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Green hydrogen produced from renewable sources such as wind and photovoltaic (PV) power is expected to be pivotal in China's carbon neutrality target by 2060. This study assessed the potential production, levelized costs of hydrogen (LCOH), and the cost structure in diverse mainland Chinese provinces from 2020 to 2060. It considered various combinations of electrolysis technologies, specifically alkaline electrolysis (AE) and proton exchange membrane (PEM), in conjunction with green electricity sources. The analysis considers the technological learning effects of wind power, PV power, AE, and PEM. This study's primary conclusions and policy recommendations are as follows: (1) PV power would be the predominant energy for green hydrogen production in nearly all of mainland China, providing a potential 2.25–28 642.19 kt/yr hydrogen production in different provinces. (2) AE exhibits cost (with LCOH around 3.18–8.74 USD/kg) competitiveness than PEM (with LCOH around 3.33–10.24 USD/kg) for hydrogen production. Thus, policymakers are advised to focus on the PV power combined with the AE pathway for large-scale hydrogen production. PEM is suggested to be mainly used in cases with high power fluctuations and end devices. (3) The provinces (especially Inner Mongolia, Xinjiang, and Gansu Province) in the Northwest of China show the greatest potential (about 74.35%) and have the lowest LCOH (with around 3.18–4.78 USD/kg). However, these provinces are quite distant from existing energy demand hubs. Thus, decision-makers are advised to focus on developing long-distance transmission/transportation infrastructure for either green electricity or green hydrogen. [ABSTRACT FROM AUTHOR]

Recycling plastic can abate the environmental pollution as well as CO2 emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO2 price is modelled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond the current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modelling framework can be easily applied to other sectors with similar characteristics. [ABSTRACT FROM AUTHOR]

Promoting energy efficiency in iron and steel production provides opportunities for mitigating environmental impacts from this energy-intensive industry. Energy efficiency technologies differ in investment costs, fuel-saving potentials, and environmental performance. Hence the decision-making of the adoption strategy needs to prioritize technological combinations concerning these multi-dimensional objectives. To address this problem, this study proposes a hybrid multi-criteria decision-support model for adopting energy efficiency technologies in the iron and steel industry. The modeling framework integrates a linear programming model that determines the optimal technology adoption rates based on the techno-economic, energy, and environmental performance details and an interactive multi-criteria model analysis tool for diverse modeling environments. A real case study was performed in which a total number of 56 energy efficiency technologies were investigated against various criteria concerning economics, energy, and environmental performances. The results examine the tradeoffs and synergies were examined with regard to seven criteria. A balanced solution shows that a total investment of 13.4 billion USD could save 2.51 Exajoule fuel consumption, cut 67.4 million tons (Mton) CO2 emissions, and reduce air pollution of 1.5 Mton SO2, 1.41 Mton NOx, and 0.86 Mton PM, respectively. The case study demonstrates the effectiveness and applicability of the proposed multi-criteria decision-making support framework. [ABSTRACT FROM AUTHOR]

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein–Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables. [ABSTRACT FROM AUTHOR]

Organizations may face the problem of whether and when they should cooperate with others to accomplish a novel task. Prior literature answers this problem based on the transaction costs of cooperation, but ignores the effect of cooperation timing on innovation outcomes. Will postponing cooperation influence innovation outcomes? We develop a conceptual agent-based search and learning model to explore this question. We find that the timing of cooperation, the initial mental representations, and the search strategy jointly determine innovation search outcomes. Agents with initial mental representations that are inclined towards suboptimal outcomes can benefit more from postponing cooperation when the search strategy is exploitative. This study inserts the practical innovation problem into the framework of adaptive learning models and provides a novel perspective from which to understand the timing of cooperation. [ABSTRACT FROM AUTHOR]

Efficient new product concept screening plays an important role in new product development (NPD). Due to the inherent complexity, incompleteness and ambiguity in NPD, fuzzy approaches have been widely developed and used for new product concept screening. Because of the group behaviours (diversity of gate-team members and senior management’s preference), the new product concept screening is typical group decision-making problem. Moreover, it is usually more efficient to conduct the screening evaluation in a gate-team and make a screening decision outside the gate-team by senior management. Therefore, it is necessary to provide a result with a high degree of flexibility and credibility so as to better support the senior management to make a screening decision in NPD. Existing fuzzy approaches neglect these two issues. Towards this end, a three-stage fuzzy group decision-making approach is proposed to assist the senior management perform new product concept screening. A case study is borrowed from the literature to illustrate our proposed approach. Our approach is compared with two existing techniques from theoretical and empirical perspectives. The results show that the proposed approach can capture the group behaviours and present a relatively realistic and informative result which gives the senior management a high degree of flexibility and credibility in new product concept screening. [ABSTRACT FROM PUBLISHER]

