Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods

Area of Science:

• Environmental Science
• Corporate Governance
• Sustainable Development

Background:

• China's focus on climate change and sustainable development elevates attention on the solid waste treatment industry's ESG performance.
• Assessing ESG performance is crucial for understanding and improving the environmental impact of waste management.

Purpose of the Study:

• To construct a targeted ESG evaluation index system for the solid waste treatment industry.
• To evaluate the ESG performance and identify company types (static and dynamic) of 71 Chinese solid waste disposal companies (SWDCs) from 2013-2021.

Main Methods:

• Developed a targeted ESG evaluation index system using literature, SASB standards, and company reports.
• Employed random forest and K-means clustering to determine indicator weights and classify company ESG types.
• Analyzed ESG performance data for 71 SWDCs between 2013 and 2021.

Main Results:

• Overall ESG performance of SWDCs shows a significant high-low imbalance (2-8 point range).
• Identified three static ESG company types: delayed development, single-wheel-driven, and coordinated development.
• Identified five dynamic ESG company types: continual leading, growth catch-up, slow progress, fluctuating change, and retrogressive inertia.

Conclusions:

• Enhancing waste management, emergency planning, and ESG disclosure are key to improving industry performance.
• The study provides targeted recommendations for different ESG company types.
• Understanding industry ESG conditions aids regulation and support for sustainable development in this high environmental risk sector.