SlideShare ist ein Scribd-Unternehmen logo
1 von 56
Downloaden Sie, um offline zu lesen
Energy Savings through Foreign
Acquisitions? Evidence from
Indonesian Manufacturing
Plants
Arlan Brucal (with Beata Javorcik and Inessa Love)
10 June 2018 | OECD, Paris
Motivation
• FDI is widely perceived as a source of growth and development
• Foreign ownership leads to more sales, higher TFP and more
innovation
• Arnold & Javorcik (JIE 2009), Guadalupe et al. (AER 2012)
• But what about its impact on natural environment?
Anecdotal Evidence Is Mixed
• Foreign-owned textile rms dumping pollution in Citarum
River, Bandung, Indonesia
• Haze crisis resulting from increased palm oil production in
Indonesia
Anecdotal Evidence Is Mixed
• Resource Conservation
• PepsiCo initiated energy conservation programs that have
saved more than 4.6 mn kWh of electricity since their inception
• Baxter International installed energy-saving lighting systems
in 59 of its 97 worldwide sites by 1996, saving 30-40% of the
energy used ve years earlier
• Collaboration with external stakeholders on environmental
improvement projects.
• Goodyear helps design community-based recycling programs
• Texaco provides managers and sta to train employees of
Caltex Pacic in Indonesia in sound environmental practices.
• Internally-oriented Social Responsibility Practices
• MNCs certify their environmental management systems into
ISO 14001 guidelines. e.g. Sony Corporation (1998), ABB
(1997), and Goodyear (1997).
• Unilever companies have environmental certication programs
for their suppliers
This Paper
Examines the impact of foreign acquisitions on plant-level energy
intensity and CO2 emissions associated with energy use
Empirical Strategy
Within-plant output and energy use changes
• Matching based on pre-acquisition characteristics using
one-to-one Propensity Score Matching (PSM)
• Matching within year-(4-digit)industry groupings
• Dierences-in-Dierence on matched pairs
yit = αi + γPostt + β(Postt ∗ Acquiredi ) + εit (1)
where i denotes plant and t is the year. We consider two periods, i.e., t =
T − 1, T + s where T is the acquisition year and s =0,1,2. A separate model
is estimated for each s.
Decomposition of aggregate energy intensity changes
• Olley and Pakes (Econometrica, 1996)
Data
Focus on Indonesia, 1983-2001
• Large FDI inows from the early 1980s to the late 1990s
• No signicant environmental policies implemented during that
time
Data Source: Indonesian Annual Survey of Manufacturing,
1983-2001 (up to 2008 for robustness check)
• Includes manufacturing plants with 20 or more employees
• Detailed information on fuel and electricity, both in terms of
expenditure and physical units
• More than 300,000 plant-year observations for more than
40,000 plants
• Foreign acquisition dened as the change in foreign equity
share to over 20%
More on the Census of Manufacturing
Information included in the dataset:
1. employment (e.g., total, by gender, by type)
2. nancial data (e.g., output, sales, stock value, source of
capital)
3. trade data (e.g., exports, imports)
4. energy use data (fuel types in physical units and value,
electricity generated  sold, etc.)
Example: Calculation of Energy Usage
Assume: A plant using 100 barrels of diesel fuel at a certain time
period
100 barrels diesel x
5.825 million BTUs (MBTUs)
1 barrel
= 582.50 MBTUs
Using the same example above, we calculate the CO2 emissions as
below:
582.50 MBTUs x
71.80 kg CO2
1 MBTU
= 41, 845.04 kg CO2
Matched Sample: Average Output by Ownership
Acquired
Domestic
9.8
10
10.2
10.4
10.6
10.8
t-2 t-1 t t+1 t+2
t = year of acquisition
Figure: Energy Expenditure
Matched Sample: Average Energy Intensity by
Ownership
Acquired
Domestic
-4
-3.9
-3.8
-3.7
-3.6
t-2 t-1 t t+1 t+2
t = year of acquisition
Figure: Output Figure: Energy Expenditure
Matched Sample: Baseline Results
Output, Energy- and Emission-Intensity
Acquisition Year 1 Year Later 2 Years Later
Log(Output)
Post*Acquired 0.838*** 1.047*** 1.013***
(0.113) (0.117) (0.122)
R-sq. (within) 0.203 0.240 0.229
No. of Obs. 840 840 840
Log (Energy Expenditure/Output)
Post*Acquired -0.276** -0.282** -0.326**
(0.119) (0.118) (0.127)
R-sq. (within) 0.013 0.014 0.016
No. of Obs. 838 838 835
Log (Energy Use/Output)
Post*Acquired -0.304** -0.285** -0.367***
(0.120) (0.125) (0.137)
R-sq. (within) 0.015 0.014 0.019
No. of Obs. 838 838 835
Log (CO2 Emissions/Output)
Post*Acquired -0.282** -0.262** -0.357***
(0.119) (0.124) (0.136)
R-sq. (within) 0.014 0.015 0.021
No. of Obs. 838 838 835
Robustness Checks
Excluding the eect of potential local competition
• matching is not within the same county (Kabupaten) Results
Removing the eect of potential changes in mark-ups
• Normalization of output based on materials, including export
share and interaction between export share and the treatment
dummy Results
Excluding the 1997-1998 Financial Crisis
• Dropping years beyond 1997 Results
Longer Time Horizon
• Extending to 5 years after acquisition Results
Dierent measures of intensities
• Energy Use (in MBTUs)/Output Results
• CO2 Emission/Output Results
Dierent Matching Procedures
• Coursened Exact Matching Balancing Results
• Inverse Probability Weights Balancing Results
Do Divestments Have the Opposite Effect?
Output, Energy- and Emission-Intensity
Acquisition Year One Year Later Two Years Later
Log(Output)
Post*Acquired -0.318*** -0.397*** -0.313***
(0.081) (0.092) (0.091)
R-sq. (within) 0.030 0.038 0.035
No. of Obs. 1024 1024 1024
Log (Energy expenditure/Output)
Post*Acquired 0.296*** 0.406*** 0.290**
(0.099) (0.108) (0.121)
R-sq. (within) 0.021 0.035 0.016
No. of Obs. 1022 1022 1022
Log (Energy use/Output)
Post*Acquired 0.296*** 0.454*** 0.258**
(0.106) (0.119) (0.126)
R-sq. (within) 0.019 0.036 0.017
No. of Obs. 1022 1022 1022
Log (CO2 emissions/Output)
Post*Acquired 0.289*** 0.453*** 0.249**
(0.106) (0.120) (0.126)
R-sq. (within) 0.019 0.036 0.018
No. of Obs. 1022 1022 1022
Balancing
Preacquisition energy intensity matters!
Eect of acquisition on energy and emission intensities at varying
preacquisition energy intensities.
-2
-1
0
1
2
-8 -6 -4 -2 0
Log(energy expenditure/output)
-2
-1
0
1
2
-8 -6 -4 -2 0
Log(energy use/output)
-2
-1
0
1
2
-8 -6 -4 -2 0
Log(CO2 emission/output)
0
.1
.2
.3
-8 -6 -4 -2 0
0
.1
.2
.3
-8 -6 -4 -2 0
0
.1
.2
.3
-8 -6 -4 -2 0
Marginaleffect
Preacquisition log(energy expenditure/output)
The gure illustrates estimated combined coecients of foreign acquisition dummy and its interaction
with preacquisition energy intensity in equation 1 using the matched sample. The dashed lines correspond
to the 95-percent condence interval. The period focuses at one year after the acquisition (i.e., t + 1)
and estimates are relative to the preacquisition period.
Conclusion
• Foreign acquisitions increase production volume, which in
turn increases energy use and emissions
• But they reduce energy and emission intensities by 28 and
30%, respectively.
• Foreign divestment results have the opposite eect.
• Pre-acquisition energy-intensity matters.
• FDI contributes to aggregate improvements in energy
eciency, both through within-plant improvement and
reallocation
Your comments and suggestions
are welcome!
Email: a.z.brucal@lse.ac.uk
APPENDICES
Distribution of Foreign Equity, Pre and
Post-Acquisition
0.00
0.20
0.40
0.60
0.80
1.00
0 25 50 75 100 0 25 50 75 100
Pre-acquisition Post-Acquisition
Fraction
Foreign ownership (%)
Distribution of Foreign Acquisitions, by Industry
25.6
21.8
19.1
14.4
7.6
3.6
3.2
2.7
2.0
0 5 10 15 20 25
Percent Share
Machinery
Textile
Chemicals
Food
Wood Products
Others
Minerals
Paper Products
Basic Metal
IndustryClassification
Distribution of Foreign Acquisitions, by Year
0.00
0.04
0.08
0.12
0.16
0.20
Fraction
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Conversion Metrics
Input Conversion Factor Source
Conversion to Energy (in MBTUs)
Gasoline 1 barrel = 5.600 MBTUs Silverman, D.; University of California, Irv
Diesel 1 barrel = 5.825 MBTUs US Energy Information Administration (EI
Fuel Oil/ Bunker Oil 1 barrel ≈ 6.287 MTBUs EIA
Kerosene 1 barrel = 5.670 MBTUs EIA
Lubricants 1 barrel = 6.065 MBTUs EIA
Coal 1 short ton = 21.090 MBTUs EIA (average between sub- to bituminous
Coke 1 short ton = 24.800 MBTUs EIA
Public Gas 1 ft3 ≈ 0.001 MBTUs US Bureau of Mines
Liqueed Petroleum Gas 1 barrel = 3.861 MBTUs US Environmental Protection Agency (EP
Firewood 1 cord = 20 MBTUs Silverman, D.; University of California, Irv
Charcoal 1 lb = 0.128 MBTUs Oak Ridge National Laboratory
Electricity 1 kWh ≈ 0.101 MBTUs EIA (assumes coal-red generation)
Conversion to Carbon Dioxide (in KgCO2)
Gasoline 1 MBTU = 71.26 KgCO2 EIA
Diesel 1 MBTU = 71.80 KgCO2 EPA
Fuel Oil/ Bunker Oil 1 MBTU = 78.80 KgCO2 EPA
Kerosene 1 MBTU = 72.31 KgCO2 EPA
Lubricants 1 MBTU = 74.20 KgCO2 EIA
Coal 1 MBTU = 95.25 KgCO2 EIA
Coke 1 MBTU = 114.10 KgCO2 EIA
Public Gas 1 MBTU = 53.06 KgCO2 EIA
Liqueed Petroleum Gas 1 MBTU = 62.28 KgCO2 EIA
Firewood 1 MBTU ≈ 96.62 KgCO2 Partnership for Policy Integrity
Charcoal 1 MBTU ≈ 2.39 KgCO2 Akagi (2011)
Electricity 1 MBTU = 95.52 KgCO2 EIA
Summary Statistics (Domestic vs. Foreign-Owned)
Economic Variables
Variables Domestic Firms Foreign Owned
Obs Mean Obs Mean
Output (in Million RP) 288988 25.36 27142 172.49
Employment (no. of workers) 288973 150.49 27136 471.73
Unskilled workers 238225 119.93 20639 374.13
Skilled workers 237822 25.32 20580 78.11
Capital (in MillionRP) 189156 25.62 17896 132.54
Materials (in Million RP) 288990 13.26 27142 87.38
Per worker wage (in '000 RP) 288990 2.59 27142 8.54
Investment in machineries (in Million RP) 233694 9.21 23705 57.05
Exporter Dummy 289266 0.08 27182 0.27
Share of exports 289266 6.31 27182 21.45
Share of imported materials 289266 11.70 27182 38.23
Capital-Labor ratio 189146 117.45 17894 326.25
Share of skilled workers 237822 13.59 20580 21.81
Public ownership dummy 289266 0.03 27182 0.04
Age 273844 13.41 25098 11.94
Balancing Test: Variables Used in Matching
Variables
Unmatched sample Matched sample
(pre-acquisition, N=258,827) (pre-acquisition, N=420)
Acquired Domestic p-value Treated Control p-value
Used in matching
Log (Real output)t−1 10.62 7.71 0.000 9.89 9.88 0.951
Log (Energy expenditure/output)t−1 -4.10 -3.81 0.000 -3.87 -3.83 0.752
Log (Real output)t−2 10.58 7.76 0.000 9.74 9.74 0.997
