In recent years, ecological problems in the middle and lower reaches of the Yellow River have been frequent. Therefore, exploring its core influences can advance the implementation of “Ecological Protection and High-quality Development of the Yellow River Basin”. This paper constructs an indicator system based on PSR guidelines, evaluates the ecological protection level of 55 cities in the middle and lower reaches of the Yellow River from 2009 to 2019, and uses correlation analysis with geographically and temporally weighted regression (GTWR) model to explore the spatial distribution characteristics of influencing factors, such as intensity of fertilizer application, amount of agricultural film applied, afforestation area per capita, and green technology innovation level on the ecological protection level. It is found that the overall level of ecological protection has shown a steady increase, but the spatial distribution varies widely. The ecological level increased from 0.2218 to 0.3357, showing a decreasing distribution trend from coastal to inland. Furthermore, it is found that the ecological protection level has a significant positive spatial correlation, mainly for similar clustering. The Global Moran’s I for ecological protection level is greater than 0, and the Moran scatter plot has a high number of cities distributed in the first and third quadrants. There is a heterogeneity in the spatial and temporal distribution of factors influencing the level of ecological protection. Fertilizer application, the agricultural film uses, and afforestation area per capita are mainly negatively affected, while green innovation level has a strong positive effect, and agricultural film use, afforestation area per capita, and green innovation level become the core influencing factor of different regions. Therefore, in the middle and lower reaches of the Yellow River, the ecological protection level should be improved by implementing a regional differentiated development strategy, realizing cross-regional linkages between cities and focusing on differences in core driving factors.

The development of digitalization is a crucial aspect of agricultural progress, and expediting the establishment of digital systems is a significant driving force behind high-quality agricultural advancements in the current era. Utilizing data from 16 cities within Shandong Province in China between 2014 and 2020, we created an assessment system to measure the degree of agricultural digitalization, utilized the entropy technique to assess the level of digitalization, scrutinized the general trends and time-dependent features of each city, and then utilized the obstacle degree model to pinpoint the primary hindrances to digitalization in agriculture. Lastly, the ESDA method was utilized to examine the differences in spatial distribution among regions and the spatial characteristics of agricultural digitalization at different stages and levels. Overall, the degree of agricultural digitalization can be categorized into three stages: deceleration and upswing (2014–2015), steady fluctuation (2016–2017), and high-level upswing (2018–2020). From the perspective of obstacles, the main hurdles to agricultural digitalization are e-commerce transaction volume and the total amount of telecommunication business. To accelerate the development of the entire agricultural industry chain, it is required to leverage the strengths of high-value areas and reinforce the coordination mechanism among various departments while hastening the construction of rural infrastructure in low-value areas. Additionally, it is necessary to improve inter-regional communication and cooperation to nurture different regional development models in line with local conditions.

This study examines the impact of green finance on optimizing China’s agricultural industrial structure. It emphasizes the importance of innovative green financial services, improved efficiency, tailored regional approaches, and enhanced foresight to foster a high-level development in the agricultural sector amid China’s economic transformation. Based on the provincial panel data of 31 provinces in China from 2012 to 2021, this study empirically tests the effect mechanism of green finance on the optimization process of the agricultural industrial structure by constructing a fixed-effects model. This study finds that green finance can effectively promote the development of the optimization of the agricultural industrial structure. Under the current trend of China’s economic structural transformation and optimization, we suggest that China should innovate green financial service products, improve the efficiency of green finance, and enhance the depth of green financial services for the optimization of the agricultural industrial structure. It is required to strengthen foresight and improve relevant laws, regulations, policies, and standards. To help green finance be better promoted, a high-level development of the agricultural industrial structure is required.

This study utilized panel data from 31 Chinese provinces over a period of nine years to investigate the impact of green finance on the upgrading of the agricultural industrial structure. A fixed-effect model was employed, and the findings indicate that green financing has a positive effect on the growth of China's agricultural industry. However, regional disparities exist, particularly in the uneven distribution of green financing across the eastern, central, and western regions. Moreover, it emphasizes the need to consider regional differences and tailor development strategies accordingly. To promote further development and transformation of China's agricultural industrial structure, the study recommends innovative green financial products, improved regulations and policies, and the integration of digital technologies.