Emissions trading schemes have been widely implemented by many countries to enforce the "cap and trade" concept for mitigating CO2 emissions. Thus, the carbon price influences the manufacturing costs in all stages of production, recycling, and disposal. Consideration of the carbon price is especially important for the economic efficiency of the downstream manufacturing sectors, such as in plastic product manufacturing, to substantially reduce their costs through the design and management of networked supply chains, which results in purchasing feedstocks from different technological routes, as well as choosing plants, warehouses and various transportation modes with diverse CO2 emission intensities. Supporting the decision-making in such situations requires the integration of life cycle analysis and networked supply chain management methodologies with an analysis of the carbon-market uncertainties. Such approaches have not been sufficiently quantified in the existing literature. This study presents a stochastic mixed-integer linear programming model developed for polyvinyl chloride pipe manufacturing in China, which is used to evaluate the effects of the life cycle emissions of procurement on the whole supply chain under carbon market uncertainty. Our results illustrate that the carbon market uncertainty would not only significantly influence the carbon-intensive production sectors but also the downstream manufacturing sectors. The five scenarios with carbon price variation exhibit distinctively different choices in procurement and supply chain configurations, as well as in their performances regarding total emissions and associated costs. [ABSTRACT FROM AUTHOR]

The adoption of new technologies with high efficiency and low emissions is of great importance in achieving sustainable development. Most studies of technology adoption have been criticized for idealistically assuming only one global decision agent. In this paper, an optimization model of technology adoption with heterogeneous agents is proposed. These agents have different market shares, and each one attempts to identify the optimal technology adoption for a portion of the entire system. The carbon emission trading mechanism is implemented to reduce carbon emissions. Agents' acceptance of uncertain carbon prices is characterized by calculating their willingness to pay, and a bargaining process is introduced to reasonably allocate the profit. Computational tests are conducted with different market shares and different discounting factors. Numerical results show that implementing the carbon emission trading mechanism is an effective way to promote technology adoption and carbon emission reduction, although it does not certainly lead to less carbon emissions than implementing only a carbon cap. A small gap between agents' market shares and an increase in the seller's discounting factor will lead to more adoption in the entire system. A seller's market may lead to less carbon emissions than implementing only a carbon cap, while a buyer's market may lead to more carbon emissions. Moreover, it is suggested that governments can propose incentive policies to support small companies to develop and maintain carbon prices at a reasonable level to benefit sellers to promote technology adoption. [ABSTRACT FROM AUTHOR]

Technological knowledge can be created via R&D investments, but it can also be eroded through depreciation. Knowing how fast knowledge depreciates is important for various reasons for practitioners and decision makers alike; especially if it comes to questions regarding how to "recharge" knowledge production processes within an ever changing global system. In this study, we use patent citation data to identify technological knowledge depreciation rates by adjusting for exogenous citation inflation and by disentangling the preferential-attachment dynamics of citation growth. Solar photovoltaic (PV) technology is employed as a case study. The rates calculated with our method are comparable to the few available estimates on technology depreciation rates in the PV industry. One of the advantages of the proposed method is that its underlying data are more readily available, and thus more replicable for the study of the knowledge depreciation rates in other relevant technology fields. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer requirements (CRs) into the design requirements (DRs) of a product. Deriving the priority order of DRs from input variables is a crucial step in applying QFD. Due to the inherent vagueness or impreciseness in QFD, the use of fuzzy linguistic variables for prioritising DRs has become more and more important in QFD applications. Existing approaches make use of the associated fuzzy membership functions of linguistic labels based on the fuzzy extension principle. However, an inherent limitation of such fuzzy linguistic approaches is the information loss caused by approximation processes, which eventually implies a lack of precision in the final results. This paper proposes an alternative approach to prioritising engineering DRs in QFD based on the order-based semantics of linguistic information and fuzzy preference relations of linguistic profiles, under random interpretations of customers, design team and CRs. Ultimately, this approach enhances the fuzzy-computation-based models proposed in the previous studies by overcoming the mentioned limitations. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous techniques based on fuzzy computation. [ABSTRACT FROM PUBLISHER]

