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With the rapid economic growth and urbanization development, regional environmental problems have attracted increasing attention. The study on analyzing cities’ capacity on resource utilization and environmental protection is practically significant because of the industrial agglomeration in an urban area. This study aims to measure environmental efficiency of urban industrial water utilization by comparing input, output and undesirable outputs variables of cities.

This paper combines data envelopment analysis basic approach with regression to discover the environmental efficiency score of cities and their influence factors. A set of slack-based measure (SBM) model is constructed for calculating the environmental efficiency score by considering both desirable and unendurable outputs.

This paper analyzes the industrial water utilization efficiency for China from the city level by evaluating the performance for up to 200 mainland Chinese cities during 2012–2016 under SBM model. Then, 2C Tobit regression is used to measure the determining factors of industrial water utilization efficiency from the angle of natural, social, municipal and industrial structure factors. The empirical study results show huge room for improvement in industrial water utilization in China, while the average citizen efficiency scores maintain a level of about 0.5.

Significant differences exist in the regional efficiency in different cities in China. Per capita GDP, a total length of drainage pipe and whether the area is coastal or not have significant positive impacts on the water utilization efficiency, while the proportion of secondary industry to GDP has a significant negative influence. Specific recommendations are proposed based on the local industrial water utilization efficiency scores, such as improving urban infrastructure and adopting a more flexible water pricing system.

The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment.

This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters.

The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method.

There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study.

The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

Purpose of this study is to classify the states of Markov Chain for the implementation of Markov Password for effective security. Password confirmation is more often required in all authentication process, as the usage of computing facilities and electronic devices have developed hugely to access networks. Over the years with the increase in numerous Web developments and internet applications, each platform needs ID and password validation for individual users.

In the technological development of cloud computing, in recent times, it is facing security issues. Data theft, data security, denial of service, patch management, encryption management, key management, storage security and authentication are some of the issues and challenges in cloud computing. Validation in user login authentications is generally processed and executed by password. To authenticate universally, alphanumeric passwords are used. One of the promising proposed methodologies in this type of password authentication is Markov password. Markov passwords – a rule-based password formation are created or generated by using Markov chain. Representation of Markov password formation can be done by state space diagram or transition probability matrix. State space classification of Markov chain is one of the basic and significant properties. The objective of this paper is to classify the states of Markov chain to support the practice of this type of password in the direction of effective authentication for secure communication in cloud computing. Conversion of some sample obvious password into Markov password and comparative analysis on their strength is also presented in this paper. Analysis on strength of obvious password of length eight has shown range of 7%–9% although the converted Markov password has shown more than 82%. As an effective methodology, this password authentication can be implemented in cloud portal and password login validation process.

The objective of this paper is to classify the states of Markov chain to support the practice of this type of password in the direction of effective authentication for secure communication in cloud computing. Conversion of some sample obvious password into Markov password and comparative analysis on their strength is also presented in this paper.

Validation in user login authentications is generally processed and executed by password. To authenticate universally, alphanumeric passwords are used. One of the promising proposed methodologies in this type of password authentication is Markov password.

This paper aims to explore the impact of the COVID-19 pandemic on the market timing skills of Islamic equity funds in Asia, Europe and North America.

The authors employed a two-step process. First, a Granger causality test is applied to test the bivariate relationship between Islamic fund indices and stock market ones by highlighting the impact of the COVID-19 pandemic. Second, the methodology of Treynor and Mazuy (1966) is deployed to account for the market timing abilities skills of Islamic fund managers during the pandemic period.

The investigation revealed mixed results. The European Islamic funds were positively impacted by the stock market as well as by the COVID-19 pandemic context. Additionally, compared to their Asian and North American peers, only European Islamic fund managers have the ability to time the market during the health crisis period.

