Learning EOQ Model with Trade-Credit Financing Policy for Imperfect Quality Items under Cloudy Fuzzy Environment

An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model.

Does Green Credit Policy Move the Industrial Firms Toward a Greener Future? Evidence From a Quasi-Natural Experiment in China

How to utilize financial instrument to deal with environmental issues has been a focal topic. Taking the introduction of green credit program as a “quasi-natural experiment,” the propensity score matching and difference-in-difference approach (PSM-DID) are used to investigate the impact of the green credit policy implemented by Chinese government on firm-level industrial pollutant emissions. The estimation results indicate that the green credit policy significantly reduces corporate sulfur dioxide emissions. Heterogeneity analysis shows this impact is more pronounced for large-scale enterprises and enterprises located in the eastern region. The estimated mediation models reveal that after the implementation of the green credit policy, reduction in sulfur dioxide emissions can be attribute to the increased environmental investment and improved energy consumption intensity. Moreover, the green credit policy is also significantly effective in mitigating the discharge of other common industrial pollutants. Our findings highlight the importance of green credit policies in achieving greener industrial production and more sustainable economic development.

The Impact of Green Credit on the Green Innovation Level of Heavy-Polluting Enterprises—Evidence from China

This article uses the “Green Credit Guidelines” promulgated in 2012 as an example to construct a quasi-natural experiment and uses the double difference method to test the impact of the implementation of the “Green Credit Guidelines” on the green innovation activities of heavy-polluting enterprises. The study found that, in comparison to non-heavy polluting enterprises, the implementation of green credit policies inhibited the green innovation of all heavy-polluting enterprises. In the analysis of heterogeneity, this restraint effect did not differ significantly due to the nature of property rights and the company’s size. The mechanism test showed that green credit policy limits the efficiency of business investment and increases the cost of financing business debt. Eliminating corporate credit financing, particularly long-term borrowing, negatively impacts the green innovation behavior of listed companies.

ONCE AGAIN ABOUT THE NEGATIVE SYNERGY OF THE DESTRUCTIVE POLICY OF THE RUSSIAN MONETARY AUTHORITIES

The article discloses an interconnected set of strategic defects in the regulatory activities of the Bank of Russia in cooperation with the Ministry of Finance, which predetermine the significant contribution of the neoliberal financial and credit policy pursued by them both to the development of an autonomous recession and the aggravation of the coronary crisis in our country. Based on a comparative analysis of the post-default and post-sanction devaluation of the ruble, a conclusion is made about the predominantly negative impact of the latter on the dynamics of Russian GDP and on inflationary processes in the country. The premature transition of the central bank to inflation targeting and, especially, to the free-floating ruble regime, the leading beneficiaries of which are disclosed commodity exporters, financial speculators and the Ministry of Finance, have been critically examined. The continued dominance of the foreign exchange channel in the Bank of Russia’s issuing activities over the credit channel and the inability of the financial authorities to cover the budget deficit through monetary financing are regarded as decisive factors preventing overcoming the coronavirus crisis on the way to a reasonable diversification of the domestic economy. The unjustified transition already in 2021 to a super-tight monetary and fiscal policy, which does not fit into the global practice of anti-crisis regulation, is seen as a kind of renaissance of the false monetarist approach in the activities of leading Russian regulators, their traditional reliance on the quantitative theory of money and the ensuing desire to overcome cost inflation using methods characteristic of combating demand inflation. As the end result of the noted manifestations of the fiasco of the Russian state in the money market, an extremely low coefficient of monetization of the domestic economy is considered, which prevents its breakthrough high-quality growth in the foreseeable future.