The theory on the carbon footprint of agriculture can systematically evaluate the carbon emissions caused by artificial factors from the agricultural production process, which is the theoretical basis for constructing low-carbon agriculture and has important guiding significance for realizing low-carbon agriculture. Based on farm production survey data from Jilin Province in 2014, this paper aims to obtain a clear understanding of the carbon footprint of maize production through the following method: (1) one ton of maize production was evaluated systematically by using the Life Cycle Assessment (LCA); (2) the carbon emissions of the whole system were estimated based on field measurement data, (3) using the emission factors we estimated Jilin’s carbon footprint for the period 2006–2013, and forecasted it for the period from 2014 to 2020 using the grey system model GM (1, 1).

With the outbreak of COVID-19 in late 2019, people’s awareness of actively searching for health information has been growing. Coupled with the promotion of “Internet + medical and health,” social media, as an important platform for health information dissemination, has become one of the important information sources for users to obtain health information. However, health information seeking behavior in public health emergencies are quite different from those in daily life. Therefore, the research question of this paper is to explore the influencing factors of health information seeking behavior of social media users in the context of COVID-19. To this end, based on the research framework of social cognition theory, this paper selects six variables to construct a structural equation modeling, including information and platform quality, experience, social support, emotional arousal, self-efficacy, and social media users’ health information seeking behavior. The empirical study is carried out by collecting 219 valid questionnaires. The research results show that: (1) Information and platform quality, and use experience have a significant positive effect on emotional arousal; (2) Experience, social support, and emotional arousal have a significant positive effect on self-efficacy; (3) Emotional arousal and self-efficacy have a significant positive effect on social media users’ health information seeking behavior. The research provides an important theoretical reference for social media users and operators to cope with the huge demand for health information in the post-COVID-19 era.

In order to improve the global search performance of the Komodo Mlipir Algorithm, this paper proposed two adaptive Komodo Mlipir Algorithms with variable fixed parameters (IKMA-1; IKMA-2). Among them, IKMA-1 adaptively controls the parthenogenesis radius of female Komodo dragons to achieve more efficient conversion of global search and local search. Second, IKMA-2 introduces adaptive weighting factors to the “mlipir” movement formula of Komodo dragons to improve the local search performance. Both IKMA-1 and IKMA-2 were tested on 23 benchmark functions in CEC2013 and compared with the other seven optimization algorithms. The Wilcoxon rank-sum test and Friedman rank test were used to compare the performance of different algorithms. Furthermore, IKMA-1 and IKMA-2 are applied to two constrained engineering optimization problems to verify the engineering applicability of the improved algorithm. The results show that both IKMA-1 and IKMA-2 have better convergence accuracy than the initial KMA. In terms of the benchmark function simulation results, IKMA-1 improves the performance by 17.58% compared to KMA; IKMA-2 improves by 10.99%. Both IKMA-1 and IKMA-2 achieve better results than other algorithms for engineering optimization problems, and IKMA-2 outperforms IKMA-1.

Purpose: Corporate environmental responsibility (CER) and green supply chain management (GSCM) have become important factors in improving corporate sustainable performance. Among them, GSCM can be divided into green procurement, green logistics, customer relationship collaboration, and internal environmental management, which are interrelated yet independent of each other. Therefore, based on the stakeholder theory, this study aims to explore how different dimensions of GSCM collectively or differentially transmit the impact of CER on sustainable performance outcomes, and to discuss the complex relationships among corporate environmental performance (ENP), economic performance (ECP), and social performance (SOP). Design/methodology/approach: This study conducted data collection through a structured questionnaire among managers of medium-sized and above manufacturing enterprises in China. Subsequently, empirical analysis was performed using Structural Equation Modeling (SEM) on 332 valid samples. Findings: The study revealed significant positive correlations among CER, GSCM, and corporate sustainable performance. Additionally, the four dimensions of GSCM (green procurement, green logistics, customer relationship collaboration, and internal environmental management) all exerted significant positive mediating effects on the enterprise’s economic, social, and environmental performance. Originality/value: This study expands the application of stakeholder theory in the field of sustainable supply chains, demonstrating how environmental pressures from stakeholders drive enterprises to adopt multidimensional GSCM, thereby translating CER into measurable ECP. By elucidating the differential transmission mechanisms of GSCM’s four dimensions (including green procurement, green logistics, customer relationship collaboration, and internal environmental management) between CER and corporate sustainable performance. This research provides actionable insights for guiding enterprises to enhance ECP, facilitating the alignment of stakeholder expectations with economic and environmental goals within supply chain systems.

Purpose: This paper explores the key factors inhibiting algorithm aversion from the perspectives of information, emotion, control and social interaction based on the interactive experience framework, and discusses the moderating role of emotion types. Design/methodology/approach: Based on offline retail scenarios, this study designed two sub-studies using a combination of between-group experiments and PLS-SEM; a total of 541 online sample data were used to verify the relevant factors affecting consumers' algorithm aversion from the perspective of the interaction experience framework. Findings: The findings show that: (1) Interactive (compared to advisory) AI-assisted product selection can promote higher-quality interactive experiences for consumers and inhibit consumers' algorithm aversion. (2) Information interaction, emotional interaction, control interaction and social interaction all significantly affect consumers' algorithm aversion and have significant mediating effects. (3) Emotion types play a significant moderating role in the path between AI-assisted product selection modes and algorithm aversion. Among them, positive emotions help consumers project their self-emotions into the interactive process of AI algorithm decision-making, which is conducive to further alleviating algorithm aversion in the advisory AI-assisted product selection mode. Originality/value: Compared with previous studies, this paper enriches the understanding of algorithm aversion, expands the value of the interaction framework and emotion theory in guiding consumer behavior and can provide practical guidance and insights for brands and AI developers.