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In response to the problems of lack of planning in management and high difficulty in law enforcement in urban and rural planning (URP for short here) and construction, this article proposes to apply the concept of low-carbon ecology (LCE) to URP and construction and reasonably optimize URP and construction. This article provided a relevant analysis of urban and rural energy planning issues and applied the concept of LCE to URP and construction. This article constructed a framework for evaluating urban and rural carbon emissions, which is used to evaluate carbon emissions issues in URP and construction. This article also combines artificial neural network algorithms to further conduct experimental analysis on the scale of URP and construction land. In contrast, during the same period, the construction land scale of our algorithm increased by 179,800 acres less than that of the machine learning algorithm. The per capita area of urban and rural construction land decreased by 16.5 m 2 , and the gross domestic product (GDP) output of urban and rural construction land increased by 1.95 billion yuan. The algorithm in this article has increased by 155,200 acres compared to the construction land scale under deep learning. The per capita area of urban and rural construction land decreased by 7.1 m 2 , and the GDP output of urban and rural construction land increased by 915 million yuan. In summary, this algorithm can effectively increase the GDP output of urban and rural construction land, slow down the expansion of construction land scale, and play a good auxiliary role in URP and construction.

Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism