Journal of Management Science and Engineering最新文献

The Conference of the Parties (COP26 and 27) placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon neutrality. However, studies on the implementation of this policy proposition are limited. To address this gap in the literature, this study employs machine learning techniques, specifically natural language processing (NLP), to examine 77 climate bond (CB) policies from 32 countries within the context of climate financing. The findings indicate that “sustainability” and “carbon emissions control” are the most outlined policy objectives in these CB policies. Additionally, the study highlights that most CB funds are invested toward energy projects (i.e., renewable, clean, and efficient initiatives). However, there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and 2019. This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry, potentially leading to the greenwashing of climate funds. Furthermore, policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change, which may negatively influence climate actions. Thus, the findings highlight that the effective implementation of CB policies depends on policy goals, objectives, and sentiments. Finally, this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents with higher moral levels were more likely to choose higher effort and lower manipulation. Simultaneously, the principal offers lower incentives and receives more significant payoff. We also extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general and learning models and find that the learning contract can bring more profit to the principal.

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.

In March 2018, the US used an immense trade deficit as an excuse to provoke trade friction with China. This study uses the EGARCH model and event study methods to study the impact of the major risk event of Sino-US trade friction on soybean futures markets in China and the United States. Results indicate that the Sino-US trade friction weakened the return spillover effect between the soybean futures markets in China and the US, and significantly increased market volatilities. As the scale of additional tariffs increased, the volatility of the Chinese soybean futures market declined; however, the volatility of the US soybean futures market did not weaken. In addition, expanding the sources of soybean imports helped ease the impact of tariffs on China’s soybean futures market, while the decline in US soybean exports to China intensified the volatility of the US soybean futures market. In addition, while the release of multiple tariff increases has had a short-term impact on the returns of soybean futures markets, the impact of trade friction has gradually decreased.

The rapid development of Chinese online loan platforms (OLPs), as well as their risks, has attracted widespread attention, increasing the demand for a complete credit rating mechanism. The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators, including platform transaction volume and average expected rate of return. We also consider two qualitative indicators of online loan background, namely platform background and guarantee mode, that reflect Chinese characteristics. Subsequently, a factor analysis was conducted to reduce the 14 indicators’ dimensions. The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor, fund dispersion factor, security factor, and profitability factor. Finally, a K-means clustering algorithm was employed to cluster the factor scores of each OLP, thereby obtaining credit rating results. The empirical results indicate that the proposed machine learning–based credit rating method effectively provides early warnings of problem platforms, yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China, namely, Wangdaitianyan and Wangdaizhijia.

Some research on climate change has been the basis of climate change denialism (hereafter, CCD is used to refer to denial, denialism, and deniers). There is formative knowledge about the role of political propaganda in climate policies and resulting outcomes. To contribute to the understanding of political ideology and the extent of CCD, we adopt econometric techniques to study the impact of the United States of America's withdrawal from the Paris Agreement. The results show that ideological political propaganda has psychological and philosophical consequences that impact climate change policies. In addition, we find that the effect of a state's economic growth on climate change is mediated by CCD. In particular, low-income capitalist states have a higher propensity to become CCDs because they are more likely to engage in economic expansion even at the expense of environmental sustainability. Global climate change policies depend on high-income nations and industries' willingness to adopt economic policies to achieve sustainable future development. Thus, this study fills the literature gap on the relationship between political ideology and climate change. The findings show that CCD significantly influences voting patterns and socioeconomic outcomes. It impedes states from achieving net-zero emissions and carbon neutrality and it is used as a political propaganda. Subject to these findings, relevant policy suggestions are offered.

To infer product fitness information, consumers often consult digital influencers who exert effort to learn and evaluate product features and then propagate information to their followers. However, influencers' recommendations are not limited to products that exactly match their expertise, which may lead followers to unfollow them. This study investigates which product (high or low congruence) an influencer should endorse and how much endorsement effort should be exerted. Using a theoretical model, we obtain several interesting results. First, endorsing a low-congruence product can surprisingly benefit the influencer if the penalty per lost follower is moderate or if the influencer has sufficiently high efficiency in reducing the perception error of this product. Second, although the influencer has high expertise in the high-congruence product, she will lose more followers when endorsing it than when endorsing the low-congruence product if a particularly small or large proportion of her followers are potential consumers of the low-congruence product. Third, when the influencer endorses the low-congruence product, the number of new followers increases with the size of the influencer's follower base only when the follower base is small and a large proportion of followers are potential consumers of the low-congruence product. Interestingly, as the size of the follower base increases, the influencer always exerts more effort when endorsing the high-congruence product but will exert less effort in learning about the low-congruence product if a sufficiently small proportion of followers are potential consumers. Finally, after taking consumers' knowledge about product features into account, the main results still hold, and when considering the competition between influencers, we uncover the conditions under which both influencers will choose to endorse the high-congruence product.

Transshipment is an effective method for reducing mismatches between supply and demand among retailers. Consumers attempting to purchase out-of-stock items may wait for transshipment, purchase at another store, or choose not to buy. In this paper, the consumer behavioral heterogeneity is characterized using the transshipment request rate and consumer switching rate. It affects the replenishment and transshipment decisions, as well as the system profits. The inventory replenishment and transshipment decisions are studied in both centralized and decentralized two-location inventory systems with consumer behavioral heterogeneity. We characterize optimal replenishment decisions in a centralized system, prove the existence of a unique Nash equilibrium in a decentralized system with a specific demand distribution, and determine the coordinating transshipment price for some decentralized systems. In numerical studies, the performance of various systems is analyzed for consumers with identical (symmetric) or differing (asymmetric) behavior between retailers. For scenarios with symmetric consumers, a higher transshipment request rate and consumer switching rate resulted in increased total profit in all systems. For scenarios with asymmetric consumers, the retailer with the higher consumer switching rate should reduce ordering in a centralized system but increase ordering in a decentralized system. Moreover, the retailer with the higher transshipment request rate reduces ordering to increase profit, whereas the other retailer increases order quantity yet earns less profit.

As a fundamental logistics service, the express delivery (ED) industry provides production and consumption activities to maintain efficient supply chain operations. It plays an important role in promoting industrial development and maintaining daily life and consumption. This study examined the effects of COVID-19 on the ED industry using ED package delivery data from 31 provinces (autonomous regions and municipalities) in China. Using a difference-in-differences method, the COVID-19 outbreak was found to have a positive effect and epidemic prevention and control (EPC) policies were found to have negative effects on the development of the ED industry. In addition to EPC policies, the Chinese government has implemented other short-term economic interventions (SEI), such as the resumption of work and production (RWP), policies to balance economic growth, and epidemic prevention. This study divided the pandemic period into three stages to analyze the different impacts of EPC and SEI policy changes on the ED industry. The RWP policies adopted during the pandemic positively impacted the volume and income of packages delivered to the ED industry. The conclusions of this study are significant for the ED industry in addressing public health emergencies and for the Chinese government to formulate EPC and SEI policies during epidemic periods.