The Learning Process and Technological Change through International

Collaboration: Evidence From China's CDM Wind Projects

Tian Tang David Popp

Maxwell School

Syracuse University

Public Policy in Asia, Singapore, May 26-27, 2014

Research Question• Research Question: How does the learning process lead to 

technological change in wind power?

• Technological Change: - Overall production cost: reduction in unit cost of wind power- Wind farm installation: reduction in unit capital costs- Wind farm operation: capacity factor

• Learning Process: How the knowledge related to wind power is acquired and diffused among project  participants

• Case: China’s wind power projects supported by the Clean Developed Mechanisms (CDM)

Background: CDM and China’s Wind Industry• CDM:  A  project-based carbon transaction mechanism under 

the Kyoto Protocol  that allows developed countries with emission constraints to purchase emission credits by financing projects that reduce carbon emissions in developing countries. 

• Goals of CDM: - Help developing countries reduce carbon emissions- Stimulate sustainable development in developing countries 

through technology transfer from developed countries

• The Role of CDM in China’s Wind Industry- China has actively engaged in CDM since 2002 and used it to 

provide financial support for over 80% of wind projects-    Standardized and transparent process and validated data

Background: International Collaboration in CDM Wind Projects

• Engage both the public and private sectors of the carbon trading countries, and international organizations

CDM Project hosting country

Central and local government, wind power companies, carbon trade consultants

Emission credits buying country

Central government, investment banks or carbon trading firms

International organizations administering and monitoring CDM process

Executive Board of CDM, 3rd party validating and monitoring agencies

Theory: Learning Process and Technological Change

• Following the technological learning and collaboration theories, we identify the following channels of learning that could lead to the reduction of electricity production cost: 

Project Developer- Site selection - Construction- Operation - Connecting with the grid for delivery

Wind Turbine Manufacturer

-Research and development-Produce wind turbines

Installation, Training, O&M

Learning through R&D (Learning by

Searching, LBS)

Learning by doing (LBD)

Learning by interacting (LBI)

Learning by doing

Knowledge spillovers

Data and Empirical Model• Unit of Analysis:  CDM wind power project• Data- Pooled cross-sectional- 510 registered CDM wind projects in China that started from 2002 to 2009

- Including 92 developers and 33 turbine manufacturers • Sources: 1) Validated CDM project design document and  its 

attached financial analysis spreadsheet for each project2) Yearbook from Chinese Wind Energy Association3) DELPHION patent database

Data and Empirical Model

  

• Dependent Variables:

 1) Projected unit cost of electricity production of project i started construction in year t - Levelized cost

2) Projected unit capital cost of project i started in year t

3) Projected capacity factor of project i started in year t

Data and Empirical Model• Explanatory Variables: Learning Effects

𝑳𝑩𝑺𝒎𝒇𝒕 Manufacturer’s knowledge stock: Cumulative patents related to wind

power that the manufacturer has in year t-1 𝑳𝑩𝑫𝒎𝒇𝒕 Experience from manufacturer: manufacturer’s cumulative installed

capacities in year t-1

𝑳𝑩𝑫𝒅𝒆𝒗 Experience from project developer in CDM projects: project

developer’s cumulative installed capacities in CDM projects in year t-1

𝑺𝒑𝒊𝒍𝒍𝒑𝒓𝒐𝒗 Experience from wind projects in a province: Cumulative installed

capacities in the province in year t-1 𝑺𝒑𝒊𝒍𝒍𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚 Experience from the whole industry: cumulative installed capacities

of the whole industry in year t-1 𝑳𝑩𝑰 Cooperating experience between project developer and

manufacturer: cumulative capacities installed by this developer and

the same manufacturer in previous CDM projects in year t-1.

 

