Search results

This study investigates the causal relationship and mechanisms between the development of digital finance and household carbon emissions. Its objective is to explore how digital finance can influence the carbon footprint at the household level, aiming to contribute to the broader understanding of financial innovations' environmental impacts.

The research combines macro and micro data, employing input-output analysis to utilize data from the China Household Finance Survey (CHFS) for the years 2013, 2015, 2017, and 2019, national input-output tables, and Energy Statistical Yearbooks. This approach calculated CO2 emissions at the household level, including the growth rate of household carbon emissions and per capita emissions. It further integrates the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) for 2012–2018 and corresponding urban economic data, resulting in panel data for 7,191 households across 151 cities over four years. A fixed effects model was employed to examine the impact of digital finance development on household carbon emissions.

The findings reveal that digital finance significantly lowers household carbon emissions. Further investigation shows that digital transformation, consumption structure upgrades, and improved household financial literacy enhance the restraining effect of digital finance on carbon emissions. Heterogeneity analysis indicates that this mitigating effect is more pronounced in households during the nurturing phase, those using convenient payment methods, small-scale, and urban households. Sub-index tests suggest that the broadening coverage and deepening usage of digital finance primarily drive its impact on reducing household carbon emissions.

The paper recommends that China should continue to strengthen the layout of digital infrastructure, leverage the advantages of digital finance, promote digital financial education, and facilitate household-level carbon emission management to support the achievement of China's dual carbon goals.

The originality of this paper lies in its detailed examination of the carbon reduction effects of digital finance at the micro (household) level. Unlike previous studies on carbon emissions that focused on absolute emissions, this research investigates the marginal impact of digital finance on relative increases in emissions. This method provides a robust assessment of the net effects of digital finance and offers a novel perspective for examining household carbon reduction measures. The study underscores the importance of considering heterogeneity when formulating targeted policies for households with different characteristics.

The purpose of this study is to address the low localization accuracy and frequent tracking failures of traditional visual SLAM methods in low-light and weak-texture situations, and we propose a mobile robot visual-inertial localization method based on the improved point-line features VINS-mono algorithm.

First, the line feature information is introduced into VINS-mono. Subsequently, the EDlines line feature extraction algorithm is optimized with a short line merging strategy and a dynamic length suppression strategy to reduce redundant short lines and fragmented segments. In the back-end sliding window optimization, line feature reprojection errors are incorporated, and Huber kernel functions are added to the inertial measurement unit residuals, point-line feature residuals and loop closure constraints to reduce the impact of outliers on the optimization results.

Comparison and verification experiments are carried out on the EuRoC MAV Data set and real weakly textured environment. In the real low-light and weak-texture scenarios, the improved mobile robot localization system achieves over 40% higher accuracy compared to VINS-mono.

The main contribution of this study is to propose a new visual-inertial SLAM method combining point-line features, which can achieve good localization effect in low-light and weak-texture scenes, with higher accuracy and robustness.

Augmented reality (AR) technology presents novel opportunities for marketers to captivate consumers, enhancing their experiences with brands. Although recent research underscores consumers' favorable responses to AR within retail contexts, there remains a paucity of studies examining the specific factors motivating these responses, i.e. reasons for, such as the appeal of entertainment, trendiness, customization, interaction and augmentation quality. Conversely, reasons against AR acceptance, such as perceived lack of realism, skepticism or perceived deception, are also underexplored.

Guided by the behavioral reasoning theory, the current study delves into consumers' personal values and beliefs, their reasons for and reasons against using AR apps and how these factors influence their engagement and relationship performance with AR apps. The data were collected from 343 AR app users to test the developed theoretical framework.

Results reveal that while perceived value compatibility does not influence reasons for using AR apps, it negatively influences reasons against using AR apps and positively influences connectedness to AR apps. Interestingly, openness to change does not significantly influence users’ connection to AR apps, and no association was found between reasons-against and connectedness to AR apps and relationship performance. However, a positive relationship was observed between users’ connectedness to AR apps and their relationship performance.

