Firms operating in regions with strict carbon regulations often face foreign competition from exports in regions with laxer regulations. Carbon tariffs, taxes imposed on imported goods, significantly affect these firms' technology choices and production. This study evaluates the efficiency of three prevalent carbon tariffs: the default low/high-pollution tariff and the nondefault tariff. The former is levied based on default green/existing technology, while the latter is based on firms' actual applied technology. We investigate the equilibrium decisions made by domestic and offshore firms regarding technology and production given the carbon tariffs established by the domestic government. The carbon tariffs are evaluated in terms of green technology incentivization, market share retainment, and total greenhouse gas (GHG) emissions reduction. Moreover, social welfare, a composite indicator of concern to policymakers, is considered so that a social maximum can be achieved. Our findings reveal that although the imposition of carbon tariffs incentivizes domestic firms to utilize green technology, it can disincentivize offshore firms from doing so. In terms of market share retainment and total GHG emissions reduction, the default high-pollution tariff performs at least as well as the default low-pollution tariff and the nondefault tariff. Moreover, for policymakers with the objective of social welfare maximization, it is not always optimal to impose carbon tariffs as carbon tariffs can fail to improve social welfare. Further, when the imposition of carbon tariffs improves social welfare, it is the default high-pollution tariff that improves the most.

Carbon Tax and Carbon Cap-and-Trade policies, as the primary carbon pricing instruments, are designed to reduce greenhouse gas emissions. However, inconsistent global regulations for emissions may lead to emissions leakage, as firms strategically relocate production to regions with less stringent controls. This study contributes to existing research by evaluating Carbon Tax and Carbon Cap-and-Trade policies in conjunction with the impending Carbon Border Tax (CBT), designed to mitigate emissions leakage from cross-regional production. Specifically, we examine a firm's equilibrium strategies in investment and production in both domestic and offshore regions under two different carbon pricing instruments while subject to the CBT. The analysis includes a comparative assessment of technology investment, total greenhouse gas emissions, and social welfare. Our findings indicate that the Carbon Tax policy can be associated with lower emission intensity, albeit generating greater total emissions compared to the Carbon Cap-and-Trade policy. Notably, the CBT can serve as an equivalent stimulant for technology investment and a reducer of aggregate emissions under both pricing instruments. Moreover, the Carbon Cap-and-Trade policy proves more beneficial in terms of social welfare when the firm's optimal strategy is confined to either domestic or offshore production. Conversely, the Carbon Tax policy yields higher social welfare in cases of high emission prices when the firm's optimal strategy is cross-regional production. The introduction of the CBT enlarges the set of conditions under which the Carbon Tax policy proves more advantageous for social welfare.

Governments are providing an increasing number of subsidy policies for biomass-based industries. Although one of the main barriers to the development of the biomass industry is the high cost of feedstock, it remains unclear how effective different subsidies are in addressing that barrier. In this paper, we categorize biomass subsidies and explore their effectiveness by developing a biomass feedstock supply model. First, we examined biomass subsidies in China and the United States and found that all biomass subsidies can be grouped into four categories based on the types of costs they seek to mitigate: production and transport subsidies for biomass utilization improvement and product and operating subsidies for specific biomass industry development. Then, we developed a game-theoretic model of the interactions between the government and biorefineries and compared the effectiveness of the subsidies. The results indicate that transport subsidies are more cost-effective at increasing biomass utilization. However, the production subsidy allows for more even use of biomass across different biorefineries. A combination of operating and product subsidies is more cost-effective in enhancing the profitability of specific biomass industries if the industry to be supported is quite unprofitable; otherwise, the product subsidy is better.

Given the rise in consumers' ecological awareness, many firms currently offering only nongreen products are extending their product lines with products applying green technology. We examine a monopolist's product line extension strategy and pricing strategy by explicitly considering the environment-related utility and spillover effects of a green product. The results show that a firm should introduce its green product when the spillover effects and the cost of the green product are small or the spillover effects are large enough. The firm should utilize a penetration pricing strategy for the functionally inferior green product when the environment-related utility is small or when the environment-related utility is moderate and the spillover effects are relatively small. Otherwise, the firm should adopt a skimming pricing strategy. However, the firm should adopt a penetration pricing strategy for the functionally superior green product when the spillover effects are relatively large and a skimming pricing strategy otherwise. Interestingly, the firm's total profit may decrease with the spillover effects when the spillover effects are small.

