This paper employs a sample of Chinese A-share listed companies spanning from 2011 to 2022 to empirically investigate the influence of climate policy uncertainty on the corporate cost of debt, based on the theory of financial friction. We find that climate policy uncertainty significantly increases the corporate cost of debt, and the result is supported by robustness tests. To avoid biases arisen from endogeneity, this paper introduces an instrumental variable approach and propensity score matching method for verification. The endogeneity test results support the baseline regression results as well. Finally, this paper also discovers that financing constraints are the potential mechanism behind the impact of climate policy uncertainty on the corporate cost of debt.

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

The global economic recession has caused pessimism in terms of prospects of sales recovering in the future. The present study is an attempt to investigate the cost stickiness behavior by focusing on specific characteristics of companies. The research was done through documentary analysis and access to quantitative data, with the use of statistical methods for analysis as panel data. The statistical population of the actual study included all companies listed on the India stock exchange from 2017 to 2021. They were selected after screening 128 listed companies. The regression method was used to examine the relationship between variables and to present a forecast model. The results of testing the first hypothesis showed that companies’ costs are sticky and according to the results of this hypothesis, an increase in costs when the level of activity increases is greater than the level of reduction in costs when the volumes of the activities are decreased. The results of the second hypothesis showed a remarkable relationship between the cost stickiness and specific characteristics of companies (size, number of employees, long-term assets, financial leverage, and accuracy of profits forecast). Based on the third hypothesis, there is a notable difference between cost stickiness at different levels of specific characteristics of companies. Therefore, the results show that environmental uncertainty such as COVID-19, increases cost stickiness.

There are several methods in the literature to find the fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems. However, in all these methods, it is assumed that the product of two trapezoidal (triangular) fuzzy numbers will also be a trapezoidal (triangular) fuzzy number. Fan et al. (“Generalized fuzzy linear programming for decision making under uncertainty: Feasibility of fuzzy solutions and solving approach”, Information Sciences, Vol. 241, pp. 12–27, 2013) proposed a method for finding the fuzzy optimal solution of FFLP problems without considering this assumption. In this paper, it is shown that the method proposed by Fan et al. (2013) suffer from errors and to overcome these errors, a new method (named as Mehar method) is proposed for solving FFLP problems by modifying the method proposed by Fan et al. (2013) . To illustrate the proposed method, some numerical problems are solved.