期刊：
International Journal of Information Technology and Management,2024年23(2):137-155 ISSN：1461-4111
作者机构：
[Yanyin Li] School of Finance, Renmin University of China, Beijing,100872, China;[Xing Yu] School of Economics and Business Administration, Central China Normal University, Wuhan, China;[Dongwei Shi] Sichuan University of Science and Engineering, Zigong, 643000, China
关键词：
Budget control;Commerce;Investments;Sales;Dual interior point algorithm;Interior point;Interior point algorithm;Margin call;Mark to market;Mark-to-market risk;Market risks;Option hedging;Point approach;Put options;Sensitivity analysis
关键词：
Air pollution environmental regulation;markup;manufacturing firms;China;Q53;Q58;O14
摘要：
A comprehensive evaluation of the costs and benefits of environmental regulations provides empirical evidence for policymakers to formulate and introduce environmental policies. Firms' profitability and competitiveness manifest their market power and provide the micro foundation for economic development and social stability. Therefore, this paper focus on investigating the impact of air pollution environmental regulation on manufacturing firms' market power, measured by markups, with a difference-in-differences (DID) method. Using air pollution control policy, requiring China's Key Cities for Air Pollution Control (KCAPC) to meet air quality standards within a specified time limit as a natural experiment, our DID estimations show that air pollution regulation significantly leads to an increase in firms' markup rate by 0.002 in key cities that did not meet air quality standards. Heterogeneous effects show that this impact is larger for polluting firms, private-owned and foreign-owned firms, and firms located in western and central cities and cities with high marketization. Productivity and market share may be two main influencing channels to explain the positive impact of air pollution environmental regulation on firms' markups. Our findings suggest that strengthening environmental regulation is conducive to improving the competitiveness and market power of manufacturing firms, lending support to the enforcement of environmental regulations.
摘要：
ABSTRACT Existing studies on comparative price advertisements mainly address how the physical factors of price presentation (e.g., font size, physical distance, background color) influence consumers' numerical processing and affect their discount perception and purchase intentions. However, this research takes a visual processing perspective to examine the visual balance effect of comparative price presentation (horizontal vs. vertical) on product preference. We used a scenario‐based experimental design to imagine an online shopping scenario. Study 1 indicates that horizontal (vs. vertical) comparative prices lead to heightened product attitudes/purchase intentions. Study 2 verified that perceived stability mediates the visual balance effect. In addition, Study 3 shows that the visual balance effect is moderated by the fit of perceived stability with product characteristics. The conclusions contribute to the knowledge structure of comparative price presentation from a visual processing perspective and can guide more effective comparative price promotion.
期刊：
INTERNATIONAL JOURNAL OF CLIMATE CHANGE STRATEGIES AND MANAGEMENT,2024年16(1):1-18 ISSN：1756-8692
通讯作者：
Tong, QM
作者机构：
[Liu, Xuan; Tong, Qingmeng; Ran, Shan; Tong, QM] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan, Peoples R China.;[Zhang, Lu; Zhang, Junbiao] Huazhong Agr Univ, Coll Econ & Management, Wuhan, Peoples R China.
通讯机构：
[Tong, QM ] C;Cent China Normal Univ, Sch Econ & Business Adm, Wuhan, Peoples R China.
