Permit markets,carbon prices and the creation of innovation clusters

Innovation clusters combining public and private effort to develop breakthrough technologies promise greater technological advances to slow down climate change. We use a multi-country model with an emission trading system to examine whether and how international climate policy can incentivize countries to create such innovation clusters. We find that a minimal carbon price is needed to attract applied research firms, but countries may nevertheless fail to invest in complementary research infrastructure. We construct a mechanism that leads to innovation clusters when emissions targets are set before uncertainty surrounding technological developments is resolved. It is a combination of low permit endowments for the country with the lowest costs to build the needed infrastructure, compensation for this country by profits from permit trade, and maximal possible permit endowments for the remaining countries. We outline how the EU-ETS can be further refined according to this mechanism.     

The impact of research collaboration on academic performance: An empirical analysis for some European countries

The aim of this paper is to investigate the impact of internal and external research collaborations on the scientific performance of academic institutions. The data are derived from the international SCOPUS database. We consider both quantity (the number of publications) and quality indicators (the field-weighted citation impact and the share of publications in the 10% most-cited articles) to evaluate universities' performance in some European countries (Germany, France, Italy, the UK and Russia). To this end, we develop a non-overlapping generations model to evidence the theoretical idea of research externalities between academic institutions. Moreover, we implement an empirical model to determine the extent to which the impact of internal and external collaborations on universities' performance is sensitive to the geographical dimension of the data.     

Hi-tech start-ups: legitimacy challenges and funding dynamics

Studies on the legitimacy challenges faced by hi-tech start-ups are still scarce, leaving room for research, and policymaking debate. Legitimacy issues may become difficult to face for hi-tech firms in comparison with other start-ups, because they work in an extreme environment where the technological challenges are at the edge of the scientific possibility. The paper proposes a conceptual model to grasp the relevance of three legitimacy drivers of the hi-tech start-ups for investment decisions. We investigated the ability of 30 hi-tech start-up firms to obtain financial resources from the US investors, employing a set-theoretic approach and carried out fuzzy-set Qualitative Comparative Analysis. Our findings clarify that the funding decisions of US investors are driven by the location of headquarter and R&D of hi-tech start-ups and by the educational experience of the founders. Our study has significant theoretical, practical, and policymaking implications.     

Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

跨国公司技术联盟:动因、效应及启示

跨国公司技术联盟是在经济全球化与一体化的国际背景下发展起来的，它被愈来愈多的人认为是一种知识联盟，是一种独立的组织形式。跨国公司进行技术联盟的动因在于：获取与企业核心技术相关的上下游技术、适应网络竞争的需要及促进研究与开发等。跨国公司技术联盟既给合作方带来一定的积极效应，同时也产生相当的负面影响。在当前，跨国公司技术联盟对我国企业进行技术联盟与合作有积极的借鉴意义。     

Researching with whom? Stability and manipulation

This paper explores the existence of stable research teams, when the preferences of each agent depend on the set of researchers who are collaborating. We introduce a property over researchers’ preferences, called top responsiveness, guaranteeing the existence of stable research teams configurations. We also provide a stable mechanism, induced by the so-called top covering algorithm, which is strategy-proof when researchers preferences satisfy top responsiveness. Furthermore, we find that, in this framework, the top covering mechanism is the only strategy-proof mechanism that always selects stable allocations.     

A Cross‐national Comparison of R&D Expenditure Decisions: Tax Incentives and Financial Constraints*

We provide evidence on the impact of tax incentives and financial constraints on corporate R&D expenditure decisions. We contribute to extant research by comparing R&D expenditures in the United States and Canada, thereby exploiting the differences in the two countries' R&D tax credit mechanisms and generally accepted accounting principles. The two tax incentive mechanism designs are consistent with differing views of the degree of financial constraints faced by firms in these economies. Our sample also allows us to explore the effects of capitalizing R&D on Canadian firms. Employing a matched design, we document relations between tax credit incentives and R&D spending consistent with both Canadian and U.S. public companies responding as though they are not financially constrained. We estimate that the Canadian credit system induces, on average, $1.30 of additional R&D spending per dollar of taxes forgone while the U.S. system induces, on average, $2.96 of additional spending. We also find that firms that capitalize R&D costs in Canada spend, on average, 18 percent more on R&D. Collectively, this evidence is important to the ongoing debates in both countries concerning the appropriate design of incentives for R&D and is consistent with the assumptions found in the U.S. tax credit system, but not those found in the Canadian system.     

Definitions and progress of ecosystem health and ecological security

1. Introduction Development of human industrial civilization and predatory exploitation of natural resources have in- duced tremendous changes of ecological environment. In recent years, territorial and global environmental problems have increased, such as greenhouse effect, depletion of ozone, global warming, water scarcity, water pollution, acid rain, decrease of biodiversity, soil erosion desertification etc. These changes seriously intimidate the health and security of ecosystems, and dist…