A low carbon management model for regional energy economies based on blockchain technology

Abstract

As the issue of sustainable energy development becomes more and more important in national economic construction, the potential dangers of climate change are gradually attracting widespread attention from countries around the world. In order to better carry out the low-carbon management of the regional energy economy, based on the analysis of the characteristics of blockchain technology, the present study utilized this technology to achieve intelligent and digital management of carbon emissions, and established a carbon emission prediction system. The cuckoo algorithm is used to improve the long-term memory network, and the improved algorithm is used in carbon emission prediction and management. The experimental results show that the improved Long Short Term Memory networks are close to the target precision in 240 iterations, and the convergence speed is fast. In the short-term regional carbon emission prediction, the average absolute error of the method is only 2%, which is highly consistent with the actual carbon emission. In the long-term carbon emission prediction, the average prediction accuracy of the upgraded long-term short-term memory networks can reach 97.26%, and the running time is only 19.46s. With high precision and running efficiency, the upgraded Long Short Term Memory networks can efficiently monitor regional carbon emission and provide a technical reference for the low-carbon management of the regional power industry.

Keywords

• Blockchain
• Energy economy
• Carbon emissions
• Long short term memory
• Management mode
• Cuckoo algorithm