As the Chinese economy enters the stage of new normal, high-quality economic development has become the current priority. Economic development should not be achieved at the cost of environment and resources. Clarifying the interactive relationship between water environment pollution and the quality of economic growth can help realize water environment protection and sustainable economic development. Based on the data of 30 provinces and cities in China, this paper studies the bidirectional mechanism between water environmental pollution and the quality of economic growth with the dynamic simultaneous equations model. The paper concludes that environmental pollution and the quality of regional economy growth have strong hysteretic nature; And that water environmental pollution and the development of regional economy display the relationship of bidirectional feedback. Discharge of pollutants into the water environment can inhibit high-quality development of regional economy. As the intensity of water environmental pollution increases by 1%, the quality of regional economic growth decreases by 0.0230%; Hence as the quality of regional economic growth increases by 1%, the intensity of water environmental pollution increases by 0.148%. Therefore, sustainable economic development should be attached with due significance, by removing the factors of environmental pollution, timely adjusting environmental policies, and promoting harmony between ecological environment and economic growth.

The Loess Plateau exemplifies the type of ecologically fragile region that faces severe poverty challenges in China. Orchards have expanded rapidly over the past few decades and now constitute a considerable part of local economy. Not only do the characteristics of orchard expansion affect local economic development, but also exert additional pressure on the ecological environment. Therefore, it is essential for sustainable development on the Loess Plateau to investigate the characteristics and driving forces of orchard expansion. The Fuxian, Luochuan, Huangling, three typical orchard planting counties were chosen as the study area. Firstly, the orchard was extracted from the land use/cover classification from 1990–2020. It broadens the research approach to the identification of expansion cash crops by using the combination of linear spectral mixture analysis (LSMA) and decision tree. Secondly, the spatiotemporal dynamics of orchard expansion were quantitatively investigated based on spatial geometry center shift, physical geographical features, landscape pattern and orchard planting suitability. Then, we constructed an evaluation indicators system to detect the feature importance and partial dependence of different factors by random forest regression. It is more innovative to employ the machine learning method to investigate driving forces. Finally, the linkages between planting suitability and orchard expansion were further discussed, and subsequent policies were proposed. Findings demonstrated the orchard had continuously expanded over the past 30 years, with the fastest expansion rate during 1990–2005. Increased cohesion was accompanied by a shift in the orchard’s spatial distribution to the north central region and highly suitable planting regions. Slope turned out to be the primary factor affecting the orchard expansion. In the future, regions with aging orchard but high planting suitability should be the preferred choice for orchard expansion. Additionally, the transportation connectivity and governmental assistance are crucial considerations for the future planning of the orchard.

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In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.