United Nations University Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT)

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1 The Role of Technological Trajectories in Catching-up-based Development: An Application to Energy Efficiency Technologies UNIDO Inclusive and Sustainable Development Working Paper Series 6/2016 Sheng Zhong & Bart Verspagen United Nations University Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT) Iseo, Italy June 2017

2 Outline 1.Introduction 2.Classification and data 3.Research strategies 4.Results 5.Conclusions

3 1. Introduction Comparative advantage Develop the right industries / sectors The role of technology Problems: incremental and radical innovations, complex technological trends and too many technologies The important technologies for latecomer countries to focus on Measuring technologies: R&D, productivity & patents Based on patent citation: Cited patent (source) Citing patent (sink)

4 1. Introduction What to do: Quantify technological trajectories in three fields of energy efficiency technologies Countries contributions & vest interests Technological trajectories: optimal paths Potential business opportunities & smart policy-making

5 2. Classification and data Data: European Patent Office (EPO) Worldwide Patent Statistical Database (PATSTAT) More than 90 million patent documents from all the leading economies in the world ; 1830s Present; Bibliographical data; patent citation data based on publication and application.

6 2. Classification and data Data: Name of the table in EPO PATSTAT TLS201_APPLN TLS202_APPLN_TITLE TLS206_PERSON TLS207_PERS_APPLN TLS211_PAT_PUBLN TLS_212_CITATION TLS218_DOCDB_FAM TLS224_APPLN_CPC Description Patent application bibliographical data Patent application title Data on patent applicants and inventors Links between applicants and applications Patent publication bibliographical data Citation data linking between publications, applications and non-patent literature Patent family data, based on EPO DOCDB patent family Cooperative Patent Classification (CPC) data

7 2. Classification and data What are energy efficiency technologies? OECD s classification of energy efficiency technologies OECD patent search strategies for the identification of selected environment-related technologies(env-tech) Energy efficiency technologies related energy generation, transmission and distribution Energy efficiency technologies related to transportation Energy efficiency technologies related to buildings

8 2. Classification and data Example: Energy efficiency technologies related to energy generation Category in OECD ENV-TECH CPC class Used in this paper Renewable energy generation Y02E10 No Energy generation from fuels of non-fossil origin Y02E50 No Combustion technologies with mitigation potential (e.g. using fossil fuels, biomass, waste, etc.): technologies for improved output efficiency and input efficiency Y02E20 Yes Nuclear energy Y02E30 No Technologies for an efficient electrical power generation, Y02E40 No transmission and distribution Enabling technologies (technologies with potential or indirect contribution to emissions mitigation) Y02E60 No Other energy conversion or management systems reducing GHG emissions Y02E70 No

9 2. Classification and data Unit of analysis: patent family (EPO s DOCDB classification) Double counting and self-citation The same technology is patented in different countries; A technology is represented in three levels in PATSTAT: patent publication, patent application and patent family

10 2. Classification and data To solve the problem of double counting and self-citation Step 1: link all the publications to their corresponding patent applications and choose those citation pairs under the CPC classes of interest; Step 2: link all the patent applications to their corresponding patent families and remove duplicated pairs and self-citations.

11 3. Research strategies Perfectly acyclical directed citation networks (complete datasets) Energy efficiency technologies related to energy generation; Cited vertices: ; citing vertices: ; citation pairs; 7230 patent families 2324 source vertices, 2264 sink vertices Energy efficiency technologies related to ICT in buildings; Cited vertices: ; citing vertices: ; citation pairs; patent families 3695 source vertices, 5432 sink vertices Energy efficiency technologies related to vehicles; Cited vertices: ; citing vertices: ; citation pairs; patent families source vertices, sink vertices

12 3. Research strategies Global optimal solution: optimal paths technological trajectory Compute Search Path Count (SPC) for every edge; SPC: number of times that citation lies on a path from a source to a sink; higher SPC, more important; Cited patent (source) Citing patent (sink) Target function: sum of SPC weights of a path from a source to a sink; Technological trajectory: the optimal path whose sum of SPC weights is the maximum among all the possible paths connecting any sources to any reachable sinks. Similar to the Shortest Path Problem in Graph Theory

13 3. Research strategies Global optimal solution: optimal paths technological trajectory Simple example (2 sources & 4 sinks): Number: SPC weight; Red color: optimal paths

14 3. Research strategies How countries related to the technological profiles? Forward linkage: how many patents are cited by the optimal paths; Country s contribution to the field; Backward linkage: how many patents cite the optimal paths; Experience: how many years the country has been in the field Estimation methods: Basic method: OLS; Quantile regression (at 25%, median & 75%); Robust to outliers and heavy-tailed distributions; Relax Gaussian hypotheses on error terms.