Log (Energy expenditure/output)t−2 -4.10 -3.81 0.000 -3.93 -3.86 0.574
Balancing Test: Variables NOT Used in Matching
Variables
Unmatched sample Matched sample
(pre-acquisition, N=258,827) (pre-acquisition, N=420)
Acquired Domestic p-value Treated Control p-value
Unused in matching
Log (Energy expenditure)t−1 6.52 3.98 0.000 6.02 6.04 0.868
Log(Energy use)t−1 9.48 6.93 0.000 8.95 9.00 0.779
Log (CO2 emission)t−1 13.88 11.32 0.000 13.33 13.38 0.760
Log (Employment)t−1 5.52 4.10 0.000 5.18 5.29 0.338
Exporter dummyt−1 0.30 0.08 0.000 0.19 0.18 0.706
Share of imported materialst−1 0.37 0.08 0.000 0.26 0.19 0.050
Share of skilled workerst−1 0.22 0.14 0.000 0.24 0.22 0.291
Log(Investment in machinery)t−1 8.62 5.43 0.000 8.19 7.80 0.105
Log (Energy expenditure/output)t−1 -4.10 -3.81 0.000 -3.87 -3.83 0.752
Log(Energy use/output)t−1 -1.15 -0.86 0.000 -0.94 -0.87 0.645
Log(CO2 emission/output)t−1 3.26 3.53 0.000 3.44 3.50 0.612
Log(Energy exp./materials exp.)t−1 -3.23 -2.98 0.000 -2.87 -3.07 0.201
∆ Log (Real output)t−1 0.22 0.05 0.000 0.15 0.14 0.893
∆ Log (Energy expenditure)t−1 0.20 0.06 0.000 0.21 0.17 0.644
∆ Log (Energy use)t−1 0.21 0.07 0.000 0.22 0.20 0.887
∆ Log (CO2 emission)t−1 0.22 0.08 0.000 0.21 0.20 0.857
∆ Log (Energy expenditure/output)t−1 -0.02 0.01 0.014 0.06 0.03 0.684
∆ Log(Energy use/output)t−1 -0.00 0.03 0.038 0.06 0.06 0.975
∆ Log(CO2 emission/output)t−1 0.00 0.03 0.063 0.06 0.05 0.938
∆ Log(Energy exp./materials exp.)t−1 -0.02 0.01 0.068 0.02 0.04 0.842
OLS Results: Always Domestic vs. Acquired Plants
t t+1 t+2
Log(Output) 2.156*** 2.339*** 2.423***
Log(Energy Expenditure) 1.770*** 1.938*** 1.985***
Log(Energy Use, in MBTUs) 1.724*** 1.913*** 1.967***
Log (CO2 Emissions) 1.743*** 1.927*** 1.971***
Log(Energy Expenditure/Output) -0.347*** -0.373*** -0.414***
Log(Energy Use/Output) -0.393*** -0.398*** -0.431***
Log (CO2 Emissions/Output) -0.374*** -0.384*** -0.428***
Industry-year xed eects Yes Yes Yes
Firm xed eects No No No
Sample Size 169,141 - 180,901
FE Results: Always Domestic vs Acquired Plants
t t+1 t+2
Log(Output) 1.034*** 1.197*** 1.245***
Log(Energy Expenditure) 0.766*** 0.906*** 0.916***
Log(Energy Use, in MBTUs) 0.712*** 0.868*** 0.891***
Log (CO2 Emission) 0.727*** 0.878*** 0.890***
Log(Energy Expenditure/Output) -0.249*** -0.269*** -0.323***
Log(Energy Use/Output) -0.302*** -0.307*** -0.348***
Log (CO2 Emission/Output) -0.287*** -0.297*** -0.349***
Industry-year xed eects Yes Yes Yes
Firm rm eects Yes Yes Yes
Sample Size 165,905 - 177,840
always domestic vs matched acquired plants
Results from OLS
(Always domestic and acquired plants)
t t+1 t+2
Log(Output) 1.034*** 1.197*** 1.245***
Log(Energy Expenditure) 0.766*** 0.906*** 0.916***
Log(Energy Use, in MBTUs) 0.712*** 0.868*** 0.891***
Log (CO2 Emission) 0.727*** 0.878*** 0.890***
Log(Energy Expenditure/Output) -0.249*** -0.269*** -0.323***
Log(Energy Use/Output) -0.302*** -0.307*** -0.348***
Log (CO2 Emission/Output) -0.287*** -0.297*** -0.349***
Log(Energy Expenditure/Materials) -0.295*** -0.288*** -0.395***
Log(Energy Use/Materials) -0.342*** -0.327*** -0.418***
Log (CO2 Emission/Materials) -0.330*** -0.321*** -0.423***
Industry-year xed eects Yes Yes Yes
Firm rm eects Yes Yes Yes
Sample Size 165,905 - 177,840
Results from OLS
(Always domestic and matched acquired plants)
t t+1 t+2
Log(Output) 0.839*** 0.974*** 0.949***
Log(Energy Expenditure) 0.647*** 0.763*** 0.703***
Log(Energy Use, in MBTUs) 0.584*** 0.721*** 0.703***
Log (CO2 Emission) 0.621*** 0.746*** 0.721***
Log(Energy Expenditure/Output) -0.194** -0.214** -0.266***
Log(Energy Use/Output) -0.257*** -0.256*** -0.267**
Log (CO2 Emission/Output) -0.220** -0.230*** -0.248**
Log(Energy Expenditure/Materials) -0.252** -0.217** -0.384***
Log(Energy Use/Materials) -0.308*** -0.264*** -0.378***
Log (CO2 Emission/Materials) -0.280*** -0.245** -0.370***
Industry-year xed eects Yes Yes Yes
Firm rm eects Yes Yes Yes
Sample Size 165,307 - 177,155
Average Energy Expenditure, by Ownership
(Matched Sample)
Acquired
Domestic
5.8
6
6.2
6.4
6.6
6.8
t-2 t-1 t t+1 t+2
t = year of acquisition
Figure: Energy Intensity
Robustness Check: Matches from Another
Kabupaten
Acquisition Year 1 Year Later 2 Years Later
Log(Output)
Post*Acquired 0.829*** 1.037*** 1.008***
(0.114) (0.116) (0.123)
R-sq. (within) 0.199 0.238 0.225
No. of Obs. 836 836 836
Log (Energy Expenditure in Rps)
Post*Acquired 0.573*** 0.758*** 0.701***
(0.118) (0.126) (0.134)
R-sq. (within) 0.145 0.173 0.161
No. of Obs. 834 834 831
Log (Energy Expenditure/Output)
Post*Acquired -0.262** -0.286** -0.324**
(0.119) (0.119) (0.128)
R-sq. (within) 0.012 0.015 0.016
No. of Obs. 834 834 831
Matched Sample: Is It Just about Markups?
Acquisition Year 1 Year Later 2 Years Later
Log(Energy Expenditure/Materials Expenditure)
Post*Acquired -0.310** -0.266** -0.382**
(0.123) (0.128) (0.147)
R-sq. (within) 0.021 0.011 0.018
No. of Obs. 808 810 807
Log(Energy Expenditure/Output)
Post*Acquired -0.266** -0.290** -0.331***
(0.117) (0.117) (0.126)
Export share -0.002 0.001 0.001
(0.002) (0.002) (0.002)
R-sq. (within) 0.016 0.015 0.016
No. of Obs. 838 838 835
Log(Energy Expenditure/Output)
Post*Acquired -0.317** -0.382*** -0.406***
(0.134) (0.136) (0.146)
Export share -0.003 -0.001 -0.001
(0.002) (0.002) (0.002)
Post*Acquired*Export share 0.003 0.004 0.004
(0.003) (0.003) (0.003)
R-sq. (within) 0.018 0.023 0.021
No. of Obs. 838 838 835
Matched Sample: Dropping years beyond 1997
Acquisition Year 1 Year Later 2 Years Later
Log(Output)
Post*Acquired 0.793*** 0.777*** 0.798***
(0.125) (0.134) (0.156)
R-sq. (within) 0.236 0.281 0.291
No. of Obs. 714 654 614
Log (Energy expenditure in Rps)
Post*Acquired 0.519*** 0.647*** 0.492***
(0.133) (0.152) (0.184)
R-sq. (within) 0.134 0.174 0.136
No. of Obs. 714 654 613
Log (Energy expenditure/Output)
Post*Acquired -0.273** -0.130 -0.310*
(0.131) (0.130) (0.160)
R-sq. (within) 0.019 0.012 0.031
No. of Obs. 714 654 613
Back
Matched Sample: Longer Time Period and
Constant Sample
Output, Energy Expenditure and Energy Intensity
Acquisition Year 1 Year Later 2 Years Later 3 Years Later 4 Years Later 5 Yea
Log(Output)
Post*Acquired 0.728*** 0.839*** 0.813*** 1.033*** 1.104*** 1.1
(0.135) (0.139) (0.150) (0.170) (0.180) (0
R-sq. (within) 0.247 0.316 0.281 0.299 0.292 0
No. of Obs. 462 462 462 462 462
Log (Energy Expenditure in Rps)
Post*Acquired 0.430*** 0.593*** 0.500*** 0.306 0.420** 0.6
(0.152) (0.159) (0.183) (0.198) (0.186) (0
R-sq. (within) 0.124 0.184 0.141 0.138 0.206 0
No. of Obs. 454 454 454 454 454
Log (Energy Expenditure/Output)
Post*Acquired -0.308** -0.272** -0.345** -0.718*** -0.675*** -0.4
(0.134) (0.126) (0.161) (0.157) (0.155) (0
R-sq. (within) 0.025 0.024 0.019 0.087 0.084 0
No. of Obs. 454 454 454 454 454
Back
Balancing Test: Variables used in matching
Variables
CEM PSM (no same county) IPTW
(N=440) (N=418) (N=143,216)
Treated Control p-value Treated Control p-value F-Stat p-value
Used in matching
Log (Real Output) 9.03 9.03 0.99 9.86 9.86 0.90 4.43 0.04
Log (Energy Expenditure/Output) -3.71 -3.71 0.99 -3.82 -3.82 0.65 0.80 0.37
Log (Real Output) 9.59 9.62 0.90 9.86 9.86 0.90 5.91 0.02
Log (Energy Expenditure/Output) -3.77 -3.79 0.92 -3.82 -3.82 0.65 0.02 0.89
Back
Balancing Test: Variables NOT used in matching
Variables
CEM PSM (no same county) IPTW
(N=440) (N=418) (N=143,216)
Treated Control p-value Treated Control p-value F-Stat p-value
Unused in matching
Log (Energy Expenditure) 5.33 5.33 0.99 6.03 6.03 0.84 6.23 0.01
Log (Energy Use) 8.25 8.26 0.95 8.99 8.99 0.77 5.42 0.01
Log (CO2 Emission) 12.65 12.66 0.94 13.36 13.36 0.78 0.02 0.02
Log (Employment) 4.85 4.72 0.26 5.26 5.26 0.40 4.59 0.00
Exporter Dummy 0.15 0.18 0.30 0.18 0.18 0.80 14.42 0.03
Share of Imported Materials 0.20 0.18 0.56 0.19 0.19 0.05 12.14 0.00
Share of Skilled Workers 0.19 0.20 0.63 0.21 0.21 0.25 17.91 0.00
Log(Investment in Machineries) 7.15 6.93 0.43 7.86 7.86 0.20 0.61 0.00
Log(Energy Use/Output) -0.80 -0.79 0.93 -0.87 -0.87 0.56 0.22 0.43
Delta Log (Energy Expenditure) 0.14 0.08 0.19 0.15 0.15 0.55 0.03 0.90
Delta Log (Energy Use) 0.17 0.09 0.19 0.18 0.18 0.72 0.00 0.87
Delta Log (CO2 Emission) 0.17 0.09 0.22 0.18 0.18 0.70 10.36 0.97
Log(CO2 Emission/Output) 3.61 3.62 0.92 3.51 3.51 0.57 7.45 0.64
Log(Energy Exp./Materials) -2.80 -2.81 0.93 -3.03 -3.03 0.32 0.39 0.01
Delta Log (Real Output) 0.13 0.05 0.14 0.14 0.14 0.86 2.05 0.53
Delta Log (Energy Expenditure/Output) 0.02 0.03 0.81 0.02 0.02 0.60 1.93 0.15
Delta Log(Energy Use/Output) 0.05 0.04 0.87 0.04 0.04 0.81 1.48 0.16
Delta Log(CO2 Emission/Output) 0.04 0.04 0.96 0.04 0.04 0.78 1.66 0.22
Delta Log(Energy Exp./Materials) 0.08 -0.02 0.08 0.04 0.04 0.81 0.00 0.20
Back
Coasened Exact Matching-DID Estimates
Acquisition Year 1 Year Later 2 Years Later
Log(Output)
Post*Acquired 1.392*** 1.499*** 1.530***
(0.141) (0.144) (0.148)
R-sq. (within) 0.350 0.383 0.392
No. of Obs. 876 876 876
Log (Energy Expenditure in Rps)
Post*Acquired 1.012*** 1.189*** 1.159***
(0.140) (0.149) (0.158)
R-sq. (within) 0.221 0.248 0.253
No. of Obs. 871 868 868
Log (Energy Expenditure/Output)
Post*Acquired -0.372*** -0.297** -0.382***
(0.113) (0.121) (0.123)
R-sq. (within) 0.059 0.054 0.048
No. of Obs. 871 868 868
Back
IPW-DID Estimates
Acquisition Year 1 Year Later 2 Years Later
Log(Output)
Post*Acquired 1.659*** 1.763*** 1.906***
(0.184) (0.218) (0.221)
R-sq. (within) 0.803 0.807 0.809
No. of Obs. 138750 138750 138750
Log (Energy Expenditure in Rps)
Post*Acquired 1.353*** 1.421*** 1.399***
(0.176) (0.199) (0.220)
R-sq. (within) 0.806 0.802 0.800
No. of Obs. 138011 138009 138008
Log (Energy Expenditure/Output)
Post*Acquired -0.324*** -0.358*** -0.516***
(0.120) (0.133) (0.159)
R-sq. (within) 0.640 0.649 0.638
No. of Obs. 138011 138009 138008
Back
Matched Sample: Energy Use and CO2 Emissions
Acquisition Year 1 Year Later 2 Years Later
Log(Energy Use in MBTUs)
Post*Acquired 0.539*** 0.770*** 0.664***
(0.118) (0.130) (0.136)
R-sq. (within) 0.138 0.178 0.168
No. of Obs. 838 838 835
Log (CO2 Emissions)
Post*Acquired 0.562*** 0.792*** 0.673***
(0.120) (0.130) (0.137)
R-sq. (within) 0.150 0.188 0.176
No. of Obs. 838 838 835
Log (Energy Use/Output)
Post*Acquired -0.304** -0.285** -0.367***
(0.120) (0.125) (0.137)
R-sq. (within) 0.015 0.014 0.019
No. of Obs. 838 838 835
Log (CO2 Emissions/Output)
Post*Acquired -0.282** -0.262** -0.357***
(0.119) (0.124) (0.136)