The high logistics costs of biomass feedstock and the involvement of other firms that use biomass and independent suppliers make feedstock acquisition increasingly difficult for biomass power plants. Governments provide feed-in tariffs (FiT) for biomass power plants to help reduce the negative impacts of high raw material costs. Using a game-theoretic approach, we study the optimal government FiT for biomass power plants and explore the effects of biomass feedstock competition and the presence of independent biomass feedstock suppliers on FiT strategy. Our results show that governments should subsidize biomass power plants whose competitiveness exceeds a certain threshold. The entry of a feedstock competitor into the biomass supply chain raises this threshold, but the involvement of an independent biomass supplier will not. However, the involvement of an independent biomass supplier reduces the efficiency of the FiT. In addition, FiT for biomass power plants should not be provided if government funds are below a certain threshold, as it does not increase social welfare. By comparing FiT with an alternative use of government funds, the technology subsidy, we find that the technology subsidy should be adopted instead of FiT if the government funds are below a threshold.

In a market with a technology provider and two competing manufacturers, we examine the provider's technology introduction strategy and the manufacturers' product rollover strategies. Our main results show that, first, the provider sells the new technology to both manufacturers in the case of small-level technology improvement, and sells to only one of them otherwise. Specifically, under the niche strategy (i.e., the provider sells the new technology to only one manufacturer), the provider sells the new technology to the low-quality manufacturer in the case of moderate-level technology improvement; otherwise, the provider sells to the high-quality manufacturer. Interestingly, the provider's profit may decrease with increased technology improvement. Second, the manufacturers will remove the old version of the product from the market when adopting the new technology, as long as the quality of the new version of the product exceeds a critical threshold. Finally, if the provider has a limited production capacity and can only satisfy the demand of one manufacturer, the provider sells the new technology to the low-quality manufacturer when the technology improvement is moderate; otherwise, the provider sells to the high quality manufacturer. (c) 2021 Elsevier B.V. All rights reserved.

"应用统计学"是经管类学科的基础专业课程,在大数据背景下,传统的"应用统计学"课程已经不能满足大数据分析应用的需求.从大数据时代经管类专业"应用统计学"课程在思维、知识和能力培养的变化上出发,总结了大数据背景下经管类专业"应用统计学"课程的改革需求,探讨了"应用统计学"课程改革思路.结合课程改革实践经验,从教学模式、教学内容、课堂形式、课程考核规则等方面提出了课程改革的具体举措,借此希望能为全国其他高校该类专业课程的改革进程提供参考.

Complex projects, such as product development projects, usually involve myriads of interrelated activities. Identifying an appropriate sequence of these activities is a challenge to project managers because of the existence of rework iterations. Researchers have developed a series of methods to find a schedule that minimizes the first-order rework of interrelated activities using the design structure matrix (DSM). By contrast, this study presents a more accurate approach for determining a sequence that minimizes the high-order rework. First, a new objective function is proposed to describe the first-order and second order rework time (FSRT) of complex projects. Second, a parallel branch-and-prune algorithm and two local search heuristics are proposed, which can be conveniently used to reduce the FSRT. Experimental results show that we can reduce more rework of complex projects through minimizing FSRT than using other objective functions. Finally, the efficiency of the proposed methods is validated using random experiments. (c) 2021 Elsevier Ltd. All rights reserved.

The linear ordering problem (LOP) is an NP-hard combinatorial optimization problem with wide applications. As the problem is computationally intractable, the only practical alternative is to develop efficient heuristics to solve it. Here we present four new properties of block insertion for the LOP, and show that changing the node order in one sub-problem does not affect the objective values of the remaining sub-problems. Based on these properties, we then propose three local search schemes for solving the LOP. Our experimental results show that the block insert with the first strategy often outperforms other local search schemes within the same computational time. To further improve the performance of this local search scheme, we incorporate it into the iterated local search and genetic algorithm frameworks, and develop the block-insertion-based iterated local search (ILSb) and memetic algorithm (MA(b)), respectively. The computational results show that both the ILSb and MA(b) outperform the state-of-the-art meta-heuristics. Moreover, with appropriate parameter settings, the MA(b) frequently outperforms the ILSb. Finally, we design a parallel computing framework, which divides the LOP problem into independent subproblems that are solved in parallel by exact methods. This parallel framework can further improve the solutions derived by MAb or other heuristics. (C) 2019 Elsevier Ltd. All rights reserved.

The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We find that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten treatment time is markedly reduced and the importance of contact quarantine and social distancing increases.

面对需求不确定性,柔性设备可以根据需求的变化为多种产品调配产能,但相比专用设备成本较大且有限产能无法满足所有需求;延迟策略可以通过收集信息准确预测需求提供匹配的产能,但会推迟产品的市场投放时间、缩短销售周期.文章研究了需求不确定下多品种相关产品的柔性设备与延迟策略选择问题,给出相关系数与不确定性的临界值指导决策.结果 表明:相关系数较小且不确定性较大,或者相关系数较大且不确定性中等时,企业选择柔性设备;相关系数较小且不确定性中等,或者相关系数较大且不确定性较大时,企业选择延迟策略;不确定性较小时,企业无措施.

