Zhaoqiu Wang

The reduction of carbon emissions has emerged as a critical issue that requires urgent attention in the Jiangsu and Zhejiang regions as environmental concerns continue to grow. This paper examined how to achieve carbon emission reduction through industrial restructuring. The influence rela-tionship between industrial restructuring and carbon emissions was investigated using the Kaya constant equation LMDI decomposition method, while the coefficient of variation (CV) method was utilized to explore practical ways of promoting carbon emission reduction through industrial re-structuring. Data on carbon emissions and the economy from 12 core cities and 24 industries in the Jiangsu and Zhejiang regions from 2010 to 2020 were analyzed. The key findings of this study in-dicate that economic growth remains the primary driver of local carbon emission growth, while industrial restructuring and carbon emission intensity changes exhibit both positive and negative effects on carbon emission growth. The inhibitory effect of industrial structure upgrading on carbon emission growth can be weakened by regional industrial isomorphism. Furthermore, regional dis-parities in carbon emission intensity exist among some industries in the Jiangsu and Zhejiang regions, and industrial restructuring based on carbon productivity variations has greater potential for emission reduction. The cities in these regions can encourage the development of industries with superior carbon productivity while regulating the growth of industries with inferior carbon productivity, allowing the optimal allocation of carbon emission credits from industries with lower productivity to those with higher efficiency, resulting in carbon emission reduction.

Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder-Decoder architecture for landslide recognition, called the Separable Channel Attention Network (SCANet). The SCANet consists of a Poolformer encoder and a Separable Channel Attention Feature Pyramid Network (SCA-FPN) decoder. Firstly, the Poolformer can extract global semantic information at different levels with the help of transformer architecture, and it greatly reduces computational complexity of the network by using pooling operations instead of a self-attention mechanism. Secondly, the SCA-FPN we designed can fuse multi-scale semantic information and complete pixel-level prediction of remote sensing images. Without bells and whistles, our proposed SCANet outperformed the mainstream semantic segmentation networks with fewer model parameters on our self-built landslide dataset. The mIoU scores of SCANet are 1.95% higher than ResNet50-Unet, especially.