explaining firm compliance in a large-scale chinese energy .... t10000p.pdf · explaining ﬁrm...

Explaining firm compliance in a large-scale Chinese energyefficiency program

Valerie J. Karplus, Xingyao Shen, Da Zhang

MIT Joint Program on the Science and Policy of Global ChangeInstitute of Energy, Environment and Economy, Tsinghua University

Sloan School of Management, MIT

June 21st, 2017

Motivation Overview of firm-compliance Why firms fail to comply? Discussion Conclusion

Motivation

China’s Industrial Sector:

Accounts for more than 15% of world’s total energy use and CO2 emissions.

Is expected to have large potential for energy efficiency improvement.

Firm compliance in energy efficiency programs:

Have been rarely studied in the literature.

May differ in developing countries because ofa) More acute trade-offs with environmental constraints (Greenstone and Jack,2015), andb) Less deterrence against data manipulation (Duflo et al., 2013)

Da Zhang (MIT Joint Program) China’s Energy Saving Program June 21st, 2017 2 / 23


Motivation

China’s Industrial Energy Conservation Program:

The flagship program in China’s comprehensive set of command-and-controlpolicies for energy saving.

Implemented as the “Top 1,000 Firms Program” in the Eleventh Five-Year Plan(FYP, 2006-2010).1,008 firms involved accounted for about 30% of China’s total energy use in 2005,and ended up with about 170 million tons of coal-equivalent energy savingreported.

Expanded to the “Top 10,000 Firms Program” during the Twelfth FYP(2011-2015), involving 14,641 industrial firms, and hundreds of transportationoperators, hotels and restaurants, commercial and trade enterprises, and schools,in total 16,078 institutions,accounting for more than 60% of China’s total energy use, targeting 250 milliontons of coal-equivalent energy saving, about 650 million tons of CO2 emissionsmitigation.



Motivation

Our primary contribution from assessing firm compliance outcomes inChina’s Industrial Energy Conservation Program:

We develop a new and unique data set that combines data on participating firmcharacteristics.

We use a statistical method to evaluate the plausibility of compliance datasubmitted by firms.We find statistical evidence that firms deliberately exaggerated performance in thefirst phase of the program.

We assess the correlation between pre-existing firm and location characteristicsand compliance outcomes after the program was expanded.We find larger firms not controlled by the state and firms in cities with slow growthshowed significantly lower likelihood of compliance.



Firms covered and non-compliant firms

Numbers of total firms and non-compliance firms for the two programs:

Top 1,000 Firms Program Top 10,000 Firms ProgramOriginallist

Evaluationin 2008

Evaluationin 2009

Evaluationin 2010

Originallist

Evaluationin 2012

Evaluationin 2013

Evaluationin 2014

Total firms 1,008 922 901 881 16078 14,542 14,119 13,328Non-compliantfirms

- 36 28 15 - 1,377 1,191 948

Non-compliantrates

- 3.9% 3.1% 1.7% - 9.5% 8.4% 7.1%



A closer look at the Top 10,000 Firms Program

Non-industrial firms have higher non-compliance rate, especially in 2012.

Tranportation firms and schools, most of which are shiyedanwei or large localSOEs, have fairly high non-compliance rate in 2012, but greatly improved in 2013and 2014.

Non-compliance rate of industrial firms in the Top 10,000 Firms Programs is stillsignificantly higher than that of the Top 1,000 Firms Program.

Originallist

Evaluation in 2012 Evaluation in 2013 Evaluation in 2014

Total num-ber

Non-compliancenumber

Estimatednon-compliancerate





Industrial firms 14,641 1,174 9.0 % 1038 8.2% 693 5.8%Transportationfirms

548 63 11.5% 37 6.8% 28 5.1%

Hotels and restau-rants

195 14 7.2% 13 6.7% 12 6.2%

Commercial andtrade firms

260 28 10.8% 29 11.2% 41 15.8%

Schools 434 88 20.3% 48 11.1% 21 4.8%



Matching with the CIC data

We match the firm information of the Top 10,000 Firms Program with acomprehensive firm-level data set (the China Industrial Census) that includesvery detailed corporate finance information.