This article proposes an uncertain target-oriented QFD approach to service standardization-based service design with an application to bank window service, based on a probabilistic interpretation of weighting information. On the one hand, the proposed approach performs computations solely based on the order-based semantics of linguistic labels and comparisons of linguistic profiles, without needing to quantify the qualitative concepts. It can thus guarantee the robustness of QFD and easy of use in practice. On the other hand, the proposed approach sets uncertain targets for customer needs (WHATs) and service standards (HOWs) by competitors' uncertain service performance on WHATs and HOWs, and conducts satisfactory-oriented competitive analysis from the perspective of uncertain target-oriented decision analysis. Moreover, the proposed approach is applied to an empirical case study of window service design based on service standardization in the Shanghai Branch of Bank JT. The results show that the bank should pay more attention to "Service specifications", "Service providing specifications", and "Service evaluation and improvement standards". Industry feedback shows that the results are consistent with service acceptance and provide valuable insights to the service standardization in the bank. Comparisons with existing studies show that our proposed approach is comparable with existing studies. [ABSTRACT FROM AUTHOR]

A lot of researches have shown that geographic proximity and technological proximity influence firms' innovative performance. Little work has been done to explore how technological proximity interacts with geographic proximity. Using the theories of recombinant innovation and economic geography, this study develops and tests a theory of how firms' abilities to reap local benefits and generate innovation are influenced by technological features. Data on technological and geographic proximity of the top 100 firms in China's electronics industry from 1985 to 2012 are used to test the hypotheses. This study shows that, for a firm with high geographic proximity or located in a dense area of R&D, a low technological proximity in the industry facilitates the process of recombinant innovation. [ABSTRACT FROM AUTHOR]

When a nation is making strategies in a technology domain, it makes sense to know where the nation is currently in the domain and to know where other nations are as well. This paper presents a method to position nations’ efforts in a technology domain. The method builds a network of International Patent Classifications (IPCs) based on their co-occurrence in patent applications and applies an index called the Revealed Patent Application Advantage (RPAA), which reflects where nations’ efforts have been spent in a technology domain denoted as the IPC network. We applied the proposed method to position the efforts that have been spent in the electric vehicle (EV) technology domain by mainland China, Germany, Japan, and the United States. [ABSTRACT FROM AUTHOR]

Energy transportation is an important issue for China's electricity system due to the unbalanced distribution of energy sources and electrical demands. The traditional method of energy transportation (e.g., coal transportation) is challenging due to both the long distance of transportation and the large amount of energy that it requires. In recent years, China has been developing ultra-high voltage (UHV) as an alternative way of transmitting energy over a long distance, and many UHV lines are being planned for construction during the 13th Five-Year Plan (FYP) period. This paper develops a system optimization model for exploring the optimal pathway for long-distance energy transportation in China during the 13th FYP period from the perspective of the minimum cost of an energy transportation system. The results show that (a) although UHV transmission will be increasingly adopted in the 13th FYP period, coal transportation will still play an important role; (b) eight UHV lines are suggested to be built and put into use as a priority in the 13th FYP period; (c) the high technology learning effect, electricity demand, and investment costs of power plants would promote the adoption of UHV. Based on these results, some policy suggestions are provided. [ABSTRACT FROM AUTHOR]

Nowadays, with the increasing of aesthetic products, it becomes more and more important and quite difficult for consumers to choose their preferred products, especially the ones whose artistic and aesthetic aspects play a crucial role in consumer purchase decisions. Taking Kansei as one quality aspect of products, consumer-oriented Kansei evaluation focuses on evaluation of existing commercial products based on consumers' Kansei preferences. This paper proposes a stochastic dominance based approach to consumer-oriented Kansei evaluation with multiple priorities. Particularly, given a consumer's preferences, the concept of stochastic dominance is used to build an evaluation function for each Kansei attribute. Then, the importance weights captured by a priority hierarchy of Kansei attributes, together with the fuzzy majority, are incorporated into the aggregation of individual stochastic dominance degrees into an overall one. An application to the hand-painted Kutani cups in Ishikawa, Japan, is conducted to illustrate the effectiveness and efficiency of the proposed approach. It is seen that the proposed approach outperforms the existing research in terms of easy of use and better decision-support to the consumers. [ABSTRACT FROM AUTHOR]