R-sq. (within) 0.014 0.015 0.021
No. of Obs. 838 838 835
Back
Why Acquired Firms Did Not Reduce Energy
Intensity for Electricity?
Industrial Electricity Rates for Select ASEAN Counties, 1984-1992.
Source: Malhotra, et al. 1994.
Back
PSM-DID results: energy input prices.
Acquisition Year 1 Year Later 2 Years Later
Fuel price
Acquired -0.080 0.021 -0.059
(0.090) (0.014) (0.063)
R-sq. (within) 0.005 0.013 0.005
No. of Obs. 812 815 806
Log(fuel price)
Acquired 0.049 0.083 0.014
(0.057) (0.054) (0.057)
R-sq. (within) 0.004 0.008 0.001
No. of Obs. 812 815 805
Electricity price
Acquired -0.001 -0.003 0.006
(0.003) (0.003) (0.007)
R-sq. (within) 0.001 0.026 0.004
No. of Obs. 714 713 711
Log(electricity price)
Acquired -0.020 -0.043 0.016
(0.040) (0.044) (0.051)
R-sq. (within) 0.002 0.038 0.045
No. of Obs. 714 713 711
Back
Regression Result: Scale Effect
Dependent Variable: Log(Energy Expenditure)
All Sample Matched Sample
(1) (2) (3) (4)
Acquired 0.836** 2.334*** 0.997** 1.931***
(0.329) (0.250) (0.404) (0.462)
ln(output) 0.571*** 0.621***
(0.005) (0.040)
ln(output)t-1 0.272*** 0.250***
(0.005) (0.046)
Acquired*ln(output) -0.060** -0.086**
(0.030) (0.038)
Acquired*ln(output)t-1 -0.176*** -0.144***
(0.022) (0.043)
Firm xed eect Yes Yes Yes Yes
Year-xed eect Yes Yes Yes Yes
R-sq. (within) 0.261 0.097 0.389 0.134
No. of Obs. 255450 228733 2994 2571
Back
PSM-DID estimates: employment, capital and
investments
Acquisition Year 1 Year Later 2 Years Later
Log(Capital)
Post*Acquired 0.622*** 0.738*** 0.794***
(0.147) (0.178) (0.208)
R-sq. (within) 0.109 0.099 0.082
No. of Obs. 658 644 627
Log(Employment)
Post*Acquired 0.319*** 0.349*** 0.361***
(0.050) (0.055) (0.061)
R-sq. (within) 0.153 0.152 0.109
No. of Obs. 840 840 840
Log(Capital-labor ratio)
Post*Acquired 0.349** 0.406** 0.449**
(0.145) (0.174) (0.201)
R-sq. (within) 0.034 0.030 0.030
No. of Obs. 658 644 627
Log(Investment in machineries)
Post*Acquired 0.745*** 0.729*** 0.861***
(0.178) (0.202) (0.245)
R-sq. (within) 0.087 0.070 0.067
No. of Obs. 650 637 620
Matched difference-in-differences estimates:
Testing for non-linear effect of acquisition at
varying preacquisition energy intensity.
Log(energy expenditure
output ) Log(energy use
output ) Log( CO2
output)
Post*Acquired -1.759*** -1.738*** -1.643***
(0.359) (0.382) (0.358)
Post*Acquired*Log(energy expenditure
output )t−1 -0.409*** -0.392*** -0.378***
(0.092) (0.097) (0.093)
R-sq. (within) 0.213 0.191 0.185
No. of Obs. 814 814 814
Threshold -4.30 4.43 -4.35
Pre-acq. Energy Intensitythreshold
(% share of treated plants) 60.48 68.57 64.29
Back
Balancing Test: Variables used in matching
Variables
Divestment
(N=512)
Treated Control p-value
Used in matching
Log (Real Output) 10.94 10.96 0.87
Log (Energy Expenditure/Output) -4.21 -4.19 0.86
Log (Real Output) 10.83 10.76 0.60
Log (Energy Expenditure/Output) -4.13 -4.08 0.68
Back
Is There Reallocation across Energy Inputs?
Price-based measures Physical Units
Acquisition Year 1 Year Later 2 Years Later Acquisition Year 1 Year Later 2 Years Later
Log(Total fuel, in Rps) Log(Total fuel expenditure, in MBTUs)
Post*Acquired 0.392*** 0.596*** 0.543*** 0.343** 0.513*** 0.547***
(0.149) (0.158) (0.172) (0.165) (0.173) (0.189)
R-sq. (within) 0.044 0.069 0.058 0.028 0.045 0.045
No. of Obs. 812 815 805 812 815 806
Log(Net electricity expenditure, in Rps) Log(Net electricity expenditure, in MBTUs)
Post*Acquired 0.761*** 0.775*** 0.696*** 0.781*** 0.818*** 0.679***
(0.204) (0.206) (0.217) (0.201) (0.208) (0.219)
R-sq. (within) 0.092 0.115 0.118 0.099 0.137 0.142
No. of Obs. 714 713 711 714 713 711
Log(Total fuel expenditure/Output) Log(Total fuel use/Output)
Post*Acquired -0.422*** -0.452*** -0.429*** -0.471*** -0.535*** -0.428**
(0.148) (0.153) (0.160) (0.164) (0.171) (0.182)
R-sq. (within) 0.025 0.023 0.020 0.026 0.027 0.017
No. of Obs. 812 815 805 812 815 806
Log(Net electricity expenditure/Output) Log(Net electricity use/Output)
Post*Acquired -0.103 -0.343* -0.389* -0.083 -0.300 -0.406*
(0.203) (0.202) (0.213) (0.201) (0.204) (0.219)
R-sq. (within) -0.001 0.011 0.015 -0.001 0.015 0.025
No. of Obs. 714 713 711 714 713 711
Power Rates (Regression table)
Energy Intensity vs Scale
Predicted energy expenditure and output
(a) Unmatched sample
012345
log(EnergyExpenditure)t
2 6 10 14 18
log(output) t-1
Acquired Domestic
(b) Matched sample
12345
log(EnergyExpenditure)t
5 10 15
log(output) t-1
Acquired Domestic
Note:
Regression Table: Scale Regression Table:Structural Change
Balancing Test: Variables NOT used in matching
Variables
Divestment
(N=512)
Treated Control p-value
Unused in matching
Log (Energy Expenditure) 6.72 6.76 0.79
Log (Energy Use) 9.66 9.69 0.80
Log (CO2 Emission) 14.05 14.08 0.82
Log (Employment) 5.76 5.69 0.46
Exporter Dummy 0.33 0.35 0.64
Share of Imported Materials 0.30 0.36 0.05
Share of Skilled Workers 0.22 0.21 0.39
Log(Investment in Machineries) 8.84 8.96 0.55
Log(Energy Use/Output) -1.28 -1.26 0.87
∆ Log (Energy Expenditure) 0.03 0.08 0.51
∆ Log (Energy Use) 0.02 0.10 0.40
∆ Log (CO2 Emission) 0.02 0.10 0.43
Log(CO2 Emission/Output) 3.11 3.13 0.89
Log(Energy Exp./Materials) -3.54 -3.37 0.19
∆ Log (Real Output) 0.11 0.19 0.24
∆ Log (Energy Expenditure/Output) -0.09 -0.11 0.78
∆ Log(Energy Use/Output) -0.09 -0.09 0.95
∆ Log(CO2 Emission/Output) -0.09 -0.10 0.91
∆ Log(Energy Exp./Materials) -0.11 -0.13 0.77
Back
Decomposition of Aggregate Changes in Energy
Intensity
Following Olley and Pakes (Econometrica, 1996):
Wt =
i
sit lnEIP
Aggregate weighted
energy intensity
= lnEIP
Unweighted average
energy intensity
+
i
(sit − st)(lnEIPit − lnEIP)
Covariance
Table: All Industries
Decomposition of Aggregate Changes in Energy
Intensity
Following Olley and Pakes (Econometrica, 1996):
Wt =
i
sit lnEIP
Aggregate weighted
energy intensity
= lnEIP
Unweighted average
energy intensity
+
i
(sit − st)(lnEIPit − lnEIP)
Covariance
Table: All Industries
Yjst = βForeignjt + γj + λst + εjst
where j denotes kabupaten (48), s sector (9) and t year (19).
Decomposition of Aggregate Energy Intensity
Measure based on number of FAs Measure based on output share of FAs
Wt lnEIP Covariance Wt lnEIP Covariance
Log (Energy Expenditure/Output)
Foreign Aliates -0.226*** -0.086** -0.140*** -0.772* -0.552** -0.219
(0.041) (0.034) (0.044) (0.410) (0.276) (0.318)
Adj. R-sq. 0.853 0.829 0.774 0.842 0.827 0.764
Observations 1408 1408 1408 1408 1408 1408
Log (Energy Use/Output)
Foreign Aliates -0.215*** -0.070** -0.146*** -0.740* -0.490* -0.250
(0.039) (0.034) (0.040) (0.401) (0.271) (0.336)
Adj. R-sq. 0.859 0.852 0.784 0.850 0.851 0.775
Observations 1408 1408 1408 1408 1408 1408
Log (CO2 Emission/Output)
Foreign Aliates -0.217*** -0.077** -0.140*** -0.761* -0.521* -0.239
(0.039) (0.035) (0.040) (0.405) (0.277) (0.328)
Adj. R-sq. 0.853 0.834 0.783 0.844 0.833 0.775
Observations 1408 1408 1408 1408 1408 1408
No. of industries (4-digit ISIC) 79 79 79 79 79 79
No. of sectors (2-digit ISIC) 9 9 9 9 9 9
No. of years 19 19 19 19 19 19
Table: Dierent normalization
Decomposition of Changes in Aggregate Energy
Intensity
All Industries
Year Aggregate Unweighted Covariance Number of
Energy Intensity Energy Intensity Foreign-owned
1984 -0.10 -0.10 -0.11 -2
1985 -0.18 -0.25 0.00 103
1986 -0.45 -0.35 -0.11 78
1987 -0.26 -0.29 0.04 102
1988 -0.27 -0.33 0.06 166
1989 -0.23 -0.35 0.07 169
1990 -0.14 -0.45 0.23 266
1991 -0.17 -0.37 0.20 410
1992 -0.27 -0.39 0.11 581
1993 -0.41 -0.32 -0.09 674
1994 -0.34 -0.39 0.05 792
1995 -0.43 -0.40 -0.03 864
1996 -0.42 -0.44 0.01 978
1997 -0.60 -0.60 0.00 1071
1998 -0.39 -0.48 0.08 1216
1999 -0.18 -0.37 0.08 1289
2000 -0.08 -0.34 0.24 1226
2001 -0.31 -0.43 0.07 1106
Decomposition of Changes in Aggregate Energy
Intensity
Industries with below-median increases in foreign aliates
Year Aggregate Unweighted Covariance Number of
Energy Intensity Energy Intensity Foreign-owned
1984 -0.14 -0.07 -0.08 0
1985 -0.29 -0.22 -0.08 38
1986 -0.47 -0.31 -0.16 42
1987 -0.50 -0.24 -0.26 37
1988 -0.49 -0.27 -0.23 51
1989 -0.52 -0.30 -0.22 45
1990 -0.52 -0.31 -0.21 53
1991 -0.60 -0.27 -0.33 96
1992 -0.56 -0.23 -0.33 134
1993 -0.50 -0.10 -0.42 142
1994 -0.52 -0.17 -0.35 169
1995 -0.62 -0.16 -0.46 190
1996 -0.60 -0.19 -0.41 247
1997 -0.59 -0.48 -0.11 278
1998 -0.56 -0.41 -0.14 234
1999 -0.32 -0.26 -0.09 281
2000 -0.26 -0.20 -0.11 271
2001 -0.59 -0.32 -0.30 242
Decomposition of Changes in Aggregate Energy
Intensity
Industries with above-median increases in foreign aliates
Year Aggregate Unweighted Covariance Number of
Energy Intensity Energy Intensity Foreign-owned
1984 -0.08 -0.13 -0.11 -1
1985 -0.12 -0.26 0.05 63
1986 -0.49 -0.38 -0.11 33
1987 -0.16 -0.33 0.17 64
1988 -0.18 -0.36 0.18 108
1989 -0.12 -0.37 0.18 116
1990 -0.01 -0.51 0.39 227
1991 -0.03 -0.41 0.38 328
1992 -0.17 -0.47 0.27 461
1993 -0.46 -0.45 -0.01 546
1994 -0.29 -0.52 0.22 637
1995 -0.39 -0.54 0.16 688
1996 -0.38 -0.60 0.20 745
1997 -0.62 -0.66 0.04 807
1998 -0.35 -0.51 0.13 996
1999 -0.15 -0.42 0.12 1022
2000 -0.02 -0.42 0.38 969
2001 -0.21 -0.48 0.22 878
Decomposition of aggregate energy intensity
(Different Normalization)
Measure based on number of FAs Measure based on output share of FAs
Wt lnEIP Covariance Wt lnEIP Covariance
Log (Energy Expenditure/Materials)
Foreign Aliates -0.254*** -0.093** -0.161** -0.822* -0.698** -0.125
(0.061) (0.043) (0.065) (0.454) (0.307) (0.381)
Adj. R-sq. 0.873 0.874 0.789 0.863 0.874 0.779
Observations 1407 1407 1407 1407 1407 1407
Log (Energy Use/Materials)
Foreign Aliates -0.243*** -0.076* -0.167*** -0.788* -0.628** -0.160
(0.058) (0.041) (0.060) (0.444) (0.299) (0.392)
Adj. R-sq. 0.881 0.882 0.804 0.872 0.883 0.794
Observations 1407 1407 1407 1407 1407 1407
Log (CO2 Emission/Materials)
Foreign Aliates -0.244*** -0.082** -0.162*** -0.804* -0.657** -0.147
(0.057) (0.041) (0.060) (0.450) (0.301) (0.386)
Adj. R-sq. 0.877 0.872 0.805 0.868 0.873 0.796
Observations 1407 1407 1407 1407 1407 1407
No. of industries (4-digit ISIC) 79 79 79 79 79 79
No. of sectors (2-digit ISIC) 9 9 9 9 9 9
No. of years 19 19 19 19 19 19
Back
Emissions, Output and FDI in Indonesia, 1983-2001.
0
500
1000
1500
2000
2500
-4000
-2000
0
2000
4000
6000
1983 1986 1989 1992 1995 1998 2001
Net FDI inflow, BOP current Million US$ (left axis)
GDP, current 100 Million US$ (right axis)
Foreign Owned Firms (right axis)
CO2 Emission, in 100K MT (right axis)