7,006 out of 12,000+ Top 10,000 industrial firms are matched with 2011 CIC data.

435 out of 7,006 firms were in the Top 1,000 Firms Program. Theirnon-compliance rates are 7.8% in 2012 and 7.4% in 2013 respectively.⇒ Even for the same group of firms, compliance is more difficult under the

Top 10,000 Firms Program.

4,995 of 7,006 firms are from the sectors that are covered in the Top 1,000 FirmsProgram. Their non-compliance rates are 7.7% in 2012 and 7.6% in 2013respectively.⇒ Expanding sector coverage does not fully explain the significant increase

of non-compliance rate.

Top 1,000 Firms Program Top 10,000 Firms ProgramOriginallist

Evaluationin 2008

Evaluationin 2009

Evaluationin 2010

Original list

Industrial firms total 1,008 922 901 881 14,641Industrial firms matched 1,001 862 840 824 7,006Percentage matched 99.3% 93.5% 93.2% 93.5% 47.9%



Data manipulation? A discontinuity test

We test whether there are possibilities that the low non-compliance rate inthe Top 1,000 Firms Program is a result of data gaming.

China’s energy and environmental data should not be taken for granted.Previous studies either found huge disparity in reported values for the sameenergy statistics (Guan et al., 2012), and discovered data gaming in air qualitydata by observing “bunching” the PM10 numbers below the “Blue Sky Day”threshold (Chen et al., 2013; Ghanem and Zhang, 2014).

We apply the statistical method of Chen et al. (2013) to check if there isevidence that firms’ energy saving data is “bunched” above the target.

Firm’s energy saving achievement in percentage term in 2010 is calculated.There must be a discontunuity around 100% if data is revised upward.This discontinuity does not necessarily mean data manipulation because firmsmay stratigically achieve the energy saving target by a small margin.Burgstahler and Dichev test (BDT) statistics (Burgstahler and Dichev, 1997):compare each bin’s observed probability density with its average neighboringprobability density:

BDT =

p̂j−1+p̂j+12 − p̂j√

1n p̂j (1− p̂j ) +

14n (p̂j−1 + p̂j+1)(1− p̂j−1 − p̂j+1) +

1n p̂j (p̂j−1 + p̂j+1)

(1)




Dash lines are the critical values with the confidence level of 99%. Significantdiscontinuity is found in the neighborhood of 100%.Results are robust for the bin size of 2.5% and 5%.

Achievement in Percentage (bin size: 2.5) (%)0 50 100 150 200 250 300

Den

sity

0

0.02

0.04

0.06

0.08

0.1

0.12(a)

Achievement in Percentage (bin size: 2.5) (%)0 50 100 150 200 250 300

BD

T S

tati

stic

s

-20

0

20

40

60(b)

Achievement in Percentage (bin size: 5) (%)0 50 100 150 200 250 300

Den

sity

0

0.02

0.04

0.06

0.08

0.1

0.12(a)

Achievement in Percentage (bin size: 5) (%)0 50 100 150 200 250 300

BD

T S

tati

stic

s

-20

0

20

40

60(b)




No evidence of “bunching" is found from the non-compliant firm data of theTop 10,000 Firms Program.

