Recently, the National Data Bureau published an expert interpretation article entitled "Expert Interpretation | Competition Promotes Quality Development of New Quality Productivity in Green and Low Carbon Fields". General Secretary
Xi Jinping emphasized that "green development is the background of high-quality development, and new quality productivity itself is green productivity", which pointed out an important direction and provided a practical path for the development of green productivity. The Ministry of Ecology and Environment thoroughly implements Xi Jinping's thought of ecological civilization, gives full play to the role of data elements in promoting innovation and development in the field of green and low carbon, actively constructs a new data-driven green development model, promotes the modernization of ecological environment governance system and governance capacity, and injects strong impetus into the comprehensive green transformation of economic and social development. The green low-carbon track of the 2025 "Data Elements *" Competition closely follows the key action plan of the "Data Elements *" Three-year Action Plan (2024-2026), and sets up four competition topics, namely, optimizing ecological environment management services, promoting energy efficiency, promoting resource recycling, and promoting production emission reduction and carbon reduction. In order to further promote the deep integration of data elements and green low-carbon industries, cultivate new formats and new models, we focus on the following four aspects to explore the application of "data elements * green low-carbon".
(1) Optimizing ecological environment management services
1. Meteorological and hydrological coupling forecast and disaster analysis
integrate meteorological satellite, ground station network, hydrological stations and other data, integrate numerical model and artificial intelligence engine, and construct meteorological and hydrological dynamic coupling deduction model. Improve the prediction accuracy of floods, droughts and other disasters caused by extreme weather events, dynamically assess the scope and level of disasters, and provide scientific basis for early warning, emergency rescue and post-disaster reconstruction.
2. Monitoring of river and lake shorelines and fine management
of urban water environment use satellite remote sensing, ground information and observation station data to dynamically monitor river and lake shorelines, and timely detect shoreline changes, illegal occupation and other issues; Through the fusion analysis of water quality monitoring data, sewage treatment plant operation data and drainage network data, the sewage treatment process is optimized and the fine management of urban water environment is realized.
3. Heavy pollution weather response and environmental quality monitoring and assessment
integrate meteorological, air quality, pollution source emission data, establish heavy pollution weather prediction and early warning model, predict the development trend of heavy pollution weather in advance, and provide support for the formulation of scientific and effective response measures; Environmental quality monitoring and assessment are carried out by integrating multi-source data to support environmental policy formulation and scientific decision-making of environmental management.
4. Pollution source analysis and tracking and environmental risk early warning and emergency
integration of industrial production, transportation, agricultural activities, meteorological conditions and other related data to build a pollution traceability model to achieve accurate tracking of pollution sources. Using real-time monitoring data and forecasting model, real-time early warning of environmental risks is carried out to support the scientific formulation of contingency plan and the command of environmental emergency dispatch.
5. Environmental pollution liability insurance and green finance
combine data such as enterprise risk rating, accident files and industry standards to accurately price environmental liability insurance and enhance the ability of enterprises to resist risks. Gather enterprise environmental information, financial data, credit records, etc., build a green financial wind control model, provide quantitative basis for green credit audit and green bond issuance, and help the development of green industry.
(2) Promoting the improvement
of energy efficiency 1. Industrial energy consumption prediction and multi-energy complementary optimization
collect order data, production scheduling data, power consumption data and equipment operation data in the industrial production process to achieve accurate prediction of industrial production energy consumption. Through the supply and cost of different energy sources, optimize the multi-energy complementary scheme, realize the coordinated utilization of electricity, heat, natural gas and other energy sources, and improve the efficiency of energy utilization.
2. Energy gradient pricing and intelligent management
Integrate energy market pricing, user information, power grid operation and other data, formulate energy gradient pricing strategy, realize differentiated pricing according to user's energy consumption period, energy intensity and energy cost, and guide users to rationally adjust their energy consumption behavior. Through real-time monitoring and analysis of energy data, the intelligent dispatch and optimal allocation of energy can be realized, and the reliability and stability of energy supply can be improved.
(3) Promoting the recycling
of resources 1. Intelligent management
of the whole process of solid waste opens up the whole link data of solid waste from generation to disposal, relying on the Internet of Things and big data technology to build an intelligent dynamic management system, real-time tracking of production, types and transfer. Optimize the process flow through data analysis to improve the efficiency of resource utilization.
2. Intelligent recycling and resource utilization
aggregate user preferences, market fluctuations and recycling industry data, link up supply and demand information links, improve recycling efficiency and public participation; Relying on image recognition and machine learning technology to optimize the intelligent collection and sorting system, to achieve accurate classification of solid waste, and to promote the development of resource utilization industry.
(4) Promoting production emission reduction and carbon
reduction 1. Carbon emission monitoring, accounting and dynamic tracking
collect data on energy consumption, raw materials and production processes, and use scientific accounting methods to achieve accurate monitoring and accounting of carbon emissions. The carbon emissions of products or enterprises are tracked throughout the life cycle to provide data support for enterprises to formulate emission reduction strategies.
2. Energy system optimization and carbon trading assistant decision-making
integrate energy supply, energy consumption, carbon emissions and other data to achieve intelligent optimization of energy systems and reduce energy consumption and carbon emissions. Combined with the data of carbon trading market and the carbon emissions of enterprises, service enterprises can rationally allocate resources in the carbon trading market and reduce transaction costs.
3. Carbon reduction technology innovation and application
in key areas focus on key carbon emission areas such as power, steel and cement. Through in-depth analysis of industry production data, it supports carbon reduction technology screening, serves carbon reduction process monitoring and effect evaluation, and taps deep carbon reduction potential and technology path.
Author: Huang Mingxiang, Director of
Big Data Development Department, Information Center, Ministry of Ecology and Environment
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