This paper analyzes the causal relationship between trade policy uncertainty, government subsidies, and the role of political connections using annual data on Chinese energy firms from 2003 to 2018. The results show that when trade policy uncertainty increases, the Chinese government tends to increase subsidies for energy firms, and firms with political ties may receive more subsidies. Furthermore, firms with poor sales performance and in less marketized regions will be granted even more subsidies when uncertainty rises and firms are politically connected. We also observe that subsidies promote firms' fixed asset investments and innovations but decrease their overall investment efficiency.

Coal is one of the main fuel sources in China. This paper sheds light on the evolution of China's interregional differences in CO2 emissions from coal by constructing a Gini coefficient and decoupling elasticity index for emissions from 1997 to 2012 and explains why emission differences deviate from economic growth differences. The study decomposed the Gini coefficient of CO2 emissions from coal by source, incremental source, and region. It also divided the decoupling elasticity of carbon emissions into two components: effects of environmental expenditure and effects of emission reduction policy. The findings of the study are as follows: First, interregional differences in China's overall CO2 emissions from coal are characterized by periodic fluctuation. Second, the differences in emissions from raw coal, the concentration effect of emissions, and the emission differences within regions are the three main factors in the overall difference changes in coal's carbon emissions in China. Last but not least, the decoupling between provincial CO2 emissions from coal and economic growth is on the whole weak. Based on the above findings, the author offers four suggestions for emission reduction.

This paper intends to demonstrate how open innovation systems could be developed by tackling the challenging knowledge management problems that are encountered when aiming at involving very large audiences. This is the case when generalizing open innovation approach beyond companies to a wider societal context like in the case of national innovation systems. The Open Innovation Banking System (OIBS) project, funded by the European Social Fund (ESF) and the participating higher education institutions in Finland, is used as a basis for our discussion. It specifically aims at bringing the largely underutilized creativity of students and senior citizens to play. Among several technologies to develop OIBS, mashups as hybrid web applications can play an important role in such constantly evolving system and contents. However, relying only on unstructured text inputs, the services of textual content sharing for OIBS would require intelligent text processing that far exceeds the capability of such applications. In this paper, we propose an "idealet"-centric solution for representing the data submitted by users, enabling concise description, refinement and linking of ideas as input for innovation processes. An idealet is defined as the core knowledge about an innovative idea. The relationships among idealets and essays can be represented in a semantic network in terms of their relationships. This scheme allows the mashup applications for OIBS to more effectively retrieve, process, extract, and deliver the most important knowledge from an ocean of information contributed by participating information composer, reviewers, and users. The paper also discusses how the idealet-centric approach can be employed for a functional open innovation system.

While navigation within complex information spaces is uneasy for all users, it is extremely difficult for visually impaired users who can not simply searching and browsing digital contents with a mouse. These users have to listen line by line using a screen reader program, which may be particularly inefficient in a large documents with complex structures and loose connections of relevant information that are hard to search and navigate. Consequently, they are especially penalized when the information being searched is hidden deeply. In this article, we introduce a partition-annotate-recommend (PAR) paradigm to improve the accessibility of digital libraries for visually impaired users. Our evaluation, involving the participation of visually impaired users, show that the RAIN system, built based on the PAR paradigm, reduces the navigational overhead significantly and enables visually impaired users to access complex digital libraries effectively.

Collaborative filtering recommendation is more successful personalized recommendation algorithm.However,the data sparsity decreases the performance of recommendation.The two-phase clustering-based collaborative filtering algorithm is proposed.The algorithm not only reduces the sparsity of data,but also improves the accuracy of the nearest neighbor and the recommendation accuracy.And it reduces the time complexity.