renewable, clean, and smart energy technologies Kimmo Ollikka*, - - PowerPoint PPT Presentation

Knowledge spillovers from renewable, clean, and smart energy technologies

Kimmo Ollikka*, Hanna-Liisa Kangas**, Kim Yukyeong** * VATT Institute for Economic Research, Finland ** Finnish Environment Institute SYKE IAEE Wien, September 2017

Note: Results are very preliminary!

Climate Change – two externalities

• Negative externality of greenhouse gas pollution

– Policies: Carbon pricing by Pigouvian tax or emissions trading

• Positive externality of technology developement

– Mitigation of climate change requires a switch from fossil fuels to renewable energy sources – Learning related to R&D and technology diffusion lowers the costs of renewable energy – However, knowledge spillovers lower firms’ incentives of renewable energy R&D and investments – Policies: R&D support, feed-in-tariffs, green certificates… – Problem: The choice and targeting of policy instruments??

Smart energy transition

• Increasing share of intermittent electricity

production poses challenges to electric power systems.

• Intermittent energy production causes more and

frequent demand-supply mismatches and thus a need of flexibility to the power grid.

• New energy innovations that combine renewable

energy solutions together with ICT solutions are becoming more valuable.

OBJECTIVES

• We investigate the value of renewable, clean, and smart energy

innovations in terms of knowledge spillovers METHODS

• We use patent citations as indicator of knowledge spillovers generated

by patents in 1970 – 2011

• World Patent Statistical database PATSTAT (EPO)
• We study

1. do smart energy inventions that are co-classified both as an energy technology and ICT receive more citations than pure energy technology inventions 2. differences in knowledge spillovers between energy technologies

• We utilize Poisson fixed effects (and zero-inflated Poisson regression)

model RESULTS

• Smart energy patents receive more citations than pure energy patents.
• Particularly, co-classified patents receive more citations from ICT

patents.

• PV and wind energy patents are two most cited technology fields of

energy technologies.

Technologies (299 373 inventions) Cooperative patent classification (CPC) International Patent Classification (IPC)

1. Renewable energy technologies

– Solar thermal energy (CPC: Y02E 10/40) – Photovoltaic (PV) energy (CPC: Y02E 10/50) – Wind energy (CPC: Y02E 10/70)

2. Clean combustion technologies

– Combustion technologies with climate change mitigation potential (CPC: Y02E 20/10, Y02E 20/30)

3. Combustion technologies

– Combustion apparatus, combustion processes (IPC: F23)

• Smart technologies

– ICT-related patents are classified based on the OECD identification – Semiconductor devices (IPC: H01L) are exluded from ICT class, because most PV patents are also H01L-patents.

Numbers of renewable energy inventions have risen dramatically since the millennium

(A) Annual energy inventions (B) Shares of smart energy inventions

Dependent variable: forward citations

• Count of citations one invention, filed in year t, has received

from other patents, which have been applied after year t

• Patent data and citation counts have been widely used in

studying knowledge spillovers

• Citation counts underestimate the total effect of knowledge

spillovers and are a noisy measure of knowledge flows:

– All inventions are not patented – Patent offices have different citation practices – Self-citations: citation counts include also citations made to patents by the same inventor – Truncation: new patents have not had time to receive as much citations than old patents

No clear trends in average citation counts

(A) Citations without self- citations (B) Citations from ICT patents

Dependent variables:

Variable Mean SD Min Max Forward citations from all patents without self-citations:

• all

4.24 11.23 1177

• within 5 years

1.76 5.36 864 Forward citations from ICT patents without self-citations:

• all

0.36 3.97 900

• within 5 years

0.20 2.54 678 ICT 0.05 0.21 1 Solar thermal 0.13 0.33 1 Photovoltaic 0.23 0.42 1 Wind 0.11 0.31 1 Combustion CCMT 0.04 0.20 1 Combustion apparatus 0.53 0.50 1 Family size 1.93 2.40 1 76 Triadic 0.06 0.23 1 Granted 0.56 0.50 1 Number of inventions 50.95 43.64 153.92 Year 1996 12.39 1970 2011 Patent office Categorical variable Inventor country Categorical variable

Technology variables: Controls:

Estimation equation – Poisson fixed effects model

where

– 𝐷𝑗 - number of citations to invention 𝑗 (count data) – 𝐽𝐷𝑈𝑗 – 1 if technology is co-classified as Energy + ICT – 𝑈𝑓𝑑ℎ𝑗 - technology of invention 𝑗 – 𝑌𝑗 - vector of controls: family size, triadic (1/0), granted (1/0), number

• f inventions, country of inventor

– Fixed effects: year-by-office – 𝜁𝑗 - error term

• Over-dispersion problem

– Robust standard errors by Poisson pseudo-maximum likelihood.

