The OECD have created a search strategy for environment-related technologies (ENV-TECH) based on more than 200,000 different classification symbols, containing both International Patent Classification (IPC) symbols and Cooperative Patent Classification (CPC) symbols. The classifications cover a broad spectrum of technologies related to environmental pollution, water scarcity and climate change mitigation. The classifications found in the ENV-TECH search strategy have been grouped according to their relevance within 12 innovation systems. Not all the classifications found in the ENV-TECH search strategy have been used. For each innovation system, a list of the relevant CPC and IPC schemes is created. The raw patent data found in the OECD REGPAT database (which contains all patent filed to the EPO) is then filtered for each list. This yields the number of patent applications relevant within each innovation system. These are allocated fractionally to the inventor(s) country according to inventor share. The patents are then sorted according to the priority year of filing. Only patents filed to the EPO are listed in the data. This contains inventions sought protected within the jurisdiction of the EPO and also captures international patents filed under the Patent Cooperation Treaty (PCT), which must also be filed to the EPO. The method does, however, not list patents which are filed to either the United States Patents and Trademark Office (USPTO) or the Japan Patent Office (JPO) alone. Hence, inventions which are sought protected in the ERA countries and all the PCT member states are covered, but not inventions which are sought protected under the jurisdiction of either the USPTO or JPO alone. The patents are also listed according to the country of the inventor(s), though an invention may have been developed in a different country. Many patents are never used in any industrial application, and do therefore not contribute to innovation directly. Many inventions are also not sought patented, either because they cannot be patented or because the inventors attempt to protect the invention through other means. These inventions are not captured through patent statistics, which are then not a perfect indicator for innovation. Be aware that due to delayed data entries in the OECD patent database the values for the last couple of years might be underestimated and could possibly increase over the next years. Have this in mind when working with data from recent years.

1 DataSets for Paper-Patent comparison} Three datasets for paper/patent related data. The first three files are in Excel format the 4th in Python. The initial dataset is located first. Then selected subset and calculations are generated. 1.1 Article/patent ratio according to Global Innovation Index} World Bank declared this dataset is classified as Public under the Access to Information Classification Policy under Definition: Scientific and technical journal articles refer to the number of scientific and engineering articles published in a lot of fields. Scientific and technical article counts are from journals classified by the Institute for Scientific Information's Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). We isolated two codes: 1. WIPO, Patent applications, residents indicator code 2012, 2. SCIMAGO, Scientific and technical journal articles indicator code 2015

Dutta, S., Lanvin, B., Wunsch-Vincent, S., 2020. Global innovation index 2020. WIPO, Johnson Cornell University. URL: https://www.globalinnovationindex.org/about-gii.

1.2 Article - patent ratio according to Global Innovation Index} All classifications and numbers are per country GDP. We focus on three items: 1. PCT patents by origin/bn PPP USD GDP, 2. Scientific and technical articles/bn PPP USD GDP and 3. Citable documents H-index OECD, 2014. Patents by main technology and by international patent classification (ipc) URL: https://www.oecd-ilibrary.org/content/data/data-00508-en , doi: https://doi.org/10.1787/data-00508-en

1.3 Article Patent Ratio according European Patent office Scientific and technical journal articles - tcdata360. URL: https://tcdata360.worldbank.org/indicators/IP.JRN.ARTC.SC?country=BRA&indicator=2015&viz=line_chart&years=2003,2016 . Download link https://tcdata360-backend.worldbank.org/api/v1/datasets/56/dump.csv

1.4 PYTHON sheet to solve min-max scaling data for PATENT/PAPER ratio To compare patents with papers, normalize my data with min-max scaling method The formula for calculating normalized score is the following X new = (X — X min)/ (X max — X min) 1 Python 1 PDF file

this graph was created in PowerBi,Loocker Studio and Tableau :

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This dataset provides a comprehensive analysis of the technological landscape in three of China’s most prominent innovation hubs: Beijing, Shanghai, and Shenzhen. It focuses on invention patents and offers insights into the complexity, technology density, and evolution of patent technologies in each of these cities. Rather than simply counting the number of patents, the dataset explores the depth and breadth of technological knowledge involved, the concentration of innovation in specific fields, and the dynamic changes in focus over time. Technological complexity is a key dimension of the dataset, reflecting how advanced and interdisciplinary a particular patent is. In Beijing, this complexity is largely fueled by prestigious academic institutions and research centers such as Tsinghua University and the Chinese Academy of Sciences. Patents in Beijing often combine science and engineering in highly sophisticated ways, particularly in areas like artificial intelligence, aerospace, and advanced materials. Shanghai presents complexity through its integration of industrial systems and smart technologies. The city’s innovation is heavily influenced by the automotive, chemical, and semiconductor industries, creating complex patents that serve both commercial and industrial applications. Shenzhen, widely regarded as China’s Silicon Valley, showcases complexity through rapid, iterative development and the fusion of software, hardware, and telecommunications technologies. Its ecosystem, driven by startups and tech giants alike, results in disruptive, high-impact innovations in electronics, AI, and consumer devices. In terms of technology density, Shenzhen shows a strong concentration in electronics, telecommunications, and integrated circuits. This reflects the dominance of companies like Huawei, Tencent, and DJI, and a culture of product engineering that fuels dense clusters of technological advancement. Shanghai, meanwhile, exhibits a balanced density profile, rooted in traditional sectors like chemical engineering and automotive design while simultaneously expanding into digital finance, green technologies, and industrial IoT. Beijing has a wider spread of technology domains, influenced by national research agendas and policy-driven innovation. While it has dense areas in AI and defense, the city also supports advances in biotechnology, clean energy, and cybersecurity.