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Financial market sustainability is gaining attention as investors and stakeholders become more aware of environmental, social and governance issues, pushing demand for responsible and ethical investment practices. Therefore, this study aims to investigate the impact of carbon (CO2) emissions from three sources, oil, gas and coal, on the stock market sustainability via effective government policies.

The eight countries belong to two different regions of world: Asian economies such as Pakistan, India, Malaysia and China, and OECD economies such as Germany, France, the UK and the USA are selected as a sample of the study. The 22-year data from 2000 to 2022 are collected from the DataStream and the World Bank data portal for the specified countries. The generalized methods of movement (GMM) and wavelet are used as the econometric tool for the analysis.

Our findings show that the CO2 emission from coal and gas significantly negatively impacts stock market sustainability, but CO2 emission from oil positively impacts stock market sustainability. Moreover, all the emerging Asian economies’ CO2 emissions from coal and gas have a much greater significant negative impact on the stock market sustainability than the OECD countries due to the critical situation. However, the government’s effective policies have a positive significant moderating impact between them, reducing the effect of CO2 emission on the stock market.

This study advocated strong implications for policymakers, governments and investors.

Effective government policies can protect the environment and make business operations suitable, leading to market financial stability. This study advocated strong implications for policymakers, governments and investors.

This study provides fresh evidence of the government’s effective role to control the carbon environment that provide the sustainability to the organizations with respect to OECD and emerging economy.

This study aims to address these challenges by enhancing the resistance of Ag-based pastes to corrosion and sulfurization, thereby improving their performance and weatherability in high-power and high-frequency electronic applications.

This study investigates the influence of Sn doping in W-doped Ag paste to enhance resistance against electrochemical corrosion and sulfurization. A systematic examination was conducted using transient liquid phase sintering and solid–liquid inter-diffusion techniques to understand the microstructural and electrochemical properties.

This study found that Sn addition in W-doped Ag paste significantly improves its resistance to electrochemical corrosion and sulfurization. The sintering process at 600°C led to the formation of an Ag₂WO₄ phase at the grain boundaries, which, along with the presence of Sn, effectively inhibited the growth of Ag₂WO₄ grains. The 0.5% Sn-doped samples exhibited optimal anti-corrosion properties, demonstrating a longer grain boundary length and a passivation effect that significantly reduced the corrosion rate. No Ag₂S phase was detected in the weatherability tests, confirming the enhanced durability of the doped samples.

The findings of this study highlight the potential of Sn-doped Ag-W composites as a promising material for electronic components, particularly in environments prone to sulfurization and corrosion. By improving the anti-corrosion properties and reducing the grain size, this study offers a new approach to extending the lifespan and reliability of electronic devices, making a significant contribution to the development of advanced materials for high-power and high-frequency applications.

The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.

This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context.

This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography.

This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency.

In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

In the 21st century, libraries are experiencing a significant decline in users due to shifting reading habits and the impact of technology, necessitating library transformation and a heightened emphasis on library marketing. Special libraries, in particular, rely heavily on patrons for survival, often due to their private ownership and limited resources. This paper aims to discuss the aforementioned objectives.

This paper examines the Taste Library, a special library in Hong Kong, and analyzes its current practices based on an interview with its founder, website content, and social media presence. The 7Ps Marketing Mix model is employed to assess the strengths and weaknesses of the library's current market position.

The Taste Library's existing practices exhibit limitations in attracting young patrons. To address this issue, we propose marketing strategies focused on enhancing social network presence, offering digitized content, and engaging in school outreach.

By concentrating on youth marketing, this study offers valuable insights for special libraries in developing strategic plans for transitioning and maintaining sustainability.

Few studies concentrate on marketing small special libraries, particularly in the East, within today's digitized economy.

This study addresses the empirical results of the spillover effect with export as the primary economic activity that enhances local businesses' total factor productivity (TFP). A learning mechanism is expected to be generated and used as the basis for the policy implication.

This study adopted the Cobb–Douglas function and multiple estimation approaches, including the generalized method of moments, the Olley–Pakes and the Levinsohn–Petrin estimation techniques. The findings were estimated based on the panel data of a Vietnamese local businesses survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2019.

The results showed that the highest TFP belongs to the businesses in the Southeast region, the Mekong Delta region, the mining industry and the foreign-invested enterprises. The lowest impacted TFP are businesses in the Northwest region and agricultural, forestry and fishery sectors. In addition, the estimated results also show that the positive spillover effect on TFP is shown through forward and backward linkage. The negative spillover effect is expressed through the backward and horizontal channels.

