Le Tao, Yun Su and Xiuqi Fang
The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future…
Abstract
Purpose
The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future scenario for carbon emissions. This paper aims to obtain the fine spatial pattern of carbon emissions in 2030, identify hot spots and analyze changes of carbon emissions with a spatial grid method.
Design/methodology/approach
Based on the integrated quantified INDCs of each economy in 2030, the authors predict the population density pattern in 2030 by using the statistics of current population density, natural growth rates and differences in population growth resulting from urbanization within countries. Then the authors regard population density as a comprehensive socioeconomic indicator for the top-bottom allocation of the INDC data to a 0.1° × 0.1° grid. Then, the grid spatial pattern of carbon emissions in 2030 is compared with that in 2016.
Findings
Under the unconditional and conditional scenarios, the global carbon emission grid values in 2030 will be within [0, 59,200.911] ktCO2 and [0, 51,800.942] ktCO2, respectively; eastern China, northern India, Western Europe and North America will continue to be the major emitters; grid carbon emissions will increase in most parts of the world compared to 2016, especially in densely populated areas.
Originality/value
While many studies have explored the overall global carbon emissions or warming under the INDC scenario, attention to spatial details is also required to help us make better emissions attributions and policy decisions from the perspective of the grid unit rather than the administrative unit.
Details
Keywords
Jiahua Jin, Tingting Zhang and Xiangbin Yan
Online Q&A communities have been widely highlighted as an important knowledge exchange market. Although motivations for users’ initial knowledge-seeking behavior have been widely…
Abstract
Purpose
Online Q&A communities have been widely highlighted as an important knowledge exchange market. Although motivations for users’ initial knowledge-seeking behavior have been widely investigated, the factors that affect online Q&A users’ continued knowledge-seeking behavior are still vague. This study aims to investigate the factors that affect users continuously seeking knowledge from online social Q&A communities.
Design/methodology/approach
Based on social information processing theory, social capital theory, social exchange theory and social cognitive theory, this study used a negative binomial regression model to explore what would affect people’s continued knowledge-seeking behavior. Empirical data was collected from a popular Chinese online social Q&A community.
Findings
The results indicate that while previous knowledge sharing behavior, peer responses for previous seeking behavior, identity-based trust have a positive impact on knowledge-seeking behaviors, social exposure has a negative impact. In addition, self-presentation negatively moderates the relationship between social exposure and knowledge-seeking behavior.
Originality/value
This study contributed to the theoretical basis for knowledge-seeking behavior in online Q&A communities. The research findings can be used to derive guidelines for the development and operation of online social Q&A communities.
Details
Keywords
Aurojyoti Prusty and Amirtham Rajagopal
This study implements the fourth-order phase field method (PFM) for modeling fracture in brittle materials. The weak form of the fourth-order PFM requires C1 basis functions for…
Abstract
Purpose
This study implements the fourth-order phase field method (PFM) for modeling fracture in brittle materials. The weak form of the fourth-order PFM requires C1 basis functions for the crack evolution scalar field in a finite element framework. To address this, non-Sibsonian type shape functions that are nonpolynomial types based on distance measures, are used in the context of natural neighbor shape functions. The capability and efficiency of this method are studied for modeling cracks.
Design/methodology/approach
The weak form of the fourth-order PFM is derived from two governing equations for finite element modeling. C0 non-Sibsonian shape functions are derived using distance measures on a generalized quad element. Then these shape functions are degree elevated with Bernstein-Bezier (BB) patch to get higher-order continuity (C1) in the shape function. The quad element is divided into several background triangular elements to apply the Gauss-quadrature rule for numerical integration. Both fourth-order and second-order PFMs are implemented in a finite element framework. The efficiency of the interpolation function is studied in terms of convergence and accuracy for capturing crack topology in the fourth-order PFM.
Findings
It is observed that fourth-order PFM has higher accuracy and convergence than second-order PFM using non-Sibsonian type interpolants. The former predicts higher failure loads and failure displacements compared to the second-order model due to the addition of higher-order terms in the energy equation. The fracture pattern is realistic when only the tensile part of the strain energy is taken for fracture evolution. The fracture pattern is also observed in the compressive region when both tensile and compressive energy for crack evolution are taken into account, which is unrealistic. Length scale has a certain specific effect on the failure load of the specimen.
