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At present, China's economic development has entered a "new normal." Exploring Industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the Super-Efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that: The IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend whereby Yellow River Basin's regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.

To find a solution regarding sustainable arable land use pattern in the important grain-producing area during the rapid urbanization process, this study combined agricultural production, locational condition, and ecological protection to determine optimal arable land use. Dongting Lake basin, one of the major grain producing areas in China, was chosen as the study area. The analysis of land use transition, the calculation of arable land barycenter, the landscape indices of arable land patches, and the comprehensive evaluation of arable land quality(productivity, economic location, and ecological condition) were adopted in this study. The results showed that (1) in 1990-2000, the arable land increased by 11.77%, and the transformation between arable land and other land use types actively occurred; in 2000-2010, the arable land decreased by 0.71%, and more ecological area (forestland, grassland, and water area) were disturbed and transferred into arable land; (2) urban expansion of the Changsha-Zhuzhou-Xiangtan city cluster (the major economy center of this area) induced the northward movement of the arable land barycenter; (3) the landscape fragmentation and decentralization degree of arable land patches increased during 1990-2010; (4) potential high-quality arable land is located in the zonal area around Dongting Lake, which contains the Li County, Linli County, Jinshi County, Taoyuan County, Taojiang County, Ningxiang County, Xiangxiang County, Shaoshan County, Miluo County, and Zhuzhou County. The inferior low-quality arable land is located in the northwestern Wuling mountainous area, the southeastern hilly area, and the densely populated big cities and their surrounding area. In the optimized arable land use pattern, the high-quality land should be intensively used, and the low-quality arable land should be reduced used or prohibitively used. What is more, it is necessary to quit the arable land away from the surrounding area of cities appropriately, in order to allow more space for urban expansion. This study could provide guidance for sustainable arable land use by both satisfying the future agricultural production and the local economic development, which can be used for the other major grain-producing areas in this rapid developing country.

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This study primarily examined the assessment of environmental risk in high intensity agricultural areas. Dongting Lake basin was taken as a case study, which is one of the major grain producing areas in China. Using data obtained from 1989 to 2012, we applied Material Flow Analysis (MFA) to show the material consumption, pollutant output and production storage in the agricultural-environmental system and assessed the environmental risk index on the basis of the MFA results. The results predicted that the status of the environmental quality of the Dongting Lake area is unsatisfactory for the foreseeable future. The direct material input (DMI) declined by 13.9%, the domestic processed output (DPO) increased by 28.21%, the intensity of material consumption (IMC) decreased by 36.7%, the intensity of material discharge (IMD) increased by 10%, the material productivity (MP) increased by 27 times, the environmental efficiency (EE) increased by 15.31 times, and the material storage (PAS) increased by 0.23%. The DMI and DPO was higher at rural places on the edge of cities, whereas the risk of urban agriculture has arisen due to the higher increasing rate of DMI and DPO in cities compared with the counties. The composite environmental risk index increased from 0.33 to 0.96, indicating that the total environmental risk changed gradually but seriously during the 24 years assessed. The driving factors that affect environmental risk in high intensity agriculture can be divided into five classes: social, economic, human, natural and disruptive incidents. This study discussed a number of effective measures for protecting the environment while ensuring food production yields. Additional research in other areas and certain improvements of this method in future studies may be necessary to develop a more effective method of managing and controlling agricultural-environmental interactions.