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• Chinese soil loss equation model was used to estimate long-term soil erosion. • Soil erosion rates increased in the basin from 2010 to 2017. • Interactions between main driving factors analyzed using boosted regression trees. • The B factor was the primary driver of soil erosion. Soil erosion reduces soil fertility and land productivity while enhancing desertification, which seriously threatens ecological security, carbon sequestration, and sustainable economic and social development. Therefore, it is fundamental to precisely assess the long-term dynamics of soil erosion and explore its drivers to control the risk of soil erosion. Currently, machine learning-based analyses of the factors driving soil erosion are lacking in the Beiluo River Basin (BRB), China. This paper aimed to investigate the spatiotemporal dynamics of soil erosion by the Chinese soil loss equation coupled with the gully erosion factor, and then reveal its driving factors using the Boosting Regression Tree method (BRT) and estimate the influence of drivers' interactions on soil erosion in the BRB between 1990 and 2017. The findings demonstrated that the expanded Chinese soil loss equation accurately explained the variations in soil erosion in the BRB. The range of average soil erosion rates was 2,852.83–800.35 t km−2 a−1. Steep gully farmland, particularly areas with relatively rough terrain, was more vulnerable to intense erosion. Soil erosion rates for different geomorphologies decreased as follows: hilly–gully areas > gully–plateau areas > alluvial river plains > Rocky Mountains. The major drivers affecting the soil erosion rate were the biomass control factor (B) and slope, followed by slope length (L) and slope steepness (S). B–soil erodibility (1990), B–LS (2000), LS–rainfall erosivity (2010), and slope–rainfall erosivity (2017) interactions had the greatest influence on soil erosion, with interaction intensities of 32.3%, 17.75%, 46.05%, and 35.69%, respectively. The average soil erosion rates according to land classification types decreased as follows: farmland > grassland > forest. The results indicate that water and soil conservation in the study area benefited greatly from implementation of the Grain for Green Program, with forests reducing erosion more effectively than grassland. [ABSTRACT FROM AUTHOR]

• The high-quality development index is introduced into STIRPAT model. • The average yield of ecological footprint is updated. • High-quality development can effectively reduce the increase of ecological footprint. • The improvement of population quality helps ease pressure on the environment. • Lower energy consumption intensity helps improve ecological quality. It is urgent to coordinate the development of economy and environment under the "dual carbon" goal. As China's new development stage and mode, high-quality development takes into account both the speed and quality of economic, providing the possibility of achieving a "win-win" situation for the economy and the environment. In order to test the practical impact of high-quality development on ecological, based on the STIRPAT model, this paper introduces high-quality development index instead of per capita GDP, so as to measure the current economic situation in China, and explore the relationship between high-quality development and ecological footprint, and demonstrates the feasibility from theory and practice. At the same time, it pays attention to the complex effects of population and technology on environmental quality. The results show that the contradiction between economy and ecology still exists in China. The ecological footprint index of 30 provinces in the past 21 years has increased as a whole, and the fworst ecological footprint has concentrated in the Yellow River basin. But since 2012, the growth of the footprint has slowed. The slow down time and low value region of ecological footprint were observed, and it was found that they overlapped with the proposed time and the high value area of high-quality development. The level of high-quality development in China has also continued to rise, with regional differences but gradually narrowing. Among them, green development and shared development contribute more to the improvement of high-quality development level. Compared with the traditional economic development mode, high-quality development can reduce the increase of ecological footprint, and the longer the time, the smaller the increase, more conducive to long-term sustainable development. In addition, population and technology factors are also profoundly affecting China's ecological pattern. The increase in population size and population density, as well as the increase in the proportion of coal-based energy consumption structure, will lead to increased environmental pressure. The improvement of population quality and the reduction of energy consumption intensity will help alleviate ecological pressure. The research is helpful to provide policy guidance and path combination reference for China's sustainable development, and also provide new ideas for the global coordination of the relationship between economy and environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Trying to break out of the stereotypical thinking of inherent study areas. • Use of a three-stage DEA model to make efficiency results more scientific. • Multidimensional identification of differences between strategic areas. • Valuable regionally targeted policy recommendations are made. The Yangtze River Economic Belt and the Yellow River Basin are important economic regions and ecological barriers in China. Promoting the development of the Yangtze River Economic Belt and promoting ecological protection and high-quality development in the Yellow River Basin are major regional strategies implemented by the Chinese government, and the green growth of the two regions is important for the high-quality sustainable development of the whole China. To investigate the regional differences in green growth efficiency of the same type of geographical units, this paper measures the green growth efficiency and decomposition indicators of the Yangtze River Economic Belt and the Yellow River Basin from 2010 to 2020 using a three-stage DEA model and the Malmquist index method and establishes a panel Tobit model to identify the influencing factors of green growth efficiency. The results show that: ①After using the three-stage DEA model to remove the influence of external environment and stochastic factors, the mean values of green growth efficiency of Yangtze River Economic Zone and Yellow River Basin from 2010 to 2020 are 0.996 and 1.089, respectively. The change of green growth efficiency of Yangtze River Economic Zone is slightly higher than that of Yellow River Basin. ②The Malmquist indexes of the Yangtze River Economic Belt and the Yellow River Basin have generally increased, with the Technological Progress Index, which characterizes technological innovation, being the main endogenous driver of green growth efficiency in the Yellow River Basin, while the technical efficiency index, which characterizes factor mix and management level, is more significant in the Yangtze River Economic Belt. ③The Tobit model regression results show that the factors influencing green growth efficiency are also different in the two regions. Among them, the urbanization rate has a significantly positive effect on the two regions, while the effects of environmental regulation and research intensity are not significant. External openness has a suppressive effect on green growth efficiency in the Yangtze River Economic Belt, while the level of financial development and human capital negatively affect green growth efficiency in the Yellow River Basin. Therefore, green development in the new era should pay attention to the differences between different regions, and make appropriate development policies according to the local conditions of the development status of different regions. [ABSTRACT FROM AUTHOR]

