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The Grain for Green program in China, a nationwide cropland set-aside program aimed at soil erosion prevention and poverty alleviation, was begun in 1999 and quickly expanded to 25 provinces, covering 32 million households. Its effects on participating households are well studied, but the role of ethnic characteristics is less well investigated. Given the overlap of areas covered by Grain for Green and areas inhabited by ethnic minorities, where development is a long-unresolved problem, it is important to determine how ethnic minorities react to, and benefit from, the Grain for Green program. This study investigates participation in the program by ethnic minorities and estimates its impact on their off-farm labor supply, compared with that of the ethnic majority, Han. We find that ethnic minorities were more likely to participate in the program, but enrolled similar area of land per household. However, ethnic minorities did not increase off-farm labor supply after participation in Grain for Green, while Han participants increased their off-farm labor supply significantly. These findings raise concerns that Grain for Green may have widened the income gap between Han and ethnic minorities. This study also provides important policy implications on sustainable land management for less-developed regions.

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Since the China State Grid opened the market for infrastructure construction of electric charging stations and allowed Tesla, Potevio, BAIC BJEV and other enterprises to provide their own charging stations and other infrastructure construction, the development of electric vehicles has been greatly affected. How to maintain a sustainable governance in the opened electric vehicle charging and upgraded facilities market is an important policy issues. This paper presents a monopolistic competition model for the differentiated products market and addresses several issues related to Cournot equilibrium to illustrate why the expected free market actually operates in a monopolistic competition market structure. The analytic solution of the model shows that whether the extent of firm entry is insufficient, excessive or optimum is determined by consumers' time preference, level of production differentiation and features of cost structure, including fixed cost and marginal cost. The sensitivity analysis has been performed among the above factors and tracked some other factors which would determine the effect of the new policy issues. The main policy suggestion is that the government should optimize entry regulations and lay down the criterion of charging interface standards for charging stations to avoid the electric vehicle charging and upgraded facilities marketization process of a one-size-fits-all solution and form a monopolistic competition market.

Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and student counterpart share similar proposal information. This explains why existing KD methods are less effective for 1-bit detectors, caused by a significant information discrepancy between the real-valued teacher and the 1-bit student. This paper presents an Information Discrepancy-aware strategy (IDa-Det) to distill 1-bit detectors that can effectively eliminate information discrepancies and significantly reduce the performance gap between a 1-bit detector and its real-valued counterpart. We formulate the distillation process as a bi-level optimization formulation. At the inner level, we select the representative proposals with maximum information discrepancy. We then introduce a novel entropy distillation loss to reduce the disparity based on the selected proposals. Extensive experiments demonstrate IDa-Det's superiority over state-of-the-art 1-bit detectors and KD methods on both PASCAL VOC and COCO datasets. IDa-Det achieves a 76.9% mAP for a 1-bit Faster-RCNN with ResNet-18 backbone. Our code is open-sourced on https://github.com/SteveTsui/IDa-Det.