By offering these insights, this study contributes to a foundational understanding of the behavioral intricacies and dynamic interaction patterns within the rapidly emerging AR user base.

The pandemic has caused severe disruptions and significant losses in various industries. In particular, the nursing service industry has been greatly affected, leading to increased service costs and attrition of nursing service provider (NSP) residents. Although prior studies suggest that outsourcing may mitigate losses from disruptions, there still lacks a detailed analysis of whether and when to adopt such a disruption solution.

This study develops a two-period game-theoretical model to explore the impacts of demand and cost disruptions caused by the pandemic on NSPs’ operational strategies, suppliers’ strategy choices and equilibrium prices and demand.

The results present several novel managerial insights. First, we suggest that higher demand and cost disruptions decrease service demand, but do not necessarily prompt an NSP to outsource nursing services. Interestingly, we find that even when the service cost of the outsourcing strategy is low, the NSP may still insist on the in-house strategy. Additionally, the equilibrium strategy does not always result in lower prices and higher demand.

Our findings provide insightful takeaways for NSPs to cope with the pandemic in the nursing service industry. The results also offer theoretical support for other industries to recover from demand and cost disruptions.

To meet an ever - increasing urbanization demand, urban complex projects have evolved to form the development type of HOPSCA (an acronym for Hotel, Office, Park, Shopping mall, Convention and Apartment, representing a new type of urban complex). Its integrated functions, complex structures and superior siting expose HOPSCA’s construction phase to higher and more uncertain safety risks. Despite this, research on construction safety risks of large urban complexes is scarce. This study addresses this by introducing the interval ordinal priority approach (Interval-OPA) method to build a safety risk assessment model for HOPSCA, targeting its construction safety risk management.

This study initially identifies risk factors via literature review, field survey and three Delphi method rounds, forming a construction safety risk list of HOPSCA projects. Then, Interval-OPA is employed to create a safety risk assessment model, and its validity confirmed through a representative case study of an ongoing project. Lastly, uncertainty and weighting analyses of the model results identify the most probable major construction accidents, safety risk factors and targeted prevention strategies for the urban complex projects construction phase.

The findings reveal that (1) there are 33 construction safety risks in HOPSCA’s construction phase across 4 aspects: “man-machine-environment-management”; (2) object strikes are the most prominent of accidents and need to be prioritized for prevention, especially when managerial risks are arising; (3) falls from heights are evaluated with the highest level of uncertainty, which represents an ambiguous area for safety management and (4) the result of the risk evaluation shows that there are nine critical construction safety risk factors for the HOPSCA project and that most of the management-level risk factors have high uncertainty. This study explores and provides effective measures to combat these factors.

This study innovatively applies the Interval-OPA method to risk assessment, offering a fitting method for evaluating the HOPSCA project’s construction safety risks and accidents. The model aids decision-makers in appropriate risk classification and selection of scientific risk prevention strategies, enhances HOPSCA’s construction safety management system and even benefits all under-construction projects, promoting the construction industry’s sustainable development.

Green entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.

The study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.

Results indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.

The study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.

This study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

This paper aims to elucidate the pivotal role of Digital Twin (DT) technology in addressing the adverse impacts of Urban Heat Island (UHI) and consolidate the fragmented knowledge of DT technology in urban environments by identifying applied actions, proposing an approach and revealing challenges for tackling UHI effects.

Using a systematic literature review, 24 materials were retrieved from scholarly databases to provide a comprehensive understanding of DT technology and propose a conceptual framework for mitigating UHI effects.

The results revealed three major study categories within the DT and UHI domains: (1) DT-enabled actions for urban greenery optimisation, (2) DT implementation for enhancing resilience in urban planning and (3) increasing the fidelity level of DT for addressing UHI effects. Additionally, this paper introduces REFLECT, a conceptual DT-enabled framework consisting of seven layers: Retrieve, Establish, Facilitate, Lump, Examine, Cognition and Take. The framework proposes developing a systems-based model with identifiable scopes, strategies and factors through a multilayered platform, specifying model input, process and output towards mitigating UHI effects.