Originality/value
Fourth-order PFM is implemented using C1 non-Sibsonian type of shape functions. The derivation and implementation are carried out for both the second-order and fourth-order PFM. The length scale effect on both models is shown. The better accuracy and convergence rate of the fourth-order PFM over second-order PFM are studied using the current approach. The critical difference between the isotropic phase field and the hybrid phase field approach is also presented to showcase the importance of strain energy decomposition in PFM.
Details
Keywords
Gunjan Malhotra and Manjeet Kharub
Artificial intelligence (AI) usage improves e-commerce logistics efficiency. However, many actors can play significant roles, such as supply chain consistency (SCC), last-mile…
Abstract
Purpose
Artificial intelligence (AI) usage improves e-commerce logistics efficiency. However, many actors can play significant roles, such as supply chain consistency (SCC), last-mile logistics (LML) performance and collaboration and coordination among logistics firms. This study aims to assess how SCC and LML performance mediate and collaboration and coordination moderate the relationship between AI usage and logistics efficiency.
Design/methodology/approach
A structured questionnaire was used to collect the data. A total of 245 valid responses were received from Indian e-commerce businesses. The data were then analysed using AMOS v25 and structural equational modelling using SPSS for regression, PROCESS macro for mediation and moderated mediation analysis.
Findings
The findings show that AI usage independently impacts logistics efficiency, with SCC and last-mile delivery performance as mediating variables. Collaboration and coordination among logistic firms are also critical moderators in enhancing AI’s efficacy in logistic operations. The study findings suggest the integration of AI into logistic operations and provide implications to managers on the urgency of fostering a collaborative and synchronised environment to utilise the full potential of AI in e-commerce businesses.
Originality/value
This study not only contributes to the field of logistics theory by presenting empirical data on the various ramifications of AI but also offers practical guidance for logistics firms, particularly those operating in developing economies, on how to strategically employ AI to enhance operational efficiency and attain a competitive advantage in the era of e-commerce logistics in the digital age.
Details
Keywords
This study aims to assess the impact of functional green advertising receptivity and emotional green advertising receptivity on consumers' green purchase intention. The authors…
Abstract
Purpose
This study aims to assess the impact of functional green advertising receptivity and emotional green advertising receptivity on consumers' green purchase intention. The authors then examine the mediating role of perceived competence and perceived warmth. Furthermore, the authors explore the moderating effect of power distance belief (PDB) on the relationships between green advertising receptivity with different appeals and consumers' perceived competence and perceived warmth respectively.
Design/methodology/approach
Based on the online survey platform, a total of 468 responses were obtained in China from January to March 2022. 408 valid replies were collected and analyzed in this study. The research hypotheses were empirically verified with bootstrap approach.
Findings
The empirical results indicate that both functional green advertisi\ng receptivity and emotional green advertising receptivity are significantly positively correlated with green purchase intention, and perceived competence and perceived warmth play the mediating role. Besides, PDB significantly strengthens the relationship between functional green advertising receptivity and perceived competence, but weakens the incentive effect of emotional green advertising receptivity on perceived warmth.
Originality/value
The psychological mechanism of the receptivity of green advertising with different appeals affecting green purchase intention remains unclear. This is one of the first studies to uncover how functional green advertising receptivity and emotional green advertising receptivity influence green purchase intention. Besides, the impact of PDB on the formation process of consumer perception is also a black box. By clarifying and comparing the moderating role of PDB on the relationships between advertising receptivity with different appeals and consumers' perceived competence and perceived warmth, this study contributes to the research on the effectiveness of green advertising.
Details
Keywords
Examines the sixteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects…
Abstract
Examines the sixteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.