• Problem: an excess of zero counts

– Zero-inflated Poisson model is also estimated

 

exp

i i i i i i i

ICT T T

C ICT Tech ICT Tech X               

All Within 5 years All Within 5 years (1) (2) (3) (4) Family size 0.056*** 0.060*** 0.056*** 0.057*** (0.002) (0.003) (0.005) (0.005) Triadic 0.224*** 0.139***

• 0.074
• 0.196

(0.029) (0.033) (0.082) (0.101) Granted 0.521*** 0.439*** 0.594*** 0.499*** (0.025) (0.023) (0.047) (0.050) Number of inventions

• 0.004***
• 0.005***

0.004**

• 0.000

(0.000) (0.001) (0.001) (0.001) ICT 0.309*** 0.273*** 2.713*** 2.474*** (0.063) (0.063) (0.130) (0.135) Solar thermal 0.305*** 0.244*** 0.224*** 0.079 (0.022) (0.039) (0.066) (0.074) Photovoltaic 0.661*** 0.563*** 1.599*** 1.363*** (0.020) (0.026) (0.085) (0.058) Wind 0.598*** 0.361*** 0.911*** 0.817*** (0.024) (0.031) (0.113) (0.091) Combustion CCMT 0.173*** 0.014 0.051

• 0.016

(0.028) (0.043) (0.095) (0.090) Combustion app.

• 0.059

0.056

• 0.856***
• 0.320***

(0.037) (0.097) (0.135) (0.091) ICT : Solar thermal 0.023

• 0.098
• 0.211*
• 0.183

(0.061) (0.078) (0.097) (0.124) ICT : Photovoltaic

• 0.209***
• 0.063
• 1.325***
• 0.962***

(0.055) (0.053) (0.119) (0.100) ICT : Wind

• 0.142*
• 0.001
• 0.371**
• 0.292*

(0.059) (0.057) (0.127) (0.119) ICT : Combustion CCMT

• 0.221*
• 0.132

0.020 0.005 (0.090) (0.108) (0.162) (0.177) ICT : Combustion app.

• 0.055

0.030 0.258 0.176 (0.092) (0.085) (0.150) (0.140) Controls / Fixed effects: Year-by-office yes yes yes yes Inventor country yes yes yes yes N 298448.0 296000.0 278946.0 276301.0 Log pseudolikelihood

• 979318.4
• 561742.5
• 201439.8
• 130860.5

Chi2 9956.1 8935.1 12232.6 14276.3 Dependent variable: Forward citations without self-citations From all patents From ICT patents

Estimation results:

Estimation results: Technology effects

All Within 5 years All Within 5 years (1) (2) (3) (4) ICT 0.309*** 0.273*** 2.713*** 2.474*** (0.063) (0.063) (0.130) (0.135) Solar thermal 0.305*** 0.244*** 0.224*** 0.079 (0.022) (0.039) (0.066) (0.074) Photovoltaic 0.661*** 0.563*** 1.599*** 1.363*** (0.020) (0.026) (0.085) (0.058) Wind 0.598*** 0.361*** 0.911*** 0.817*** (0.024) (0.031) (0.113) (0.091) Combustion CCMT 0.173*** 0.014 0.051

• 0.016

(0.028) (0.043) (0.095) (0.090) Combustion app.

• 0.059

0.056

• 0.856***
• 0.320***

(0.037) (0.097) (0.135) (0.091) ICT : Solar thermal 0.023

• 0.098
• 0.211*
• 0.183

(0.061) (0.078) (0.097) (0.124) ICT : Photovoltaic

• 0.209***
• 0.063
• 1.325***
• 0.962***

(0.055) (0.053) (0.119) (0.100) ICT : Wind

• 0.142*
• 0.001
• 0.371**
• 0.292*

(0.059) (0.057) (0.127) (0.119) ICT : Combustion CCMT

• 0.221*
• 0.132

0.020 0.005 (0.090) (0.108) (0.162) (0.177) ICT : Combustion app.

• 0.055

0.030 0.258 0.176 (0.092) (0.085) (0.150) (0.140) Dependent variable: Forward citations without self-citations From all patents From ICT patents

Smart energy inventions receive more citations than pure energy inventions

From all patents From ICT patents All citations Within 5 years All citations Within 5 years

 

Mean |

i i

C ICT 

4.09 1.67 0.23 0.13

 

IRR, exp ICT  1.36 1.31 15.08 11.87

 

 

Mean | 1

i i

C ICT

E

  5.56 2.20 3.46 1.56

 

Mean | 1

i i

C ICT 

7.34 3.60 3.01 1.70

On average (incidence rate ratio, IRR):

• 1.3 – 1.4 times more citations from all patents
• 11.9 – 15.1 times more citations from ICT patents.

