Transcript Innovation Environnementale et Dynamique Industrielle

DIMETIC, 19th october
2007, Maastricht, The
Netherlands
Coevolution of supply and demand:
the case of environmental innovations
Maïder Saint Jean
GREThA, Bordeaux University
[email protected]
Groupe de Recherche en Economie Théorique et appliquée – UMR CNRS 5113
References
• Belis-Bergouignan M-C., Oltra V., Saint Jean M., 2004,
Trajectories towards clean technology: example of
volatile organic compound emission reductions,
Ecological Economics, 48, pp201-220.
• Saint Jean M., 2005, Coevolution of suppliers and users
through an evolutionary modelling – The case of
environmental innovations, European Journal of
Economic and Social Systems, 18 (2), pp255-284.
• Saint Jean M., Polluting emissions standards and clean
technology trajectories under competitive selection and
supply chain pressure, Forthcoming in Journal of
Cleaner Production.
Groupe de Recherche en Economie Théorique et appliquée – UMR CNRS 5113
Outline of the presentation
1. Some stylised facts on environmental
innovations
2. The main building blocks of the model
3. Results
4. Further developments: analysis of policy
instruments
5. Conclusions
Groupe de Recherche en Economie Théorique et appliquée – UMR CNRS 5113
1. Some “stylised facts” on environmental
innovations
• Environmental innovations: innovations that consist of
new or modified processes, practices, systems and
products which benefit the environment and contribute to
environmental sustainability  regulatory push-pull effect
• Clean technology vs end-of-pipe technology
– Clean technology implies an integrated change in the
production process and a reduction of pollution at source;
– End-of-pipe technology controls and treats pollution after it
has been generated
 multi-dimensionality of clean technology; innovation
offsets
• Environmental R&D dedicated to the improvement of
environmental quality of processes and products
• “Green paradigms” for the generation of heat, electricity
and motion  radical questioning of existing production
processes; technological irreversibility and lock-in
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Evolution of anti-pollution investments
Millions Euros
1 400,00
1 200,00
Specific investments
1 000,00
800,00
Anti-pollution share of
integrated investments
600,00
Total of anti-pollution
investments
400,00
200,00
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
0,00
source: SESSI
Environmental R&D of firms (% share in the total
R&D)
Evolution of environmental R&D
2,5
1 500
2
Administration
1 000
Firms
Total
500
%
1,5
1
0,5
20
02
20
01
20
00
19
99
19
98
19
97
19
96
19
95
19
94
19
93
19
92
Source: Ifen, Com m ission des com ptes et de
l’économ ie de l’environnem ent, avril 2006 –
Ministère chargé de la Recherche.
0
19
91
20
04
p
20
02
20
00
19
98
19
96
19
94
19
92
19
90
-
19
90
Million Euros
2 000
Source: Ifen, Com m ission des com ptes et de l’économ ie de
l’environnem ent, avril 2006 – Ministère chargé de la Recherche.
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The space of clean technology trajectories
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Case 1: the paintings
• Diversity of users and
market segments
• Public concern
• Significant range of
environmental
innovations with no or low
solvents
• Change in the knowledge
base of the producers
and the users: paintings 
organic chemicals
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Case 2: the surface treatment
activities
• Clean technologies for the
surface treatment, ex.: low
temperature plasma
• SMEs of the metal-work sector
subcontractors of car
manufacturers and aerospace
firms
• Technological irreversibilities
in the solvent paradigm
• Obstacles to the adoption of
clean technology:
– High adopion costs related
to weak financial and
absorptive capacities
– Product performance
constraints
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2. The main building blocks of the
model
Scheme of supply-demand interactions
SUPPLY n firms
Competition
among
suppliers
DEMAND m firms
Allocation of R&D
Investment
Process/Product
innovation
Performance achieved
for each characteristic
Market
share
Profile of
each client:
Preferences
Purchase
Average performance of industry
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Requirement
Levels: maximum
price, minimum
quality standards,
environmental
regulatory
standards
Defection
Procedure of supplier’s selection by a client at
time t
Supplier t-1 of client j: S(t-1)
Requirement thresholds
of client j at time t
At time t, performances of supplier S
Requirement thresholds reached?
YES
Product replacement
with the same supplier S
Transfer of information
from client j to S(t):
priority characteristics
NO
Defection of client j
Selection of a new supplier
remaining on the market on
the basis of its global performance
Choice of a new supplier S’
Transfer of information
from client j to S’(t)
Groupe de Recherche en Economie Théorique et appliquée – UMR CNRS 5113
Models of reference
• Chiaromonte F., Dosi G., 1993, Heterogeneity, competition and
macroeconomic dynamics, Structural Change and Economic
Dynamics, 4, 39-46.
• Malerba F., Nelson R., Orsenigo L., Winter S., 1999, ‘ Historyfriendly ’ models of industry evolution: the computer industry,
Industrial and Corporate Change, 8, 3-40.
• Laffond G., Lesourne J. and Moreau F., 1998, Stratégies de
différenciation environnementale et dynamique des structures de
marché, Colloque AFSE, Toulouse, May.
• Valente M., 1999, Evolutionary economics and computer simulations
- A model for the evolution of markets, PhD thesis, University of
Aalborg, Denmark.
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Table of correspondence
1. R&D investments and innovation
activities of suppliers
2. The product price
3. Technology space
4. Decision rules of clients
5. Inter-firm interactions
6. Exit process
Groupe de Recherche en Economie Théorique et appliquée – UMR CNRS 5113
1. R&D investments and innovation
activities of suppliers
Innovation is a mean to acquire dominant position
and technological lead
R&D activities are an important source of
innovation
Environmental R&D dedicated to the
improvement of environmental performance of
processes and products is mainly financed by
firms and performed independently. It is often a
response to government regulation
The innovation process is characterised by radical
uncertainty
RDi,t  i .Pi,t .Bi,t
P is the price of the product i at time t and B the
install base of firm I at time t. Mu is the fraction of
turnover allocated to R&D.
h
h
RDi ,t   i ,t . RDi ,t
Amount of resources allocated to the
characteristic h
h
h
h
Ri ,t   . RDi ,t  (1   ). Ri ,t 1
Research level achieved for the characteristic h
h
h
 i ,t   1 ( 2   3 . exp( 4 . Ri ,t ))
Probability of innovation
Innovative activities take place within paradigms
so that they are strongly selective, finalised in
rather precise directions and often cumulative
Technological change is firm-specific, cumulative
and path-dependent
Performance level performed by firm i for the
characteristic h at time t: X ih,t
Innovation output:


