Industrial innovation characteristics and spatial differentiation of smart grid technology in China based on patent mining

Abstract Based on the perspective of smart grid patent, this study crawls the smart grid patent data from 2009 to 2020, and extracts the hotspots of innovation space in different stages. By mining the patent information, this paper identifies the hot innovation fields of smart grid in China and the world, and selects IPC classifications of H02J, G01R and H04L as the main hot smart grid technologies for innovation space analysis. In further study, methods such as global autocorrelation analysis are used to comprehensively analyze the spatial distribution of smart grid innovation in China. It is found that innovation activities of China's smart grid industry are spatially agglomerated. However, during 2017–2020, such spatial distribution represents China's smart grid industry has approached the distribution pattern of normal innovation industries which tends to be discrete or competitive. According to autocorrelation analysis, China's smart grid innovation space has obvious agglomeration characteristics, showing a gradual decline trend from the center to the periphery, and the diffusion effect has initially appeared. Finally, the smart grid patent statistics and the spatial ellipse parameters intuitively present the innovation characteristics of China's smart grid industry in terms of the evolution of the spatial differentiation during 2009–2012, 2013–2016, and 2017–2020.

[1]  B. Boots,et al.  A Note on the Extremities of Local Moran's Iis and Their Impact on Global Moran's I , 2010 .

[2]  Thomas Brenner,et al.  Geographic concentration of innovative activities in Germany , 2009 .

[3]  Marek Vaculík,et al.  Spatial Distribution of Innovation Activities in Czech Republic, 2010-2012 , 2017 .

[4]  Dirk Fornahl,et al.  Radical or not? The role of clusters in the emergence of radical innovations , 2019, European Planning Studies.

[5]  M. Amin,et al.  The Electric Power Grid: Today and Tomorrow , 2008 .

[6]  Li Haifeng,et al.  Questionnaire Survey and Analysis of Natural Disaster Defense Techniques of Power Grids in China , 2010 .

[7]  Damir Novosel,et al.  Energy challenge, power electronics & systems (PEAS) technology and grid modernization , 2017 .

[8]  Edward Feser,et al.  On the Ellison-Glaeser geographic concentration index , 2000 .

[9]  Yifei Sun Geographic patterns of industrial innovation in China during the 1990s , 2003 .

[10]  M. Mathur Spatial autocorrelation analysis in plant population: An overview , 2015 .

[11]  M. Hazelton Variable kernel density estimation , 2003 .

[12]  Isabell M. Welpe,et al.  Monitoring Innovation in Electrochemical Energy Storage Technologies: A Patent-based Approach , 2014 .

[13]  Lieven Nachtergale,et al.  Spatial methods for quantifying forest stand structure development: a comparison between nearest-neighbor indices and variogram analysis , 2003 .

[14]  Quanxi Wang,et al.  The Spatial-Temporal Characteristics of Cultivated Land and Its Influential Factors in The Low Hilly Region: A Case Study of Lishan Town, Hubei Province, China , 2019, Sustainability.

[15]  Inchae Park,et al.  Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network , 2018, Journal of Informetrics.

[16]  Xuehua Zhang,et al.  Impact of technological progress on industrial structure upgrading based on spatial panel measurement model in Beijing-Tianjin-Hebei region in China , 2021, Arabian Journal of Geosciences.

[17]  L. King,et al.  Statistical Analysis In Geography , 1969 .

[18]  Lei Zhu,et al.  Impact of Industrial Agglomeration on Regional Economy in a Simulated Intelligent Environment Based on Machine Learning , 2021, IEEE Access.

[19]  T. Hägerstrand The propagation of innovation waves , 1952 .

[20]  Xiao-Zhi Gao,et al.  Review analysis on cloud computing based smart grid technology in the oil pipeline sensor network system , 2020 .

[21]  David Pinder,et al.  The Nearest-Neighbor Statistic: Archaeological Application and New Developments , 1979, American Antiquity.

[22]  Hong Zhang,et al.  Scalability for Smart Infrastructure System in Smart Grid: A Survey , 2018, Wirel. Pers. Commun..

[23]  Nasser Hosseinzadeh,et al.  Development of an enterprise Geographic Information System (GIS) integrated with smart grid , 2018 .

[24]  O. Prokopenko,et al.  Information and communication technologies support for the participation of universities in innovation networks (comparative study) , 2018, Innovative Marketing.

[25]  Gu Wei Study on the development and technology of strong smart grid , 2009 .

[26]  Vernon W. Ruttan,et al.  Usher and Schumpeter on Invention, Innovation, and Technological Change , 1959 .

[27]  K. Bi,et al.  Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China , 2019, Sustainability.

[28]  Zhanqi Wang,et al.  Exploring the Spatial Pattern of Urban Block Development Based on POI Analysis: A Case Study in Wuhan, China , 2019 .

[29]  Shu-Hao Chang The technology networks and development trends of university-industry collaborative patents , 2017 .

[30]  Liu Lin Survey of Demand Response Research in Deregulated Electricity Markets , 2008 .

[31]  L. Chou,et al.  The effect of CO2 emissions and economic performance on hydrogen-based renewable production in 35 European Countries , 2019, International Journal of Hydrogen Energy.

[32]  Everett M. Rogers,et al.  Innovation Diffusion As a Spatial Process , 1967 .

[33]  Jay Lee,et al.  Spatio-Temporal Nearest Neighbor Index for Measuring Space-Time Clustering among Geographic Events , 2020 .

[34]  Rahman Saidur,et al.  Comparative study of stand-alone and hybrid solar energy systems suitable for off-grid rural electrification: A review , 2013 .

[35]  Mengdi Chen,et al.  Purchase intention for hydrogen automobile among Chinese citizens: The influence of environmental concern and perceived social value , 2021, International Journal of Hydrogen Energy.

[36]  G. Heimeriks,et al.  Knowledge flows in global renewable energy innovation systems: the role of technological and geographical distance , 2021, Technol. Anal. Strateg. Manag..

[37]  Wei Song,et al.  Triple helix in the science and technology innovation centers of China from the perspective of mutual information: a comparative study between Beijing and Shanghai , 2019, Scientometrics.

[38]  Comparison of sampling methods for estimation of nearest-neighbor index values , 2017 .

[39]  Corinne Autant-Bernard,et al.  Spatial Econometrics of Innovation: Recent Contributions and Research Perspectives , 2011 .

[40]  B. Godin Innovation Without the Word: William F. Ogburn’s Contribution to the Study of Technological Innovation , 2010 .

[41]  L. Chou,et al.  The influence of democracy on emissions and energy efficiency in America: New evidence from quantile regression analysis , 2020, Energy & Environment.

[42]  Fahui Wang,et al.  Location analysis of retail stores in Changchun, China: A street centrality perspective , 2014 .

[43]  Liu Jinsong A Study on Compatibility of Smart Grid Based on Large-scale Energy Storage System , 2010 .

[44]  A. Piccaluga,et al.  The impact of technology transfer and knowledge spillover from Big Science: a literature review , 2020 .

[45]  Sumei Zeng The Marine Property Rights Operating Platform Built on the Transformation of Scientific and Technological Achievements Is Constructed under the New Economic Normal of Coastal Areas: An Example of Guangzhou City , 2020, Journal of Coastal Research.

[47]  Jing Enbo Overview of Development and Technology of Smart Grid , 2010 .

[48]  Amy J. C. Trappey,et al.  A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis , 2019, Applied Sciences.

[49]  A. Pred,et al.  City-systems in advanced economies: Past growth, present processes and future development options , 1977 .