耦合活动的排程直接影响新产品开发的周期和成本,因而受到了学者和研发管理人员的普遍关注.本文针对最小化总反馈长度这一耦合活动排程常用目标,将遗传算法与局部搜索算法相结合,提出了一种新的混合优化算法,并系统分析了参数对算法性能的影响.然后将算法应用到实际案例和大量随机算例中,实验结果表明混合优化算法较大幅度提高了现有局部搜索算法解的质量;同等情形下,混合优化算法所获得解比单纯运用遗传算法所获得解更好.

Planning the sequence of interrelated activities of production and manufacturing systems has become a challenging issue due to the existence of cyclic information flows. This study develops efficient exact algorithms for finding an activity sequence with minimum total feedback length in a design structure matrix. First, we present two new properties of the problem. Second, based on the properties, we develop an efficient Parallel Branch-and-Prune algorithm (PBP). Finally, the proposed PBP is further improved by adopting hash functions representing activity sequences, which is referred as hash function-based PBP. Experimental results indicate that the proposed hash function-based PBP can find optimal solutions for problems up to 25 interrelated activities within 1 h, and outperforms existing methods.

引入技术学习理论,通过建立一个两阶段博弈模型,探讨了政府环境规制下的企业绿色生产决策及技术学习因素对该决策的影响.研究表明,是否考虑技术学习因素会造成政府环境规制下的企业决策差异,进而导致整体绿色生产水平的不同和政府环境规制政策效率上的差别.具体来说,合理的政府环境规制水平能够有效提升整体绿色生产水平;但在不考虑技术学习因素影响的情境下,当期绿色生产对后期利润的正向作用被忽视,造成企业决策上的利润短视,进而导致整体绿色生产不足;而在考虑技术学习因素影响的情境下,企业更偏好于通过增加绿色生产投入追求长期利润最大化.

基于汤森路透的Essential Science Indicators(ESI)数据库,文章探讨了留学经历对我国科研人员产出高被引国际论文的影响,并进一步研究了学科对这种影响的调节作用.主要结论如下:(1)整体而言取得国外博士学位对中国学者发表高被引国际论文有显著的促进作用;(2)出国访问交流对发表高被引国际论文的促进作用不显著;(3)相比理工科,取得国外博士学位对医学和人文学科的学者发表高被引国际论文的促进作用更强.

随着市场竞争的日益加剧,产品生命周期的不断缩短,企业需要快速研发出符合市场需求的新产品,而关键路径上耦合活动的执行顺序是影响产品开发效率的关键因素.本文针对最小化总反馈长度这一耦合活动排序常用目标,提出了新的局部搜索算法.在此基础上进一步将局部搜索算法嵌入到遗传算法中,提出了文化基因算法.随机试验表明,本文提出的局部搜索算法能在更短的时间内求得更优的解.另外,同等情形下,本文的文化基因算法性能也优于现有的文化基因算法.

Market acceptance of new products has a high degree of uncertainty, and traditional investment theo-ries can not apply to investments of new products.For capacity investments of new products, we study how to make capacity investment opportunity and scale decisions of new products with short cycles for a monopoly enter-prise and two competitive enterprises with different costs.There are two investment opportunities as"earlier"and"later"for enterprises to choose,and"earlier"investment is defined when enterprises know the mean and covar-iance of market size for the new product; and"later"investment when enterprises know real market size.A monopoly enterprise can't collect sale information before entering the market,and it will choose"earlier"invest-ment or no investment,therefore we give the condition of choosing"earlier"investment and corresponding opti-mal expected profit.There are four kinds of circumstances for two competitive enterprises with different costs, and we give optimal capacity investment scale decisions and corresponding optimal expected profits respectively. The optimal investment opportunity decision is given by comparing optimal expected profits for these four circum-stances.

PurposeThis study aims to clarify the effect of team effort allocation between knowledge exploration and exploitation on the generation of extremely good or poor innovations. The influence of previous collaborative experience among team members on the effect of team effort allocation is also investigated to understand the relationship between team members’ collaboration networks and knowledge learning.Design/methodology/approachThis study uses data of all patents granted by the US Patent and Trademark Office between 1984 and 2010. The inventors involved in a patent are regarded as members of the focal team. Logistic regression is used to analyze the data.FindingsAllocating greater effort to exploration than to exploitation is beneficial to achieving breakthrough innovations despite the risk of generating particularly poor innovations. This benefit increases with collaborative experience among team members. Placing an equal emphasis on knowledge exploration and exploitation is not particularly effective in achieving breakthrough innovations; it is, however, the best strategy for avoiding particularly poor innovations.Originality/valueThis research not only provides valuable insights for research on innovation and knowledge management by studying the team effort allocation strategy used to achieve breakthroughs and avoid particularly poor innovations but also represents an advancement in bridging two streams of research – knowledge learning and social networks – by highlighting the influence of the team members’ collaborative networks on the effect of team effort allocation between knowledge exploration and exploitation.