Search results

This study explores the interconnectedness and complexity of risk-varied climate initiatives such as green bonds (GBs), emissions trading systems (ETS) and socially responsible investments (SRI). The analysis covers the period from September 2011 to August 2022, using six indices: three representing climate initiatives and three indicating uncertainty.

To achieve this, the study first examines dynamic lead-lag relations and correlation patterns in the time-frequency domain to analyse the returns of the series. Additionally, it applies an innovative approach to investigate the predictability of uncertainty measurements of climate initiatives across various market conditions and frequency spillovers in the short, medium and long run.

The findings indicate changing relationships between the series, increased linkages during turbulent market periods and strong co-movements within the network. The ETS is recommended for diversification and hedging against uncertainty indices, whereas the GB may be suitable for long-term diversification.

This study highlights the role of climate initiatives as potential hedges and contagion amplifiers during crises, with implications for policy recommendations and the asymmetric effects on market connectedness.

The paper answers questions that previous studies have not and contributes to the literature regarding financial risk management and social responsibility.

This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

This study aims to investigate whether and how public air environmental concern (PAEC) affects corporate environmental, social and governance (ESG) performance in emerging markets.

This study measured PAEC using the Baidu index search keyword “雾霾 (PM2.5)” and assessed its impact on corporate ESG among Chinese A-share listed companies from 2011 to 2020 through regression analysis.

The empirical results indicate a positive relationship between PAEC and corporate ESG. Moreover, PAEC facilitates enhanced corporate ESG performance by mediating through corporate reputation and government environmental regulations. Heterogeneity analysis shows that the promotion effect of PAEC on ESG is more pronounced in the subgroups of companies with an excellent green image, low perceived uncertainty, strong management political connections, low short-termism, high industry technological levels and low pollution levels.

The practical implications of this study underscore the importance for policymakers, investors and companies to prioritize PAEC and its influence on corporate ESG performance.

This study contributes to ESG literature by highlighting the positive impact of external oversight, such as PAEC.

The purpose of this paper is to investigate whether air pollution has significant impact on corporate cash holdings and financial leverage.

The data of 199 firms listed on Istanbul Stock Exchange during the period 2009–2020 is analyzed by using pooled ordinary least squares and two-step system generalized method of moments models.

The results indicate that firms in regions with high air pollution tend to increase cash level. In addition, the positive effect of air pollution on cash level is stronger and more significant for environmentally sensitive firms and firms with low operational and distress risk. The results also show insignificant effect of air pollution on financial leverage.

Firms in regions with high air pollution should conduct proactive environmental protection procedures and enhance their eco-efficiency instead of holding excess cash that could negatively affect financial performance. In this context, policymakers should provide financial facilities to firms located in regions with high air pollution and that have low ability to finance environmental investments. On the other hand, the environmental laws and regulations introduced by regulatory authorities can enhance the economic development and firm performance by decreasing the adverse influences of air pollution on corporate financial policies.

To the best of the author’s knowledge, this research is one of few that examines the impact of air pollution on corporate cash holdings and financial leverage in emerging markets.

Previous studies have mostly discussed the impact of environmental policy on enterprise innovation, but the discussion on how turbulence in environmental policy may affect firms' green innovation has been insufficient. This paper explores the effect of environmental policy uncertainty on corporate green innovation in the turnover of environmental protection officials (EPOT) context.

The authors manually collected the data on the EPOT of 280 Chinese prefecture-level cities, and used the Poisson regression model to conduct empirical analyses based on the panel data of 1472 Chinese listed manufacturing firms from 2008 to 2017.

The results show that environmental policy uncertainty leads firms to reduce their green patent applications only for green invention patent applications. Such an effect is more pronounced in non-state-owned enterprises (non-SOEs). In addition, when the new directors of the Ecology and Environmental Bureau take office through promotions or are no more than 55 years old, the negative effect is more obvious, but there is no significant difference regardless of whether new directors have worked in environmental protection departments.

First, this paper supplements the research on the antecedents of corporate green innovation from the perspective of environmental policy uncertainty and extends the applications of real options theory. Second, this paper expands the research on the government–business relationship from the EPOT perspective.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

This chapter analyzes how the macro-environment determines corporate dividend decisions. First, political factors including political uncertainty, economic policy uncertainty, political corruption, and democracy may have two opposite effects on dividend decisions. For example, firms learn democratic practices to improve their corporate governance, but dividend policy may be the outcome of strong corporate governance or the substitute for poor corporate governance. Second, firms in countries of high national income, low inflation, and highly developed stock markets tend to pay more dividends. A monetary restriction (expansion) reduces (increases) dividend payments, as economic shocks like financial crises and the COVID-19 may negatively affect corporate dividend policy through higher external financial constraint, economic uncertainty, and agency costs. On the other hand, they may positively influence corporate dividend policy through agency costs of debt, shareholders' bird-in-hand motive, substitution of weak corporate governance, and signaling motive. Third, social factors including national culture, religion, and language affect dividend decisions since they govern both managers' and shareholders' views and behaviors. Fourth, firms tend to reduce their dividends when they face stronger pressure to reduce pollution, produce environment-friendly products, or follow a green policy. Finally, firms have high levels of dividends when shareholders are strongly protected by laws. However, firms tend to pay more dividends in countries of weak creditor rights since dividend payments are a substitute for poor legal protection of creditors. Furthermore, corporate dividend policy changes when tax laws change the comparative tax rates on dividends and capital gains.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.