Weitere ähnliche Inhalte

Was ist angesagt?

Mec presentation for distribution 030111
Mec presentation for distribution 030111Mec presentation for distribution 030111
Mec presentation for distribution 030111Cathyann Burns
 
Pollution Prevention at CST Storage...
Pollution Prevention at CST Storage...Pollution Prevention at CST Storage...
Pollution Prevention at CST Storage...Sarif Patwary, Ph.D.
 
Energy Concept For Future Use Sreevidhya@Students
Energy Concept For Future Use Sreevidhya@StudentsEnergy Concept For Future Use Sreevidhya@Students
Energy Concept For Future Use Sreevidhya@StudentsB Bhargav Reddy
 
Strategies for Energy Efficiency and Conservation
Strategies for Energy Efficiency and ConservationStrategies for Energy Efficiency and Conservation
Strategies for Energy Efficiency and ConservationThe Brewer-Garrett Company
 
2015-07-24_ Hassan Presentation - BFN (Final Presentation)
2015-07-24_ Hassan Presentation - BFN (Final Presentation)2015-07-24_ Hassan Presentation - BFN (Final Presentation)
2015-07-24_ Hassan Presentation - BFN (Final Presentation)Hassan Shahrukh
 
Improving residential sector in TIMES-Kazakhstan
Improving residential sector in TIMES-KazakhstanImproving residential sector in TIMES-Kazakhstan
Improving residential sector in TIMES-KazakhstanIEA-ETSAP
 
ABA Energy Star Challenge for the Industry
ABA Energy Star Challenge for the IndustryABA Energy Star Challenge for the Industry
ABA Energy Star Challenge for the Industryamericanbakers
 
How can we Reduce the Carbon Emissions from our Church
How can we Reduce the Carbon Emissions from our ChurchHow can we Reduce the Carbon Emissions from our Church
How can we Reduce the Carbon Emissions from our ChurchP4X
 
Introducing Electricity Dispatchability Features in TIMES modelling Framework
Introducing Electricity Dispatchability Features in TIMES modelling FrameworkIntroducing Electricity Dispatchability Features in TIMES modelling Framework
Introducing Electricity Dispatchability Features in TIMES modelling FrameworkIEA-ETSAP
 
Renewable Options Report Final (2)
Renewable Options Report Final (2)Renewable Options Report Final (2)
Renewable Options Report Final (2)Jacob Dent
 
Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...
Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...
Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...Luis Ram Rojas-Sol
 
Green Key Performance Indicators for Healthcare
Green Key Performance Indicators for HealthcareGreen Key Performance Indicators for Healthcare
Green Key Performance Indicators for HealthcareGraham Takata
 
Low-grade fuel to high-quality energy by gasification
Low-grade fuel to high-quality energy by gasificationLow-grade fuel to high-quality energy by gasification
Low-grade fuel to high-quality energy by gasificationIlkka Hannula
 
Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...
Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...
Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...IRJET Journal
 

Was ist angesagt? (20)

Mec presentation for distribution 030111
Mec presentation for distribution 030111Mec presentation for distribution 030111
Mec presentation for distribution 030111
 
Pollution Prevention at CST Storage...
Pollution Prevention at CST Storage...Pollution Prevention at CST Storage...
Pollution Prevention at CST Storage...
 
Energy Concept For Future Use Sreevidhya@Students
Energy Concept For Future Use Sreevidhya@StudentsEnergy Concept For Future Use Sreevidhya@Students
Energy Concept For Future Use Sreevidhya@Students
 
Strategies for Energy Efficiency and Conservation
Strategies for Energy Efficiency and ConservationStrategies for Energy Efficiency and Conservation
Strategies for Energy Efficiency and Conservation
 
2015-07-24_ Hassan Presentation - BFN (Final Presentation)
2015-07-24_ Hassan Presentation - BFN (Final Presentation)2015-07-24_ Hassan Presentation - BFN (Final Presentation)
2015-07-24_ Hassan Presentation - BFN (Final Presentation)
 
Improving residential sector in TIMES-Kazakhstan
Improving residential sector in TIMES-KazakhstanImproving residential sector in TIMES-Kazakhstan
Improving residential sector in TIMES-Kazakhstan
 
TGE PRESENTATION
TGE  PRESENTATIONTGE  PRESENTATION
TGE PRESENTATION
 
ABA Energy Star Challenge for the Industry
ABA Energy Star Challenge for the IndustryABA Energy Star Challenge for the Industry
ABA Energy Star Challenge for the Industry
 
How can we Reduce the Carbon Emissions from our Church
How can we Reduce the Carbon Emissions from our ChurchHow can we Reduce the Carbon Emissions from our Church
How can we Reduce the Carbon Emissions from our Church
 
Introducing Electricity Dispatchability Features in TIMES modelling Framework
Introducing Electricity Dispatchability Features in TIMES modelling FrameworkIntroducing Electricity Dispatchability Features in TIMES modelling Framework
Introducing Electricity Dispatchability Features in TIMES modelling Framework
 
Korea's Energy Standards & Labeling
Korea's Energy Standards & Labeling Korea's Energy Standards & Labeling
Korea's Energy Standards & Labeling
 
Renewable Options Report Final (2)
Renewable Options Report Final (2)Renewable Options Report Final (2)
Renewable Options Report Final (2)
 
Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...
Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...
Technical-Economic Assessment of Energy Efficiency Measures in a Mid-Size Ind...
 
Chiller replacement
Chiller replacement Chiller replacement
Chiller replacement
 
Green Key Performance Indicators for Healthcare
Green Key Performance Indicators for HealthcareGreen Key Performance Indicators for Healthcare
Green Key Performance Indicators for Healthcare
 
Zolotyi Korovai
Zolotyi KorovaiZolotyi Korovai
Zolotyi Korovai
 
Low-grade fuel to high-quality energy by gasification
Low-grade fuel to high-quality energy by gasificationLow-grade fuel to high-quality energy by gasification
Low-grade fuel to high-quality energy by gasification
 
Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...
Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...
Estimation & Analysis of a 5KWP Solar Photovoltaic Power Plant at JIS College...
 
Progress of Standards and Labeling Program in India
Progress of Standards and Labeling Program in India Progress of Standards and Labeling Program in India
Progress of Standards and Labeling Program in India
 
Halychyna Milk Company
Halychyna Milk CompanyHalychyna Milk Company
Halychyna Milk Company
 

Ähnlich wie Arlan Brucal

Modelling the costs and benefits of clean air policy scenarios for residentia...
Modelling the costs and benefits of clean air policy scenarios for residentia...Modelling the costs and benefits of clean air policy scenarios for residentia...
Modelling the costs and benefits of clean air policy scenarios for residentia...Environmental Protection Agency, Ireland
 
purohit_igp_19.10.2020_v1.pptx
purohit_igp_19.10.2020_v1.pptxpurohit_igp_19.10.2020_v1.pptx
purohit_igp_19.10.2020_v1.pptxAnoopLekhwar
 
Technology-based Approach for the Impacts of Global Warming on the Energy Use...
Technology-based Approach for the Impacts of Global Warming on the Energy Use...Technology-based Approach for the Impacts of Global Warming on the Energy Use...
Technology-based Approach for the Impacts of Global Warming on the Energy Use...IEA-ETSAP
 
Climate Change Mitigation & Adaptation
Climate Change  Mitigation & AdaptationClimate Change  Mitigation & Adaptation
Climate Change Mitigation & AdaptationLaurence Mills
 
Energy Policy & Enonomics.
Energy Policy & Enonomics.Energy Policy & Enonomics.
Energy Policy & Enonomics.Paul Derwin
 
eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...
eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...
eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...Gianmarco Rossi
 
Department of alternative energy development and efficiency (dede)
Department of alternative energy development and efficiency (dede)Department of alternative energy development and efficiency (dede)
Department of alternative energy development and efficiency (dede)PSPSolutions.net
 
"Overview of Renewable Technologies" Laurie Tennant, Engenius
"Overview of Renewable Technologies" Laurie Tennant, Engenius"Overview of Renewable Technologies" Laurie Tennant, Engenius
"Overview of Renewable Technologies" Laurie Tennant, EngeniusSustainable Business Partnership
 
Elements of Sustainable Construction and Design Parameters
Elements of Sustainable Construction and Design ParametersElements of Sustainable Construction and Design Parameters
Elements of Sustainable Construction and Design ParametersAjit Sabnis
 
Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...
Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...
Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...Global CCS Institute
 
001 conservation-Energy Audit -Methods
001 conservation-Energy Audit -Methods 001 conservation-Energy Audit -Methods
001 conservation-Energy Audit -Methods Anil Palamwar
 
Australian Energy Efficiency and Demand Side Management Activities
Australian Energy Efficiency and Demand Side Management ActivitiesAustralian Energy Efficiency and Demand Side Management Activities
Australian Energy Efficiency and Demand Side Management ActivitiesIEA DSM Implementing Agreement (IA)
 
Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...
Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...
Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...ESD UNU-IAS
 
ENGR40 – Clean Energy Technology Course Overview
ENGR40 – Clean Energy Technology Course OverviewENGR40 – Clean Energy Technology Course Overview
ENGR40 – Clean Energy Technology Course OverviewRobert Cormia
 
2016.12.14 DryFining Coal Gen presentation FINAL
2016.12.14 DryFining Coal Gen presentation FINAL2016.12.14 DryFining Coal Gen presentation FINAL
2016.12.14 DryFining Coal Gen presentation FINALSandra Broekema
 

Ähnlich wie Arlan Brucal (20)

Modelling the costs and benefits of clean air policy scenarios for residentia...
Modelling the costs and benefits of clean air policy scenarios for residentia...Modelling the costs and benefits of clean air policy scenarios for residentia...
Modelling the costs and benefits of clean air policy scenarios for residentia...
 
purohit_igp_19.10.2020_v1.pptx
purohit_igp_19.10.2020_v1.pptxpurohit_igp_19.10.2020_v1.pptx
purohit_igp_19.10.2020_v1.pptx
 
Technology-based Approach for the Impacts of Global Warming on the Energy Use...
Technology-based Approach for the Impacts of Global Warming on the Energy Use...Technology-based Approach for the Impacts of Global Warming on the Energy Use...
Technology-based Approach for the Impacts of Global Warming on the Energy Use...
 
Climate Change Mitigation & Adaptation
Climate Change  Mitigation & AdaptationClimate Change  Mitigation & Adaptation
Climate Change Mitigation & Adaptation
 
Energy Policy & Enonomics.
Energy Policy & Enonomics.Energy Policy & Enonomics.
Energy Policy & Enonomics.
 
eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...
eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...
eni_Rossi Gianmarco - Energy Management System for the Optimization of the Up...
 
Department of alternative energy development and efficiency (dede)
Department of alternative energy development and efficiency (dede)Department of alternative energy development and efficiency (dede)
Department of alternative energy development and efficiency (dede)
 
EnMS Article, World Cement
EnMS Article, World CementEnMS Article, World Cement
EnMS Article, World Cement
 
energy audit
energy auditenergy audit
energy audit
 
"Overview of Renewable Technologies" Laurie Tennant, Engenius
"Overview of Renewable Technologies" Laurie Tennant, Engenius"Overview of Renewable Technologies" Laurie Tennant, Engenius
"Overview of Renewable Technologies" Laurie Tennant, Engenius
 
Thailand: Green Technology Investment
Thailand: Green Technology InvestmentThailand: Green Technology Investment
Thailand: Green Technology Investment
 
Elements of Sustainable Construction and Design Parameters
Elements of Sustainable Construction and Design ParametersElements of Sustainable Construction and Design Parameters
Elements of Sustainable Construction and Design Parameters
 
Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...
Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...
Perspectives on the role of CO2 capture and utilisation (CCU) in climate chan...
 