Despite its contribution to the Islamic finance literature, this study has some flaws. Indeed, the selected sample of three regions, namely Asia, Europe and North America, precludes extrapolating these conclusions. Other regions should be investigated to further our understanding of Islamic equity funds. Furthermore, due to data availability and accessibility, the study period was limited to a specific time of the COVID-19 pandemic. This shortcoming can be addressed through a multiwave investigation, especially since each region was exposed differently to the pandemic.

The paper provides scholars, portfolio managers and investors with insights regarding the investment dilemma during the COVID-19 pandemic period, especially for those wishing to hedge their pandemic risk exposure and/or diversify their portfolios. Equally, the depiction of potential market timing abilities of Islamic fund managers across the three regions would serve as a guide to identifying the most suitable internationally focused investment strategy.

The paper provides scholars, portfolio managers and investors with insights regarding the investment dilemma during the COVID-19 pandemic period, especially for those wishing to hedge their pandemic risk exposure and/or diversify their portfolios. Equally, the depiction of potential market timing abilities of Islamic funds managers across the three regions would serve as a guide to identify the most suitable internationally focused investment strategy.

The originality of this investigation is that it is the first to examine Islamic equity fund managers and their skills to time the stock markets during the COVID-19 pandemic period in Asia, Europe and North America. The current paper extends the Islamic finance literature.

This study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.

The study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.

The findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.

Multifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.

This research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.

The purpose of the present study is to contribute to the existing literature by examining the nexus and the connectedness between classes S&P Green Bond Index, S&P GSCI Crude Oil Index, S&P GSCI Gold, MSCI Emerging Markets Index, MSCI World Index and Bitcoin, during the pre-and post-Covid period beginning from August 2011 to July 2021 (10 years).

The study employs time-varying parameter vector autoregression and Quantile regression methods to understand the impact of events on traditional and upcoming asset classes. To further understand the connectedness of assets under consideration, the study used Geo-Political Risk Index (GPR) and Global Economic Policy and Uncertainty index (GPEU).

Findings show that these markets are strongly linked, which will only expand in the post-pandemic future. Before the pandemic, the MSCI World and Emerging Markets indices contributed the most shocks to the remaining market variables. Green bond index shows a greater correlation and shock transmission with gold. Bitcoin can no longer be used as a good hedging instrument, validating the fact that the 21st-century technology assets. The results further opine that under extreme economic consequences with high GPR and GPEU, even gold cannot be considered a safe investment asset.

Financial markets and the players who administer and communicate their investment logics are heavily reliant on conventional asset classes such as oil, gas, coal, nuclear and allied groupings, but these emerging asset classes are attempting to diversify.

The purpose of this study is to investigate the relationship between environmental, social and governance (ESG) performance and shareholder wealth in the context of mergers and acquisitions (M&As) before and during the coronavirus (COVID-19) pandemic.

This paper uses a sample of 889 completed M&As announced by US firms between 1 January 2018 and 31 July 2020. Announcement abnormal returns are estimated using an event study methodology and the relation of ESG performance to shareholder value creation is tested with univariate and multivariate cross-sectional regressions.

This study provides evidence for a significant negative value effect of ESG performance for the shareholders of acquiring firms during the entire sample period. The negative effect appears to be stronger, as the onset of the COVID-19 crisis. This suggests that, during the pandemic-driven economic turmoil, the costs of sustainability activities outweigh any possible gains, providing evidence in support of the overinvestment hypothesis.

The results of the study have important implications for firms, investors and policymakers. Firms should be more cautious with regard to extensive investments in ESG activities, particularly during economic turmoil. For shareholders, the results suggest that ESG engagement is not a resilience factor in an exogenous shock such as the COVID-19 pandemic. In terms of policymaking, the sustainability disclosure framework should remain voluntary allowing firms to report material ESG-related issues. The main limitation of the study is related to data availability regarding ESG performance.

To the best of the knowledge, this is the first study that investigates the effect of ESG performance on shareholder value in the market for corporate control before and during the COVID-19 pandemic.