Developement of the ICT coefficient estimate 𝜷𝑱𝑫𝑼

Technology effects - Incidence rate ratios

Pure energy patents:

𝒇𝒚𝒒 𝜸𝑼𝒇𝒅𝒊 𝒇𝒚𝒒 𝜸𝑫𝒑𝒏𝒄.𝒃𝒒𝒒.

Smart energy patents:

𝒇𝒚𝒒 𝜸𝑼𝒇𝒅𝒊+𝜷𝑱𝑫𝑼+𝜹𝑼𝒇𝒅𝒊 𝒇𝒚𝒒 𝜸𝑫𝒑𝒏𝒄.𝒃𝒒𝒒. All citations Within 5 years All citations Within 5 years Solar thermal 1.4 1.2 2.9 1.5 Photovoltaic 2.1 1.7 11.6 5.4 Wind 1.9 1.4 5.9 3.1 Combustion CCMT 1.3 1.0 2.5 1.4 Combustion apparatus 1 1 1 1 ICT - Solar thermal 2.0 1.4 35.9 14.7 ICT - Photovoltaic 2.3 2.1 46.7 24.4 ICT - Wind 2.3 1.8 60.9 27.6 ICT - Combustion CCMT 1.4 1.1 38.1 16.2 ICT - Combustion apparatus 1.3 1.4 19.5 14.2 From all patents From ICT patents Pure energy patents Smart energy patents

Conclusions

• On average smart energy inventions receive more citations

than pure energy inventions.

• PV and wind energy patents are most cited technology

fields.

• When technology fields are converging, as it is the case

with new energy and ICT technologies, the knowledge spreads widely also to other fields of technology.

• Supporting renewable energy R&D is a recommended

policy option

– not only for supporting the development of renewable energy technologies narrowly – but supporting the development of clean and reliable energy markets more widely.

Thank you!

kimmo.ollikka@vatt.fi

Related literature

• Dechezleprêtre et al. (2014)

– Patent citations as indicator of knowledge spillovers – Relative intensity of knowledge spillovers in clean and dirty technologies in (i) energy production, and (ii) transportation. – Main result: Clean patents receive on average 43% more citations than dirty patents.

• Noailly and Shestalova (2016)

– Knowledge spillovers generated by different renewable energy technologies and flows of them to other technological fields – Wind, storage and solar patents tend to be the most frequently cited patents. – Innovations in solar energy and storage technological fields find applications outside the field of power generation. – Wind technologies mainly find applications within their own technological field.

Renewable and cumbustion technologies with climate change mitigation potential - Cooperative patent classification (CPC)

Model variable Technology CPC code Number of inventions Energy + ICT inventions

• Geothermal energy

Y02E 10/10 4175 99 (2%)

• Hydro energy

Y02E 10/20 25233 394 (2%)

• Energy from sea

Y02E 10/30 8077 60 (1%) Solar thermal Solar thermal energy Y02E 10/40 47009 1437 (3%) Photovoltaic Photovoltaic [PV] energy Y02E 10/50 88598 7592 (9%)

• Thermal-PV hybrids

Y02E 10/60 1572 23 (1%) Wind energy Wind energy Y02E 10/70 43504 2119 (5%) Combustion CCMT Combined combustion Y02E 20/10 12015 436 (4%) Technologies for a more efficient combustion or heat usage Y02E 20/30 3522 61 (2%) Model variable Technology IPC code Number of inventions Energy + ICT inventions Combustion apparatus Combustion apparatus, combustion processes F23 178533 5335 (3%)

Combustion technologies - International Patent Classification (IPC)

Information and communication technologies - International Patent Classification (IPC)

Model variable Technology IPC code ICT Telecommunications G01S, G08C, G09C, H01P, H01Q, H01S 3/(025, 043, 063, 067, 085, 0933, 0941, 103, 133, 18, 19, 25), H01S5, H03B, H03C, H03D, H03H, H03M, H04B, H04J, H04K, H04L, H04M, H04Q Consumer electronics G11B, H03F, H03G, H03J, H04H, H04N, H04R, H04S Computer, office machinery B07C, B41J, B41K, G02F, G03G, G05F, G06, G07, G09G, G10L, G11C, H03K, H03L Other ICT G01B, G01C, G01D, G01F, G01G, G01H, G01J, G01K, G01L, G01M, G01N, G01P, G01R, G01V, G01W, G02B6, G05B, G08G, G09B, H01B11, H01J (11/, 13/, 15/, 17/, 19/, 21/, 23/, 25/, 27/, 29/, 31/, 33/, 40/, 41/, 43/, 45/)

• ICT-related patents are classified based on the OECD

identification

– Semiconductor devices (H01L) are exluded from ICT class, because most PV patents are also H01L-patents.

