Jingneng Thermal(SZ:002893)2025-11-27 09:30Group 1: Market Management and Dividend Policy - The company has established a systematic and regular market value management mechanism to maintain its capital market image and protect shareholders' rights [1] - In 2022, the company distributed cash dividends of 0.43 CNY per 10 shares, totaling 8,720,400 CNY; in 2023, the dividend increased to 0.80 CNY per 10 shares, totaling 21,091,200 CNY; and in 2024, it will be 1.02 CNY per 10 shares, totaling 26,891,280 CNY [2] - The company will continue to adhere to relevant laws and regulations while actively implementing cash dividends to reward investors [2] Group 2: Business Expansion and Market Position - The heating industry in Beijing has over 1,000 companies, leading to a fragmented market; thus, regional consolidation is a key development direction [3] - The company aims to strengthen its core market in northern China while seeking quality acquisition targets to expand its competitive advantage in the national heating market [4] Group 3: Subsidiary Operations and Synergies - The company acquired a controlling stake in Jingneng Huqing, focusing on energy efficiency in construction, utilizing renewable energy sources for comprehensive energy services [4] - The acquisition enhances the company's capabilities in energy system planning, construction, and carbon asset management, aligning with national industrial policies [4] Group 4: Technological Advancements in Energy Supply - The company is advancing integrated geothermal heating and cooling technologies, aiming to optimize energy costs and enhance operational efficiency [5] - Notable projects include the Zhongguancun Life Science Park and the proposed energy system for the new campus of the Capital Sports Academy, utilizing a combination of geothermal and solar energy [6] Group 5: Smart Heating Initiatives - The company is implementing smart heating projects in Beijing, covering an area of approximately 2 million square meters, focusing on digital management across the heating supply chain [7] - The project aims to optimize energy consumption and enhance management efficiency through advanced predictive systems and AI-driven mechanisms [8]