-200 -150 -100 -50 0 50 100Achievement in Percentage (bin size: 5) (%)

0

0.02

0.04

0.06

0.08

0.1

0.12

Den

sity

(a)


-20

0

20

40

60

BD

T S

tati

stic

s

(b)

-200 -150 -100 -50 0 50 100Achievement in Percentage (bin size: 2.5) (%)

0

0.02

0.04

0.06

0.08

0.1

0.12

Den

sity

(c)


-20

0

20

40

60

BD

T S

tati

stic

s

(d)






0

0.02

0.04

0.06

0.08

0.1

0.12

Den

sity

(a)


-20

0

20

40

60

BD

T S

tati

stic

s

(b)


0

0.02

0.04

0.06

0.08

0.1

0.12

Den

sity

(c)


-20

0

20

40

60

BD

T S

tati

stic

s

(d)





Achievement in Percentage (bin size: 5) (%)-200 -150 -100 -50 0 50 100

Den

sity

0

0.02

0.04

0.06

0.08

0.1

0.12(a)

Achievement in Percentage (bin size: 5) (%)-200 -150 -100 -50 0 50 100

BD

T S

tati

stic

s

-20

0

20

40

60(b)

Achievement in Percentage (bin size: 2.5) (%)-200 -150 -100 -50 0 50 100

Den

sity

0

0.02

0.04

0.06

0.08

0.1

0.12(c)

Achievement in Percentage (bin size: 2.5) (%)-200 -150 -100 -50 0 50 100

BD

T S

tati

stic

s

-20

0

20

40

60(d)



The Anna Karenina Principle

NDRC publishes reasons why firms failed to achieve the target for the Top10,000 Firms Program.We collect all the 803 records, and are surprised to find that there are allkinds of reasons that could explain firms’ non-compliance, consistent with thefamous “Anna Karenina” principle that firms that comply are all alike, andfirms that do not comply have their own reasons.

No. Reasons Numberoffirms

Description

1 Did not cooperatewith the evaluationor not provide com-plete data

245 Firms refused or failed to completely submit the selfcheckingreport, energy data, or any other materials required by theevaluation.

2 Ceased pro-duction, merge,product portfolioschange, or newbuildings

179 Those are firms that faced bankruptcy or stopped a signifi-cant part of or entire production temporarily or permanently,merged with other firms, or added energy-intensive productsto their portfolios. Energy saving targets of schools werelinked to the number of students, therefore constructing newbuildings made targets harder to achieve if the number of stu-dents was unchanged.

3 Low or unstableproduction

161 Production level was lower than expected, therefore the en-ergy saving linked to production was limited. Lower produc-tion level could increase the unit energy consumption of theproduct as more frequent starts and stops reduce efficiencyof operation.



The Anna Karenina Principle

No. Reasons Numberoffirms

Description

4 Uncertain energy useper unit of production

26 Some firms had production process with fluctuating energyuse, especially for some transportation firms.

5 Product price de-crease

5 Decreasing product price could bring down the output invalue term of a firm with same input use, therefore energyconsumption per unit of output in value term increased.

6 Lower heating valuefuels

5 Fuels with a lower heating value than previously used couldlead to an increase in reported energy use if firms did notmeasure the heating value change to correctly convert theenergy use to coal equivalent units.

7 Ongoing constructionproject

5 Energy use increased due to new projects under construc-tion.

8 Little room to improveenergy efficiency

34 Firms were very efficient already, with little room to im-prove.

9 Lack of capacity infirms newly includedin the program

28 Firms newly included in the evaluation might not have ac-curate historical data or an energy management system,or lack capacity to achieve energy saving or complete theevaluation.

10 Poor energy manage-ment

12 Firms had very low energy management scores.

11 Ongoing or plannedtechnology upgrading

11 Firms were implementing technology upgrades, or hadplans to enhance energy efficiency in later years duringthe Twelfth FYP.



