Leveraging R-Studio for Predicting and Enhancing the Green Development of Regional Economies: A Biotechnological Approach

Abstract

The primary task for the realization of "green economy" is to accurately measure the regional economy and green development level and effectively identify regional weaknesses. As a macro abstract indicator, it is necessary to build an evaluation system of economic green development to quantify the level of economic green development. Sustainable development capability is an effective evaluation of the future development trend of the region, which is of great significance for the region to find its own weaknesses and targeted optimization. This paper takes the green development trend of regional economy as the research object, uses R-studio for data processing, and constructs regional economic development indicators for theoretical analysis. Then this paper uses the attention mechanism based on digital twins and time series network model to verify the actual data. Finally, the regional economy is predicted according to the theoretical model. The specific research work mainly includes the following aspects: 1) This paper introduced the development status of research on time series network and economic forecasting at home and abroad. 2) This paper introduces the basic principles and structures of LSTM and CNN networks, and constructs an improved CNN-LSTM model combined with the attention mechanism, and then constructs a regional economic prediction index system. 3) The best parameters of the model are selected through experiments, and the trained model is used for simulation experiment prediction. The results show that the CNN-LSTM model based on attention mechanism proposed in this paper has a high accuracy in predicting regional economy.