WIOD Consortium Meeting
Sevilla, 25 – 26, May, 2011
Embodied and induced technical
change and the price of carbon
Kurt Kratena
Michael Wueger
Technical change in E3 modelling
Literature: embodied and induced technical change
•
Models increasingly integrate features of endogenous technical change:
WITCH (Bosetti et al., 2006), CGE (Otto, Loeschel and Reilly, 2008)
• Endogenous innovation: (i) energy saving R&D, (ii) learning by doing for
carbon-free technologies
• Popp (2002): energy saving innovation, Sue Wing (2006): technical
change induced by climate policy  climate policy creates the cost
savings of it’s own measures
Critical issues and questions
• Induced innovation or diffusion of (existing) technologies?
• Difference between substitution of factors (for example K and E) and
technical change (Binswanger and Ruttan, 1978 and Sue Wing, 2006)
• Are K and E substitutes or complements ? Does embodied and induced
technical change only work with K and E substitutability ?
Technical change in E3 modelling
K,L,E,M (Translog) model of production with embodied
and induced technical change
• Factor bias & TFP (Binswanger and Ruttan, 1978; Jorgenson and
Fraumeni, 1981, Jorgenson, 1984)
•
Embodied technical change (Berndt, Kolstad and Lee,1993; Sue
Wing and Eckaus, 2007)
• From embodied to induced technical change: K as short-run fixed
factor plus investment function (first results with EUKLEMS data in
Kratena, 2007)
• Model of dynamic factor demand (Pindyck and Rotemberg, 1983):
forward looking, but no explicit adjustment costs
• Application to WIOD data (YL files, environmental satellites accounts)
combining with EUKLEMS (release March 2008, including energy
inputs) and IEA Energy Prices and Taxes
Technical change in production
Dynamic factor demand model: general
• Dynamic cost functions with short-run variable costs VC (prices pv of
variable factors L, E, M), investment costs (K = capital stock, pI =
investment price), gross output Q, labour L (second nest: different
skills) , energy E, TFP, returns to scale and technical change bias



min  e r (t  ) VC t ( pv , K , Q, t )  p I ( K  K ) dt

• Shephard’s Lemma and Euler condition:
VC
v
p v
p I (r   ) 
VC
0
K
• Euler condition without explicit adjustment costs just states that the
shadow price of K equals the user costs of K.
Dynamic factor demand model
Translog model with non-constant returns to scale
logVC   0   Q log Q   E log( p E / p M )  log p M   L log( p L / p M )
1
1
1
  K log K   t t   tt t 2   QQ (logQ) 2   LL (log( p L / p M )) 2 
2
2
2
1
1
  LE log( p L / p M ) log( p E / p M )   EE (log( p E / p M )) 2   KK (log K ) 2 
2
2
  QL log Q log( p L / p M )   QE log Q log( p E / p M ) 
  QK log Q log K   KL log K log( p L / p M )   KE log K log( p E / p M ) 
  tQ t log Q   tK t log K   tL t log( p L / p M )   tE t log( p E / p M )




pL L
 s L   L   LL log( p L / p M )   LE log( p E / p M )   KL log K   QL log Q   tL t
VC
pE E
 s E     LE log( p L / p M )   EE log( p E / p M )   KE log K   QE log Q   tE t
VC
Dynamic factor demand model
Translog model with constant returns to scale
logVC   0   Q log Q   E log( p E / p M )  log p M   L log( p L / p M )  1   Q log K
1
1
  t t   tt t 2   LL (log( p L / p M ))2   LE log( p L / p M ) log( p E / p M )
2
2
K
K
1
  EE (log( p E / p M ))2   KL log  log( p L / p M )   KE log  log( p E / p M )
2
Q
Q
1
1

2
2
  QK  log Q log K  log Q   log K   
2
2


K
  tL t log( p L / p M )   tE t log( p E / p M )   tK t log 
Q


K
pL L
 s L   L   LL log( p L / p M )   LE log( p E / p M )   KL log    tLt 
VC
Q




K
pE E
 s E   E   LE log( p L / p M )   EE log( p E / p M )   KE log    tE t 
VC
Q


Dynamic factor demand model
Embodied and induced technical change
• Derivation of the shadow price of capital  derivation of the
optimal stock K*:
log K * 
log K * 
1
 KK
1
 QK
 
K
(1  
  QK log Q   KL log( p L / p M )   KE log( p E / p M )   tK t  s K

)   QK log Q   KL log( p L / p M )   KE log( p E / p M )   tK t  s K

Q
• Impact of energy price on K* (long-run)

 log K *
  KE
 log p E
 KK
 log K *  KE

 log p E  QK
• Returns to scale ( measures the cost effect of Q):


 logVC
  Q   QQ logQ   QL log( p L / p M )   QE log( p E / p M )   QK log K   tQt
 logQ
• Increasing returns to scale:  < 1.

Dynamic factor demand model
Capital stock adjustment and energy
• Forward looking adjustment of K to K*:


log Kt 1  log Kt   1  t (logKt*1 )  log Kt   2 log Kt  log Kt 1 
• Investment function (non-constant returns to scale)
  s K ,t 1   K 

 1

 log(K t 1 )   1 

log
K
t

  t  KL log( p L,t 1 / p M ,t 1 )   KE log( p E ,t 1 / p M ,t 1 )   QK log Qt 1   tK t  1  KK

  2 log K t  log K t 1 
• Input-output spill-overs of technical change through production of
capital goods:
p = VC/Q + pKK/Q


p I  p I  Mˆ B Bij  pim Mˆ B Bij
• With Bij as the investment matrix (industries * commodities) for
imports (MB) and domestic deliveries
Dynamic factor demand model
Embodied and induced technical change:energy
• Elasticity of E to K:
 KE
 log E  KE z K K



 log K
sE
VC
• Long-run elasticity of E:
 EE
s E2  s E   EE
d log E
 log E  log K *



d log p E
sE
 log K *  log p E
 EE
s E2  s E   EE   KE z k K   KE



 
sE
s
VC
 E
  KK



 EE
s E2  s E   EE   KE z k K   KE




sE
s
VC
 E
  QK




Dynamic factor demand model
Estimation methodology
• Balanced panel for: AUT, DNK, FIN, NLD, GB, from 1980-2006
• GMM estimation with lagged exogenous (factor prices, output, capital
stock, depreciation rate, rate of return) as instruments  the
expectation of K*t+1 is determined by all information in t  capital
stock adjustment can be due to expectation errors or shocks
• Estimating the full system with VC-function, factor demand for L and E
and investment function ( log Kt+1) for non-constant and constant
returns to scale
• Deriving returns to scale, own price elasticities, capital/energy and
capital/labour elasticities (calculating shadow price of K), as well as
long-run elasticities of E
• Restrictions: homogeneity, symmetry and concavity of the cost
function (individual, for ‘problem cases’)
Dynamic factor demand model
Data from WIOD, EUKLEMS and IEA (energy prices)
• Energy inputs in physical units (TJ) by about 20 energy carriers from
WIOD environmental accounts
• IEA Energy Prices and Taxes for most of the energy carriers/countries
(estimating missing data to fill gaps)
• Constructing an aggregate energy input in current prices (pE *E),
volumes (E), and price of energy pE. The volume measure is based on
aggregation of energy contents  effective input (‘energy services’
like ‘labour servives’ and ‘capital srvices’ in EUKLEMS)
• Capital stock, GFCF deflators, depreciation rate (capital input files
from EUKLEMS), benchmark interest rate (EUROSTAT), deflated with
VA deflator (YL files, WIOD)  user costs pI(r + )