Firm characteristics associated with non-compliance

We speculate that firm size, profitability, ownership status, and whether wasincluded in the Top 1,000 Firms Program may affect the compliance.We observe a wide range of non-compliance rate across provinces andsectors, therefore we include province and sector dummies in our regression.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

No

n-c

om

plia

nce

rat

e

Ningxia

Guang

xi

Beijing

Xinjian

g

Heilon

gjian

g

Gansu

Tianjin

Shang

hai

Hainan

Shanx

i

Jiang

xiTibe

t

Anhui

Shaan

xi

Liaon

ing

Hunan

Guizho

u

Chong

qing

Jilin

Henan

Inne

r Mon

golia

Shand

ong

Zhejia

ng

Yunna

n

Fujian

Qingha

i

Guang

dong

Sichua

n

Hebei

Jiang

su

Hubei

All the firmsSample firms matched with CIC

0

0.05

0.1

0.15

Non-fe

rrous

Ferro

us

Other

mini

ng

Petro

chem

ical

Electri

city

Paper

Other

man

ufac

turin

g

Non-m

etall

ic

Chem

ical

Textile

Coal m

ining

No

n-c

om

plia

nce

rat

e

Non-compliance rate of firms matched with CIC in 2012Non-compliance rate of firms matched with CIC in 2013Non-compliance rate of firms matched with CIC in 2014




The dependent variable is the binary variable for non-compliance.Non-compliance: 1, Compliance: 0.

(1) (2) (3) (4) (5)

Log (main business revenue) 0.07* 0.08* 0.08* 0.10** 0.17***(0.03) (0.03) (0.03) (0.04) (0.04)

Profit rate -0.31 -0.33 -0.34 -0.31(0.39) (0.38) (0.39) (0.37)

Central SOE -0.05 -0.04 2.18***(0.18) (0.18) (0.59)

Local SOE -0.12 -0.11 0.82**(0.11) (0.11) (0.29)

Top 1,000 enterprise -0.20 -0.11(0.16) (0.16)

Log (main business revenue) * Central SOE -0.31***(0.08)

Log (main business revenue) * Local SOE -0.16**(0.05)

Province fixed effects YES YES YES YES YESSector fixed effects YES YES YES YES YES

Year fixed effects YES YES YES YES YESNumber of years 3 3 3 3 3

Number of observations 19974 19974 19974 19974 19974

Note: Standard errors are clustered at the province and sector level.




We further explore if the macroeconomic and institutional factors matter.We include per-capita GDP and GDP growth rate at the prefecture citylevel in 2012, 2013 and 2014 in the regression.

(1) (2) (3) (4)

Log (main business revenue) 0.10* 0.09* 0.10* 0.18***(0.04) (0.04) (0.04) (0.04)

Profit rate -0.43 -0.43 -0.43 -0.38(0.32) (0.32) (0.32) (0.31)

Central SOE -0.03 -0.01 -0.01 2.69***(0.19) (0.19) (0.19) (0.59)

Local SOE -0.10 -0.09 -0.09 1.01**(0.12) (0.12) (0.12) (0.31)

Top 1,000 enterprises -0.22 -0.23 -0.24 -0.12(0.17) (0.17) (0.17) (0.17)

Log (main business revenue) * Central SOE -0.38***(0.08)

Log (main business revenue) * Local SOE -0.19***(0.05)

Per-capita GDP -0.01 -0.01 -0.01(0.02) (0.02) (0.02)

GDP growth rate -0.03*** -0.03*** -0.03***(0.01) (0.01) (0.01)

Province fixed effects YES YES YES YESSector fixed effects YES YES YES YES

Year fixed effects YES YES YES YESNumber of years 3 3 3 3

Number of observations 17657 17657 17657 17657

Note: Standard errors are clustered at the province and sector level.



When command-and-control meets market-based approach

As China has confirmed that a national emission trading system (ETS) will belaunched in 2017, the role of energy efficiency programs will be weakened,because these two policies with very similar focus may be incompatible.

ETS encourages flexible compliance by allowances trading, whilecommand-and-control programs require a hard target for each firm tosave energy or reduce emissions within firm’s own fence.

We find evidence that this inflexibility has caused difficulty for some firmsthat are already very energy efficient to achieve the target.

Most Top 10,000 firms in the ETS pilots are allocated with both energysaving targets and emissions allowances. They have to achieve theenergy saving target, which undermines the efficiency of ETS.




