Myopic Versus Farsighted Behaviors in a Low-Carbon Supply Chain with Reference Emission Effects

The increased carbon emissions cause relatively climate deterioration and attract more attention of governments, consumers, and enterprises to the low-carbon manufacturing. This paper considers a dynamic supply chain, which is composed of a manufacturer and a retailer, in the presence of the cap-and-trade regulation and the consumers’ reference emission effects. To investigate the manufacturer’s behavior choice and its impacts on the emission reduction and pricing strategies together with the profits of both the channel members, we develop a Stackelberg differential game model in which the manufacturer acts in both myopic and farsighted manners. By comparing the equilibrium strategies, it can be found that the farsighted manufacturer always prefers to keep a lower level of emission reduction. When the emission permit price is relatively high, the wholesale/retail price is lower if the manufacturer is myopic and hence benefits consumers. In addition, there exists a dilemma that the manufacturer is willing to act in a farsighted manner but the retailer looks forward to a partnership with the myopic manufacturer. For a relatively high price of emission permit, adopting myopic strategies results in a better performance of the whole supply chain

Potential applications of randomised graph sampling to invasive species surveillance and monitoring.

Abstract Many invasive plants and animals disperse preferentially through linear networks in the landscape, including road networks, riparian corridors, and power transmission lines. Unless the network of interest is small, or the budget for surveillance is large, it may be necessary to draw inferences from a sample rather than a complete census on the network. Desired features of a surveillance system to detect and quantify invasion include: (1) the ability to make unbiased statements about the spatial extent of invasion, the abundance of the invading organism, and the degree of impact; (2) the ability to quantify the uncertainty associated with those statements; (3) the ability to sample by moving within the network in a reasonable fashion, and with little wasted non-measurement time; and (4) the ability to incorporate auxiliary information (such as remotely sensed data, ecological models, or expert opinion) to direct sampling where it will be most fruitful. Randomised graph sampling (RGS) has all of these attributes. The network of interest (such as a road network) is recomposed into a graph, consisting of vertices (such as road intersections) and edges (such as road segments connecting nodes). The vertices and edges are used to construct paths representing reasonable sampling routes through the network; these paths are then sampled, potentially with unequal probability. Randomised graph sampling is unbiased, and the incorporation of auxiliary information can dramatically reduce sample variances. We illustrate RGS using simplified examples, and a survey of Polygonum cuspidatum (Siebold & Zucc.) within a high-priority conservation region in southern Maine, USA