LBS

LBD

Spill-over

LBI

𝒍𝒏(𝑼𝒏𝒊𝒕_𝑪𝒐𝒔𝒕𝒊𝒕) = 𝜷𝟎 + 𝜷𝟏𝑳𝑩𝑺𝒎𝒇𝒕+ 𝜷𝟐(𝑳𝑩𝑫𝒎𝒇𝒕−𝑳𝑩𝑰) + 𝜷𝟑(𝑳𝑩𝑫𝒅𝒆𝒗 − 𝑳𝑩𝑰) + 𝜷𝟒 (𝑺𝒑𝒊𝒍𝒍𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚− 𝑳𝑩𝑫𝒅𝒆𝒗 − 𝑳𝑩𝑫𝒎𝒇𝒕 + 𝑳𝑩𝑰) + 𝜷𝟓𝑳𝑩𝑰 +𝜷𝟔𝑻𝒖𝒓𝒃𝒊𝒏𝒆_𝒔𝒊𝒛𝒆𝒊 +𝜷𝟕 𝑷𝒓𝒐𝒋𝒆𝒄𝒕𝒔𝒊𝒛𝒆𝒊 +𝜷𝟖𝑾𝟏𝒊 + 𝜷𝟗𝑾𝟐𝒊 +𝜷𝟏𝟎𝑾𝟑𝒊+𝜷𝟏𝟏𝑭𝒐𝒓𝒆𝒊𝒈𝒏𝒎𝒇𝒕+𝜷𝟏𝟐𝑺𝑶𝑬𝒅𝒆𝒗 +𝜷𝟏𝟑𝑳𝑺𝑶𝑬𝒅𝒆𝒗 + 𝑷𝒓𝒐𝒗𝒊𝒏𝒄𝒆_𝑫𝒖𝒎𝒎𝒊𝒆𝒔 +𝒀𝒆𝒂𝒓_𝑫𝒖𝒎𝒎𝒊𝒆𝒔+ 𝒖𝒊

Data and Empirical Model

• Full Model: Manufacturer’s knowledge stock

Manufacturer’s experience and developer’s experience

Spillover from the industry Interacting experience

Wind resource

Descriptive Statistics:Projected Unit Cost of Electricity Production

2004 2005 2006 2007 2008 2009

Unit cost 0.4677754 0.5845055 0.5570971 0.5308354 0.5090697 0.514052599999997

Unit capital cost

0.411085 0.4695642 0.450379900000001

0.428056700000002

0.4147976 0.419378900000003

Unit O&M cost

0.0566904 0.1149413 0.1067171 0.1027787 0.0942721 0.0946737

0.05

0.15

0.25

0.35

0.45

0.55

0.65

Trends of Average Unit Costs

Mean of Costs (R

MB/kW

h)

Decreases  from 2005-2009

Unit cost: 12.1%Unit capital cost: 10.8%Unit O&M cost: 17.4%

Empirical Results

• Effect of aggregate level experience

• Effect of developer’s and manufacturer’s internal experience v. spillover effects

• Effects of interacting experience and other channels of learning 

Empirical Results

1. Learning by searching

- Wind projects benefit from the knowledge stock of their turbine manufacturers.

- The effect is small though.

Unit Production Costs

Note:1. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 2. Industrial level experience omitted in model (2), (6) and (7) because the sum of different levels of experience equals to the total industry-wide capacity in a given year, which is correlated with the year dummies.

 Dependent Variable: (1) (2) (3) (4) (5) (6) (7)

ln (unit_cost) Aggregate level Internal experience and spillover

LBI and other channels of learning

Manufacturer’s knowledge -0.00043*** -0.00037** -0.00035** -0.00029** -0.00035** -0.00029** -0.00025*

stock (0.00016) (0.00015) (0.00014) (0.00013) (0.00014) (0.00013) (0.00013)

Manufacturer’s experience -0.00488 -0.01816 -0.00309 -0.00211 -0.00216

alone (GW) (0.00489) (0.02285) (0.00515) (0.00572) (0.00571)

Developer’s experience in -0.02718*** -0.03938* -0.02418*** -0.02333*** -0.02230***

CDM projects alone (GW) (0.00456) (0.02017) (0.00611) (0.00658) (0.00655)

Cooperating experience in -0.04976** -0.04149** -0.03971**

CDM (GW) (0.02037) (0.01874) (0.01876)

Spillover from the province -0.00060 0.00457 -0.00027

(GW) (0.00553) (0.00723) (0.00252)

Spillover from the industry -0.00767***   -0.00001 -0.01605    

(GW) (0.00296)   (0.00248) (0.02444)    

Foreign manufacturer*   -0.13107**

cooperating experience   (0.06000)

Control variables Yes Yes Yes Yes Yes Yes Yes

Province fixed effects Yes Yes Yes Yes Yes Yes Yes

Year fixed effects No Yes No Yes No Yes Yes

Observations 486 486 486 486 486 486 486

R-squared 0.604 0.668 0.674 0.684 0.660 0.685 0.687

Empirical Results

1. Learning by searching2. Learning by doing and spillover- Wind projects learn from developer’s internal experience in both wind farm installation and operation.