• ‘Labour services’ (L) , compensation of employees from (pL*L)  price
of labour (pL) , data from YL files, WIOD
Dynamic factor demand model
Estimation results: non-constant returns to scale
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
K
LL
LE
EE
KK
KL
KE
-0.3064
(0.1235)
-0.7704
(0.1366)
-1.8256
(0.3106)
-1.1688
(0.6125)
-0.1471
(1.0648)
1.5429
(0.2262)
-0.8230
(0.6449)
-1.5436
(0.2738)
-0.3972
0.0577
(0.0245)
0.0168
(0.0329)
0.1528
(0.0262)
0.0109
(0.0285)
-0.0091
(0.0195)
0.1510
(0.0207)
0.0839
(0.0195)
-0.0326
(0.0219)
0.1629
-0.0144
(0.0022)
-0.0051
(0.0035)
-0.0156
(0.0061)
0.0085
(0.0094)
-0.0526
(0.0062)
-0.0008
(0.0092)
0.0027
(0.0099)
-0.0249
(0.0074)
-0.0188
0.0121
(0.0009)
0.0131
(0.0018)
0.0110
(0.0000)
0.0250
(0.0001)
0.0658
(0.0146)
0.0229
(0.0078)
0.0011
(0.0122)
0.0550
(0.0001)
0.0417
-0.0337
(0.0811)
0.3874
(0.1214)
-1.1409
(0.1446)
0.6118
(0.3202)
-0.8335
(0.2745)
1.2024
(0.2953)
-1.1749
(0.4804)
0.4000
(0.1644)
-0.1735
0.0619
(0.0156)
0.0041
(0.0160)
0.0340
(0.0173)
0.0421
(0.0165)
-0.0240
(0.0085)
0.1722
(0.0178)
0.1260
(0.0259)
0.0138
(0.0109)
0.1723
0.0148
(0.0015)
0.0025
(0.0017)
-0.0003
(0.0045)
-0.0045
(0.0097)
0.0050
(0.0181)
-0.1248
(0.0136)
-0.0149
(0.0275)
0.0441
(0.0105)
-0.0581
***
***
***
**
***
***
(0.1947) **
-0.2171
(0.2187)
-0.0358
(0.1468)
-0.2724
(0.3201)
-1.2236
(0.1927) ***
**
***
***
***
(0.0211) ***
0.1076
(0.0132) ***
0.0690
(0.0316)
0.1700
(0.0000) ***
0.0358
(0.0328)
***
*
***
***
***
***
(0.0074) ***
-0.0012
(0.0025)
-0.0005
(0.0045)
0.0134
(0.0065) **
-0.0082
(0.0076)
(0.0052)
0.0054
(0.0011)
0.0070
(0.0000)
0.0072
(0.0031)
0.0136
(0.0006)
***
***
***
***
***
***
**
***
***
***
***
**
***
(0.1242)
0.4780
(0.1223)
-0.1320
(0.0443)
-0.5835
(0.1525)
-0.1043
(0.0295)
***
***
**
***
***
*
**
***
***
***
***
(0.0104)
0.1012
(0.0066)
0.0639
(0.0130)
0.0574
(0.0187)
0.1448
(0.0144)
***
**
**
***
***
***
***
***
***
***
***
***
*
***
*
***
(0.0063) ***
0.0000
(0.0014)
-0.0019
(0.0017)
0.0009
(0.0032)
0.0016
(0.0057)
25 parameters out of 91 are insignificant
Embodied energy saving technical change: wood and cork, pulp and
paper/printing, chemicals, rubber and plastics, basic metals and
fabricated metal, electrical and optical equipment
Dynamic factor demand model
Estimation results: constant returns to scale
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
Q
LL
LE
EE
KL
KE
QK
1.7855
(0.1243)
1.5012
(0.0785)
1.1515
(0.2228)
0.0481
(0.2789)
3.3571
(0.5714)
0.9078
(0.0889)
0.9011
(0.1146)
0.6591
(0.2074)
1.1697
0.0859
(0.0100)
-0.0390
(0.0213)
0.1358
(0.0185)
-0.1672
(0.0392)