Experience:

h
h
h
h max
h
X i,t  0 .( Ri ,t ) 1 .( Ei ,t ) 2 .( X
 X i,t 1 ) 3
Ap
Ap
Ap
Ei ,t   .( MaxE  Ri ,t )  (1   ). Ei ,t 1
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2. The product price
Mark-up rule over the
production costs
1
Pi,t  (1  i ).(1 X i ,t 1 )
Teta is the mark-up rate of firm i
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3. Technology space
Different paradigms are
competing or coexisting
with different
environmental and
technological
opportunities
Competence-destroying
effect when changing
paradigm
Cf. figure
Environmental quality of the
process
X3max2
PARADIGM 2
X3max1
PARADIGM 1
Productive efficiency of the
process
The switch carried out by a firm in the paradigm with
high environmental potential leads to three effects: a
shift in the frontier achievable on the dimension
‘environmental quality of process’, a drop in the
product performance and a decrease in the
cumulated experience.
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4. Decision rules of clients
a) Purchase Clients take into account the
product characteristics they perceive from
the suppliers and evaluate the product
according to their preferencesMimetic
behaviour and/or switching costs affect the
product’s selection
Purchase probability:
~
~
x1
x2
x3
x4
Pr oba ij ,t  (1 Pi ,t 1 ) j .( X i2,t 1 ) j .( X i3,t 1 ) j .( X i4,t 1 ) j .(MS i ,t 1 )e
Preference that client j attributes to the
1
2
3
4
characteristic x j x j x j x j
Parameter e expresses the intensity of
‘bandwagon effect’ that a supplier with high
market shares may exert on client.
n
MSi,t  Bi,t  Bi,t
i 1
b) Product replacement Durable goods
are only replaced after a certain period of
use
Each client replaces its product after T
periods, with T settled randomly between 1
and 10.
c) DefectionThe continuation of the
relationships is conditional to the
achievement of some minimum
requirements imposed by demand or
regulationImplicit threat of an exit/voice
mechanism governing interfirm
relationships
If the supplier fulfils the client’s requirement
criteria, then the supplier records a sale
N i,t  1
while its install base remains unchanged.
If not, the supplier loses one client from its
stock Bi,t  1
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5. Inter-firm interactions
a) Transfer of information Supplier’s R&D
allocation are guided by demand needs
both in terms of the priority characteristics
for the client and in terms of the minimum
quality standard required in the industry
Gathering of data: a positive score is given
to characteristics that are both priority for
the clients and represent a source of
technological lead for the supplier; Z ih,t  1
A negative score is registered for the
characteristic with a performance level
below the one required by the client Wih,t  1
b) Evolution of the allocation of R&D
investment of suppliers Bounded
rationalityAdaptive decision rules through
individual learning and demand feedback
Adjustment of R&D index:


