Purpose This study aims to focus on the analysis of the internal mechanism of farmers’ ecological cognition and the behaviour of Grain for Green Project (GGP), and the further relationship between ecological cognition and ecological aspiration, proposing climate change strategies and management from the perspective of farmers. Design/methodology/approach Theory of planned behaviour and social exchange theory were used to construct a theoretical framework and an ecological cognition under the influence of external factors, the aspiration and the behaviour of GGP, using ecological fragile areas in Bazhou and Changji, Xinjiang of 618 peasant households’ survey data. The structural equation model and Heckman two-step model were applied to analyse the relationship between ecological cognition and ecological aspiration of farmers, the impact of peasant households’ ecological cognition and aspiration to the behaviour of GGP and the influence factors of GGP behaviour. Findings This research’s results show that the three characterizations of ecological cognitive variables, attitude towards the behaviour (AB), subjective norms (SN) and perceived behaviour control (PBC), have significant positive impact on farmers’ GGP ecological aspiration. The comprehensive impact path coefficients of ecological cognition are PBC (0.498) > SN (0.223) > AB (0.177). Also, income change is a moderating variable, which has a significant moderating effect on the influence of AB and SN on ecological aspiration. Further, farmers’ ecological cognition has an influence on the behaviour of GGP, and the change of farmers’ income has a significant positive effect on farmers’ choice of returning farmland to forests. Practical implications The ecological protection policy suggestions and countermeasures can be drawn from the research conclusions, adapted to China’s ecologically fragile regions and even similar regions in the world to response the climate change. Originality/value Combining the theory of planning behaviour and social exchange, this paper empirically analyses the path of farmers’ ecological cognition and ecological aspiration, as well as the influencing factors.

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For farmers, the more fragile the state of the ecology becomes, the more their awareness of the need for environmental protection grows. China’s Grain for Green Project (G.G.P.) policy of returning farmland to forests and grassland, as an external shock to the environment, has sparked people’s ecological aspirations. Many people have noticed the phenomenon of ecosystem degradation and overlapping poverty. Analyzing the environmental and income changes brought about by the G.G.P., and this study considers farmers’ self-selection problems due to their lack of subjective thinking regarding this initiative. Our study aims to fill this gap by using a forest–grass model to assess the level of farmers’ ecological aspirations in ecologically vulnerable areas of Xinjiang, China. This article is based on aspiration theory and a theoretical model assessing the economic impact of ecological aspiration on the G.G.P. in China. The results show that farmers’ ecological aspirations can increase their enthusiasm to participate in the G.G.P. Under counterfactual conditions, participation in the G.G.P. initially reduces farmers’ total income to a certain extent; however, in the long run, it can significantly increase the total income of farmers. When the intermediary effect is used to analyze the economic effect of ecological aspiration on returning farmland to forest, it is found that farmers’ ecological aspirations affect household income by influencing income expectations. Our findings have essential practical implications and provide an important reference for consolidating poverty alleviation efforts and effectively promoting rural revitalization. In addition, the results suggest a way to achieve the goals of carbon peak and carbon neutrality, and it is necessary for building environmental-friendly regions.

The high range resolution profile samples are numerous and sparse. But less radar target recognition algorithms based on high range resolution profiles (HRRP) employed the sparseness of HRRP samples. A new radar target recognition algorithm using a fast sparse decomposition method is presented here. This algorithm was to be carried out in three major steps. First, the Gabor redundant dictionary was partitioned according to its atom characteristics to decrease the atoms storage. Then, the matching pursuit algorithm was improved by the genetic algorithm and the fast cross-correlations calculation to accelerate training samples decomposition and generate the taxonomic dictionaries. Finally, the reconstruction errors of testing samples were used to recognize different radar targets. The simulations show that this method can resist noise disturbs and its recognition rate is high.