- Knowledge spillover effects from the industry as a whole are less important.  

v

Unit Production Costs

Note:1. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 2. Industrial level experience omitted in model (2), (6) and (7) because the sum of different levels of experience equals to the total industry-wide capacity in a given year, which is correlated with the year dummies.

 Dependent Variable: (1) (2) (3) (4) (5) (6) (7)

ln (unit_cost) Aggregate level Internal experience and spillover

LBI and other channels of learning

Manufacturer’s knowledge -0.00043*** -0.00037** -0.00035** -0.00029** -0.00035** -0.00029** -0.00025*

stock (0.00016) (0.00015) (0.00014) (0.00013) (0.00014) (0.00013) (0.00013)

Manufacturer’s experience -0.00488 -0.01816 -0.00309 -0.00211 -0.00216

alone (GW) (0.00489) (0.02285) (0.00515) (0.00572) (0.00571)

Developer’s experience in -0.02718*** -0.03938* -0.02418*** -0.02333*** -0.02230***

CDM projects alone (GW) (0.00456) (0.02017) (0.00611) (0.00658) (0.00655)

Cooperating experience in -0.04976** -0.04149** -0.03971**

CDM (GW) (0.02037) (0.01874) (0.01876)

Spillover from the province -0.00060 0.00457 -0.00027

(GW) (0.00553) (0.00723) (0.00252)

Spillover from the industry -0.00767***   -0.00001 -0.01605    

(GW) (0.00296)   (0.00248) (0.02444)    

Foreign manufacturer*   -0.13107**

cooperating experience   (0.06000)

Control variables Yes Yes Yes Yes Yes Yes Yes

Province fixed effects Yes Yes Yes Yes Yes Yes Yes

Year fixed effects No Yes No Yes No Yes Yes

Observations 486 486 486 486 486 486 486

R-squared 0.604 0.668 0.674 0.684 0.660 0.685 0.687

A typical CDM project by the same developer will lead to around 0.23% to 0.25% decrease in future unit costs. 

Empirical Results

1. Learning by searching2. Learning by doing and spillover

3. Cost reduction through repeated collaboration- The repeated partnership between project developer and manufacturer matters, which leads to lower electricity production costs, particularly for capital costs.

- However, the cooperating experience does not significantly improve capacity factor.

Effects of collaborating experience and technology transfer

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

  (1) (2) (3) (4) (5)

Dependent Variables ln(unit cost) ln(unit cost) ln(unit capital cost) ln(unit capital cost) ln(capacity factor)

Knowledge stock of -0.00029** -0.00025* -0.00031* -0.00027 0.00030**

manufacturer (0.00013) (0.00013) (0.00018) (0.00017) (0.00012)

Manufacturer’s experience -0.00211 -0.00216 -0.00768 -0.00772 0.00512

alone (GW) (0.00572) (0.00571) (0.00704) (0.00703) (0.00556)

Developer’s experience in -0.02333*** -0.02230*** -0.03393*** -0.03304*** 0.01126**

CDM projects alone (GW) (0.00658) (0.00655) (0.00800) (0.00798) (0.00573)

Cooperating experience in -0.04149** -0.03971** -0.05406** -0.05252** 0.00505

CDM (GW) (0.01874) (0.01876) (0.02299) (0.02314) (0.01987)

Turbine size (MW) 0.01068 0.00929 0.03342* 0.03221* 0.05332***

  (0.01481) (0.01493) (0.01817) (0.01825) (0.01093)

Project size (GW) -0.34063*** -0.33709*** -0.34582*** -0.34276** 0.14699**

  (0.12296) (0.12246) (0.13293) (0.13291) (0.07374)

Wind category 1 -0.14092*** -0.14541*** -0.21581*** -0.21969*** 0.13427***

  (0.02805) (0.02850) (0.02739) (0.02766) (0.03026)

Wind category 2 -0.09387*** -0.09348*** -0.16919*** -0.16885*** 0.10052***

  (0.02688) (0.02706) (0.02500) (0.02479) (0.02982)

Wind category 3 -0.01251 -0.01419 -0.02476 -0.02621 -0.00811

  (0.01974) (0.01983) (0.01851) (0.01852) (0.01573)

Foreign manufacturer 0.03097* 0.03969** 0.03535* 0.04288** 0.00991

  (0.01607) (0.01751) (0.01951) (0.02138) (0.01422)

Central SOE developer -0.01624 -0.01554 -0.00684 -0.00624 0.00899

  (0.01126) (0.01128) (0.01453) (0.01453) (0.01001)