Two anectdotal stories:

Datang Jixi Thermal Power Company in 2013:“The company’s two 125 MW generation units are already the mostenergy efficient ones compared to other units with simialr type in China.There is no room to improve the energy efficiency, therefore the energysaving target is not achieved. In order not to add a negative impact onJixi City’s Twelfth FYP energy saving target, Datang Heilongjiang PowerCompany has negotiated with Datang Jixi No.2 Thermal PowerCompany, and signed an agreement regarding the target sharing of theenergy saving target during the Twelfth FYP. Datang Jixi No.2 ThermalPower Company will carry the 22,000 tons of coal-equivalent energysaving target for Datang Jixi Thermal Power Company. This case hasbeen reported to the Jixi Development and Reform Commission forapproval."

Some firms reported that they signed a contract with ESCOs buyingenergy from them, so this part of energy consumption can be accountedas energy saving to fulfill the target.




There are firms which overfulfill their energy saving targets significantly. 25 of theprovinces have already achieved their Five-Year energy saving targets in 2014.Overfulfillment rates of central SOEs are mostly higher than the rates of all the firms.

0

50

100

150

200

250

300

Pro

gre

ss t

ow

ard

pro

vin

cial

tar

get

(%

)

Qingha

i

Guang

dong

Henan

Shaan

xi

Anhui

Guang

xi

Hebei

Heilon

gjian

g

Fujian

Hubei

Chong

qing

Liaon

ing

Sichua

n

Shanx

i

Inne

r Mon

golia

Tianjin

Jiang

su

Guizho

u

Hunan

Ningxia

Jiang

xi

Gansu Jil

in

Zhejia

ng

Shand

ong

Xinjian

g

Yunna

n

Shang

hai

Beijing

Hainan

Tibet

Progress of provincial energy saving target in 2012 (%)Pass line of provincial energy saving target progress in 2012 (40%)Progress of provincial energy saving target in 2013 (%)Pass line of provincial energy saving target progress in 2013 (60%)Progress of provincial energy saving target in 2014 (%)Pass line of provincial energy saving target progress in 2014 (80%)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ove

rfu

lfill

rat

e

Guang

dong

Guizho

u

Shanx

i

Beijing

Henan

Fujian

Liaon

ing

Chong

qing

Inne

r Mon

golia

Shaan

xi

Hainan

Guang

xi

Zhejia

ng

Tianjin

Ningxia

Hubei

Qingha

i

Shang

hai

Heilon

gjian

g

Yunna

n

Sichua

n

Hebei

Shand

ong

Jilin

Xinjian

g

Gansu

Anhui

Hunan

Jiang

xi

Jiang

su

Sample central SOEsAll the sample firms




Nevertheless, the Top 1,000 and 10,000 Firms Program provided a veryimportant common basis for future policy, regardless of whethercommand-and-control or market based.

Company executives are now aware of the importance of energy saving.

Financing problem is addressed to some extent by central and provincialfunds.

The energy management and online measurement system promoted bythe program are prerequisites for any effective energy saving oremissions reduction policy.

NDRC has made many relevant program documents available,increasing the transparency of the policy and offering a chance toengage the public participation in the energy saving campaign.



Concluding remarks

Summary

Non-compliance rate is increasing for many reasons:We find evidence of data manipulation in the Top 1,000 Firms Program.Larger firms not controlled by the state and firms in cities with slow growthshowed significantly lower likelihood of compliance⇒ Those are firms lesssubject to state control and related incentive structures.

The programs have built important institutions:E.g. energy data direct reporting, monitoring, verification and importantlytransparency that did not previously exist.Surfaced some related challenges for program administration such asdouble counting. structures.

Policy recommendations:Evaluate the trade off between firm-level targets and market-based policy.

a) Strengthen the measuring, reporting and verification (MRV) of energyand emissions.

b) Address potential incompatibilities to arise between the nationalemission trading system (ETS) and energy conservation programs.



Thank you for your attention!

[email protected]


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