This study examines the relationship between energy consumption and economic growth for 29 provinces of China during the period of 1995-2007. Panel unit root, panel cointegration and Granger causality are employed to infer the causal relationship. The results show there is a panel cointegration relationship between energy consumption and economic growth. The Granger causality results indicate the presence of bi-directional causality running from energy consumption to economic growth with feedback effect. The policy implications are that Chinese government should ensure energy supply safety, improve energy efficiency, defmitely put forward the strategy oflow Carbon economy. China should build up the low-carbon innovation system, and carry out institution innovation of energy-saving and low-carbon economy. Firms are required to undertake social responsibility and encouraged to take measures to low energy consumption and carbon emission.

Deep generative technology has seen a lot of success in the speech and image fields, thanks to its vigorous development and widespread application. The goal of this paper is to use the improved image restoration and depth generation algorithms to improve the English intelligent translation teaching system. This will make a bigger difference in the classroom when it comes to teaching intelligent translation in English. To address the problem of data noise, this paper proposes a data augmentation method that can efficiently exploit large-scale monolingual data in semi-supervised scenarios, as well as a data augmentation method that exploits the robustness of statistical machine translation in unsupervised scenarios. The CNN-Dueling-DQN-NMT model outperforms the CNN machine translation baseline model on the WMT14 English-French dataset by 1.92. The BLEU value of the Transformer machine translation baseline model is improved by 1.63 when using the Transformer-Dueling-DQN-NMT model. With a BLEU value of 44.63, the Transformer-Dueling-DQN-NMT model performs best.

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