Construction of Low-carbon Economic Development Level Model Based on BP Neural Network Algorithm

The Low-Carbon Economy (LCE) is an all-encompassing topic that combines economic, social, and environmental concerns with technological ones. It encompasses the ideas of low-carbon industry, low-carbon technology, low-carbon energy, low-carbon consumption, and carbon sinks. The expansion of LCE in India is hampered by a number of causes, one of which is India's illogical industrial structure, which is also an important component. As a result, the BPNN (BP neural network) technique is utilized in this study in order to construct the LCE development level model. First, an index system for controlling total carbon emissions should be created for LCE development. Next, an evaluation method that is based on the BPNN model should be developed. Examine the precision of the second generation prediction model by applying the test sample set to it, and continue this process until you have gotten the final prediction model that satisfies the convergence conditions. The findings of the research indicate that the maximum error that can occur between the actual output value of BPNN and the expected output value of BPNN is 8.36%, and the error is less than 10%, which indicates that the training results of the BPNN evaluation model constructed in this paper are good, and the evaluation results obtained are quite satisfactory.

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