There are occasions when people want to optimize the initial setting of a CAS (complex adaptive system) so that it evolves in a desired direction. A CAS evolves by heterogeneous actors interacting with each other. It is difficult to describe the evolution process with an objective function. Researchers usually attempt to optimize an intervening objective function, which is supposed to help a CAS evolve in a desired direction. This article puts forward an approach to optimize the initial setting of a CAS directly (instead of through an intervening objective function) by nesting agent-based simulations in a genetic algorithm. In the approach, an initial setting of a CAS is treated as a genome, and its fitness is defined by the closeness between the simulation result and the desired evolution. We test the applicability of the proposed approach on the problem of optimizing the layout of initial AFV (alternative fuel vehicle) refueling stations to maximize the diffusion of AFVs. Computation experiments show that the initial setting generated with the approach could better induce the desired evolving result than optimizing an intervening objective function. The idea of the approach can also be applied to other decision making associated with a complex adaptive process. © 2015 Wiley Periodicals, Inc. Complexity 21: 275-290, 2016 [ABSTRACT FROM AUTHOR]

This study looks at the historical reliability of the agent-based model of the global energy system. We present a mathematical framework for the agent-based model calibration and sensitivity analysis based on historical observations. Simulation consistency with the historical record is measured as a distance between two vectors of data points and inference on parameter values is done from the probability distribution of this stochastic estimate. Proposed methodology is applied to the model of the global energy system. Some model properties and limitations followed from calibration results are discussed. [ABSTRACT FROM AUTHOR]

There are a limited number of initial refueling stations for AFVs (alternative-fuel vehicles). With different layouts for these initial refueling stations, potential AFV adopters will have different concerns regarding the availability of refueling facilities, which will result in different starting points for the diffusion of AFVs. This paper explores the issue of optimizing layouts of initial AFV refueling stations to provide a better starting point for the diffusion of AFVs. This paper first puts forward the optimization models in terms of minimizing drivers’ total concern in two situations: in the first situation, the number of initial AFV refueling stations is fixed, and in the second situation, the number of initial AFV refueling stations is flexible within a certain scope. The paper then introduces a genetic algorithm to find nearly optimal solutions for the optimization models. Finally, experiments are conducted with agent-based simulations for the case of Shanghai, which shows that the close-to-optimal layouts generated using the models can promote the diffusion of AFVs. [ABSTRACT FROM AUTHOR]

This study develops a conceptual system optimization model of adoption of a new infrastructure technology with multiple resource sites and multiple demand sites. With the model, this paper analyzes how the distance, spillover effect, demand, initial investment cost, and learning rate influence the adoption of the new infrastructure technology and presents optimization results of the model in different scenarios. The main findings of the study are: from the perspective of system optimization, (1) different distances among different resource-demand pairs will result in different adoption time of a new infrastructure; (2) technological spillover among different resource-demand pairs will accelerate the adoption of a new infrastructure; (3) it is hard to say that higher demand will pull faster adoption of a new infrastructure, and the optimal time of adopting of a new infrastructure is very sensitive to its technological learning rate. [ABSTRACT FROM AUTHOR]

ABSTRACT Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer needs (CNs) into technical attributes (TAs) of a product. It is a crucial step to derive the prioritization of TAs from CNs in QFD. However, it is not so straightforward to prioritize TAs due to two types of uncertainties: human subjective perception and user variability. The main focus of this article is to propose a group decision-making approach to uncertain QFD with an application to a flexible manufacturing system design. The proposed approach performs computations solely based on the order-based semantics of linguistic labels to eliminate the burden of quantifying qualitative concepts in QFD. Moreover, it incorporates the importance weights of users and the concept of fuzzy majority into aggregations of individual fuzzy preference relations of different TAs in order to model the group behaviors in QFD. Finally, based on a quantifier-guided net flow score procedure, the proposed approach derives a priority ranking with a classification of TAs into important and unimportant ones so as to provide a better decision-support to the decision-maker. Due to the easiness in articulating preferential information, our approach can reduce the cognitive burden of QFD planning team and give a practical convenience in the process of QFD planning. [ABSTRACT FROM AUTHOR]

