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Water and energy consumption and carbon emissions caused by humans have vital impacts on the natural environment. Due to the dramatic global environmental pollution problem, understanding the relationship between these factors is emerging as an important approach to realising sustainable development. However, with the strengthening of interregional trade links, it is difficult to manage and evaluate their relationship. Therefore, from the perspective of regional and industrial sectors, using the multiregional input-output (MRIO) model and social network analysis (SNA), our research explores an innovative analytical methodology to evaluate the characteristics of the energy-water-carbon spatial network, and a scheme is proposed to improve it. The results demonstrate that the characteristics of water scarcity and energy enrichment could lead to a net inflow of the water footprint and a net outflow of the energy and carbon footprints. Moreover, traditional high-energy-consumption industrial sectors contribute significantly to the energy and carbon footprints. The energy-water-carbon spatial network correlation is low and unstable, and it lacks rationalisation and balance in resource-based areas. Network-based energy-water-carbon research provides more insights toward understanding the carbon emission reduction responsibilities of industrial supply chains. Our findings provide a reference for reducing the energy-water-carbon footprint and achieving the carbon reduction goal of China. [ABSTRACT FROM AUTHOR]

Behaviour-based pricing (BBP) is commonly observed in the information goods industry. Information goods providers typically sell multi-version products and have implemented different BBP strategies for their products. However, previous research on BBP is mostly limited to single-version products, which may not effectively guide practical decision-making regarding multi-version products. This gap sparks interest in how BBP can be applied to multi-version information goods. We show that the equilibrium pricing strategy depends on the quality gap between the products and how much consumers and the firm value future benefits. The firm should implement BBP for both high- and low-quality products if it values future benefits more than consumers do. If consumers value future benefits more than the firm, the firm should not implement BBP for either product. Moreover, when the firm and consumers value future benefits equally, the firm should only practice BBP for the low-quality product. Our calculations of consumer surplus and social welfare reveal that the absence of BBP doesn't always lead to the maximum benefits for consumers and society. Interestingly, if the firm values future benefits more than its consumers do, applying BBP only to high-quality products or both products may lead to higher consumer surplus and social welfare. [ABSTRACT FROM AUTHOR]

Online medical communities (OMCs) are a type of online healthcare, in which physician‐patient interaction can be comprised of a variety of media options such as pictures, text, and voice. These media formats are often used to create a personalized patient experience in AI‐driven conversational healthcare platforms. To explore how physician media usage affects patient experience, we propose a counterfactual causal inference model using AI‐driven data mining methods on 131,083 online consultation records and 7,666,111 messages sent by physicians from one of the largest OMCs in China. Our study reveals the negative impact of physician use of voice on patient experience, compared to text. Drawing upon social support theory, we identify the mechanism by which physician media usage for voice produces a negative effect. The findings indicate that the negative effect of physicians' voice‐media usage occurs mainly in low‐risk disease conditions, by weakening the role of professional and emotional support. In contrast, in high‐risk disease conditions, voice‐media usage strengthens the role of professional and emotional support in improving the patient's experience. Our study is one of the first to focus on the social support attributes of the different media formats used in OMCs. We use advanced AI text‐analysis algorithms to extract features related to social support in physician‐patient conversations, and thus contribute to the use of AI in feature extraction for research. [ABSTRACT FROM AUTHOR]

Fixed costs of ordering items or setting up a production process arise in many real‐life scenarios. In their presence, the most widely used ordering policy in the stochastic inventory literature is the (s,S)$(s,S)$ policy. Optimality of (s,S)$(s,S)$ policies and (s,S)$(s,S)$‐type policies have been examined for various inventory models, including those with the inventory level being reviewed in every period or continuously, finite and infinite horizons, discounted‐cost and average‐cost criteria, backlogging and lost‐sales practices, standard and generalized demands and/or costs, deterministic and stochastic lead times, single‐product and multi‐product settings, and coordinated pricing‐inventory decisions. We comprehensively survey the vast literature accumulated over seven decades in two papers. This paper is devoted to discrete‐time models, and the companion paper, also published in this journal issue, reviews continuous‐time models. We go over model specifications, proof techniques, specific results, and limitations of the articles published in the literature. We conclude each paper by providing corresponding suggestions for extensions and directions for future research. [ABSTRACT FROM AUTHOR]

Fixed costs of ordering items or setting up a production process arise in many real‐life scenarios. In their presence, the most widely used ordering policy in the stochastic inventory literature is the (s,S)$(s,S)$ policy. Optimality of (s,S)$(s,S)$ policies and (s,S)$(s,S)$‐type policies have been examined for various inventory models, including those with the inventory level being reviewed in every period or continuously, finite and infinite horizons, discounted‐cost and average‐cost criteria, backlogging and lost‐sales practices, standard and generalized demands and/or costs, deterministic and stochastic lead times, single‐product and multi‐product settings, and coordinated pricing‐inventory decisions. We comprehensively survey the vast literature accumulated over seven decades in two papers. This paper is devoted to continuous‐time models, and the companion paper, also published in this journal issue, reviews discrete‐time models. We go over model specifications, proof techniques, specific results, and limitations of the articles published in the literature. We conclude each paper by providing corresponding suggestions for extensions and directions for future research. [ABSTRACT FROM AUTHOR]