and a local

  • pposition

Centre de MathématiquesAppliquées – MINES ParisTech

Citizens: people living near the project who might oppose or support it. Industrials: companies locally or nationally involved in the design or the realization of the project. Decision makers: local and national politicians who have an impact on location decisions, public investments, etc.

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Three ree-dim dimen ensional sional as asses essm sment nt of social ial ac acceptance eptance

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Social acceptance as a three-dimensional assessment (Wüstenhagen et al, 2007):

  • Community acceptance : Stakeholders concerned by a local

project

  • Socio-political acceptance : broad, policy making
  • Market acceptance : adoption and diffusion of technologies

Centre de MathématiquesAppliquées – MINES ParisTech Market Community Socio- political

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Diver ersity sity of the e literature erature / / then en interest erest for for a a map ap

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  • Differences in :

– Aim of the articles – Geography studied – Technology / kind of project studied

  • Are they technologies more studied in specific areas ? Need

for a geographical representation

Centre de MathématiquesAppliquées – MINES ParisTech

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Ge Geogra graphical phical focus focus

9 Centre de MathématiquesAppliquées – MINES ParisTech

Social acceptance of energy projects: A geographical focus based on literature (based on the analysis of 96 papers)

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Map ap an anal alysis sis

10 Centre de MathématiquesAppliquées – MINES ParisTech

  • Most studied zones: Western Europe, Middle East, North

America.

  • Average studied zones: South America and Oceania.
  • Least studied zones: Africa, Asia, former USSR.

Sorted by area :

  • Nuclear mostly in Asia.
  • Wind power in Western Europe.
  • Solar power in developing countries.
  • Energy policy in developed countries.
  • North America: mostly oil & gas & bioenergies.

Sorted by technology / policy :

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Extracting social acceptance characteristics to feed the energy model

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Goal : Extracting key parameters that explain social acceptance to feed the energy model Method :

  • Identification of « measure » articles
  • Spot parameters put forward in the articles

Centre de MathématiquesAppliquées – MINES ParisTech

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From the map to TIAM FR (1/2)

12 Centre de MathématiquesAppliquées - MINES ParisTech

Geographical zones of TIAM

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From the map to TIAM FR (2/2)

13 Centre de MathématiquesAppliquées - MINES ParisTech

100000 200000 300000 400000 500000 600000 700000 2C_2100 BAU_2100_415 2C_2100 BAU_2100_415 2C_2100 BAU_2100_415 2010 2050 2100 PJ Year / Scenario

Energy mix evolution - BAU and 2°C scenarios

ALL ALCOHOLS ALL BIO ALL COALS ALL ELECTRICITY ALL GAS ALL HEAT ALL HYDROGEN ALL NUCLEAR ALL OIL PRODUCTS ALL OTHER RNW

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Most st impor portan tant t par arame ameter ers (1/2) 2)

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Community parameters :

Centre de MathématiquesAppliquées – MINES ParisTech

  • Individual parameters : gender, age, level of education, political

ideology or lack of knowledge and pre-conceived ideas on the project ;

  • Projects parameters (projects’ characteristics) : technology

chosen, stakeholders involved, and communication on the project

  • Local parameters : type of landscape, history of the region, power

sources already in operation, etc.

  • General context :

– Paris Agreement -> non-fossil energies

  • Specific events :

– Fukushima-Daishi nuclear disaster -> specific technologies

Socio political parameters :

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Most st impor portan tant t par arame ameter ers (2/2) 2)

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Dynamics of the acceptance

Centre de MathématiquesAppliquées – MINES ParisTech

Market acceptance (technology):

  • will of industrials to diffuse a

technology

  • will of customers to use it
  • Opposition between :
  • envy for green power
  • ffers and
  • reluctance to local

projects (NIMBY).

  • > lack in green power
  • intra-firm acceptance.

Dreyer et al, 2017 –Temporal change in acceptability and acceptance

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Diffi ficul cultie ties / / lac ack of quan antif ified ied dat ata

16 Centre de MathématiquesAppliquées – MINES ParisTech

  • Qualitative parameters, but TIMES model is quantitative
  • Few data in the literature
  • Very diverse literature and methodology
  • > non coherent set of data
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Key issues ues (1/2) 2)

17 Centre de MathématiquesAppliquées – MINES ParisTech

  • A risk to forget developing countries in our

analysis:

  • In developed countries: the transition is mainly electric,

from big thermal power plants toward small renewable installations.

  • In developing countries: the transition is mostly from

firewood toward off-grid renewable power.

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Key issues ues (2/2) 2)

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  • Focus on reducing of opposition:

Most articles focus on the ways to reduce opposition to a

  • project. Our goal is to think of how to include this reluctance in
  • ur model.
  • Focus on citizens:

Articles often focus on citizens and not on the other stakeholders shaping projects, which can elude some of the important parameters.

Centre de MathématiquesAppliquées – MINES ParisTech

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Conclusion nclusion an and Next xt steps eps

19 Centre de MathématiquesAppliquées – MINES ParisTech

  • Conclusion :

– Very diverse and broad phenomenon – Qualitative phenomenon that will be difficult to quantify

  • Next steps:

– Understand the possibilities to modify of the TIMES/TIAM model

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THA HANK NK YOU FOR YOUR UR ATTEN ENTION TION

20 Centre de Mathématiques Appliquées MINES ParisTech