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This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This study aims to investigate the effect of different microstructures and its grain boundary character distribution (GBCD) on the corrosion behavior of weathering bridge steel.

The rust layer characteristics and corrosion resistance of specimens with different microstructures in the simulated industrial environment were studied by Electron Probe X-ray Micro-Analyzer, wavelength-dispersive spectrometer and electrochemical techniques. Electron backscatter diffraction technique was used to characterize the GBCD in steels with different microstructures.

Results revealed a significant difference in the corrosion susceptibility among the four microstructures, with corrosion rates decreasing in the following order: ferrite + pearlite > ferrite + bainite > bainite > martensite. The variation in corrosion resistance is primarily influenced by the microstructure type and the proportion of special grain boundaries, rather than the alloying elements. The proportion of Σ3 boundaries within the coincidence site lattice boundaries is positively correlated with improved corrosion resistance. A higher Σ3 boundary fraction resulted in a lower effective grain boundary energy, elevated self-corrosion potential, increased polarization resistance and reduced areas of localized galvanic corrosion; this led to enhanced inhibition of the electrochemical corrosion reaction, consequently reducing the corrosion rate.

This study elucidates and quantifies the intrinsic relationship between microstructure, GBCD and corrosion rate. This understanding is crucial for enhancing the corrosion resistance of weathering bridge steels in industrial atmospheric corrosion environments.

This study aims to investigate the impact of ChatGPT on users’ continuous knowledge contributions in online question-and-answer (Q&A) communities based on social exchange theory and stimulus-organism-response (SOR) theory.

Data were collected from a Chinese online Q&A community, and the difference-in-differences (DID) model was employed to verify the proposed hypotheses.

The results show that ChatGPT negatively impacts users’ continuous knowledge contributions in online Q&A communities, with variations observed across different knowledge domains.

This study is the first attempt to examine the impact of ChatGPT on users’ continuous knowledge contributions in online Q&A communities. The findings provide valuable insights for community managers to develop strategies for mitigating the effects of ChatGPT on online Q&A communities.

Using energy economically and efficiently is essential to ensure national energy security, reduce greenhouse gas emissions, and preventing climate change. Household appliances consume considerable energy, so choosing energy-efficient is to consuming energy economically and efficiently. The research applies the extended theory of planned behavior (TPB) to empirically investigate factors affecting consumers’ purchase intention toward energy-efficient household appliances (EEHA) in Ho Chi Minh City (HCM). The chapter develops both qualitative and quantitative methods. The qualitative method is conducted with five in-depth interviews to get more consumer insight, verify the research model, and elaborate the questionnaire. The quantitative method with 300 consumers living in HCM is used to address the research question. The primary data are collected via a web-based questionnaire and analyzed by IBM SPSS Statistics 20 and Amos 20. The research indicates that attitude, subjective norm (SN), perceived behavioral control (PBC), past purchase experience (PPE), and economic benefit (EB) have significant positive impacts on consumers’ purchase intention. EB has the most significant impact. Nonetheless, consumers’ purchase intention is not significantly impacted by their level of environmental knowledge (EK). Besides, PPE and EBs positively impact attitude and indirectly affect purchase intention, while EK does not. Based on the empirical results, some recommendations are drawn for policymakers to promote energy-efficient household appliance consumption by citizens, thereby ensuring national energy security and promoting environmental protection. Implications to help manufacturers improve products in the right direction and make wise investment decisions and distributors/retailers to understand consumers in HCM are also suggested.

To address challenges such as inadequate funding and inefficiency in public infrastructure construction, PPPs have gained significant global traction. This study aims to comprehensively assess the impacts and mechanisms of PPPs on the SDI and to provide rational policy recommendations based on the findings.

We collated a dataset from 30 Chinese provinces covering the years 2005–2020 as our research sample. The study’s hypotheses are tested using a double fixed-effects model, a chained mediated-effects model and a multidimensional heterogeneity analysis.

Our findings indicate that PPPs have a facilitating effect on SDI in general. This boost usually lags behind policy implementation and is cyclical in the time dimension. In the spatial dimension, PPPs contribute significantly to SDI in the eastern and western regions, but not in the central region. From the perspective of the dynamics of economic, social and industrial development, PPPs in economically backward areas are difficult to promote SDI, promote it the most in economically medium regions and are slightly less in economically developed regions than in medium regions. This promotion effect has an inverted U-shaped relationship with social development and diminishes with industrial structure upgrading. Finally, due to the negative relationship between PPPs and social development and between social development and SDI, PPPs are shown to contribute to SDI and are identified as critical paths. However, PPPs suppress SDI by inhibiting economic and industrial development.

This study makes three novel contributions to the existing body of knowledge: (1) we innovatively introduce the United Nations Sustainable Development Goals (SDGs) into the field of infrastructure research, offering fresh perspectives on SDI enhancement; (2) revealing the mechanisms by which PPPs affect SDI through the three dimensions of economic, social and industrial development enabling policymakers to better understand and optimize resource allocation and improve planning, design and management of PPP projects for sustainable infrastructure and (3) we assess the spatiotemporal variances of PPPs’ effects on SDI and the diversity across regions at different social, economic and industrial structures developmental stages, offering critical insights to global decision-makers to devise tailored policy measures.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The emerging live streaming technology has provided a novel means for streamers to interact with viewers, allowing for synchronous and vivid demonstrations of products for sale. However, individual streamers as sellers still struggle to improve sales in their live stores. Drawing upon flow theory, our study proposes and tests a moderated-mediation model that explores (1) the indirect influences of telepresence and social presence as two important live streaming affordances on viewers’ purchase intentions through the immersive state of flow and (2) the dynamic contingency embedded in the indirect relationships between presence and purchases through flow as created by mindfulness.

We collected survey data from 251 experienced consumers of a three-year Kuaishou store run by an Inner Mongolian singer in China. We applied a covariance-based structural equation modeling approach to examine the first-stage moderated-mediation model.

Our results show that viewers’ flow state mediated the effects of telepresence and social presence on purchase intentions of both virtual gifts and physical products. Additionally, mindfulness toward live streaming strengthened the mediation effect of flow on the relationship between telepresence and purchase intentions but weakened its mediation effect between social presence and purchase intentions.

Our study not only expands the existing knowledge on live commerce but also systematically addresses the theoretical tension between flow and mindfulness as two important user states that coexist in the live commerce context. Our findings also reveal practical implications for streamers, managers and designers of live commerce.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.