Existing agent-based models of co-diffusion of alternative fuel vehicles and their corresponding filling stations are very stylized. In addition, there is a lack of methodologies for linking such models with a real social and economic background. Aiming at solving this problem, this paper puts forward a method that uses widely available social, economic, and spatial data to generate driver agents’ commute trips, which play an important role in such models. We tested the method with the data of Shanghai and Beijing, two of the largest cities in China, and found the commute times resulting from the method were in accordance with survey results, which validates the potential usefulness of the method. [ABSTRACT FROM PUBLISHER]

Purpose – Diffusion of a new technology and its required infrastructure is a complex process involving various adaptive decision makers interacting with each other, and this is extremely true in diffusion of alternative fuel vehicles. The purpose of this paper is to explore the dynamics of alternative fuel vehicles/infrastructure diffusion through a case study about the diffusion of NGVs (natural gas vehicles) in Shanghai, China. Design/methodology/approach – This paper first provides a case study of the adoption of NGVs in Shanghai; then the major infrastructure and economic indicators, obstacles in the market penetration process are analyzed. Finally, an agent-based model is established to simulate the diffusion of NGVs in Shanghai. Findings – The findings show that several factors limited the adoption of NGV in Shanghai. The initial distribution of refueling stations can be critical to determining whether the technology/infrastructure system survives to become mature and successful. Therefore, the strong support offered to consumers and equipment suppliers from government is important for the market penetration. These findings are also applicable to other alternative fuel vehicles such as hydrogen. Originality/value – Little real-world observations have been carried out on the dynamics of alternative fuel vehicles/infrastructure. The paper can enrich people's understanding about the dynamics of diffusion of alternative fuel vehicles/infrastructure and gives policy implications for initializing and keeping a successful demonstration project. [ABSTRACT FROM AUTHOR]

The relationship between tacit knowledge and informal networks has not been fully explored. Tacit knowledge cannot be well managed directly, because it is deeply embedded in individual actions and experiences. This study proposes an approach to managing informal networks as a new middle way to facilitate the sharing and transferring of tacit knowledge, owing to the fact that tacit knowledge is mostly shared and transferred through informal networks. To support the idea of the approach, an empirical case study of applying the approach to management of a large scientific project is also included. Our findings suggest that informal networks play essential roles for management of tacit knowledge, and thus good management of informal networks can lead to efficient sharing and transferring of tacit knowledge. [ABSTRACT FROM AUTHOR]

To date, optimization models of endogenous technological change commonly deal with uncertainty in technological learning with risk-factor methods, i.e., by adding expected risk costs resulting from overestimating technological learning rates into an objective function with a subjective risk factor. This paper argues that another way of coping with the uncertainty, risk-constrained methods that have been ignored in existing literatures, could be more practicable (at least as a supplement) for decision support. With a simplified model, this paper explores technology development paths generated by two risk-constrained methods, and compares the two risk-constrained methods with a risk-factor method. Our study shows that comparing with the risk-factor method, the two risk-constrained methods also generate an S-shaped technology diffusion pattern, which accords with historical observations, and they can result in earlier as well as later adoption of an advanced but currently expensive technology, depending on different combinations of uncertainty levels of the technology learning rate and the upper limit on the expected risk cost. Another finding of our research is that two totally different technology development paths can both be optimal solutions, which implies that with early policy interventions there is the possibility that an economy could be led to a low-carbon economy with little additional cost. [ABSTRACT FROM AUTHOR]