This study offers original empirical evidence on the learning mechanisms via which exports contribute to productivity improvement in a developing Asian economy, so making a valuable contribution to the existing academic literature in this domain. The findings of this research make a valuable contribution to the advancement of understanding on the many ways via which spillover effects manifest such as horizontal, forward, backward and supplied-backward linkage.

The study's findings indicate that it is advisable for governments to give priority to the development and improvement of forward and supply chain linkages between exporters and local suppliers. This approach is recommended in order to optimize the advantages derived from export spillovers. At the organizational level, it is imperative for enterprises to strengthen their technological and managerial skills in order to efficiently incorporate knowledge spillovers that originate from overseas partners and trade counterparts.

This study sheds new evidence on the export spillover effect on productivity in emerging economies, with Vietnam as the case study. The paper contributes to the research's originality by adopting novel methodological aspects to estimate local businesses' impact on total factor productivity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0373

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

This paper studies the relation between corporate tax evasion, job creation and optimal fiscal policy. Job creation depends on firms’ profits since firms open (“create”) new jobs when profits increase. In turn, firms’ profits depend on incentives (rewards) and disincentives (penalties) to comply with tax rules. Hence, any fiscal policy to combat tax evasion also has repercussions on job creation.

This paper is both theoretical and empirical. From a theoretical point of view, a modified and extended version of the search and matching model of the labor market is used. In this framework, moreover, the welfare function of workers and firms is closely related to the job creation condition. Empirically, a panel analysis of a system of two simultaneous equations that covers 54 countries (both developed and developing) and four years (2018–2021) is carried out.

The paper finds that anti-tax evasion policies should be related to job creation policies. Also, anti-tax evasion and job creation policies change according to the extent of tax evasion in the economy. Precisely, when tax evasion is widespread, a lower tax burden (tax cuts or provision of fiscal rewards) requires tighter tax audits, whereas, where most people comply with tax rules, a decrease in tax audits is possible.

The empirical analysis supports the model-generated theoretical relationships. Eventually, therefore, the optimal fiscal policy suggested by this work can counteract corporate tax evasion and, at the same time, reduce the firm’s tax burden, thus promoting job creation.

As far as we are aware, this is the first paper that considers the close and direct link between fiscal policy, corporate tax evasion and job creation.

This chapter examines the significant role of non-fungible tokens (NFTs) and blockchain technology in fostering a sustainable economy in the metaverse. Blockchain allows the saving and transfer of decentralized and secure data. As a primary component of the metaverse economy, NFTs are distinct and secure virtual assets saved on the blockchain. These assets facilitate possessing, trading, and monetizing digital assets. These advancing technologies have also revolutionized the method by which creators and artists test and exchange their digital work, introducing a novel period of ownership and value in the digital realm. However, the negative environmental effects of some blockchain technologies constitute a considerable constraint, pushing a shift to a sustainable economy. Platforms like The Sandbox have implemented initiatives to address environmental concerns. As a case study, The Sandbox play-to-earn model with tokenized assets showcases its ability to create value and encourage user participation. It shows the ability of NFTs and blockchain to support a sustainable economy.

This study aims to determine the antecedents that influence attitudes towards the use of environmentally friendly household appliance products and consumers' green purchase intention among consumers in Harare, Zimbabwe.

Data were collected from 329 consumers in Harare, Zimbabwe's commercial capital who were served from five using a structured questionnaire via an online web-based cross-sectional survey. Hypothesised relationships were tested through structural equation modelling with the aid of Smart PLS software.

Green product awareness, social influence, perceived benefit and attitude towards green appliances were found to have a significant positive effect on green purchase intention.

The study's findings may not be generalised to other contexts as sample data was only collected in Zimbabwe. Complementary cross-sectional research studies can be done in other parts of the world to enable cross-cultural comparisons and methodological validations.

The green appliance and energy saving practices are vastly growing, with many multinational appliance companies introducing green products within their product lines and adopting the concept of sustainability through modifications in production, design and consumption of household appliance products that encompass fewer harmful consequences on the environment in response to their concerns about the scarcity of natural resources, environmental well-being and the potential detriment of future generations.

Notwithstanding the limitations of the current study, the results have the potential to contribute to an improved understanding of influence attitudes towards the use of environmentally friendly household appliance products.