Can agricultural digital transformation help farmers increase income? An empirical study based on thousands of farmers in Hubei Province.

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.

Digital transformation, industrial structure change, and economic growth motivation: An empirical analysis based on manufacturing industry in Yangtze River Delta.

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.

Influencing Factors and Realization Path of Power Decarbonization-Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019.

2011-2019 was the critical period of the low-carbon transformation of the power industry, reflecting the deepening influence of market mechanisms. Decarbonization of the new power system is a systematic project that needs to strengthen the top-level design and overall planning. Therefore, the paper first evaluates the decarbonization of the existing power system and controls the grid architecture, power structure, energy utilization, supply chain, and trading market to further optimize the system by strengthening the basic theoretical research of the new power system, exploring the key elements of the low-carbon development of the power system, promoting the breakthrough of the key subjects, and formulating the new power system decarbonization path. In the international push for carbon neutrality goals, identifying key factors in the decarbonization of the power system is critical to achieving low-carbon development in the power sector. Combined with the characteristics and development trends of the power industry, the five dimensions of "Power generation decarbonization (SP)", "Energy utilization efficiency (EU)", "Supply chain decarbonization (SC)", and "Power grid decarbonization (PG)", and "the Trading system (TS)" are selected to construct an evaluation index system for the power decarbonization and identify the key factors. The Analytic Network Process (ANP) Method is used to calculate the index weight and measure the decarbonization level of the power industry in 30 provinces in China from 2011 to 2019. The evaluation results reveal that the overall decarbonization level of the power industry is on the rise and has stabilized after peaking in 2016. The regression results of the systematic GMM estimation show that "the intensity of cross-regional transmission", "the degree of carbon market participation", "technology innovation", and "policy support" can significantly promote power decarbonization, and different regions have heterogeneity. Therefore, we propose to achieve technological innovation and upgrading in the eastern region, strengthen the construction of smart grids in the central region, optimize the power structure in the western region, and improve the market mechanism as a whole, to form a low-carbon development path for the power industry.

Carbon emission quota allocation of high energy consumption industries in undeveloped areas - A case study of Inner Mongolia Autonomous Region.

With the approaching of the target year of carbon peak, all world countries have gradually strengthened carbon emission reduction actions. However, most of the undeveloped countries or regions do not belong to the Annex I countries in the Kyoto Protocol, and the development of carbon emission trading mechanisms is relatively slow. Therefore, setting emission quota standards for industries in underdeveloped regions is necessary. Considering the principles of fairness, efficiency, and sustainability, we establish a "multi-objective information entropy allocation model" (i.e., IEMMA method) for underdeveloped areas. Take China's Inner Mongolia Autonomous Region as an example, and the allocation results show that: (1) The "historical emissions", "carbon emission intensity", "cumulative emissions", "energy structure", and "energy intensity" are assigned higher weights. Such industries will carry a higher responsibility for emissions reductions, they should gradually transfer emission quotas to industries with smaller historical emissions to increase enthusiasm for reducing emissions. (2) Compared with the historical emissions allocation scheme, the IEMMA model can increase the emission limits for industries with large historical emissions. For industries with relatively small emissions, their emission quotas can be appropriately relaxed to promote the sustainable development of the industry. (3) Under the allocation scheme of the IEMMA model, the difference in the reduction of carbon emission intensity of different industries is small. Carbon emission intensity becomes a relative emission reduction indicator, which reflects the fairness of allocation while maintaining economic development. (4) The "quota gap" formed by the allocation plan of the IEMMA model is large, which is conducive to promoting the active emission reduction of high-emission enterprises. Larger "allowance gaps" are conducive to increasing the activity of the carbon trading market. In summary, the IEMMA model is conducive to achieving long-term emission reduction goals in underdeveloped regions and is more suitable for the allocation of quotas to high energy-consuming industries.