Existing techno-economic analyses of modern coal chemical technologies (MCCTs) neglect water constraints, which may underestimate the production cost of MCCTs and thus mislead investment activities. Considering this background, this study incorporated water scarcity and indirect water cost into a classic techno-economic evaluation model of MCCT. Using this model, our work evaluated and compared the techno-economic indicators with the latest data for four typical MCCTs, including coal-to-liquids (CTL), synthetic natural gas (SNG), coal-to-olefin (CTO), and coal-to-ethylene glycol (CTEG). The results demonstrate the following: 1) The production costs of CTL, SNG, CTO, and CTEG are 5185 CNY/t, 2653 CNY/kNm3, 5918 CNY/t, 4055 CNY/t, respectively. Under the current prices of oil-related products, investment in SNG and CTEG would be risky, investment in CTL should be considered cautiously, and investment in CTO could lead to a profit. 2) Under the current market price of water resources, which does not consider the water constraint of MCCTs, the production cost would be underestimated by at most 12.4% for CTL, 10.6% for SNG, 27.5% for CTO, and 32.4% for CTEG. The sensitivity of the results to some key parameters and investment recommendations considering profitability, capital investment, material consumption, water constraints, and CO 2 emissions are also discussed and provided. • We evaluate the techno-economic performance of major modern coal chemical technologies under water constraints. • We analysis the influence of water scarcity and water-related cost on modern coal chemical technologies' production costs. • Considering water constraints would largely increase the production costs of modern coal chemical technologies. • Investment recommendations and policy implications for modern coal chemical technologies are provided. [ABSTRACT FROM AUTHOR]

Roadmapping, originated from industry as a strategic planning tool, is attracting increasing applications in academy. Based on the recognition that roadmapping is a knowledge creation process, this paper analyzes what kind of support is needed or helpful for establishing a creative environment for roadmapping in academy and reviews various types of such support from the perspective of i-system methodology. As case studies of such support, this paper further introduces roadmapping pratices in Janpan Advanced Institute of Science and Technology. [ABSTRACT FROM AUTHOR]

As an interdisciplinary research field emerging recently, Knowledge Management (KM) has been given many different definitions. This paper introduces two studies we carried out to provide a holistic and better understanding of KM. By applying the methodology of domain analysis to investigate leading peer-reviewed journals regarding KM, the first study explores six fundamental issues regarding KM, which are: why is KM necessary; what enables the birth of KM and triggers actions on KM; what does KM deal with; how to implement KM; how to support KM by information technology; and where has KM been applied. By building an ontology structure of research topics within the community of the Graduate School of Knowledge Science at Japan Advanced Institute of Science and Technology (JAIST), the second study examines KM within a more general disciplinary called Knowledge Science, which gives a description of how KM is related to other research topics. [ABSTRACT FROM AUTHOR]

This paper argues that agent-based simulation can be used as a way for testing Kansei Engineering methods which deal with the human reaction from sensory to mental state, that is, sensitivity, sense, sensibility, feeling, esthetics, emotion affection and intuition. A new fuzzy linear quantification method is tested in an artificial world by agent-based modeling and simulations, and the performance of the fuzzy linear method is compared with that of a genetic algorithm. The simulations can expand people’s imagination and enhance people’s intuition that the new fuzzy linear quantification method is effective. [ABSTRACT FROM AUTHOR]

Motorola Inc. first introduced the concept of a ‘roadmap’ in the 1970s as a kind of strategic planning tool. This paper argues that roadmapping can also be a useful way to manage knowledge in academia and to support scientific research. By applying the principles of interactive planning methodology, this paper puts forward a new methodology for making personal academic research roadmaps as a way of knowledge management in academia. This methodology is composed of six phases: forming groups, explanation from knowledge coordinators, description of present situation, current status and idealized design reports from every member, research and study schedules, and implementation and control. After introducing the approach, a case study describing the application of the methodology at the Japan Advanced Institute of Science and Technology is given. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

China's fast growing economy is inducing various technology dynamics. Much attention and resources are being paid to new energy technologies in China. These eight selected papers are a collection of academic studies on modeling and policy analysis of the diffusion of new energy technologies in China. These studies were all presented during the First International Workshop on 'Diffusion of New Energy Technologies in China,' held from 25-26 March 2011, in Shanghai, China. [ABSTRACT FROM AUTHOR]

Although China is doing much on developing alternative energies, coal will still be the main energy resource for China in the near to medium term. Developing a green coal-electricity system in China is very important for reduction of carbon emissions and local air pollutants, while satisfying China's rapidly growing electricity demand. This paper first reviews the advancement in optimization models with endogenous technological change and spatial energy systems modeling. Then, present situation and technology strategy of coal-electricity industry in China are analyzed. Three interweaved issues are identified for the development of China's green coal-electricity system. It can be concluded that the future integration of spatial data with MESSAGE model is vital for the analysis of China's coal-electricity system. It will be helpful to study optimal spatial configuration and technology development strategies for the development of a green coal-electricity system in China. [ABSTRACT FROM AUTHOR]