RDIndexih,t  (1   ).RDIndexih,t 1    (  .Zih,t  (1   ).Wi ,ht ) (  . Zih,t  (1   ).Wi ,ht ) 
h
h



: Speed of adjustment;  : the relative
importance attributed to the positive
indicators compared to the negative ones
h
h
h
 i ,t  RDIndexi,t  RDIndexi,t
h
c) Evolution of minimum requirements
of clients under the influence of
technological advances in the industry
Social diffusion or collective learning
h
h
h
h
h
levelX j ,t  levelX j ,t 1   .(max( 0, x j .( X t  levelX j ,t 1 )))
Increase in the requirement level of a client
Average performance of the industry:
n
h
h
X t   MSi,t . X i,t
i 1
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6. Exit process
Inefficient firms do not
survive
Exit both when the install base is equal to 0, i.e.
the user stock of the supplier is exhausted, and
when the sales are equal to zero for a minimum of
four periods.
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3. Results
• Protocol of simulation: parameter
initialisation, battery of simulations
– The reference configuration: 12 suppliers,
200 clients, 2 groups of clients
• Two scenarios or market configurations:
– scenario ‘ homogeneous oligopoly ’
– scenario ‘ market segmentation ’
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40%
30%
20%
10%
0%
0
100
200
300
400
500
Environ men tal q u ality o f
p ro cess
M a rket sh ar es
Scenario HO
4
3
2
1
0
0
1
F2
10
5
0
100
200
300
400
F2
5
6
7
8
9 10 11 12 13 14 15
500
F2
F3
10
8
6
4
2
0
0
100
200
300
400
Ite rations
Iterations
F1
4
F1
15
0
3
F3
Env ironm e nta l qua li ty
of produc t
Pro d u ct p erfo rman ce
F1
2
Productive efficiency
It e r at io n s
F3
F1
F2
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F3
500
Environmental quality of
process
Scenario MS
M arket sh ares
100%
80%
60%
40%
20%
0%
0
100
200
300
400
15
10
5
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
500
Productive Efficiency
Ite r atio n s
F1
F2
Environmental quality of
product
Product perform ance
F1
15
10
5
0
0
100
200
300
500
15
10
5
0
0
100
200
300
Iterations
Iterations
F1
400
F2
F2
F1
F2
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400
500
4. Further developments: policy
implications
• The impact of tighter standards:
– Process standards
– Product standards
– Policy timing
 The rise in the environmental requirements of clients, generated by tighter
environmental standards, has different impacts according to the nature and timing
of the standards
• The role of procurement policy:
– Critical mass of ‘green’ users
– Strategic niche management (Kemp, Schot and
Hoogma, 1998)
• Taxes, subsidies, diffusion of information, etc.
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Process standards
Scenario MS
Process environmental
quality (Ip)
Process environmental
quality (Ip)
Scenario HO
6
5
4
3
2
1
0
0
2
4
6
8
10
12
14
16
5
4
3
2
1
0
0
2
Standard Ip/100
6
8
10
12
14
16
Productive efficiency (Ap)
Productive efficiency (Ap)
Reference case
4
Standard Ip/200
Reference case
Standard Ip/100
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Standard Ip/200
Product standards
1
Standard Im/100
480
440
400
360
320
280
240
Iterations
Iterations
Reference case
200
0
160
480
440
400
360
320
280
240
200
160
80
120
1
0
2
80
2
3
120
4
4
1
6
5
40
Average process
environmental quality
(Ip)
Scenario MS
8
40
Average process
environmental quality
(Ip)
Scenario HO
Standard Im/200
Reference case
Standard Im/100
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Standard Im/200
Limitations
• Methodological problems related to simulations :
- the stochastic characteristic of the dynamics;
- the high number of parameters;
- the empirical calibration of the model.
• Limits of the model:
-
no sectoral differences are taken into account;
there’s no real price strategies of firms;
effective financial constraints do not apply;
the role of final consumers is not explicitly incorporated;
no new innovative entrants are considered.
• Regarding environmental innovations:
- the anticipation of environmental regulation by firms and its
impact on firm’s innovation strategy;
- the issue of “transition management” and system
innovations.
Groupe de Recherche en Economie Théorique et appliquée – UMR CNRS 5113
5. Conclusions
• An evolutionary model of supply and demand
coevolution
• Process/product innovations with characteristics
of environmental quality
• Related questions cf. M. Schwoon (fuel cell
vehicles, role of infrastructures), E. Brouillat
(recycling, product life extension), impact of
REACH (Registration, Evaluation, Autorisation
and Restriction of Chemicals) on innovation
• Empirical validation?
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