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Establishing a fair and reasonable initial allocation scheme for carbon emission rights is a prerequisite for the efficient and stable operation of the carbon market and an important guarantee for China to achieve its emission reduction target. This study considers the historical transfer of carbon emissions and measures the cumulative net carbon emissions of China's provinces based on the principle of consumer responsibility. It draws up an initial allocation plan for carbon emission rights in China's provinces in 2020, based on the principles of equity, efficiency, and sustainability. The carbon emission and carbon intensity efficiencies of the provinces under this distribution scheme were also evaluated. The results show that the historical net carbon export area mainly consists of energy-intensive provinces and economically underdeveloped poor and remote provinces. Additionally, the historical net carbon import area mainly consists of energy-starved and economically developed provinces. Provinces with high-value ecological services (Xinjiang, Heilongjiang, and Inner Mongolia) or with large economies and dense populations (Shandong, Guangdong, and Sichuan) will receive higher carbon emission rights, while those with developed economies (Shanghai, Beijing, and Tianjin) will receive lower carbon emission rights. Provinces with a good ecological environment (Inner Mongolia, Heilongjiang, and Xinjiang) as well as certain economically underdeveloped western provinces (Chongqing, Sichuan, and Yunnan) will have abundant carbon space in the future. Provinces with rapid economic development (Beijing, Shanghai, and Jiangsu) and resource-based provinces (Shanxi and Liaoning) with a high historical carbon output have little carbon space. Considering the historical carbon transfer, the carbon emission rights allocation system will benefit resource-based industrial provinces (Shanxi, Inner Mongolia, and Liaoning) and less developed western provinces (Shaanxi, Ningxia, and Xinjiang) in improving their carbon emission efficiency. This study provides a new research idea for formulating a more equitable and efficient allocation scheme for carbon emission rights.

When considering a complete ecological assessment of logistics activities and the development of comprehensive decarbonization strategies, it's important to address not only transportation but the warehousing and transshipment processes as well. In national inventories, the total energy demand of warehousing can equal around one quarter of the transport emissions. The issue of energy-efficient processes at logistics facilities gains importance because of changing regulations and market requirements (e.g. the new European Energy Efficiency Directive which prioritizes the energy demand of buildings). Furthermore, logistics companies tend to realize energetic and/or ecological improvements at logistics facilities and report savings achieved.As opposed to logistics facilities, the ecological assessment of transportation and the derivation of appropriate environmental performance indicators (EPI) are widely standardized (cf. EN 16258). How to assess, monitor and manage greenhouse gas emissions (GHG) at warehouses or terminals are still major research and standardization issues. That is why the Fraunhofer Institute for Material Flow and Logistics has defined a comprehensive assessment approach for logistics facilities, calculated specific emission factors and developed a flexible yet detailed allocation scheme for different logistics services (e.g. refrigerated warehousing). This article addresses the developed method and provides an argumentative basis for further standardization. This will be realized by the presentation of a classification scheme for logistics facilities based on ecological aspects and the consideration of attributes of logistics items in the discussion about how GHG emissions of logistics facilities are usually influenced. To illustrate strengths and weaknesses of the method, exemplary implementations at facilities of the research partner, DB Schenker, will be published. Focus will be on the emissions of a cross-docking center, a warehouse and a spare parts depot.The provided assessment method for GHG emissions of logistics facilities and the allocation scheme for EPIs enable logistics companies to monitor and manage the emissions of goods storage and transshipments on a continual basis. Consequently, the method can trigger the development of decarbonization strategies at logistics facilities and foster an organizational or technological improvement process.

Recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarker's longitudinal evolution in each of the groups and can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis and show that this can decrease the number of false positives in a prognostic test, improving the positive predictive value. We also establish that by leaving some patients unclassified for a certain period, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patient's status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods.

The Hopfield neural network (HNN) has been widely used in numerous different optimization problems since the early 1980s. The convergence speed of the HNN (already in high gain) eventually plays a critical role in various real-time applications. In this brief, we propose and analyze a generalized HNN which drastically improves the convergence speed of the network, and thus allows benefiting from the HNN capabilities in solving the optimization problems in real time. By examining the channel allocation optimization problem in cellular radio systems, which is NP-complete and in which fast solution is necessary due to time-varying link gains, as well as the associative memory problem, computer simulations confirm the dramatic improvement in convergence speed at the expense of using a second nonlinear function in the proposed network.