-0.0430
(0.0177)
0.1553
(0.0000)
0.0542
(0.0174)
0.1429
(0.0156)
0.1699
-0.0036
(0.0017)
-0.0060
(0.0037)
0.0060
(0.0057)
0.0464
(0.0181)
-0.0463
(0.0066)
0.0367
(0.0072)
0.0297
(0.0102)
0.0101
(0.0059)
-0.0115
0.0151
(0.0000)
0.0123
(0.0013)
0.0163
(0.0025)
0.0212
(0.0107)
0.0393
(0.0095)
-0.0117
(0.0063)
0.0266
(0.0082)
0.0513
(0.0001)
0.0372
0.0658
(0.0058)
-0.0561
(0.0118)
0.0125
(0.0123)
-0.0966
(0.0124)
-0.0272
(0.0075)
0.1763
(0.0146)
0.0974
(0.0154)
-0.0381
(0.0077)
0.0914
0.0090
(0.0006)
0.0030
(0.0011)
0.0084
(0.0025)
0.0423
(0.0038)
-0.0080
(0.0124)
-0.1656
(0.0130)
-0.0479
(0.0169)
0.0160
(0.0041)
-0.0325
0.8386
(0.1582)
0.2458
(0.1347)
0.5162
(0.4424)
-1.2639
(0.4518)
3.7159
(0.7940)
-4.2503
(0.7219)
-0.1035
(0.0553)
-1.2287
(0.3890)
0.3441
(0.0752)
-0.0441
(0.0976)
1.1576
(0.0234)
2.0841
(0.1248)
1.0372
(0.0730)
***
***
***
***
***
***
***
***
***
***
***
(0.0000)
0.1694
(0.0141)
0.0766
(0.0218)
0.1703
(0.0000)
-0.0429
(0.0224)
***
*
***
***
**
***
***
***
***
***
***
***
*
(0.0112)
0.0205
(0.0018)
0.0062
(0.0035)
0.1035
(0.0097)
-0.0301
(0.0036)
**
*
**
***
***
***
*
***
*
***
***
(0.0040)
0.0047
(0.0013)
0.0061
(0.0013)
0.0141
(0.0030)
0.0132
(0.0004)
***
***
***
**
***
*
***
***
***
***
***
***
***
(0.0071)
0.1205
(0.0057)
0.0719
(0.0106)
0.0339
(0.0101)
0.1364
(0.0072)
***
***
***
***
***
***
***
***
***
***
***
***
(0.0034)
0.0064
(0.0008)
0.0008
(0.0010)
0.0179
(0.0031)
0.0188
(0.0030)
***
***
***
***
***
***
***
***
***
***
***
(0.1777)
-1.7631
(0.2056)
-0.0281
(0.0127)
1.0262
(0.1425)
0.4420
(0.0990)
***
*
***
***
***
*
***
*
***
**
***
***
8 parameters out of 91 are insignificant
Embodied energy saving technical change: Coke, refined petroleum and
nuclear fuel, chemicals, basic metals and fabricated metal
Dynamic factor demand model
Estimation results: non-constant returns to scale
t
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
 tt
tK
tL
tE
-0.0380
(-0.0025) ***
-0.0236
(-0.0053) ***
-0.0287
0.0000
(0.0001)
-0.0002
(-0.0001) *
0.0002
-0.0037
(-0.0010) ***
0.0048
(-0.0016) ***
0.0022
-0.0005
(0.0009)
-0.0028
(-0.0011) ***
-0.0038
0.0006
(-0.0001) ***
0.0005
(-0.0001) ***
0.0003
(-0.0070)
-0.0408
(-0.0144)
0.1636
(-0.0434)
0.0146
(0.0076)
-0.0591
(0.0102)
-0.0179
(0.0045)
0.0104
(0.0066)
-0.0305
(0.0070)
0.0159
(0.0127)
0.0019
(0.0056)
-0.0539
(0.0053)
(-0.0001)
-0.0001
(0.0001)
-0.0020
(0.0007)
0.0006
(0.0002)
0.0001
(0.0002)
-0.0001
(0.0001)
0.0004
(0.0001)
0.0002
(0.0001)
0.0013
(0.0001)
0.0003
(0.0002)
0.0001
(0.0002)
(0.0028)
-0.0062
(0.0058)
-0.0067
(0.0073)
0.0161
(0.0053)