Technology Inventions Family size (mean) Triadic (%) Granted (%) ICT (%) Solar thermal 38 481 1.74 2.5 % 51.5 % 3.3 % Photovoltaic 67 366 2.12 9.8 % 52.3 % 9.2 % Wind 32 405 2.14 4.7 % 52.7 % 4.6 % Combustion CCMT 12 984 2.81 11.1 % 59.5 % 3.2 % Combustion apparatus 158 750 1.85 4.5 % 58.8 % 3.1 % Total 299 373 1.93 5.5 % 55.8 % 4.6 %

Inventions of energy technologies in 1970 – 2011

Top 5 patent offices

N Solar thermal PV Wind Comb. CCMT Comb. app. Mean SD Max Japan 121 355 8 % 26 % 4 % 4 % 60 % 2.20 6.82 1133 USA 37 362 16 % 26 % 12 % 7 % 46 % 14.56 20.56 1177 Germany 24 221 19 % 15 % 15 % 6 % 50 % 4.51 8.19 268 China 21 885 26 % 19 % 21 % 3 % 34 % 1.77 3.05 80 Korea (South) 16 686 9 % 40 % 16 % 2 % 36 % 1.51 4.07 252 Other offices 63 335 16 % 10 % 16 % 3 % 59 % 2.29 6.60 390 Multiple offices 14 529 11 % 37 % 16 % 7 % 36 % 9.60 20.41 838 Total 299 373 13 % 23 % 11 % 4 % 53 % 4.24 11.23 1177 Innovations Forward citations without self-citations

Estimation results: Controls

All Within 5 years All Within 5 years (1) (2) (3) (4) Family size 0.056*** 0.060*** 0.056*** 0.057*** (0.002) (0.003) (0.005) (0.005) Triadic 0.224*** 0.139***

• 0.074
• 0.196

(0.029) (0.033) (0.082) (0.101) Granted 0.521*** 0.439*** 0.594*** 0.499*** (0.025) (0.023) (0.047) (0.050) Number of inventions

• 0.004***
• 0.005***

0.004**

• 0.000

(0.000) (0.001) (0.001) (0.001) Controls / Fixed effects: Year-by-office yes yes yes yes Inventor country yes yes yes yes N 298448.0 296000.0 278946.0 276301.0 Log pseudolikelihood

• 979318.4
• 561742.5
• 201439.8
• 130860.5

Chi2 9956.1 8935.1 12232.6 14276.3 Note: * p<0.05, ** p<0.01, *** p<0.001 Dependent variable: Forward citations without self-citations From all patents From ICT patents

• Incidence rate ratios for coefficient estimates:

– Non-ICT: 𝒇𝒚𝒒 𝜸𝑼𝒇𝒅𝒊 – ICT: 𝒇𝒚𝒒 𝜸𝑼𝒇𝒅𝒊 + 𝜷𝑱𝑫𝑼 + 𝜹𝑼𝒇𝒅𝒊

Technology effects: citations without self-citations

• Incidence rate ratios for coefficient estimates:

– Non-ICT: 𝒇𝒚𝒒 𝜸𝑼𝒇𝒅𝒊 – ICT: 𝒇𝒚𝒒 𝜸𝑼𝒇𝒅𝒊 + 𝜷𝑱𝑫𝑼 + 𝜹𝑼𝒇𝒅𝒊

Technology effects: citations without self-citations within 5 years

Estimation equation – Zero-inflated Poisson model where

• 𝜌𝑗 = 𝐺 𝐽𝐷𝑈𝑗, 𝑈𝑓𝑑ℎ𝑗, 𝑌𝑗; 𝛽0, 𝛾0, 𝛿0, 𝜀0 - the inflation

function estimating the probability of zero count

• Excess zeros are generated by a separate process
• The count model and the zero-inflation model

processes are independent

   

+ 1 exp ,

i i i i i i i i i

C ICT Tech ICT Tech X                   

All citations Within 5 years All citations Within 5 years Solar thermal 1.5 1.3 3.4 1.9 Photovoltaic 2.3 1.9 10.4 5.5 Wind 2.0 1.5 5.5 3.2 Combustion CCMT 1.4 1.2 3.2 2.0 Combustion apparatus 1 1 1 1 ICT - Solar thermal 2.3 1.6 37.7 16.7 ICT - Photovoltaic 2.5 2.3 47.3 26.7 ICT - Wind 2.5 2.2 70.0 32.3 ICT - Combustion CCMT 1.6 1.3 41.6 19.7 ICT - Combustion apparatus 1.3 1.3 21.5 14.1 Pure energy patents Smart energy patents From all patents From ICT patents

Zero-inflated Poisson model - Technology effects

  



, ,

exp 1 Pr

PV ICT PV ICT PV ICT PV ICT

IRR

C

  



   

     