001 conservation-Energy Audit -Methods
001 conservation-Energy Audit -Methods 001 conservation-Energy Audit -Methods
001 conservation-Energy Audit -Methods
 
Australian Energy Efficiency and Demand Side Management Activities
Australian Energy Efficiency and Demand Side Management ActivitiesAustralian Energy Efficiency and Demand Side Management Activities
Australian Energy Efficiency and Demand Side Management Activities
 
Energy Efficiency Policies in Korea
Energy Efficiency Policies in Korea Energy Efficiency Policies in Korea
Energy Efficiency Policies in Korea
 
2010_Defiglio.pptx
2010_Defiglio.pptx2010_Defiglio.pptx
2010_Defiglio.pptx
 
Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...
Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...
Linking the energy crisis with climate change, Ritu Mathu, TERI University, I...
 
ENGR40 – Clean Energy Technology Course Overview
ENGR40 – Clean Energy Technology Course OverviewENGR40 – Clean Energy Technology Course Overview
ENGR40 – Clean Energy Technology Course Overview
 
2016.12.14 DryFining Coal Gen presentation FINAL
2016.12.14 DryFining Coal Gen presentation FINAL2016.12.14 DryFining Coal Gen presentation FINAL
2016.12.14 DryFining Coal Gen presentation FINAL
 

Mehr von Structuralpolicyanalysis

“Keeping pace with technological change: The role of capabilities and dynamis...
“Keeping pace with technological change: The role of capabilities and dynamis...“Keeping pace with technological change: The role of capabilities and dynamis...
“Keeping pace with technological change: The role of capabilities and dynamis...Structuralpolicyanalysis
 
Integrated Data for Policy: A view from New Zealand
Integrated Data for Policy: A view from New ZealandIntegrated Data for Policy: A view from New Zealand
Integrated Data for Policy: A view from New ZealandStructuralpolicyanalysis
 
AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...
AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...
AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...Structuralpolicyanalysis
 
Changes in Productivity and Industry dynamics in the Digital transition: The ...
Changes in Productivity and Industry dynamics in the Digital transition: The ...Changes in Productivity and Industry dynamics in the Digital transition: The ...
Changes in Productivity and Industry dynamics in the Digital transition: The ...Structuralpolicyanalysis
 
Changing Patterns of Market Power and Contestability
Changing Patterns of Market Power and ContestabilityChanging Patterns of Market Power and Contestability
Changing Patterns of Market Power and ContestabilityStructuralpolicyanalysis
 
The Human Side of Productivity Setting the Scene
The Human Side of Productivity Setting the SceneThe Human Side of Productivity Setting the Scene
The Human Side of Productivity Setting the SceneStructuralpolicyanalysis
 
Employer Employee linked data in Italy availability and usage by institusions
Employer Employee linked data in Italy availability and usage by institusionsEmployer Employee linked data in Italy availability and usage by institusions
Employer Employee linked data in Italy availability and usage by institusionsStructuralpolicyanalysis
 
EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...
EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...
EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...Structuralpolicyanalysis
 
NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...
NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...
NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...Structuralpolicyanalysis
 
Labour market policy more than just finding round pegs for round holes
Labour market policy more than just finding round pegs for round holesLabour market policy more than just finding round pegs for round holes
Labour market policy more than just finding round pegs for round holesStructuralpolicyanalysis
 
The Effects of Management on Productivity: Evidence from Mid-20th Century
The Effects of Management on Productivity: Evidence from Mid-20th CenturyThe Effects of Management on Productivity: Evidence from Mid-20th Century
The Effects of Management on Productivity: Evidence from Mid-20th CenturyStructuralpolicyanalysis
 
HUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITY
HUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITYHUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITY
HUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITYStructuralpolicyanalysis
 
The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...
The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...
The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...Structuralpolicyanalysis
 
What can Machine Learning Do and What Does It Mean for the Economy?
What can Machine Learning Do and What Does It Mean for the Economy?What can Machine Learning Do and What Does It Mean for the Economy?
What can Machine Learning Do and What Does It Mean for the Economy?Structuralpolicyanalysis
 
Dynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit ConditionsDynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit ConditionsStructuralpolicyanalysis
 
Dynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit ConditionsDynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit ConditionsStructuralpolicyanalysis
 
Review of the evidence on market power, contestability and antitrust
Review of the evidence on market power, contestability and antitrustReview of the evidence on market power, contestability and antitrust
Review of the evidence on market power, contestability and antitrustStructuralpolicyanalysis
 

Mehr von Structuralpolicyanalysis (20)

“Keeping pace with technological change: The role of capabilities and dynamis...
“Keeping pace with technological change: The role of capabilities and dynamis...“Keeping pace with technological change: The role of capabilities and dynamis...
“Keeping pace with technological change: The role of capabilities and dynamis...
 
Integrated Data for Policy: A view from New Zealand
Integrated Data for Policy: A view from New ZealandIntegrated Data for Policy: A view from New Zealand
Integrated Data for Policy: A view from New Zealand
 
AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...
AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...
AI and Technological Anxiety: Paranoia , or are the robots out to get us Comm...
 
Wrap-up and way forward
Wrap-up and way forwardWrap-up and way forward
Wrap-up and way forward
 
Closing remarks
Closing remarksClosing remarks
Closing remarks
 
Changes in Productivity and Industry dynamics in the Digital transition: The ...
Changes in Productivity and Industry dynamics in the Digital transition: The ...Changes in Productivity and Industry dynamics in the Digital transition: The ...
Changes in Productivity and Industry dynamics in the Digital transition: The ...
 
Changing Patterns of Market Power and Contestability
Changing Patterns of Market Power and ContestabilityChanging Patterns of Market Power and Contestability
Changing Patterns of Market Power and Contestability
 
The Human Side of Productivity Setting the Scene
The Human Side of Productivity Setting the SceneThe Human Side of Productivity Setting the Scene
The Human Side of Productivity Setting the Scene
 
Employer Employee linked data in Italy availability and usage by institusions
Employer Employee linked data in Italy availability and usage by institusionsEmployer Employee linked data in Italy availability and usage by institusions
Employer Employee linked data in Italy availability and usage by institusions
 
Rent sharing across Production network
Rent sharing across Production networkRent sharing across Production network
Rent sharing across Production network
 
EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...
EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...
EMPLOYEE AUTONOMY AND THE WITHIN-FIRM GENDER WAGE GAP: THE CASE OF TRUST-BASE...
 
NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...
NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...
NATIONAL WAGE EQUALIZATION AND REGIONAL MISALLOCATION: EVIDENCE FROM ITALIAN ...
 
Labour market policy more than just finding round pegs for round holes
Labour market policy more than just finding round pegs for round holesLabour market policy more than just finding round pegs for round holes
Labour market policy more than just finding round pegs for round holes
 
The Effects of Management on Productivity: Evidence from Mid-20th Century
The Effects of Management on Productivity: Evidence from Mid-20th CenturyThe Effects of Management on Productivity: Evidence from Mid-20th Century
The Effects of Management on Productivity: Evidence from Mid-20th Century
 
HUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITY
HUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITYHUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITY
HUMAN CAPABILITIES – MANAGERIAL CAPITAL, ORGANISATIONAL PRACTICES AND MOBILITY
 
The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...
The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...
The Effects of Joining Multinational Supply Chains: New Evidence from Firm-to...
 
What can Machine Learning Do and What Does It Mean for the Economy?
What can Machine Learning Do and What Does It Mean for the Economy?What can Machine Learning Do and What Does It Mean for the Economy?
What can Machine Learning Do and What Does It Mean for the Economy?
 
Dynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit ConditionsDynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit Conditions
 
Dynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit ConditionsDynamism Diminished: The Role of Housing Markets and Credit Conditions
Dynamism Diminished: The Role of Housing Markets and Credit Conditions
 
Review of the evidence on market power, contestability and antitrust
Review of the evidence on market power, contestability and antitrustReview of the evidence on market power, contestability and antitrust
Review of the evidence on market power, contestability and antitrust
 

Kürzlich hochgeladen

VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130
VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130
VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130Suhani Kapoor
 
The Economic History of the U.S. Lecture 18.pdf
The Economic History of the U.S. Lecture 18.pdfThe Economic History of the U.S. Lecture 18.pdf
The Economic History of the U.S. Lecture 18.pdfGale Pooley
 
VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...
VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...
VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...Call Girls in Nagpur High Profile
 
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptxFinTech Belgium
 
Andheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot ModelsAndheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot Modelshematsharma006
 
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptxOAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptxhiddenlevers
 
Best VIP Call Girls Noida Sector 18 Call Me: 8448380779
Best VIP Call Girls Noida Sector 18 Call Me: 8448380779Best VIP Call Girls Noida Sector 18 Call Me: 8448380779
Best VIP Call Girls Noida Sector 18 Call Me: 8448380779Delhi Call girls
 
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...ssifa0344
 
The Economic History of the U.S. Lecture 20.pdf
The Economic History of the U.S. Lecture 20.pdfThe Economic History of the U.S. Lecture 20.pdf
The Economic History of the U.S. Lecture 20.pdfGale Pooley
 
Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...
Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...
Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...shivangimorya083
 
Q3 2024 Earnings Conference Call and Webcast Slides
Q3 2024 Earnings Conference Call and Webcast SlidesQ3 2024 Earnings Conference Call and Webcast Slides
Q3 2024 Earnings Conference Call and Webcast SlidesMarketing847413
 
The Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdfThe Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdfGale Pooley
 
Instant Issue Debit Cards - High School Spirit
Instant Issue Debit Cards - High School SpiritInstant Issue Debit Cards - High School Spirit
Instant Issue Debit Cards - High School Spiritegoetzinger
 
Call Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance BookingCall Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance Bookingroncy bisnoi
 
Instant Issue Debit Cards - School Designs
Instant Issue Debit Cards - School DesignsInstant Issue Debit Cards - School Designs
Instant Issue Debit Cards - School Designsegoetzinger
 
VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...
VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...
VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...Suhani Kapoor
 
Dividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptxDividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptxanshikagoel52
 
02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx
02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx
02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptxFinTech Belgium
 
(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...ranjana rawat
 

Kürzlich hochgeladen (20)

VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130
VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130
VIP Call Girls Service Dilsukhnagar Hyderabad Call +91-8250192130
 
The Economic History of the U.S. Lecture 18.pdf
The Economic History of the U.S. Lecture 18.pdfThe Economic History of the U.S. Lecture 18.pdf
The Economic History of the U.S. Lecture 18.pdf
 
VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...
VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...
VVIP Pune Call Girls Katraj (7001035870) Pune Escorts Nearby with Complete Sa...
 
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
 
Andheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot ModelsAndheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot Models
 
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptxOAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
 
Best VIP Call Girls Noida Sector 18 Call Me: 8448380779
Best VIP Call Girls Noida Sector 18 Call Me: 8448380779Best VIP Call Girls Noida Sector 18 Call Me: 8448380779
Best VIP Call Girls Noida Sector 18 Call Me: 8448380779
 
Veritas Interim Report 1 January–31 March 2024
Veritas Interim Report 1 January–31 March 2024Veritas Interim Report 1 January–31 March 2024
Veritas Interim Report 1 January–31 March 2024
 
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
 
The Economic History of the U.S. Lecture 20.pdf
The Economic History of the U.S. Lecture 20.pdfThe Economic History of the U.S. Lecture 20.pdf
The Economic History of the U.S. Lecture 20.pdf
 
Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...
Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...
Russian Call Girls In Gtb Nagar (Delhi) 9711199012 💋✔💕😘 Naughty Call Girls Se...
 
Q3 2024 Earnings Conference Call and Webcast Slides
Q3 2024 Earnings Conference Call and Webcast SlidesQ3 2024 Earnings Conference Call and Webcast Slides
Q3 2024 Earnings Conference Call and Webcast Slides
 
The Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdfThe Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdf
 
Instant Issue Debit Cards - High School Spirit
Instant Issue Debit Cards - High School SpiritInstant Issue Debit Cards - High School Spirit
Instant Issue Debit Cards - High School Spirit
 
Call Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance BookingCall Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Koregaon Park Call Me 7737669865 Budget Friendly No Advance Booking
 
Instant Issue Debit Cards - School Designs
Instant Issue Debit Cards - School DesignsInstant Issue Debit Cards - School Designs
Instant Issue Debit Cards - School Designs
 
VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...
VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...
VIP Call Girls LB Nagar ( Hyderabad ) Phone 8250192130 | ₹5k To 25k With Room...
 
Dividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptxDividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptx
 
02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx
02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx
02_Fabio Colombo_Accenture_MeetupDora&Cybersecurity.pptx
 
(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANIKA) Budhwar Peth Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
 

Arlan Brucal

  • 1. Energy Savings through Foreign Acquisitions? Evidence from Indonesian Manufacturing Plants Arlan Brucal (with Beata Javorcik and Inessa Love) 10 June 2018 | OECD, Paris
  • 2. Motivation • FDI is widely perceived as a source of growth and development • Foreign ownership leads to more sales, higher TFP and more innovation • Arnold & Javorcik (JIE 2009), Guadalupe et al. (AER 2012) • But what about its impact on natural environment?
  • 3. Anecdotal Evidence Is Mixed • Foreign-owned textile rms dumping pollution in Citarum River, Bandung, Indonesia • Haze crisis resulting from increased palm oil production in Indonesia
  • 4. Anecdotal Evidence Is Mixed • Resource Conservation • PepsiCo initiated energy conservation programs that have saved more than 4.6 mn kWh of electricity since their inception • Baxter International installed energy-saving lighting systems in 59 of its 97 worldwide sites by 1996, saving 30-40% of the energy used ve years earlier • Collaboration with external stakeholders on environmental improvement projects. • Goodyear helps design community-based recycling programs • Texaco provides managers and sta to train employees of Caltex Pacic in Indonesia in sound environmental practices. • Internally-oriented Social Responsibility Practices • MNCs certify their environmental management systems into ISO 14001 guidelines. e.g. Sony Corporation (1998), ABB (1997), and Goodyear (1997). • Unilever companies have environmental certication programs for their suppliers
  • 5. This Paper Examines the impact of foreign acquisitions on plant-level energy intensity and CO2 emissions associated with energy use
  • 6. Empirical Strategy Within-plant output and energy use changes • Matching based on pre-acquisition characteristics using one-to-one Propensity Score Matching (PSM) • Matching within year-(4-digit)industry groupings • Dierences-in-Dierence on matched pairs yit = αi + γPostt + β(Postt ∗ Acquiredi ) + εit (1) where i denotes plant and t is the year. We consider two periods, i.e., t = T − 1, T + s where T is the acquisition year and s =0,1,2. A separate model is estimated for each s. Decomposition of aggregate energy intensity changes • Olley and Pakes (Econometrica, 1996)
  • 7. Data Focus on Indonesia, 1983-2001 • Large FDI inows from the early 1980s to the late 1990s • No signicant environmental policies implemented during that time Data Source: Indonesian Annual Survey of Manufacturing, 1983-2001 (up to 2008 for robustness check) • Includes manufacturing plants with 20 or more employees • Detailed information on fuel and electricity, both in terms of expenditure and physical units • More than 300,000 plant-year observations for more than 40,000 plants • Foreign acquisition dened as the change in foreign equity share to over 20%
  • 8. More on the Census of Manufacturing Information included in the dataset: 1. employment (e.g., total, by gender, by type) 2. nancial data (e.g., output, sales, stock value, source of capital) 3. trade data (e.g., exports, imports) 4. energy use data (fuel types in physical units and value, electricity generated sold, etc.)
  • 9. Example: Calculation of Energy Usage Assume: A plant using 100 barrels of diesel fuel at a certain time period 100 barrels diesel x 5.825 million BTUs (MBTUs) 1 barrel = 582.50 MBTUs Using the same example above, we calculate the CO2 emissions as below: 582.50 MBTUs x 71.80 kg CO2 1 MBTU = 41, 845.04 kg CO2
  • 10. Matched Sample: Average Output by Ownership Acquired Domestic 9.8 10 10.2 10.4 10.6 10.8 t-2 t-1 t t+1 t+2 t = year of acquisition Figure: Energy Expenditure
  • 11. Matched Sample: Average Energy Intensity by Ownership Acquired Domestic -4 -3.9 -3.8 -3.7 -3.6 t-2 t-1 t t+1 t+2 t = year of acquisition Figure: Output Figure: Energy Expenditure
  • 12. Matched Sample: Baseline Results Output, Energy- and Emission-Intensity Acquisition Year 1 Year Later 2 Years Later Log(Output) Post*Acquired 0.838*** 1.047*** 1.013*** (0.113) (0.117) (0.122) R-sq. (within) 0.203 0.240 0.229 No. of Obs. 840 840 840 Log (Energy Expenditure/Output) Post*Acquired -0.276** -0.282** -0.326** (0.119) (0.118) (0.127) R-sq. (within) 0.013 0.014 0.016 No. of Obs. 838 838 835 Log (Energy Use/Output) Post*Acquired -0.304** -0.285** -0.367*** (0.120) (0.125) (0.137) R-sq. (within) 0.015 0.014 0.019 No. of Obs. 838 838 835 Log (CO2 Emissions/Output) Post*Acquired -0.282** -0.262** -0.357*** (0.119) (0.124) (0.136) R-sq. (within) 0.014 0.015 0.021 No. of Obs. 838 838 835
  • 13. Robustness Checks Excluding the eect of potential local competition • matching is not within the same county (Kabupaten) Results Removing the eect of potential changes in mark-ups • Normalization of output based on materials, including export share and interaction between export share and the treatment dummy Results Excluding the 1997-1998 Financial Crisis • Dropping years beyond 1997 Results Longer Time Horizon • Extending to 5 years after acquisition Results Dierent measures of intensities • Energy Use (in MBTUs)/Output Results • CO2 Emission/Output Results Dierent Matching Procedures • Coursened Exact Matching Balancing Results • Inverse Probability Weights Balancing Results
  • 14. Do Divestments Have the Opposite Effect? Output, Energy- and Emission-Intensity Acquisition Year One Year Later Two Years Later Log(Output) Post*Acquired -0.318*** -0.397*** -0.313*** (0.081) (0.092) (0.091) R-sq. (within) 0.030 0.038 0.035 No. of Obs. 1024 1024 1024 Log (Energy expenditure/Output) Post*Acquired 0.296*** 0.406*** 0.290** (0.099) (0.108) (0.121) R-sq. (within) 0.021 0.035 0.016 No. of Obs. 1022 1022 1022 Log (Energy use/Output) Post*Acquired 0.296*** 0.454*** 0.258** (0.106) (0.119) (0.126) R-sq. (within) 0.019 0.036 0.017 No. of Obs. 1022 1022 1022 Log (CO2 emissions/Output) Post*Acquired 0.289*** 0.453*** 0.249** (0.106) (0.120) (0.126) R-sq. (within) 0.019 0.036 0.018 No. of Obs. 1022 1022 1022 Balancing
  • 15. Preacquisition energy intensity matters! Eect of acquisition on energy and emission intensities at varying preacquisition energy intensities. -2 -1 0 1 2 -8 -6 -4 -2 0 Log(energy expenditure/output) -2 -1 0 1 2 -8 -6 -4 -2 0 Log(energy use/output) -2 -1 0 1 2 -8 -6 -4 -2 0 Log(CO2 emission/output) 0 .1 .2 .3 -8 -6 -4 -2 0 0 .1 .2 .3 -8 -6 -4 -2 0 0 .1 .2 .3 -8 -6 -4 -2 0 Marginaleffect Preacquisition log(energy expenditure/output) The gure illustrates estimated combined coecients of foreign acquisition dummy and its interaction with preacquisition energy intensity in equation 1 using the matched sample. The dashed lines correspond to the 95-percent condence interval. The period focuses at one year after the acquisition (i.e., t + 1) and estimates are relative to the preacquisition period.
  • 16. Conclusion • Foreign acquisitions increase production volume, which in turn increases energy use and emissions • But they reduce energy and emission intensities by 28 and 30%, respectively. • Foreign divestment results have the opposite eect. • Pre-acquisition energy-intensity matters. • FDI contributes to aggregate improvements in energy eciency, both through within-plant improvement and reallocation
  • 17. Your comments and suggestions are welcome! Email: a.z.brucal@lse.ac.uk
  • 19. Distribution of Foreign Equity, Pre and Post-Acquisition 0.00 0.20 0.40 0.60 0.80 1.00 0 25 50 75 100 0 25 50 75 100 Pre-acquisition Post-Acquisition Fraction Foreign ownership (%)
  • 20. Distribution of Foreign Acquisitions, by Industry 25.6 21.8 19.1 14.4 7.6 3.6 3.2 2.7 2.0 0 5 10 15 20 25 Percent Share Machinery Textile Chemicals Food Wood Products Others Minerals Paper Products Basic Metal IndustryClassification
  • 21. Distribution of Foreign Acquisitions, by Year 0.00 0.04 0.08 0.12 0.16 0.20 Fraction 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
  • 22. Conversion Metrics Input Conversion Factor Source Conversion to Energy (in MBTUs) Gasoline 1 barrel = 5.600 MBTUs Silverman, D.; University of California, Irv Diesel 1 barrel = 5.825 MBTUs US Energy Information Administration (EI Fuel Oil/ Bunker Oil 1 barrel ≈ 6.287 MTBUs EIA Kerosene 1 barrel = 5.670 MBTUs EIA Lubricants 1 barrel = 6.065 MBTUs EIA Coal 1 short ton = 21.090 MBTUs EIA (average between sub- to bituminous Coke 1 short ton = 24.800 MBTUs EIA Public Gas 1 ft3 ≈ 0.001 MBTUs US Bureau of Mines Liqueed Petroleum Gas 1 barrel = 3.861 MBTUs US Environmental Protection Agency (EP Firewood 1 cord = 20 MBTUs Silverman, D.; University of California, Irv Charcoal 1 lb = 0.128 MBTUs Oak Ridge National Laboratory Electricity 1 kWh ≈ 0.101 MBTUs EIA (assumes coal-red generation) Conversion to Carbon Dioxide (in KgCO2) Gasoline 1 MBTU = 71.26 KgCO2 EIA Diesel 1 MBTU = 71.80 KgCO2 EPA Fuel Oil/ Bunker Oil 1 MBTU = 78.80 KgCO2 EPA Kerosene 1 MBTU = 72.31 KgCO2 EPA Lubricants 1 MBTU = 74.20 KgCO2 EIA Coal 1 MBTU = 95.25 KgCO2 EIA Coke 1 MBTU = 114.10 KgCO2 EIA Public Gas 1 MBTU = 53.06 KgCO2 EIA Liqueed Petroleum Gas 1 MBTU = 62.28 KgCO2 EIA Firewood 1 MBTU ≈ 96.62 KgCO2 Partnership for Policy Integrity Charcoal 1 MBTU ≈ 2.39 KgCO2 Akagi (2011) Electricity 1 MBTU = 95.52 KgCO2 EIA
  • 23. Summary Statistics (Domestic vs. Foreign-Owned) Economic Variables Variables Domestic Firms Foreign Owned Obs Mean Obs Mean Output (in Million RP) 288988 25.36 27142 172.49 Employment (no. of workers) 288973 150.49 27136 471.73 Unskilled workers 238225 119.93 20639 374.13 Skilled workers 237822 25.32 20580 78.11 Capital (in MillionRP) 189156 25.62 17896 132.54 Materials (in Million RP) 288990 13.26 27142 87.38 Per worker wage (in '000 RP) 288990 2.59 27142 8.54 Investment in machineries (in Million RP) 233694 9.21 23705 57.05 Exporter Dummy 289266 0.08 27182 0.27 Share of exports 289266 6.31 27182 21.45 Share of imported materials 289266 11.70 27182 38.23 Capital-Labor ratio 189146 117.45 17894 326.25 Share of skilled workers 237822 13.59 20580 21.81 Public ownership dummy 289266 0.03 27182 0.04 Age 273844 13.41 25098 11.94