-0.0237
(0.0063)
0.0157
(0.0019)
-0.0088
(0.0027)
-0.0012
(0.0025)
0.0027
(0.0026)
0.0002
(0.0022)
-0.0103
(0.0021)
(-0.0007)
-0.0028
(-0.0009)
-0.0009
(0.0007)
-0.0037
(0.0009)
-0.0023
(0.0006)
-0.0007
(0.0006)
-0.0046
(0.0007)
-0.0059
(0.0003)
-0.0083
(0.0012)
-0.0073
(0.0006)
-0.0033
(0.0011)
(-0.0001)
0.0003
(0.0004)
0.0036
(0.0011)
-0.0030
(0.0005)
0.0006
(0.0005)
-0.0009
(0.0003)
-0.0003
(0.0003)
-0.0002
(0.0001)
-0.0004
(0.0002)
-0.0004
(0.0002)
0.0011
(0.0003)
***
***
***
**
***
*
***
***
*
***
***
**
***
*
***
**
***
***
***
***
***
***
***
***
***
***
***
***
***
*
***
***
***
***
***
**
***
Factor bias: (i) labour saving in all industries, (ii) energy saving in:
chemicals, other non-metallic minerals, basic metals and fabricated
metal, machinery, electrical and optical equipment, and transport
equipment
Dynamic factor demand model
Estimation results: constant returns to scale
t
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
 tt
tK
tL
tE
0.0014
(0.0047)
0.0159
(0.0046) ***
-0.0315
0.0001
(0.0002)
-0.0008
(0.0002) ***
0.0011
0.0143
(0.0039) ***
0.0247
(0.0029) ***
-0.0176
-0.0017
(0.0005) ***
-0.0016
(0.0009) *
-0.0037
0.0003
(0.0001) ***
0.0004
(0.0001) ***
-0.0002
(0.0063)
-0.0178
(0.0071)
0.0737
(0.0222)
-0.0122
(0.0031)
-0.0490
(0.0066)
-0.0298
(0.0063)
-0.0331
(0.0036)
0.0120
(0.0069)
-0.0231
(0.0019)
-0.0040
(0.0044)
-0.0371
(0.0043)
(0.0003)
0.0002
(0.0003)
-0.0043
(0.0013)
0.0011
(0.0002)
0.0026
(0.0004)
0.0019
(0.0004)
0.0017
(0.0002)
-0.0002
(0.0002)
0.0014
(0.0001)
0.0007
(0.0002)
0.0014
(0.0002)
(0.0085)
-0.0122
(0.0061)
0.0465
(0.0186)
0.0643
(0.0113)
-0.0062
(0.0061)
0.0109
(0.0041)
-0.0003
(0.0030)
0.0217
(0.0057)
0.0178
(0.0012)
0.0224
(0.0036)
-0.0141
(0.0028)
(0.0007)
0.0041
(0.0012)
-0.0004
(0.0006)
-0.0026
(0.0004)
-0.0013
(0.0006)
-0.0044
(0.0004)
-0.0037
(0.0003)
-0.0061
(0.0004)
-0.0084
(0.0008)
-0.0053
(0.0004)
-0.0008
(0.0009)
(0.0002)
-0.0013
(0.0006)
0.0044
(0.0007)
-0.0060
(0.0004)
-0.0017
(0.0004)
-0.0014
(0.0002)
-0.0009
(0.0003)
-0.0004
(0.0001)
-0.0004
(0.0001)
-0.0020
(0.0002)
0.0020
(0.0002)
***
**
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***
***
***
***
*
***
***
***
***
***
***
***
***
***
***
***
**
**
**
***
***
***
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Factor bias: (i) labour saving in all industries except pulp and paper, (ii)
energy saving in a majority of industries
Dynamic factor demand model
Estimation results: elasticities, non-constant returns to
scale
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
 EE
 LL
 EK
 LK