Details
Keywords
Jiang Qin and Björn Albin
Due to social transformation in China, more than 100,000,000 people are migrating within the country. Many parents are forced to leave their children behind when they migrate. In…
Abstract
Due to social transformation in China, more than 100,000,000 people are migrating within the country. Many parents are forced to leave their children behind when they migrate. In 2008, 58,000,000 children were living as left‐behind children, mainly in the rural parts of China (Zhang, 2009).Migration and its accompanying stressors may affect the mental health of the left‐behind children. This unique literature review of Chinese literature summarises the present state of knowledge and reviews the influential factors. Possible approaches to intervention and system reforms are discussed.A literature review was performed of published studies between 2001 and 2008. Databases used were Fujian Medical University Library Interface, Chinese National Knowledge Infrastructure, Wanfang Data, and VIP Information. The Chinese word for ‘left‐behind’ was used as a key word. Books, book chapters, monographs and studies on caring were searched electronically and by hand. Altogether, 53 items were found, discussed and grouped together. Migration affected the mental health of the left‐behind children in a passive way, especially their emotions and social behaviour.There is still controversy over how serious mental health problems are among children who have been left behind. Life events, personality, coping strategies and social suppor t can be regarded as four main factors that are predictive of mental health, which provides theoretical guidance for intervention. Suppor t and prevention of mental health problems in schools, in families and in primary care should be developed and studied.
Details
Keywords
Tao (Tony) Gao and Talin E. Sarraf
This paper explores the major factors influencing multinational companies’ (MNCs) propensity to change the level of resource commitments during financial crises in emerging…
Abstract
This paper explores the major factors influencing multinational companies’ (MNCs) propensity to change the level of resource commitments during financial crises in emerging markets. Favorable changes in the host government policies, market demand, firm strategy, and infrastructural conditions are hypothesized to influence the MNCs’ decision to increase resource commitments during a crisis. The hypotheses are tested with data collected in a survey of 82 MNCs during the recent Argentine financial crisis (late 2002). While all the above variables are considered by the respondents as generally important reasons for increasing resource commitments during a crisis, only favorable changes in government policies significantly influence MNCs’ decisions to change the level of resource commitments during the Argentine financial crisis. The research, managerial implications, and policy‐making implications are discussed.
Details
Keywords
A collection of essays by a social economist seeking to balanceeconomics as a science of means with the values deemed necessary toman′s finding the good life and society enduring…
Abstract
A collection of essays by a social economist seeking to balance economics as a science of means with the values deemed necessary to man′s finding the good life and society enduring as a civilized instrumentality. Looks for authority to great men of the past and to today′s moral philosopher: man is an ethical animal. The 13 essays are: 1. Evolutionary Economics: The End of It All? which challenges the view that Darwinism destroyed belief in a universe of purpose and design; 2. Schmoller′s Political Economy: Its Psychic, Moral and Legal Foundations, which centres on the belief that time‐honoured ethical values prevail in an economy formed by ties of common sentiment, ideas, customs and laws; 3. Adam Smith by Gustav von Schmoller – Schmoller rejects Smith′s natural law and sees him as simply spreading the message of Calvinism; 4. Pierre‐Joseph Proudhon, Socialist – Karl Marx, Communist: A Comparison; 5. Marxism and the Instauration of Man, which raises the question for Marx: is the flowering of the new man in Communist society the ultimate end to the dialectical movement of history?; 6. Ethical Progress and Economic Growth in Western Civilization; 7. Ethical Principles in American Society: An Appraisal; 8. The Ugent Need for a Consensus on Moral Values, which focuses on the real dangers inherent in there being no consensus on moral values; 9. Human Resources and the Good Society – man is not to be treated as an economic resource; man′s moral and material wellbeing is the goal; 10. The Social Economist on the Modern Dilemma: Ethical Dwarfs and Nuclear Giants, which argues that it is imperative to distinguish good from evil and to act accordingly: existentialism, situation ethics and evolutionary ethics savour of nihilism; 11. Ethical Principles: The Economist′s Quandary, which is the difficulty of balancing the claims of disinterested science and of the urge to better the human condition; 12. The Role of Government in the Advancement of Cultural Values, which discusses censorship and the funding of art against the background of the US Helms Amendment; 13. Man at the Crossroads draws earlier themes together; the author makes the case for rejecting determinism and the “operant conditioning” of the Skinner school in favour of the moral progress of autonomous man through adherence to traditional ethical values.
Details
Keywords
Bin Wang, Huifeng Li, Le Tong, Qian Zhang, Sulei Zhu and Tao Yang
This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for…
Abstract
Purpose
This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.
Design/methodology/approach
The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.
Findings
The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.
Originality/value
The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.