15 4. Results Summary statistics: energy efficiency technologies related to energy generation Average Year Min year Number United States of America Germany Japan Switzerland France United Kingdom Sweden Republic of Korea Italy Canada Netherlands India Finland Austria Israel China

16 4. Results: Temporal technological trajectories Energy efficiency technologies related to energy generation;

17 Cluster as labeled in figure description Period (min average max) Cluster 2 (Core) Gasification of fuel, mainly companies from USA and Germany Cluster 13 (Branch A final path) Oxygen combustion, started by companies from USA, France and Norway, last part Hitachi (Japan) Cluster 8 (stable part of Branch B final path) Combined cycle, mostly companies from Japan (Mitsubishi) and USA Cluster 7 (non-stable part of Branch B final Hybrid fuel and solar, frequency stabilization, path) heating, cooling, moisturization, mostly companies from USA Cluster 12 (non-stable part of Branch B final Cogeneration, mostly companies from Korea and path) Japan Cluster 4 (early dead end) Load control, combined cycle, mostly companies from Japan and USA Cluster 1 (dead ends) Combined cycle, gasification, mainly companies from Germany and USA Cluster 3 (dead ends) Use of biofuels, using fuel cells, mainly companies from USA Cluster 5 (dead ends) Using waste as fuel, companies from Finland, Germany Cluster 9 (dead ends) Combined cycle, companies from Germany, USA Cluster 6 (mostly fringe) Cluster 10 (disconnected dead end) Cluster 11 (disconnected dead end) Reducing CO2 emissions, mainly companies from USA Fuel burners, combustion, mainly companies from USA Oxygen combustion, fuel injection, mainly companies from USA and France

18 4. Results Summary statistics: energy efficiency technologies related to ICT in buildings Average Year Min year Number United States of America Japan Republic of Korea China United Kingdom Taiwan Germany Canada India France Finland Sweden Israel Netherlands Switzerland Singapore Italy

19 4. Results: Temporal technological trajectories Energy efficiency technologies related to ICT in buildings;

20 4. Results Summary statistics: energy efficiency technologies related to vehicles Average Year Min year Number Japan Germany United States of America France United Kingdom Republic of Korea Italy Sweden Austria Canada Switzerland China Netherlands Australia Taiwan Belgium Spain India Russian Federation

21 4. Results: Temporal technological trajectories Energy efficiency technologies related to vehicles;

22 4. Regression results for forward linkages OLS Quantile regression 25% Log of ( year) 1.879*** 2.321*** (0.218) (0.234) Log of the sum of backward linkages 0.547** 0.590** over all the years (0.173) (0.181) Quantile 50% 1.680*** (0.214) 0.366* (0.165) regression Quantile 75% 1.781*** (0.221) 0.588*** (0.171) regression Log of number of patent families for all the years (0.0343) (0.0306) * (0.0279) ** (0.0289) Industry dummies: reference group: ICT_10 ICT_ *** (0.0667) ** (0.166) * (0.152) (0.157) Power_ *** (0.141) *** (0.199) ** (0.181) ** (0.188) Power_ *** (0.148) *** (0.196) ** (0.179) ** (0.185) Vehicle_ *** (0.147) *** (0.197) *** (0.18) *** (0.186) Constant *** (0.559) *** (0.595) *** (0.543) *** (0.562) N R-squared Standard errors in parentheses; * p<.05, ** p<.01, *** p<.001

23 5. Conclusions Technological trajectory built on patent citation networks by using patent big data is a very useful analytical tool; Technological development over time; Enable us to measure the extent to which a country is a latecomer; Using patent family as the unit of analysis might be the best way to solve the problem of double counting and self-citation. Latecomer countries tend to contribute less, proportionally, to the main technological trajectories in the field that we analyze. Those countries with large vested interests in the dominant technological trajectories are also the ones with strongest forward linkages.

24 Acknowledgements This study was supported by the United Nations Industrial Development Organization (UNIDO). We are thankful to Mourik Jan Heupink, Ad Notten, Herman Pijpers (UNU- MERIT) and Dr. Önder Nomaler (TU/e) for their great support: providing us the SQL server with EPO raw dataset, high-performance server and very helpful suggestions on removing loops in the citation networks.

25 Thank you for your attention!