-0.2595
-0.0791
-0.1494
-0.1339
-0.1092
-0.5682
-0.4759
-0.0160
-0.0742
-0.4165
-0.0810
-0.2061
-0.0397
-0.4773
-0.6458
-0.1316
-0.6706
-1.1677
-0.0404
-0.4773
-0.7898
-0.1115
-0.3362
-0.4551
-0.0220
-0.6047
0.8845
0.1587
0.0489
-0.1159
0.2276
-1.9717
-0.2467
0.5935
-1.1123
-0.0240
-0.1248
0.1149
0.0783
0.3742
-0.0010
0.2075
0.1824
-0.3925
0.7897
0.6514
-0.0936
0.7391
0.3023
0.3502
0.2678
0.3875
1.0165
1.0126
0.8908
0.9614
0.8844
1.0518
0.7445
1.1096
0.9279
1.0028
0.8846
1.0052
1.0005
price elasticity of E : - 0.2, of L : - 0.5 .
Negative energy/capital elasticities (K and E are substitutes):
pulp and paper/printing, chemicals, rubber and plastics, basic metals and
fabricated metal, machinery, electrical and optical equipment
 Energy saving embodied technical change
Dynamic factor demand model
Estimation results: elasticities, constant returns to scale
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
 EE
 LL
 EK
 LK
-0.0766
-0.1292
-0.1035
-0.2587
-0.1422
-1.0836
-0.1107
-0.0772
-0.1676
-0.4928
-0.2030
-0.0906
-0.0702
-0.3066
-0.8412
-0.1998
-1.3081
-2.0352
-0.0191
-0.5175
-0.2258
-0.0853
-0.1390
-0.4283
-0.0208
-0.7703
0.5721
0.1972
0.5357
1.5026
-0.0056
-2.7513
-1.5797
0.3545
-0.6477
0.5788
0.1732
1.9192
1.3564
0.4304
-0.2049
-0.0466
-0.2380
-0.6921
0.6553
0.2704
-0.0330
0.3698
0.2823
0.3288
0.1459
0.4615
Negative energy/capital elasticities (K and E are substitutes): coke,
refined petroleum and nuclear fuel, chemicals, rubber and plastics,
basic metals and fabricated metal
Three industries show energy saving embodied technical change in
both specifications: chemicals, rubber and plastics, basic
metal/fabricated metal
Dynamic factor demand model
Estimation results: embodied and induced technical
change
• Two industries with embodied energy saving technical change in
the case of non-constant returns to scale, but not with constant
returns: pulp and paper/printing, electrical and optical equipment
• Are non-constant returns to scale an additional source of technical
change? (some industres with decreasing returns !)
• Main disadvantage of this approach:
• (Price) induced technical change is only possible, when K and E are
substitutes and investment reacts positive to energy price shocks 
further reduction of number of industries with embodied plus
induced energy saving technical change
Dynamic factor demand model
Estimation results: long-run price elasticities of E
non-constant rs non-constant rs
Food, beverages and tobacco
Textiles, leather and footwear
Wood and cork
Pulp and paper, printing
Coke, refined petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic minerals
Basic metals and fabricated metal
Machinery
Electrical and optical equipment
Transport equipment
Other manufacturing
constant rs
constant rs
 EE
EE
 EE
EE
-0.2595
-0.0791
-0.1494
-0.1339
-0.1092
-0.5682
-0.4759
-0.0160
-0.0742
-0.4165
-0.0810
-0.2061
-0.0397
0.1276
-0.0801
-0.1494
-0.1347
-0.1078
-0.7728
-0.9150
-0.0815
0.2981
-0.4165
-0.0792
-0.2059
-0.0364
-0.0766
-0.1292
-0.1035
-0.2587
-0.1422
-1.0836
-0.1107
-0.0772
-0.1676
-0.4928
-0.2030
-0.0906
-0.0702
0.0503
-0.1309
-0.1036
-0.2437
-0.1422
-1.3512
-0.0920
-0.1400
-0.0022
-0.4928
-0.2055
0.5446
-0.0613
Starting point: industries with embodied energy saving technical
change
Is there also induced energy saving technical change ? =
Is the long-run price elasticity of E higher than the short-run ?
Dynamic factor demand model
Embodied & induced technical change
Non-constant returns to scale
Starting point: industries with embodied energy saving technical
change: pulp and paper/printing, chemicals, rubber and plastics,
basic metals and fabricated metal, machinery, electrical and optical
equipment
Long-run elasticity of E (EE) > short-run elasticity (EE) = induced
technical change: pulp and paper/printing, chemicals, rubber and
plastics, machinery
Dynamic factor demand model
Embodied & induced technical change
Constant returns to scale
Starting point: industries with embodied energy saving technical
change: coke, refined petroleum and nuclear fuel, chemicals, rubber
and plastics, basic metals and fabricated metal
Long-run elasticity of E (EE) > short-run elasticity (EE) = induced
technical change: chemicals
Conclusions and Future Research
Conclusions
• Dynamic Translog factor demand model allows for diverse
sources/types of technical change: returns to scale, TFP, factor bias,
embodied and induced technical change.
• Technical change/progress is only partly energy saving, also for
embodied technical change
• Disadvantage: embodied technical change is only energy saving, if E
and K are substitutes  induced technical change is only energy
saving, if E and K are substitutes plus if investment reacts positively
to energy prices.
Future research
• Allowing for embodied technical change in the case that E and K are
complements  vintage model for aggregate K.