Research & Projects
Research & Projects
This project analyzes the development trends of traditional and renewable energy innovation in Chinese listed companies, using the Directed Technology Change model and incorporating machine learning techniques into traditional economic indicators to study the impact of financial data on corporate innovation.
Database Creation: Led the development of a comprehensive database covering financial statements and 1000+ indicators for Chinese listed companies from 2000 to 2023, ensuring data integrity and accuracy.
Model Integration: Integrated financial data into the Directed Technology Change model combined with machine learning algorithms to analyze the relationship between financial indicators and patent innovation, identifying key factors driving traditional and renewable energy development.
This project examines whether China's electric vehicle promotion policies have effectively stimulated technological innovation in energy storage. Independent collection and processing of nearly all patent data for Chinese companies and individuals were conducted to study the policy's impact on technological innovation.
Data Processing: Created a unique and comprehensive database, one of the few accessible to international academia.
Innovation Analysis: Designed and implemented an analysis process for patent data to evaluate the impact of EV promotion policies on energy storage innovation, ensuring the accuracy and reliability of the results.
This project explores how patent characteristics in the energy sector affect litigation risks. The key focus is on the relationship between patent characteristics and litigation risks, using a difference-in-difference model to estimate the impact of the American Inventor's Protection Act (AIPA) of 1999 on patent grant lag times.
Data Analysis: Collected and organized patent data from the U.S. Patent and Trademark Office (USPTO) for the energy sector, analyzing the relationship between patent characteristics and litigation risks, providing data support for model design.
Developed a safety solution for incidents in the New York MTA subway system. The proposal included a detailed technical overview, implementation plan, execution plan, risk management strategy, and financial budgeting.
Authored an academic paper that used fixed-effect regression methods to examine the relationship between GDP growth and the emissions of carbon dioxide and sulfur dioxide at the national level. The findings support the Environmental Kuznets Curve hypothesis.
Introduced the ERR system regarding supplier access, selection shortlists, and performance evaluation, as well as the assessment and improvement of enterprise operations.
After the successful commissioning of the project, I conducted a month-long data collection and analysis on effluent water quality, including phosphorus content, ammonia nitrogen levels, and chemical oxygen demand.