A Mechanism for Digital Finance to Drive Green and Low-Carbon Development

Given the global focus on green and low-carbon development and the increasing prominence of digital finance,it is particularly important to explore how to leverage digital finance to achieve these environmental goals.This study,through mechanism analysis,deeply examines how China’s digital finance promotes green and low-carbon development and elucidates the positive interaction between digital finance and the green industry.The study found that digital finance,through more flexible and efficient financial functions,alters the cost structure of carbon emissions,and reduces the risks and costs of green investments,thereby creating a cooperative green mechanism benefiting all parties,and guiding social groups toward a green and low-carbon transformation.Additionally,the rapid development of digital finance has strengthened the implementation of environmental protection policies,effectively promoted the expansion of the environmental protection industry,and established the green ethos as a mainstream concept in financial development.This study aims to provide reference perspectives and suggestions,assist policymakers in promoting the green and lowcarbon development of digital finance,and offer insights into the integrated development of digital finance and the green environmental protection industry.

Synergistic dance of digital economy and green finance on carbon emissions:Insights from China

China has recently implemented a dual-carbon strategy to combat climate change and other environmental issues and is committed to modernizing it sustainably.This paper supports these goals and explores how the digital economy and green finance intersect and impact carbon emissions.Using panel data from 30 Chinese provinces over the period 2011-2021,this paper finds that the digital economy and green finance can together reduce carbon emissions,and conducts several robustness tests supporting this conclusion.A heterogeneity analysis shows that these synergistic effects are more important in regions with low levels of social consumption Meanwhile,in the spatial dimension,the synergistic effect of the local digital economy and green finance adversely impacts the level of carbon emissions in surrounding areas.The findings of this paper provide insights for policymakers in guiding capital flow and implementing carbon-reduction policies while fostering the growth of China’s digital economy and environmental sustainability.

The Mediating Effect of Digital Transformation on the Link Between Executive Overconfidence and Corporate Green Innovation

Green technological innovation is crucial for the manufacturing industry’s green transformation and sustainable development.This study examines the impact of executive overconfidence on corporate green innovation,focusing on the internal drivers of corporate innovation and using a sample of Shanghai and Shenzhen A-share listed manufacturing companies from 2013 to 2020.We further examine the mediating role of digital transformation and the moderating role of external attention.The findings indicate that executive overconfidence promotes corporate green technological innovation.Overconfident executives enhance green innovation by accelerating digital transformation.Moreover,external attention from analysts and media positively moderates the relationship between executive overconfidence and corporate green innovation.Heterogeneity analysis reveals that the positive impact of executive overconfidence on green innovation is more significant in non-state-owned enterprises,high-tech firms,and enterprises with lower pollution levels.

Dynamic evaluation of digital and green development policies based on text mining of the PMC framework

Aiming to identify policy topics and their evolutionary logic that enhance the digital and green development(dual development)of traditional manufacturing enterprises,address weaknesses in current policies,and provide resources for refining dual development policies,a total of 15954 dual development-related policies issued by national and various departmental authorities in China from January 2000 to August 2023 were analyzed.Based on topic modeling techniques and the policy modeling consistency(PMC)framework,the evolution of policy topics was visualized,and a dynamic assessment of the policies was conducted.The results show that the digital and green development policy framework is progressively refined,and the governance philosophy shifts from a“regulatory government”paradigm to a“service-oriented government”.The support pattern evolves from“dispersed matching”to“integrated symbiosis”.However,there are still significant deficiencies in departmental cooperation,balanced measures,coordinated links,and multi-stakeholder participation.Future policy improvements should,therefore,focus on guiding multi-stakeholder participation,enhancing public demand orientation,and addressing the entire value chain.These steps aim to create an open and shared digital industry ecosystem to promote the coordinated dual development of traditional manufacturing enterprises.

Consideration of green intelligent steel processes and narrow window stability control technology on steel quality

In order to promote the intelligent transformation and upgrading of the steel industry, intelligent technology features based on the current situation and challenges of the steel industry are discussed in this paper. Based on both domestic and global research, functional analysis, reasonable positioning, and process optimization of each aspect of steel making are expounded. The current state of molten steel quality and implementation under narrow window control is analyzed. A method for maintaining stability in the narrow window control technology of steel quality is proposed, controlled by factors including composition, temperature, time, cleanliness, and consumption(raw material). Important guidance is provided for the future development of a green and intelligent steel manufacturing process.

How R&D investment promotes green technology innovation in the context of digitalization?-An empirical analysis based on provincial panel data

Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.

Path of green and intelligent manufacturing in the Internet era

Green and intelligent manufacturing will be the development direction of manufacturing industry because of their features: low pollution on the environment, high effi ciency and automatic production. The rapid development of internet is changing the direction of the manufacturing industry, which will impact the green and intelligent manufacturing. Based on these, the environment and conditions of green and intelligent manufacturing brought by internet is sorted out in this paper; and the new opportunities and challenges of green and intelligent manufacturing in the internet era are analyzed. Then, the path for the realizing green and intelligent manufacturing is studied; and some policy recommendations are put forward to promote the realization of green and intelligent manufacturing.

