Core Viewpoint - ESG is not merely a moral filter but an evolution in risk management, and transformation should be seen as a reconstruction of value creation rather than a cost burden [1] Group 1: ESG Infrastructure and Data Challenges - The implementation of ESG faces a data-driven dilemma, characterized by three major gaps that need to be addressed [1] - Companies must build a data-driven ESG infrastructure that is predictive, autonomous, and closed-loop [1] - Establishing such systems requires significant investment, and the financial benefits of these investments remain uncertain [1] Group 2: Scenario Testing and Decision-Making - After building the necessary systems, companies can conduct stress tests to simulate the impact of various scenarios (e.g., climate change, social conflicts) on their operations [1] - It is essential to convert relevant data into cost, revenue, and risk factors from a value chain perspective to create a "quantifiable, priceable, and manageable" basis for decision-making [1] Group 3: Market Sensitivity and Long-Term Value - The market tends to react more sensitively to negative news, causing immediate impacts on stock prices when ESG-related negative events occur [2] - Long-term resilience and value are crucial for sustainable development, testing investors' patience and perseverance in the ESG sector [2]