  • 24. Balancing Test: Variables Used in Matching Variables Unmatched sample Matched sample (pre-acquisition, N=258,827) (pre-acquisition, N=420) Acquired Domestic p-value Treated Control p-value Used in matching Log (Real output)t−1 10.62 7.71 0.000 9.89 9.88 0.951 Log (Energy expenditure/output)t−1 -4.10 -3.81 0.000 -3.87 -3.83 0.752 Log (Real output)t−2 10.58 7.76 0.000 9.74 9.74 0.997 Log (Energy expenditure/output)t−2 -4.10 -3.81 0.000 -3.93 -3.86 0.574
  • 25. Balancing Test: Variables NOT Used in Matching Variables Unmatched sample Matched sample (pre-acquisition, N=258,827) (pre-acquisition, N=420) Acquired Domestic p-value Treated Control p-value Unused in matching Log (Energy expenditure)t−1 6.52 3.98 0.000 6.02 6.04 0.868 Log(Energy use)t−1 9.48 6.93 0.000 8.95 9.00 0.779 Log (CO2 emission)t−1 13.88 11.32 0.000 13.33 13.38 0.760 Log (Employment)t−1 5.52 4.10 0.000 5.18 5.29 0.338 Exporter dummyt−1 0.30 0.08 0.000 0.19 0.18 0.706 Share of imported materialst−1 0.37 0.08 0.000 0.26 0.19 0.050 Share of skilled workerst−1 0.22 0.14 0.000 0.24 0.22 0.291 Log(Investment in machinery)t−1 8.62 5.43 0.000 8.19 7.80 0.105 Log (Energy expenditure/output)t−1 -4.10 -3.81 0.000 -3.87 -3.83 0.752 Log(Energy use/output)t−1 -1.15 -0.86 0.000 -0.94 -0.87 0.645 Log(CO2 emission/output)t−1 3.26 3.53 0.000 3.44 3.50 0.612 Log(Energy exp./materials exp.)t−1 -3.23 -2.98 0.000 -2.87 -3.07 0.201 ∆ Log (Real output)t−1 0.22 0.05 0.000 0.15 0.14 0.893 ∆ Log (Energy expenditure)t−1 0.20 0.06 0.000 0.21 0.17 0.644 ∆ Log (Energy use)t−1 0.21 0.07 0.000 0.22 0.20 0.887 ∆ Log (CO2 emission)t−1 0.22 0.08 0.000 0.21 0.20 0.857 ∆ Log (Energy expenditure/output)t−1 -0.02 0.01 0.014 0.06 0.03 0.684 ∆ Log(Energy use/output)t−1 -0.00 0.03 0.038 0.06 0.06 0.975 ∆ Log(CO2 emission/output)t−1 0.00 0.03 0.063 0.06 0.05 0.938 ∆ Log(Energy exp./materials exp.)t−1 -0.02 0.01 0.068 0.02 0.04 0.842
  • 26. OLS Results: Always Domestic vs. Acquired Plants t t+1 t+2 Log(Output) 2.156*** 2.339*** 2.423*** Log(Energy Expenditure) 1.770*** 1.938*** 1.985*** Log(Energy Use, in MBTUs) 1.724*** 1.913*** 1.967*** Log (CO2 Emissions) 1.743*** 1.927*** 1.971*** Log(Energy Expenditure/Output) -0.347*** -0.373*** -0.414*** Log(Energy Use/Output) -0.393*** -0.398*** -0.431*** Log (CO2 Emissions/Output) -0.374*** -0.384*** -0.428*** Industry-year xed eects Yes Yes Yes Firm xed eects No No No Sample Size 169,141 - 180,901
  • 27. FE Results: Always Domestic vs Acquired Plants t t+1 t+2 Log(Output) 1.034*** 1.197*** 1.245*** Log(Energy Expenditure) 0.766*** 0.906*** 0.916*** Log(Energy Use, in MBTUs) 0.712*** 0.868*** 0.891*** Log (CO2 Emission) 0.727*** 0.878*** 0.890*** Log(Energy Expenditure/Output) -0.249*** -0.269*** -0.323*** Log(Energy Use/Output) -0.302*** -0.307*** -0.348*** Log (CO2 Emission/Output) -0.287*** -0.297*** -0.349*** Industry-year xed eects Yes Yes Yes Firm rm eects Yes Yes Yes Sample Size 165,905 - 177,840 always domestic vs matched acquired plants
  • 28. Results from OLS (Always domestic and acquired plants) t t+1 t+2 Log(Output) 1.034*** 1.197*** 1.245*** Log(Energy Expenditure) 0.766*** 0.906*** 0.916*** Log(Energy Use, in MBTUs) 0.712*** 0.868*** 0.891*** Log (CO2 Emission) 0.727*** 0.878*** 0.890*** Log(Energy Expenditure/Output) -0.249*** -0.269*** -0.323*** Log(Energy Use/Output) -0.302*** -0.307*** -0.348*** Log (CO2 Emission/Output) -0.287*** -0.297*** -0.349*** Log(Energy Expenditure/Materials) -0.295*** -0.288*** -0.395*** Log(Energy Use/Materials) -0.342*** -0.327*** -0.418*** Log (CO2 Emission/Materials) -0.330*** -0.321*** -0.423*** Industry-year xed eects Yes Yes Yes Firm rm eects Yes Yes Yes Sample Size 165,905 - 177,840
  • 29. Results from OLS (Always domestic and matched acquired plants) t t+1 t+2 Log(Output) 0.839*** 0.974*** 0.949*** Log(Energy Expenditure) 0.647*** 0.763*** 0.703*** Log(Energy Use, in MBTUs) 0.584*** 0.721*** 0.703*** Log (CO2 Emission) 0.621*** 0.746*** 0.721*** Log(Energy Expenditure/Output) -0.194** -0.214** -0.266*** Log(Energy Use/Output) -0.257*** -0.256*** -0.267** Log (CO2 Emission/Output) -0.220** -0.230*** -0.248** Log(Energy Expenditure/Materials) -0.252** -0.217** -0.384*** Log(Energy Use/Materials) -0.308*** -0.264*** -0.378*** Log (CO2 Emission/Materials) -0.280*** -0.245** -0.370*** Industry-year xed eects Yes Yes Yes Firm rm eects Yes Yes Yes Sample Size 165,307 - 177,155
  • 30. Average Energy Expenditure, by Ownership (Matched Sample) Acquired Domestic 5.8 6 6.2 6.4 6.6 6.8 t-2 t-1 t t+1 t+2 t = year of acquisition Figure: Energy Intensity
  • 31. Robustness Check: Matches from Another Kabupaten Acquisition Year 1 Year Later 2 Years Later Log(Output) Post*Acquired 0.829*** 1.037*** 1.008*** (0.114) (0.116) (0.123) R-sq. (within) 0.199 0.238 0.225 No. of Obs. 836 836 836 Log (Energy Expenditure in Rps) Post*Acquired 0.573*** 0.758*** 0.701*** (0.118) (0.126) (0.134) R-sq. (within) 0.145 0.173 0.161 No. of Obs. 834 834 831 Log (Energy Expenditure/Output) Post*Acquired -0.262** -0.286** -0.324** (0.119) (0.119) (0.128) R-sq. (within) 0.012 0.015 0.016 No. of Obs. 834 834 831
  • 32. Matched Sample: Is It Just about Markups? Acquisition Year 1 Year Later 2 Years Later Log(Energy Expenditure/Materials Expenditure) Post*Acquired -0.310** -0.266** -0.382** (0.123) (0.128) (0.147) R-sq. (within) 0.021 0.011 0.018 No. of Obs. 808 810 807 Log(Energy Expenditure/Output) Post*Acquired -0.266** -0.290** -0.331*** (0.117) (0.117) (0.126) Export share -0.002 0.001 0.001 (0.002) (0.002) (0.002) R-sq. (within) 0.016 0.015 0.016 No. of Obs. 838 838 835 Log(Energy Expenditure/Output) Post*Acquired -0.317** -0.382*** -0.406*** (0.134) (0.136) (0.146) Export share -0.003 -0.001 -0.001 (0.002) (0.002) (0.002) Post*Acquired*Export share 0.003 0.004 0.004 (0.003) (0.003) (0.003) R-sq. (within) 0.018 0.023 0.021 No. of Obs. 838 838 835
  • 33. Matched Sample: Dropping years beyond 1997 Acquisition Year 1 Year Later 2 Years Later Log(Output) Post*Acquired 0.793*** 0.777*** 0.798*** (0.125) (0.134) (0.156) R-sq. (within) 0.236 0.281 0.291 No. of Obs. 714 654 614 Log (Energy expenditure in Rps) Post*Acquired 0.519*** 0.647*** 0.492*** (0.133) (0.152) (0.184) R-sq. (within) 0.134 0.174 0.136 No. of Obs. 714 654 613 Log (Energy expenditure/Output) Post*Acquired -0.273** -0.130 -0.310* (0.131) (0.130) (0.160) R-sq. (within) 0.019 0.012 0.031 No. of Obs. 714 654 613 Back
  • 34. Matched Sample: Longer Time Period and Constant Sample Output, Energy Expenditure and Energy Intensity Acquisition Year 1 Year Later 2 Years Later 3 Years Later 4 Years Later 5 Yea Log(Output) Post*Acquired 0.728*** 0.839*** 0.813*** 1.033*** 1.104*** 1.1 (0.135) (0.139) (0.150) (0.170) (0.180) (0 R-sq. (within) 0.247 0.316 0.281 0.299 0.292 0 No. of Obs. 462 462 462 462 462 Log (Energy Expenditure in Rps) Post*Acquired 0.430*** 0.593*** 0.500*** 0.306 0.420** 0.6 (0.152) (0.159) (0.183) (0.198) (0.186) (0 R-sq. (within) 0.124 0.184 0.141 0.138 0.206 0 No. of Obs. 454 454 454 454 454 Log (Energy Expenditure/Output) Post*Acquired -0.308** -0.272** -0.345** -0.718*** -0.675*** -0.4 (0.134) (0.126) (0.161) (0.157) (0.155) (0 R-sq. (within) 0.025 0.024 0.019 0.087 0.084 0 No. of Obs. 454 454 454 454 454 Back
  • 35. Balancing Test: Variables used in matching Variables CEM PSM (no same county) IPTW (N=440) (N=418) (N=143,216) Treated Control p-value Treated Control p-value F-Stat p-value Used in matching Log (Real Output) 9.03 9.03 0.99 9.86 9.86 0.90 4.43 0.04 Log (Energy Expenditure/Output) -3.71 -3.71 0.99 -3.82 -3.82 0.65 0.80 0.37 Log (Real Output) 9.59 9.62 0.90 9.86 9.86 0.90 5.91 0.02 Log (Energy Expenditure/Output) -3.77 -3.79 0.92 -3.82 -3.82 0.65 0.02 0.89 Back
  • 36. Balancing Test: Variables NOT used in matching Variables CEM PSM (no same county) IPTW (N=440) (N=418) (N=143,216) Treated Control p-value Treated Control p-value F-Stat p-value Unused in matching Log (Energy Expenditure) 5.33 5.33 0.99 6.03 6.03 0.84 6.23 0.01 Log (Energy Use) 8.25 8.26 0.95 8.99 8.99 0.77 5.42 0.01 Log (CO2 Emission) 12.65 12.66 0.94 13.36 13.36 0.78 0.02 0.02 Log (Employment) 4.85 4.72 0.26 5.26 5.26 0.40 4.59 0.00 Exporter Dummy 0.15 0.18 0.30 0.18 0.18 0.80 14.42 0.03 Share of Imported Materials 0.20 0.18 0.56 0.19 0.19 0.05 12.14 0.00 Share of Skilled Workers 0.19 0.20 0.63 0.21 0.21 0.25 17.91 0.00 Log(Investment in Machineries) 7.15 6.93 0.43 7.86 7.86 0.20 0.61 0.00 Log(Energy Use/Output) -0.80 -0.79 0.93 -0.87 -0.87 0.56 0.22 0.43 Delta Log (Energy Expenditure) 0.14 0.08 0.19 0.15 0.15 0.55 0.03 0.90 Delta Log (Energy Use) 0.17 0.09 0.19 0.18 0.18 0.72 0.00 0.87 Delta Log (CO2 Emission) 0.17 0.09 0.22 0.18 0.18 0.70 10.36 0.97 Log(CO2 Emission/Output) 3.61 3.62 0.92 3.51 3.51 0.57 7.45 0.64 Log(Energy Exp./Materials) -2.80 -2.81 0.93 -3.03 -3.03 0.32 0.39 0.01 Delta Log (Real Output) 0.13 0.05 0.14 0.14 0.14 0.86 2.05 0.53 Delta Log (Energy Expenditure/Output) 0.02 0.03 0.81 0.02 0.02 0.60 1.93 0.15 Delta Log(Energy Use/Output) 0.05 0.04 0.87 0.04 0.04 0.81 1.48 0.16 Delta Log(CO2 Emission/Output) 0.04 0.04 0.96 0.04 0.04 0.78 1.66 0.22 Delta Log(Energy Exp./Materials) 0.08 -0.02 0.08 0.04 0.04 0.81 0.00 0.20 Back
  • 37. Coasened Exact Matching-DID Estimates Acquisition Year 1 Year Later 2 Years Later Log(Output) Post*Acquired 1.392*** 1.499*** 1.530*** (0.141) (0.144) (0.148) R-sq. (within) 0.350 0.383 0.392 No. of Obs. 876 876 876 Log (Energy Expenditure in Rps) Post*Acquired 1.012*** 1.189*** 1.159*** (0.140) (0.149) (0.158) R-sq. (within) 0.221 0.248 0.253 No. of Obs. 871 868 868 Log (Energy Expenditure/Output) Post*Acquired -0.372*** -0.297** -0.382*** (0.113) (0.121) (0.123) R-sq. (within) 0.059 0.054 0.048 No. of Obs. 871 868 868 Back
  • 38. IPW-DID Estimates Acquisition Year 1 Year Later 2 Years Later Log(Output) Post*Acquired 1.659*** 1.763*** 1.906*** (0.184) (0.218) (0.221) R-sq. (within) 0.803 0.807 0.809 No. of Obs. 138750 138750 138750 Log (Energy Expenditure in Rps) Post*Acquired 1.353*** 1.421*** 1.399*** (0.176) (0.199) (0.220) R-sq. (within) 0.806 0.802 0.800 No. of Obs. 138011 138009 138008 Log (Energy Expenditure/Output) Post*Acquired -0.324*** -0.358*** -0.516*** (0.120) (0.133) (0.159) R-sq. (within) 0.640 0.649 0.638 No. of Obs. 138011 138009 138008 Back
  • 39. Matched Sample: Energy Use and CO2 Emissions Acquisition Year 1 Year Later 2 Years Later Log(Energy Use in MBTUs) Post*Acquired 0.539*** 0.770*** 0.664*** (0.118) (0.130) (0.136) R-sq. (within) 0.138 0.178 0.168 No. of Obs. 838 838 835 Log (CO2 Emissions) Post*Acquired 0.562*** 0.792*** 0.673*** (0.120) (0.130) (0.137) R-sq. (within) 0.150 0.188 0.176 No. of Obs. 838 838 835 Log (Energy Use/Output) Post*Acquired -0.304** -0.285** -0.367*** (0.120) (0.125) (0.137) R-sq. (within) 0.015 0.014 0.019 No. of Obs. 838 838 835 Log (CO2 Emissions/Output) Post*Acquired -0.282** -0.262** -0.357*** (0.119) (0.124) (0.136) R-sq. (within) 0.014 0.015 0.021 No. of Obs. 838 838 835 Back