This paper contributes to the discourse on sustainable urban development by highlighting the challenges associated with DT technology in mitigating UHI. It introduces a conceptual framework to demonstrate applications and directions for developing innovative solutions to unlock the full potential of DT technology in mitigating UHI effects.

This paper aims to design a novel hybrid terrestrial-aerial robot, FlyingDog, including its modeling and implementation. By combining the complementary advantages of a quadrotor drone and a quadruped robot, FlyingDog demonstrates excellent maneuverability and high energy efficiency, showcasing great potential for applications in industrial inspection, field exploration, and search and rescue operations.

By integrating propellers and leg mechanisms, FlyingDog achieves hybrid motion, encompassing both aerial flight and ground movement. This paper first provides an overview of the robot’s structural design, emphasizing the minimization of interactions between the aerial and ground mechanisms while balancing the thrust-to-weight ratio and payload capacity. A distributed control framework is then proposed to achieve the hybrid motion, alongside the development of corresponding control strategies to ensure stability during various movements.

Experiments conducted in real-world conditions validated FlyingDog’s performance in terms of motion stability, energy efficiency, and obstacle-crossing ability. The results demonstrate that FlyingDog exhibits outstanding mobility by combining ground locomotion with aerial flight capabilities, allowing it to overcome challenging obstacles in purely ground-based mode. In ground mode, the robot achieved an energy efficiency of up to 93.5%.

The hybrid terrestrial-aerial robot presented in this paper features stable land and aerial mobility, a lightweight structure, high energy efficiency, and low manufacturing costs, making it a valuable innovation in the field of robotics.

Using Chinese A-share listed firms between 2007 and 2020 with 21,380 observations, we aim to examine the impact of cross-ownership on firms’ innovation output and explore the underlying mechanisms.

To test the influence of cross-ownership on firms’ innovation output, this paper constructs an ordinary least square regression model. The explained variables are firms’ innovation output, including the total number of patent applications (Apply) and the number of invention patent applications (Apply_I). Considering the long period of patent R&D, we take the value of the explained variables in the following year for regression. Cross-ownership (Cross) is the explanatory variable; Control is the control variable; and ε is the regression residual term.

We find that cross-ownership significantly promotes corporate innovation output, indicating that cross-owners play an important role in “collaborative governance.” This finding remains unchanged after conducting a series of robustness tests. We also find that cross-ownership contributes to innovation output mainly through two plausible channels: the relaxation of financing constraints and reducing both types of agency costs. Further analysis shows that cross-ownership has a more pronounced influence on innovation output in those firms with higher equity restriction ratios and facing more competitive markets. Moreover, cross-ownership has a profound impact on firms’ innovation quality and innovation efficiency, thereby increasing firm value.

This study provides important policy implications. First, cross-owners should actively play their resource and supervision advantages to improve firms’ long-term development capability through the “collaborative governance” effect. Second, listed companies in China should be fully aware of the value of the cross-ownership and use the cross-ownership as a bridge to strengthen the cooperative relationship with firms in the same portfolio. Meanwhile, they need to pay attention to cross-ownership’s “collaborative governance” effect to provide an impetus for the healthy development of enterprises. Finally, government regulators should maintain appropriate supervision of the cross-ownership linkage in the market.

Our findings show that cross-ownership significantly contributes to firms’ innovation output, indicating that cross-owners play the role of “collaborative governance.” While paying attention to the collusion effect of the cross-ownership, they shall not ignore its governance effect, for example, the promotion effect on the innovation level. Government regulators should appropriately supervise the cross-ownership linkage, which is conducive to maintaining the market order and driving the healthy development of the capital market.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.