A Study of the Impact of the Digital Economy on the Efficiency of Urban Green Economy--Based on the Yangtze River Delta City Cluster

This paper adopts the non-expected output-super-efficiency SBM(Slacks-Based Model)model and principal component analysis to calculate the green economy efficiency and the digital economy level of 27 prefecture-level cities in China’s Yangtze River Delta urban agglomeration between 2011 and 2019,respectively,and examines the impact of the digital economy on the green economy efficiency by using benchmark regression and mechanism analysis.The findings show that,first,the digital economy has a significant contribution to the green economic efficiency of cities,and this conclusion still holds after robustness tests such as replacing explanatory and interpreted variables and introducing province-fixed effects.Second,through the mechanism test,it is found that the digital economy can indirectly promote urban green economic efficiency through the positive mechanism effect of promoting industrial structure upgrading.

Global energy transition revolution and the connotation and pathway of the green and intelligent energy system

The essence of energy system transition is the"energy revolution':The development of the"resource-dominated"energy system with fossil energy as the mainstay has promoted human progress,but it has also triggered energy crisis and ecological environment crisis,which is not compatible with the new demands of the new round of scientific and technological revolution,industrial transformation,and sustainable human development.It is in urgent need to research and develop a new-type energy system in the context of carbon neutrality.In the framework of"technique-dominated"new green and intelligent energy system with"three new"of new energy,new power and new energy storage as the mainstay,the"super energy basin"concepts with the Ordos Basin,Nw China as a representative will reshape the concept and model of future energy exploration and development.In view of the"six inequalities"in global energy and the resource conditions of"abundant coal,insufficient oil and gas and infinite new energy"in China,it is suggested to deeply boost"China energy revolution',sticking to the six principles of independent energy production,green energy supply,secure energy reserve,efficient energy consumption,intelligent energy management,economical energy cost;enhance"energy scientific and technological innovation"by implementing technique-dominated"four major science and technology innovation projects',namely,clean coal project,oil production stabilization and gas production increasing project,new energy acceleration project,and green-intelligent energy project;implement"energy transition"by accelerating the green-dominated"four-modernization development',namely,fossil energy cleaning,large-scale new energy,coordinated centralized energy distribution,intelligent multi-energy management,so as to promote the exchange of two 80%s"in China's energy structure and construct the new green and intelligent energy system.

Development,Characterization and Application of a Three-Layer Intelligent pH-Sensing Indicator Based on Bromocresol Green(BCG)for Monitoring Fish Freshness

As a novel food quality monitoring technology,intelligent freshness indicator has received wide attention in recent years.However,its poor safety and stability are the main problems hindering its practical application.Hence a new pH-sensing indicator based on bromocresol green(BCG)was developed in this study for nondestructive and real-time monitoring the freshness of marine fishes.The indicator was designed with a three-layer structure,using the polytetrafluoroethylene(PTFE)membrane with high hydrophobicity and air permeability as the inner layer to isolate the moisture in the package,BCG-coated filter paper as the colorchanging layer to indicate the freshness of fish,and a transparent unidirectional permeable(TUP)membrane with moisture resistance as the out layer to isolate the moisture in the environment.This contributed to weaken the influence of humidity and prevent dye migration,so as to improve the accuracy and safety of the indicator.Therefore,a highly sensitive and distinguished color variation response to trimethylamine(TMA)standard solution with different concentrations was observed on the indicator.Additionally,the indicator showed a high color stability at different storage temperatures up to 14 days with total color differences(ΔE)less than 5.0.The indicator presented visible color variations from yellow to green then eventually to blue when applied to monitor the freshness of sea bass and salmon stored at 4℃,implying that fish was spoiled.Meanwhile,indicatorsΔE value was significantly positively correlated with total volatile basic nitrogen(TVB-N)and total viable count(TVC)in sea bass and salmon samples.Thus,the pH-sensing indicator can be applied as a cost-effective and promising intelligent indicator for monitoring fish freshness.

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process- safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging informa- tion technologies such as arti cial intelligence (AI) are quite promising as a means of overcoming these dif culties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety;② knowledge-based reasoning for process safety;③ accurate fusion of heterogeneous data from various sources;and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent years.Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption.Many factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial variations.The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations.This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs.Based on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications.Realistic databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

Assessment Framework of Green Intelligent Transformation of Small Hydropower in China

With the comprehensive promoted construction of the establishment of green small hydropower,the defects of existing small hydropower station are gradually emerging,and it is necessary to implement green intelligent transformation to promote the construction of energy internet in China.This study focuses on constructing a green intelligent planning and transforming assessment framework,and assists management department to filtrate the small hydropower stations which can be transformed reasonably.Firstly,power station economy,ecological environment,technical safety management and social benefits are involved in the assessment index system.Secondly,multi-expert judgment aggregation based on fuzzed comparison scale is put forward to calculate the index value,and evidence synthesis is used to comprehensively assess the feasibility of green and intelligent planning and transformation for several small hydropower stations.The simulation case analysis shows the constructed assessment framework can reflect the actual situation of objectives properly and would provide decision-making basis for green and intelligent planning and transformation of small hydropower stations.

