This paper presents a game-theoretical analysis of joint decisions on carbon emission reduction and inventory replenishment with overconfidence and consumer’s low-carbon preference for key supply chain players when facing effort-dependent demand. We consider respectively the overconfidence of a supplier who overestimates the impacts of his emission reduction efforts on product demand and the overconfidence of a retailer who underestimates the variability of the stochastic demand. We find, surprisingly, that the supplier’s overconfidence can mitigate “double marginalization” but hurt self-profit, while the retailer’s overconfidence can be an irrelevant factor for self-profit. The retailer aiming at short-term trading should actively seek an overconfident supplier, while the supplier should actively seek a rational retailer for whom the critical fractile is more than 0.5, whereas for an overconfident retailer, the critical fractile is less than or equal to 0.5. The study also underlines the effect of regulation parameters as an important contextual factor influencing low-carbon operations.

Vegetation restoration is an essential approach to re-establish the ecological balance in subalpine areas. Changes in vegetation cover represent, to some extent, vegetation growth trends and are the consequence of a complex of different natural factors and human activities. Microtopography influences vegetation growth by affecting the amount of heat and moisture reaching the ground, a role that is more pronounced in subalpine areas. However, little research is concerned with the characteristics and dynamics of vegetation restoration in different microtopography types. The respective importance of the factors driving vegetation changes in subalpine areas is also not clear yet. We used linear regression and the Hurst exponent to analyze the trends in vegetation restoration and sustainability in different microtopography types since 2000, based on Fractional Vegetation Cover (FVC) and identified potential driving factors of vegetation change and their importance by using Geographical Detector. The results show that: (1) The FVC in the region under study has shown an up-trend since 2000, and the rate of increase is 0.26/year (P = 0.028). It would be going from improvement to degradation, continuous decrease or continuous significant decrease in 47.48% of the region, in the future. (2) The mean FVC is in the following order: lower slope (cool), lower slope, lower slope (warm), valley, upper slope (warm), upper slope, valley (narrow), upper slope (cool), cliff, mountain/divide, peak/ridge (warm), peak/ridge, peak/ridge (cool). The lower slope is the microtopographic type with the best vegetation cover, and ridge peak is the most difficult to be afforested. (3) The main factors affecting vegetation restoration in subalpine areas are aspect, microtopographic type, and soil taxonomy great groups. The interaction between multiple factors has a much stronger effect on vegetation cover than single factors, with the effect of temperatures and aspects having the most significant impact on the vegetation cover changes. Natural factors have a greater impact on vegetation restoration than human factors in the study area. The results of this research can contribute a better understanding of the influence of different drivers on the change of vegetation cover, and provide appropriate references and recommendations for vegetation restoration and sustainable development in typical logging areas in subalpine areas.

This paper proposed a new method combining infrared and visible image, which is devoted to monitor the electrical equipment. The key technologies of infrared and visible image processing include two aspects. On the one hand, it researches on targeting localization based on visible image. We can get the horizontal and vertical adjustments of the refitted cradle head camera through the coordinates of the electrical equipment in the image by means of the strong ability for BP algorithm fitting nonlinear function; on the other hand, the infrared image is processed by qualitative method based on the temperature difference The improved MLP network eliminated the harmful effect of the size of the partial derivative on the weight step. The network received some features as the inputs and classified the conditions of equipment into two categories which are defective and normal. Experimental results show that the positioning error is less than 5 pixels and the classification accuracy rate reaches 84.21%, which means that the method not only improves the measurement efficiency of the infrared detection technology, which is not affected by the limitation of measurement environment.