Local SOE developer 0.03259* 0.03183* 0.05485** 0.05419** -0.01548

  (0.01885) (0.01890) (0.02443) (0.02451) (0.01544)

Foreign manufacturer*   -0.13107**   -0.11329  

cooperating experience   (0.06000)   (0.08382)  

Province fixed effects Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes

Observations 486 486 486 486 502

R-squared 0.685 0.687 0.607 0.608 0.606

One more CDM wind project that a developer builds together with the same foreign manufacturer will reduce the unit cost by almost 1%.  -- 4 times of the effects of collaborating with domestic manufacturer

Empirical Results1. Learning by searching2. Learning by doing and spillover3. Cost reduction through repeated collaboration

4. Technology transfer through CDM- Learning-by-interacting effect occurs when a wind project developer repeatedly collaborates with a foreign manufacturer.

- Transfer of tacit knowledge through partnership

Policy Implications• For Chinese policymakers: - Increase understanding of the learning process in China’s wind industry

- Help to make more targeted policies to forge the partnership between project developers and turbine manufacturers

• For international climate change policy making: - Shed light on how CDM facilitate technology transfer  by encouraging cooperation between local project developers and foreign turbine manufacturers

THANKS!

Questions and comments are appreciated.

2003 2004 2005 2006 2007 2008 2009 20100

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000The Share of CDM Wind Projects By Year (Unit: MW)

Annual Total Installed Capacities Annual CDM Installed Capacities

From 2003 to 2010, the cumulative installed capacities in registered CDM projects account for approximately 78.7% of the total installed capacities in China. 

Background: CDM and China’s Wind Industry

Motivation: CDM as a Demand-Side Policy for Wind Technology

Project Design Document

National Approval

Validation

Registration

Monitoring and Verification

Issuance of CERs

Project Developer

National Authority

1st Designated Operational Entity (DOE)

Executive Board (EB)

Project Developer & 2nd DOE

EB

Project Development

and Construction

(1-2 years)

Operation(20-25 years)

Project Cycle CDM Cycle Responsible Party in each step

Data and Empirical Model

  

• Dependent Variables: 1) Projected unit cost of electricity production of project i   started construction in year t (Levelized production cost

2) Projected unit capital cost of project i

3) Projected capacity factor of project i

 

Descriptive Statistics:Variation of unit costs and capacity factors

.2.4

.6.8

1u

nit c

ost o

f ele

ctric

ity p

rodu

ctio

n, R

MB

/KW

.h, 2

005=

100

2004 2005 2006 2007 2008 2009

.15

.2.2

5.3

.35

capa

city

fact

or2002 2004 2005 2006 2007 2008 2009

Variable Mean Std.dev. Max Min N

Unit cost (RMB/kWh) 0.523 0.076 1.007 0.335 492

Unit capital cost (RMB/kWh) 0.425 0.066 0.780 0.256 492

Unit O&M cost (RMB/kWh) 0.098 0.028 0.227 0.011 492

Capacity factor 0.253 0.028 0.367 0.144 510

Manufacturer's knowledge stock (Decay rate = 0.15) 18.40 36.9 228.8 0 504

Manufacturer's cumulative installed capacity (GW) 0.685 0.777 2.621 0 504

Developer's cumulative installed capacity in CDM projects (GW) 0.722 0.965 3.338 0 510

Cooperating installed capacity in CDM projects (GW) 0.123 0.214 0.938 0 510

Province level cumulative installed capacity (GW) 0.791 1.029 3.679 0 510

Industrial level cumulative installed capacity (GW) 3.518 2.420 6.602 0.056 510

Average turbine size (MW) 1.34 0.38 3 0.6 510

CDM project size (GW) 0.059 0.048 0.4005 0.00935 510

Foreign manufacturer 0.176 0.382 1 0 510

Central SOE developer 0.70 0.46 1 0 510

Local SOE developer 0.11 0.32 1 0 510

Private developer 0.19 0.39 1 0 510

Wind category 1 0.2 0.400 1 0 510

Wind category 2 0.288 0.453 1 0 510

Wind category 3 0.125 0.332 1 0 510

Wind category 4 0.387 0.487 1 0 510

Background: CDM as a Demand-Side Policy for Wind Technology

Domestic International

Supply Side

-National basic research program (973 Program, 1997)-National high-tech R&D program (863 Program, 1986)- National key technology R&D program (TKPs, 1982)

Demand Side

- National wind  concession program (2003-2008)-Mandatory renewable market share (1997)-Power surcharge for wind power (2006)-Relief of VAT and import tax for wind turbines (2008)

Clean Development  Mechanisms (CDM)

Contributions to the LiteratureExisting Literature This Research

Learning process in wind power

Focus on: -Learning through R&D- Learning by doing 

(Goulder, 2004; Junginger, et al,  2005; Nemet, 2012; Qiu et al, 2012)

-Provide empirical evidence on the learning by interacting effect. 