关键词：
Agricultural internet information (AII);Climate resilience;China;Rice production;Recursive binary probit model
摘要：
<jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>The main purpose of this study is to examine the impact of agricultural internet information (AII) acquisition on climate-resilient variety adoption among rice farmers in the Jianghan Plain region of China. Additionally, it explores the influencing channels involved in this process.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>Based on survey data for 877 rice farmers from 10 counties in the Jianghan Plain, China, this paper used an econometric approach to estimate the impact of AII acquisition on farmers’ adoption of climate-resilient varieties. A recursive bivariate Probit model was used to address endogeneity issues and obtain accurate estimates. Furthermore, three main influencing mechanisms were proposed and tested, which are broadening information channels, enhancing social interactions and improving agricultural skills.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>The results show that acquiring AII can overall enhance the likelihood of farmers adopting climate-resilient varieties by 36.8%. The three influencing channels are empirically confirmed. Besides, educational attainment, income and peer effects can facilitate farmers’ acquisition of AII, while climate conditions and age significantly influence the adoption of climate-resilient varieties.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Practical implications</jats:title>
<jats:p>Practical recommendations are put forward to help farmers build climate resilience, including investing in rural internet infrastructures, enhancing farmers’ digital literacy and promoting the dissemination of climate-resilient information through diverse internet platforms.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>Strengthening climate resilience is essential for sustaining the livelihoods of farmers and ensuring national food security; however, the role of internet information has received limited attention. To the best of the authors’ knowledge, this study is the first to examine the casual relationship between internet information and climate resilience, which fills the research gap.</jats:p>
</jats:sec>
摘要：
<jats:p>Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is proposed. The raw data is preprocessed and then decomposed into trend, seasonal, and residual components using the seasonal decomposition algorithm. The deep GRU model is then used to predict each subcomponent, which is then integrated into the final prediction results. In particular, the hyperparameter optimization algorithm of tree-structured parzen estimators (TPE) is used to optimize the model. Moreover, this paper introduces the single machine learning model (including multiple linear regression (MLR), back propagation (BP), long short-term memory neural network (LSTM) and gate recurrent unit (GRU)) and a combination model (including seasonal decomposition–back propagation (SD-BP), seasonal decomposition–multiple linear regression (SD-MLR), along with seasonal decomposition–long-and-short-term-memory neural network (SD-LSTM), which are used as comparison models to verify the excellent prediction performance of the proposed model. Finally, a case study of the Qingjiang Shuibuya test set, which considers the period 1 January 2019 to 31 December 2019, is conducted. Case studies of the Qingjiang River show the proposed model outperformed the other models in prediction performance. The model achieved a mean absolute error (MAE) index of 38.5, a Nash-Sutcliffe efficiency (NSE) index of 0.93, and a coefficient of determination (R2) index of 0.7. In addition, compared to the comparison model, the NSE index of the proposed model increased by 19.2%, 19.2%, 16.3%, 16.3%, 2.2%, 2.2%, and 1.1%, when compared to BP, MLR, LSTM, GRU, SD-BP, SD-MLR, SD-LSTM, and SD-GRU, respectively. This research can provide an essential reference for the study of daily runoff prediction models.</jats:p>
关键词：
State dependence;Model-driven;HMM;Machine learning;Futures hedging
摘要：
Considering the dynamic nature of market conditions, this paper introduces a state-dependent futures hedging optimization model and methodology. This approach dynamically adjusts the traditional model-driven hedging strategy, effectively balancing the pursuit of returns with the imperative of risk mitigation. Empirical evidence shows that integrating Hidden Markov Model (HMM) with machine learning techniques, as demonstrated in this study, improves the accuracy of market state forecasts. Compared to the traditional model-driven hedging strategy, the innovative state-dependent hedging strategy introduced here significantly enhances the return-to-risk ratio, and revenue, without increasing hedging risks. Moreover, the hedging portfolio developed under this strategy achieves an average hedging efficiency of 0.76, highlighting the effectiveness of the proposed methodology. Additional robustness tests indicate that this market state-dependent hedging optimization strategy is promising under various conditions, including different position adjustment ratios, volatility benchmarks, evaluation periods, types of crude oil, and transaction costs. The research conducted in this paper not only contributes to and expands traditional hedging theories but also provides a practical risk management solution for market participants.
期刊：
POLISH JOURNAL OF ENVIRONMENTAL STUDIES,2024年33(4) ISSN：1230-1485
通讯作者：
Huang, WY
作者机构：
[Xu, Junbing; Cai, Dixin; Wang, Hao] Minjiang Univ, Newhuadu Business Sch, Fuzhou, Fujian, Peoples R China.;[Bai, Zongshang] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan, Peoples R China.;[Huang, Wenyu] Xiamen Univ, Wang Yanan Inst Studies Econ WISE, Fujian, Peoples R China.
通讯机构：
[Huang, WY ] X;Xiamen Univ, Wang Yanan Inst Studies Econ WISE, Fujian, Peoples R China.
关键词：
political connection disruptions;environmental performance;heavily polluting industries
摘要：
Employing Rule No. 18 of the China Communist Party for independent directors in 2014 as a quasi -exogenous shock, our paper investigates the effects of political connection disruptions on firms' environmental performance in heavily polluting industries. We find that political connection disruptions will worsen firms' environmental performance, which reduces firms' environmental investment by 37.65%. Further analysis shows that this effect supports the government intervention hypothesis rather than the government resource hypothesis. This effect is especially prominent for firms that are in low -market pressure, small-scale, or high environmental regulation locations. Overall, our paper provides new insights into the environmental effects of political connections.
