With the deep integration of the digital economy and agricultural development, agricultural digital transformation promotes agricultural production, and industrial upgrading and broadens sales channels to achieve the strategic goal of rural revitalization in China. To explore whether agricultural digitization can help farmers increase their income and what path can be achieved, this study is based on the theoretical framework of rural revitalization theory and digitization. Through a questionnaire survey of 1500 farmers in Hubei Province, the impact mechanism of agricultural digitization transformation on farmers’ income is empirically studied. The empirical results show that the digital transformation of agriculture can promote the increase in farmers’ income, and promote the increase in farmers’ income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. At the same time, both production efficiency and sales channels form a chain double intermediary path with the upgrading of agricultural structure. Under the background of rural revitalization, this study provides theoretical references and guidance for further promoting agricultural digital transformation to increase farmers’ income. The marginal contribution of this study is to construct a theoretical model of agricultural digitization to promote farmers’ income increase, which has important theoretical reference and guiding significance for guiding the development direction of agricultural digitization and promoting farmers’ income increase.

China is in a critical stage of economic growth mode transformation. The digital transformation of the manufacturing industry may create new impetus and new models for economic growth. Taking the manufacturing industry of 25 prefecture-level cities in the Yangtze River Delta region as the research object, we explore the digital transformation process of the manufacturing industry and verifies its theoretical mechanism of promoting economic growth through the industrial structure. A panel model based on the improved Feder two-sector model and a multiple mediating effect model are established to explore the dynamic mechanism of manufacturing digital transformation to promote economic growth through industrial restructuring. The results show that the digital transformation of the manufacturing industry in the Yangtze River Delta region of China is relatively high, and the speed of digital transformation has been accelerating in recent years. The digital transformation of the manufacturing industry can promote the change in industrial structure and form a new driving force for economic growth. The key is to improve the level of industrial structure and extend the length of the industrial chain. Based on these, we propose measures to promote the transformation and upgrading of industrial structure for the sustainable development of China’s economy.

It is of great significance to study the regional differences and temporal and spatial evolution of China’s carbon emission intensity under the carbon emissions trading mechanism, and to explore the potential for regional emission reduction. This paper uses the Theil index and Moran index to analyze the regional differences and temporal and spatial evolution trend of carbon emission intensity in China from 2010 to 2019, further constructs the emission reduction effect standard of carbon emissions trading mechanisms, discusses the emission reduction effect of the trading mechanisms, and measures the regional emission reduction potential according to the environmental learning curve. The results showed that: (1) China’s overall carbon emissions continued to increase, but the carbon emission intensity showed an overall decreasing trend. There are strong regional differences in China’s carbon emission intensity. The carbon emission intensity in the western region is higher, and the overall regional difference is decreasing year by year. (2) China’s carbon emissions trading mechanism has a significant reduction effect, but the total quota slack of the Tianjin, Beijing, and Chongqing carbon emissions trading pilot markets is loose. (3) Shanghai, Shanxi, Jiangxi, Guizhou, Inner Mongolia, and Beijing are high-efficiency carbon emission reduction provinces (more than 35%), and Fujian and Xinjiang are low-efficiency carbon emission reduction provinces (less than 15%). It is necessary to further develop the demonstration effect of high emission reduction potential areas and increase the emission reduction efforts in low emission reduction potential areas.

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