- Highlight the importance of partnership and collaboration in technological change

Technological change in China’s wind industry

- Qualitative study- Concentrated ondomestic policies

-First empirical research on  CDM projects

- Data improvement on electricity production cost

Collaboration Concentrated on public service delivery such as welfare program, health, education etc. 

Extends empirical study on collaboration  to international collaboration on carbon reduction and  renewable energy technology diffusion.

Effects of Aggregate Level Experience

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.Industrial level experience omitted in all models due to the multicollinearity.

  (1) (2) (3)

Dependent Variables ln(unit cost) ln(unit capital cost) ln(capacity factor)

Manufacturer’s knowledge stock -0.00037** -0.00042** 0.00032***

(0.00015) (0.00021) (0.00012)

Province level experience (GW) 0.00457 -0.00296 -0.00875

(0.00723) (0.00888) (0.00595)

Turbine size (MW) 0.01149 0.03683** 0.05319***

(0.01503) (0.01858) (0.01097)

Project size (GW) -0.32164*** -0.33291** 0.13045*

(0.12414) (0.13833) (0.07282)

Wind category 1 -0.13557*** -0.20836*** 0.13308***

  (0.02839) (0.02820) (0.03098)

Wind category 2 -0.09914*** -0.17539*** 0.10357***

  (0.02696) (0.02607) (0.03064)

Wind category 3 -0.00948 -0.02110 -0.00796

  (0.02184) (0.02033) (0.01626)

Foreign manufacturer 0.03793** 0.04489** 0.00644

  (0.01609) (0.01961) (0.01397)

Central SOE developer -0.04186*** -0.08436*** 0.01837**

  (0.01008) (0.02092) (0.00859)

Local SOE developer 0.02352 -0.04343* -0.01271

  (0.01862) (0.02403) (0.01521)

Province fixed effects Yes Yes Yes

Year fixed effects Yes Yes Yes

Observations 486 486 502

R-squared 0.668 0.579 0.603

Effects of Developer’s Internal Experience and Spillover

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

 Dependent Variables (1) (2) (3)

ln(unit cost) ln (unit capital cost) ln(capacity factor)Manufacturer’s knowledge stock -0.00029** -0.00031* 0.00028**

(0.00013) (0.00018) (0.00012)Manufacturer’ experience (GW) -0.01816 -0.02013 0.00529

(0.02285) (0.02752) (0.00471)Developer’s experience in CDM -0.03938* -0.04638* 0.01094**projects (GW) (0.02017) (0.02475) (0.00446)Spillover from the industry (GW) -0.01605 -0.01245 0.00107

(0.02444) (0.02942) (0.00345)Turbine size (MW) 0.01068 0.03342* 0.05349***  (0.01481) (0.01817) (0.01085)Project size (GW) -0.34063*** -0.34582*** 0.14495*  (0.12296) (0.13293) (0.07389)Wind category 1 -0.14092*** -0.21581*** 0.13587***  (0.02805) (0.02739) (0.02877)Wind category 2 -0.09387*** -0.16919*** 0.10083***  (0.02688) (0.02500) (0.02837)Wind category 3 -0.01251 -0.02476 -0.00556  (0.01974) (0.01851) (0.01512)

Foreign manufacturer 0.03097* 0.03535* 0.01231

  (0.01607) (0.01951) (0.01405)Central SOE developer -0.01624 -0.00684 0.00791  (0.01126) (0.01453) (0.00974)Local SOE developer 0.03259* 0.05485** -0.01550  (0.01885) (0.02443) (0.01528)

Province fixed effects Yes Yes Yes

Year fixed effects Yes Yes Yes

Constant -0.45777*** -0.64313*** -1.62209***

  (0.10299) (0.10847) (0.07938)

Observations 486 486 502

R-squared 0.684 0.607 0.606

The unit capital cost will fall by 4.64% and the capacity factor will increase by 1.1% if the developer increases additional 1 GW installed capacity in CDM projects.