作者机构：
[Dong, Hanmin] Cent China Normal Univ, Sch Econ & Business Adm, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Dong, Hanmin; Zhang, Lin] City Univ Hong Kong, Sch Energy & Environm, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China.;[Zheng, Huanhuan] Natl Univ Singapore, Lee Kuan Yew Sch Publ Policy, 469C Bukit Timah Rd, Singapore City, Singapore.;[Zhang, Lin] Ctr Ocean Res Hong Kong & Macau, Hong Kong, Peoples R China.
通讯机构：
[Zhang, L ] C;City Univ Hong Kong, Sch Energy & Environm, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China.
关键词：
Green bond;Green innovation;Corporate sustainability;Climate finance
摘要：
This study investigates the impact of green bond issuance on green innovation and its underlying mechanisms. We find that green bond issuance promotes green innovation, with stronger effects observed in regions with weaker climate regulation, industries exhibiting better environmental performance, and firms with more concentrated ownership. Further analysis reveals that corporate green bonds facilitate the reallocation of investment capital into research and development, effectively mitigating financial constraints on green innovation. This upsurge in green innovation not only enhances financial performance but also yields specific environmental benefits, such as improved environmental investment and ESG performance. These empirical results underscore the significance of green finance in fostering sustainable corporate innovation and advancing climate governance, rather than merely engaging in greenwashing practices.
摘要：
With the popularization of smartphones, various types of mobile applications (apps) have been integrated into every aspect of people's lives. Recently, the insertion of advertising slogans into app icons has become a new promotional method. Accordingly, this research examines the influence of advertising slogan typefaces in app icons on the downloads of consumers with different regulatory focuses. Specifically, the results of two studies demonstrate that consumers with a greater promotion (prevention) focus have a stronger download intention toward apps with handwritten (machine-written) typefaces in advertising slogans in their app icons. Moreover, this effect is mediated by consumers' risk perceptions regarding app services or functions, and the effect of time pressure as a boundary condition is emphasized. Our findings thus not only contribute to the literature concerning symbolic association, typeface inference, and regulatory focus but also highlight the risk of using handwritten typefaces and provide operational methods that can be applied to increase download intentions.
期刊：
Engineering Applications of Artificial Intelligence,2024年137:109066 ISSN：0952-1976
通讯作者：
Hegui Zhu
作者机构：
[Chong Liu; Sheng Shi; Hegui Zhu] College of Sciences, Northeastern University, Shenyang, 110819, China;[Wen-Ze Wu] School of Economics and Business Administration, Central China Normal University, Wuhan 430079, China;[Wanli Xie] Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing 210097, China
通讯机构：
[Hegui Zhu] C;College of Sciences, Northeastern University, Shenyang, 110819, China
摘要：
To accurately predict China’s carbon dioxide emissions, this paper constructs an innovative prediction algorithm based on the marine predators algorithm and a new discrete nonlinear grey Bernoulli model with fractional-order accumulation operation. In this prediction algorithm, the new model is used to complete the modeling task of nonlinear time series, and the marine predators algorithm is used to facilitate the solution process of model. It is found that the proposed model satisfies both unbiasedness and uniformity, underscoring its superiority over conventional grey prediction models. Numerical results indicate that the proposed algorithm outperforms all competing algorithms in fitting China’s carbon dioxide emissions, validating the effectiveness and feasibility of this approach.
关键词：
Executive green cognition;ESG performance;Green innovation;Environmental information disclosure quality
摘要：
This paper examines how executive green cognition influences corporate ESG performance using data from Chinese listed companies. The results indicate that higher level of executive green cognition substantially enhance ESG performance. Specially, this paper finds that this positive impact is achieved through advancements in green innovation and improvements in the quality of environmental information disclosure. Additionally, heterogeneity analysis reveals that the benefits of executive green cognition on ESG performance are more significant in industries with lower competition and among non-state-owned enterprises. This paper introduces a novel perspective on enhancing ESG performance, offering valuable insights for promoting sustainable development within enterprises.
摘要：
Industrial robots, as a core technology and essential tool in intelligent manufacturing, have brought about a new transformation in industrial production patterns. China is at a critical juncture in its green transition and urgently needs to increase investments in industrial robots. Based on the policy shock provided by implementing the Clean Air Action in 2013, we construct a quasi-natural experiment to assess the impact of escalating environmental policies on industrial robot investment. The results indicate that the stringent regulation significantly inhibited high-pollution enterprises from adopting industrial robots. Mechanism analysis reveals that the obstacle to corporate investment is the reason for the suppression of industrial robot installation. Compared to equity financing, constraints from debt financing are the primary channel inhibiting investment. Furthermore, factors such as corporate ownership and industry concentration also play a moderating role in the policy effect. The findings of this paper provide insights for supporting policies aimed at sustainable development.