  • 40. Why Acquired Firms Did Not Reduce Energy Intensity for Electricity? Industrial Electricity Rates for Select ASEAN Counties, 1984-1992. Source: Malhotra, et al. 1994. Back
  • 41. PSM-DID results: energy input prices. Acquisition Year 1 Year Later 2 Years Later Fuel price Acquired -0.080 0.021 -0.059 (0.090) (0.014) (0.063) R-sq. (within) 0.005 0.013 0.005 No. of Obs. 812 815 806 Log(fuel price) Acquired 0.049 0.083 0.014 (0.057) (0.054) (0.057) R-sq. (within) 0.004 0.008 0.001 No. of Obs. 812 815 805 Electricity price Acquired -0.001 -0.003 0.006 (0.003) (0.003) (0.007) R-sq. (within) 0.001 0.026 0.004 No. of Obs. 714 713 711 Log(electricity price) Acquired -0.020 -0.043 0.016 (0.040) (0.044) (0.051) R-sq. (within) 0.002 0.038 0.045 No. of Obs. 714 713 711 Back
  • 42. Regression Result: Scale Effect Dependent Variable: Log(Energy Expenditure) All Sample Matched Sample (1) (2) (3) (4) Acquired 0.836** 2.334*** 0.997** 1.931*** (0.329) (0.250) (0.404) (0.462) ln(output) 0.571*** 0.621*** (0.005) (0.040) ln(output)t-1 0.272*** 0.250*** (0.005) (0.046) Acquired*ln(output) -0.060** -0.086** (0.030) (0.038) Acquired*ln(output)t-1 -0.176*** -0.144*** (0.022) (0.043) Firm xed eect Yes Yes Yes Yes Year-xed eect Yes Yes Yes Yes R-sq. (within) 0.261 0.097 0.389 0.134 No. of Obs. 255450 228733 2994 2571 Back
  • 43. PSM-DID estimates: employment, capital and investments Acquisition Year 1 Year Later 2 Years Later Log(Capital) Post*Acquired 0.622*** 0.738*** 0.794*** (0.147) (0.178) (0.208) R-sq. (within) 0.109 0.099 0.082 No. of Obs. 658 644 627 Log(Employment) Post*Acquired 0.319*** 0.349*** 0.361*** (0.050) (0.055) (0.061) R-sq. (within) 0.153 0.152 0.109 No. of Obs. 840 840 840 Log(Capital-labor ratio) Post*Acquired 0.349** 0.406** 0.449** (0.145) (0.174) (0.201) R-sq. (within) 0.034 0.030 0.030 No. of Obs. 658 644 627 Log(Investment in machineries) Post*Acquired 0.745*** 0.729*** 0.861*** (0.178) (0.202) (0.245) R-sq. (within) 0.087 0.070 0.067 No. of Obs. 650 637 620
  • 44. Matched difference-in-differences estimates: Testing for non-linear effect of acquisition at varying preacquisition energy intensity. Log(energy expenditure output ) Log(energy use output ) Log( CO2 output) Post*Acquired -1.759*** -1.738*** -1.643*** (0.359) (0.382) (0.358) Post*Acquired*Log(energy expenditure output )t−1 -0.409*** -0.392*** -0.378*** (0.092) (0.097) (0.093) R-sq. (within) 0.213 0.191 0.185 No. of Obs. 814 814 814 Threshold -4.30 4.43 -4.35 Pre-acq. Energy Intensitythreshold (% share of treated plants) 60.48 68.57 64.29 Back
  • 45. Balancing Test: Variables used in matching Variables Divestment (N=512) Treated Control p-value Used in matching Log (Real Output) 10.94 10.96 0.87 Log (Energy Expenditure/Output) -4.21 -4.19 0.86 Log (Real Output) 10.83 10.76 0.60 Log (Energy Expenditure/Output) -4.13 -4.08 0.68 Back
  • 46. Is There Reallocation across Energy Inputs? Price-based measures Physical Units Acquisition Year 1 Year Later 2 Years Later Acquisition Year 1 Year Later 2 Years Later Log(Total fuel, in Rps) Log(Total fuel expenditure, in MBTUs) Post*Acquired 0.392*** 0.596*** 0.543*** 0.343** 0.513*** 0.547*** (0.149) (0.158) (0.172) (0.165) (0.173) (0.189) R-sq. (within) 0.044 0.069 0.058 0.028 0.045 0.045 No. of Obs. 812 815 805 812 815 806 Log(Net electricity expenditure, in Rps) Log(Net electricity expenditure, in MBTUs) Post*Acquired 0.761*** 0.775*** 0.696*** 0.781*** 0.818*** 0.679*** (0.204) (0.206) (0.217) (0.201) (0.208) (0.219) R-sq. (within) 0.092 0.115 0.118 0.099 0.137 0.142 No. of Obs. 714 713 711 714 713 711 Log(Total fuel expenditure/Output) Log(Total fuel use/Output) Post*Acquired -0.422*** -0.452*** -0.429*** -0.471*** -0.535*** -0.428** (0.148) (0.153) (0.160) (0.164) (0.171) (0.182) R-sq. (within) 0.025 0.023 0.020 0.026 0.027 0.017 No. of Obs. 812 815 805 812 815 806 Log(Net electricity expenditure/Output) Log(Net electricity use/Output) Post*Acquired -0.103 -0.343* -0.389* -0.083 -0.300 -0.406* (0.203) (0.202) (0.213) (0.201) (0.204) (0.219) R-sq. (within) -0.001 0.011 0.015 -0.001 0.015 0.025 No. of Obs. 714 713 711 714 713 711 Power Rates (Regression table)
  • 47. Energy Intensity vs Scale Predicted energy expenditure and output (a) Unmatched sample 012345 log(EnergyExpenditure)t 2 6 10 14 18 log(output) t-1 Acquired Domestic (b) Matched sample 12345 log(EnergyExpenditure)t 5 10 15 log(output) t-1 Acquired Domestic Note: Regression Table: Scale Regression Table:Structural Change
  • 48. Balancing Test: Variables NOT used in matching Variables Divestment (N=512) Treated Control p-value Unused in matching Log (Energy Expenditure) 6.72 6.76 0.79 Log (Energy Use) 9.66 9.69 0.80 Log (CO2 Emission) 14.05 14.08 0.82 Log (Employment) 5.76 5.69 0.46 Exporter Dummy 0.33 0.35 0.64 Share of Imported Materials 0.30 0.36 0.05 Share of Skilled Workers 0.22 0.21 0.39 Log(Investment in Machineries) 8.84 8.96 0.55 Log(Energy Use/Output) -1.28 -1.26 0.87 ∆ Log (Energy Expenditure) 0.03 0.08 0.51 ∆ Log (Energy Use) 0.02 0.10 0.40 ∆ Log (CO2 Emission) 0.02 0.10 0.43 Log(CO2 Emission/Output) 3.11 3.13 0.89 Log(Energy Exp./Materials) -3.54 -3.37 0.19 ∆ Log (Real Output) 0.11 0.19 0.24 ∆ Log (Energy Expenditure/Output) -0.09 -0.11 0.78 ∆ Log(Energy Use/Output) -0.09 -0.09 0.95 ∆ Log(CO2 Emission/Output) -0.09 -0.10 0.91 ∆ Log(Energy Exp./Materials) -0.11 -0.13 0.77 Back
  • 49. Decomposition of Aggregate Changes in Energy Intensity Following Olley and Pakes (Econometrica, 1996): Wt = i sit lnEIP Aggregate weighted energy intensity = lnEIP Unweighted average energy intensity + i (sit − st)(lnEIPit − lnEIP) Covariance Table: All Industries
  • 50. Decomposition of Aggregate Changes in Energy Intensity Following Olley and Pakes (Econometrica, 1996): Wt = i sit lnEIP Aggregate weighted energy intensity = lnEIP Unweighted average energy intensity + i (sit − st)(lnEIPit − lnEIP) Covariance Table: All Industries Yjst = βForeignjt + γj + λst + εjst where j denotes kabupaten (48), s sector (9) and t year (19).
  • 51. Decomposition of Aggregate Energy Intensity Measure based on number of FAs Measure based on output share of FAs Wt lnEIP Covariance Wt lnEIP Covariance Log (Energy Expenditure/Output) Foreign Aliates -0.226*** -0.086** -0.140*** -0.772* -0.552** -0.219 (0.041) (0.034) (0.044) (0.410) (0.276) (0.318) Adj. R-sq. 0.853 0.829 0.774 0.842 0.827 0.764 Observations 1408 1408 1408 1408 1408 1408 Log (Energy Use/Output) Foreign Aliates -0.215*** -0.070** -0.146*** -0.740* -0.490* -0.250 (0.039) (0.034) (0.040) (0.401) (0.271) (0.336) Adj. R-sq. 0.859 0.852 0.784 0.850 0.851 0.775 Observations 1408 1408 1408 1408 1408 1408 Log (CO2 Emission/Output) Foreign Aliates -0.217*** -0.077** -0.140*** -0.761* -0.521* -0.239 (0.039) (0.035) (0.040) (0.405) (0.277) (0.328) Adj. R-sq. 0.853 0.834 0.783 0.844 0.833 0.775 Observations 1408 1408 1408 1408 1408 1408 No. of industries (4-digit ISIC) 79 79 79 79 79 79 No. of sectors (2-digit ISIC) 9 9 9 9 9 9 No. of years 19 19 19 19 19 19 Table: Dierent normalization
  • 52. Decomposition of Changes in Aggregate Energy Intensity All Industries Year Aggregate Unweighted Covariance Number of Energy Intensity Energy Intensity Foreign-owned 1984 -0.10 -0.10 -0.11 -2 1985 -0.18 -0.25 0.00 103 1986 -0.45 -0.35 -0.11 78 1987 -0.26 -0.29 0.04 102 1988 -0.27 -0.33 0.06 166 1989 -0.23 -0.35 0.07 169 1990 -0.14 -0.45 0.23 266 1991 -0.17 -0.37 0.20 410 1992 -0.27 -0.39 0.11 581 1993 -0.41 -0.32 -0.09 674 1994 -0.34 -0.39 0.05 792 1995 -0.43 -0.40 -0.03 864 1996 -0.42 -0.44 0.01 978 1997 -0.60 -0.60 0.00 1071 1998 -0.39 -0.48 0.08 1216 1999 -0.18 -0.37 0.08 1289 2000 -0.08 -0.34 0.24 1226 2001 -0.31 -0.43 0.07 1106
  • 53. Decomposition of Changes in Aggregate Energy Intensity Industries with below-median increases in foreign aliates Year Aggregate Unweighted Covariance Number of Energy Intensity Energy Intensity Foreign-owned 1984 -0.14 -0.07 -0.08 0 1985 -0.29 -0.22 -0.08 38 1986 -0.47 -0.31 -0.16 42 1987 -0.50 -0.24 -0.26 37 1988 -0.49 -0.27 -0.23 51 1989 -0.52 -0.30 -0.22 45 1990 -0.52 -0.31 -0.21 53 1991 -0.60 -0.27 -0.33 96 1992 -0.56 -0.23 -0.33 134 1993 -0.50 -0.10 -0.42 142 1994 -0.52 -0.17 -0.35 169 1995 -0.62 -0.16 -0.46 190 1996 -0.60 -0.19 -0.41 247 1997 -0.59 -0.48 -0.11 278 1998 -0.56 -0.41 -0.14 234 1999 -0.32 -0.26 -0.09 281 2000 -0.26 -0.20 -0.11 271 2001 -0.59 -0.32 -0.30 242
  • 54. Decomposition of Changes in Aggregate Energy Intensity Industries with above-median increases in foreign aliates Year Aggregate Unweighted Covariance Number of Energy Intensity Energy Intensity Foreign-owned 1984 -0.08 -0.13 -0.11 -1 1985 -0.12 -0.26 0.05 63 1986 -0.49 -0.38 -0.11 33 1987 -0.16 -0.33 0.17 64 1988 -0.18 -0.36 0.18 108 1989 -0.12 -0.37 0.18 116 1990 -0.01 -0.51 0.39 227 1991 -0.03 -0.41 0.38 328 1992 -0.17 -0.47 0.27 461 1993 -0.46 -0.45 -0.01 546 1994 -0.29 -0.52 0.22 637 1995 -0.39 -0.54 0.16 688 1996 -0.38 -0.60 0.20 745 1997 -0.62 -0.66 0.04 807 1998 -0.35 -0.51 0.13 996 1999 -0.15 -0.42 0.12 1022 2000 -0.02 -0.42 0.38 969 2001 -0.21 -0.48 0.22 878
  • 55. Decomposition of aggregate energy intensity (Different Normalization) Measure based on number of FAs Measure based on output share of FAs Wt lnEIP Covariance Wt lnEIP Covariance Log (Energy Expenditure/Materials) Foreign Aliates -0.254*** -0.093** -0.161** -0.822* -0.698** -0.125 (0.061) (0.043) (0.065) (0.454) (0.307) (0.381) Adj. R-sq. 0.873 0.874 0.789 0.863 0.874 0.779 Observations 1407 1407 1407 1407 1407 1407 Log (Energy Use/Materials) Foreign Aliates -0.243*** -0.076* -0.167*** -0.788* -0.628** -0.160 (0.058) (0.041) (0.060) (0.444) (0.299) (0.392) Adj. R-sq. 0.881 0.882 0.804 0.872 0.883 0.794 Observations 1407 1407 1407 1407 1407 1407 Log (CO2 Emission/Materials) Foreign Aliates -0.244*** -0.082** -0.162*** -0.804* -0.657** -0.147 (0.057) (0.041) (0.060) (0.450) (0.301) (0.386) Adj. R-sq. 0.877 0.872 0.805 0.868 0.873 0.796 Observations 1407 1407 1407 1407 1407 1407 No. of industries (4-digit ISIC) 79 79 79 79 79 79 No. of sectors (2-digit ISIC) 9 9 9 9 9 9 No. of years 19 19 19 19 19 19 Back
  • 56. Emissions, Output and FDI in Indonesia, 1983-2001. 0 500 1000 1500 2000 2500 -4000 -2000 0 2000 4000 6000 1983 1986 1989 1992 1995 1998 2001 Net FDI inflow, BOP current Million US$ (left axis) GDP, current 100 Million US$ (right axis) Foreign Owned Firms (right axis) CO2 Emission, in 100K MT (right axis)