Influence mechanism of government subsidy on the green transformation of coal company in China

The optimization of government subsidies to enhance the efficiency of coal companies’green transformation constitutes a critical component in the pursuit of global sustainability.We investigate the influence mechanism of government subsidies on the green transformation using data from the listed coal companies in China from 2007 to 2022.According to our findings and hypothesis testing,previous government subsidies did not have a significant direct impact on coal companies’green transformation.Nevertheless,government subsidies can help coal companies transition to greener practices by promoting innovative green initiatives.Furthermore,we confirmed an indirect route:that government subsidies enable the adoption of low-carbon initiatives,which in turn could facilitate the transition of coal companies towards green practices.In addition,we discovered that the coal company’s digitization will improve this indirect route.Thus,we propose increasing the effectiveness of government subsidies in facilitating coal companies’transition to green practices by focusing on technological advancements and enhancing company digitalization.

Research on the Spillover Effects of Digital Transformation on the Sustainable Growth of Green Schools

The development of society and the advancement of science and technology have led to the widespread integration of digital transformation in the field of education.However,the current establishment of green schools faces various challenges,including non-environmental building facilities,high renovation costs,low organizational management efficiency,high energy consumption,outdated office tools,and insufficient environmental awareness among teachers and students.Through thorough research and analysis,it becomes evident that digital technology can play a pivotal role in addressing these challenges and contribute to all aspects of green school establishment.The incorporation of digital thinking concepts is essential for the construction of ecologically civilized campuses and inclusive innovation.The process of digital design and transformation proves instrumental in optimizing both software and hardware facilities within the campus,thereby reducing energy consumption.Simultaneously,comprehensive digital teaching management enhances overall efficiency in management and service delivery.Innovative digital teaching and learning models emerge as transformative tools,providing new avenues to create low-carbon,green classrooms for both teachers and students.By exploring the application of digital transformation in establishing green schools and examining the resulting spillover effects,valuable insights can be gained.These insights,in turn,serve as reference points for building diversified digital technology paths on campus and fostering the creation of green schools.

Moderating effect of corporate financialization on the impact of climate policy on corporate green innovation:Evidence from China

With intensifying global climate change,humanity is confronted with unparalleled environmental challenges and risks.This study employs the staggered difference-in-difference model to examine the relationship between climate policy and green innovation in the corporate financialization context.Using Chinese-listed company data from 2008 to 2020,our analysis reveals a favorable correlation between China’s carbon emission trading policy(CCTP)and advancements in green innovation.Furthermore,we find that the level of corporate financialization moderates this correlation,diminishing the driving effect of CCTP on green innovation.Additionally,results of heterogeneity analysis show that this moderating consequence is more evident in non-state owned and low-digitization enterprises compared with state-owned and high-digitization ones.Our findings contribute to the existing literature by clarifying the interaction between CCTP,green innovation,and corporate financialization.Our research provides valuable insights for policymakers and stakeholders seeking to strengthen climate policies and encourages green innovation in different types of businesses.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental conditions.To mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission parameters.The orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and pollution.Employing K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations.To estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is used.Exploring various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of EDs.Empirical findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs combination.Notably,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

Green Charm from Green Design

The way of making people like to use the product by emotional and aesthetic design and making product with charm is illustrated. Examples show that to solve the green charm problem and protect environment only by people＇s responsibility and duty is far from enough. The most important is to attract people loving the green product from the bottom of their heart and to realize the harmonious union of the nature and human beings.

A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider(AUG)is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications,in which path planning is an essential problem.Due to the complexity and variability of the ocean,accurate environment modeling and flexible path planning algorithms are pivotal challenges.The traditional models mainly utilize mathematical functions,which are not complete and reliable.Most existing path planning algorithms depend on the environment and lack flexibility.To overcome these challenges,we propose a path planning system for underwater intelligent internet vehicles.It applies digital twins and sensor data to map the real ocean environment to a virtual digital space,which provides a comprehensive and reliable environment for path simulation.We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters.The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing.The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility.The task-related reward function promotes the rapid convergence of the training.The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Research on digital twin technology and its application in intelligent operation and maintenance of highspeed railway infrastructure

Purpose–This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/methodology/approach–This paper provides a comprehensive overview of the definition,connotations,characteristics and key technologies of digital twin technology.It also conducts a thorough analysis of the current state of digital twin applications,with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure.Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study,the paper details the construction process of the twin system from the perspectives of system architecture,theoretical definition,model construction and platform design.Findings–Digital twin technology can play an important role in the whole life cycle management,fault prediction and condition monitoring in the field of high-speed rail operation and maintenance.Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.Originality/value–This paper systematically summarizes the main components of digital twin railway.The general framework of the digital twin bridge is given,and its application in the field of intelligent operation and maintenance is prospected.