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- (B) Citations from ICT patents citations
Variable Mean SD Min Max Forward citations from all patents Dependent without self-citations: variables: - all 4.24 11.23 0 1177 - within 5 years 1.76 5.36 0 864 Forward citations from ICT patents without self-citations: - all 0.36 3.97 0 900 - within 5 years 0.20 2.54 0 678 ICT 0.05 0.21 0 1 Technology Solar thermal 0.13 0.33 0 1 variables: Photovoltaic 0.23 0.42 0 1 Wind 0.11 0.31 0 1 Combustion CCMT 0.04 0.20 0 1 Combustion apparatus 0.53 0.50 0 1 Family size 1.93 2.40 1 76 Controls: Triadic 0.06 0.23 0 1 Granted 0.56 0.50 0 1 Number of inventions 50.95 43.64 0 153.92 Year 1996 12.39 1970 2011 Patent office Categorical variable Inventor country Categorical variable
Estimation equation – Poisson fixed effects model exp C ICT Tech ICT Tech X ICT T T i i i i i i i where – 𝐷 𝑗 - number of citations to invention 𝑗 (count data) – 𝐽𝐷𝑈 𝑗 – 1 if technology is co-classified as Energy + ICT – 𝑈𝑓𝑑ℎ 𝑗 - technology of invention 𝑗 – 𝑌 𝑗 - vector of controls: family size, triadic (1/0), granted (1/0), number of inventions, country of inventor – Fixed effects: year-by-office – 𝜁 𝑗 - 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
Dependent variable: Forward citations without self-citations From all patents From ICT patents Estimation results: 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 Estimation From all patents From ICT patents All Within 5 years All Within 5 years results: (1) (2) (3) (4) ICT 0.309*** 0.273*** 2.713*** 2.474*** (0.063) (0.063) (0.130) (0.135) Technology Solar thermal 0.305*** 0.244*** 0.224*** 0.079 effects (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)
Smart energy inventions receive more citations than pure energy inventions 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. From all patents From ICT patents All citations Within 5 years All citations Within 5 years ICT Mean | 0 4.09 1.67 0.23 0.13 C i i IRR, exp 1.36 1.31 15.08 11.87 ICT E Mean | 0 1 5.56 2.20 3.46 1.56 C ICT i i ICT Mean | 1 7.34 3.60 3.01 1.70 C i i
Developement of the ICT coefficient estimate 𝜷 𝑱𝑫𝑼
Recommend
More recommend
