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Carbon finance refers to various financial institutional arrangements and financial trading activities aimed at reducing greenhouse gas emissions, and is one of the research hotspots of environmental economics. The Clean Development Mechanism (CDM) refers to the fact that investors from developed countries obtain certified emission reductions from their emission reduction projects in developing countries that are conducive to sustainable development in developing countries. A compliance mechanism adopted by Parties to the Third Conference of the Parties to the Framework Convention on Change, COP3 (Kyoto Conference), to achieve partial emission reduction commitments abroad. At present, the number of CDM projects registered in China ranks first in the world, with about 1586 projects, accounting for 52% of the global CDM registration projects, and the carbon trading volume reached 530.7 billion tons, which is the largest supply market for CDM in the world. Strengthen the social benefits of CDM projects and promote poverty alleviation in rural China, and this will help improve the rural ecological environment in China and strengthen the construction of rural ecological civilization. This paper aims to combine the qualitative analysis and quantitative analysis of rural CDM projects, establish an external effect evaluation system, provide a quantifiable model reference for the practice of rural CDM related project effect evaluation, and explore new ideas for poverty alleviation policies to address climate change. The author intends to establish a poverty alleviation effect evaluation mechanism for rural CDM mechanism in China, analyze the poverty reduction effect of CDM mechanism and the internal logic of promotion in poverty-stricken areas, and conduct multi-dimensional effect evaluation indicators from three levels: evaluation method selection, index selection and weight setting, and model rehearsal. Then carry out the promotion feasibility analysis, and the rural CDM projects that fully consider the needs of the poor have significant poverty-prone effects, ecological benefits and social benefits. Based on the evaluation of the dual indicators of rural CDM projects, they will make suggestions and suggestions to promote poverty alleviation. China's agricultural carbon trading and organic integration of poverty reduction.

Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result in severe ringing artifacts when recovering low-light saturated blurry images. To solve this problem, we formulate the saturation deblurring problem as a nonlinear model, in which all the saturated and unsaturated pixels are modeled adaptively. Specifically, we additionally introduce a nonlinear function to the convolution operator to accommodate the procedure of the saturation in the presence of the blurring. The proposed method has two advantages over previous methods. On the one hand, the proposed method achieves the same high quality of restoring the natural image as seen in conventional deblurring methods, while also reducing the estimation errors in saturated areas and suppressing ringing artifacts. On the other hand, compared with the recent saturated-based deblurring methods, the proposed method captures the formation of unsaturated and saturated degradations straightforwardly rather than with cumbersome and error-prone detection steps. Note that, this nonlinear degradation model can be naturally formulated into a maximum-a posterioriframework, and can be efficiently decoupled into several solvable sub-problems via the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real-world images demonstrate that the proposed deblurring algorithm outperforms the state-of-the-art low-light saturation-based deblurring methods.