Long-term agricultural development has an influence on the regional landscape patterns and non-point source (NPS) pollution. The implementation of sustainable landscapes management is a key component in agricultural development together with the reduction of agricultural NPS pollution. In this paper, the interactions of land cover, landscape pattern and NPS pollution were analyzed synchronously. The land cover variation of the agricultural watershed from 1976 to 2006 in Northeast China was analyzed from the Landsat series images, and the landscape pattern was also analyzed with FRAGSTATS. The Soil and Water Assessment Tool (SWAT) was used to identify the NPS pollution response to long-term land cover dynamics. The temporal variation trends of the landscape fragmentation, shape and diversity index demonstrated the response of natural land covers to agricultural development. The six landscape metrics at landscape and watershed scale indicated that the impact of agricultural expansion on regional natural system become slightly in the recent time period. By applying redundancy analyses (RDA), the interactions of the land cover and landscape pattern dynamics with the pollution loading of NPS nutrients at four temporal scales were analyzed. The RDA results also demonstrated the close interactions among them and the proper landscape pattern design can facilitate the prevention of NPS pollution. The responses of seven NPS nutrient indices to the temporal variation of the landscape pattern were also identified, and the influence of agricultural land expansion on NPS nutrient loading was described. The applied method can analyze the interactions of three aspects simultaneously and provide potential solution to agricultural NPS pollution reduction. These results confirm the important role of sustainable landscape management and spatial arrangement during agricultural development and conservation planning. [ABSTRACT FROM AUTHOR]

Abstract: Freshwater is the essential resource element to support and ensure human life and regional sustainable development. On the basis of the concept of ecosystem service footprint proposed by Burkhard, in this paper, we introduce a new methodological approach that addresses the sustainability and capability of regional freshwater provision and consumption, which we have coined freshwater ecosystem service footprint model (FESF model). The FESF model enhances the accuracy of water provision and consumption calculations and reveals a spatial-pattern of freshwater ecosystem service footprint at the watershed scale, overcoming the shortcomings of the traditional water footprint model, which was first introduced by Rees and Wackernagel in 1992. The proposed model is tested in the Beijing–Tianjin–Hebei (BTH) freshwater supply area in China, where water use restrictions are regularly imposed on the population and economic activities. The results of the physical process and theoretical analysis reveal that FESF model is a reliable and helpful model for researchers to understand the regional freshwater situation. The average FESF is 0.94hectare per capita in BTH, with a maximum of 2.52hectare per capita and a minimum of 0.36hectare per capita. Approximately 16.8% of the considered region, primarily parts of the mid-northern region and the surrounding eastern and southern low-lying regions, is under high freshwater resource pressure. In conclusion, this model can be used by researchers and policymakers for promoting sustainable human–ecology interactions. [Copyright &y& Elsevier]