Industrial Solutions Research Articles

Navigating the Challenges and Seizing Opportunities in Implementing Closed-Loop Supply Chains

Closed-Loop Supply Chains (CLSCs) present a transformative solution for industries seeking sustainability by reintegrating returned products through recycling, refurbishment, and remanufacturing, reducing waste and dependency on added resources. While offering substantial benefits, CLSC adoption faces challenges, including high infrastructure costs, complex reverse coordination, and inconsistent regulatory support, which hinder widespread application. Through a mixed-method approach using surveys and case studies, this study reveals barriers such as financial constraints and operational restructuring needs, while highlighting enablers like digital tools (e.g., blockchain, IoT) that enhance efficiency and transparency. Furthermore, it emphasizes the critical role of regulatory incentives (subsidies, tax breaks, and extended producer responsibility mandates) and strategic partnerships that pool resources and share risks, essential for small and medium-sized enterprises (SMEs) aiming to implement CLSCs effectively. By proposing a practical CLSC framework, informed by empirical data, this research provides actionable insights for businesses to embrace circular economy principles, aligning environmental responsibility with corporate resilience, profitability, and consumer expectations. CLSCs, therefore, are not only an environmental solution but also a strategic opportunity that addresses sustainability imperatives while meeting regulatory requirements and advancing competitive advantage.

Development of Software for Hydrometallurgical Calculation of Metal Extraction

Hydrometallurgy plays a critical role in the metallurgical industry by providing an efficient method for extracting metals from ores and secondary materials using aqueous solutions. This approach is particularly advantageous for processing low-grade and complex ores, as well as secondary resources that cannot be effectively processed by traditional pyrometallurgical methods. The objective of this study is to develop specialized software to automate and optimize the calculations necessary for metal extraction in hydrometallurgical processes. The software integrates a complete computational framework for automating the various stages of the hydrometallurgical process, including ore composition initialization, total element mass calculations, and the determination of metal concentrations in both metal products (matte) and by-products (slag). The methodology involves the design and implementation of a web-based application using Django for the user interface and MySQL for robust data storage. The computational module, written in Python, automates the mathematical operations required to simulate complex chemical reactions and metal extraction processes. This module supports real-time processing and ensures accurate calculations for each stage of metal extraction, including leaching, extraction, re-extraction, sorption, and electrolysis. The software also features detailed tables and dynamic graphs that allow users to analyze the distribution of valuable metals and assess the influence of key operational parameters on extraction efficiency. The results show that the developed software successfully manages large datasets, enhances the precision of hydrometallurgical calculations, and minimizes the risk of human error. The implementation of the software leads to significant improvements in the economic efficiency of production by optimizing metal recovery rates and reducing operational costs. Additionally, it supports comprehensive process control by providing actionable insights into each stage of extraction, thus improving the quality of the final metal product. Overall, the software represents a significant technological advancement in the field, offering a scalable solution for industries aiming to streamline hydrometallurgical processes.

Effect of quaternary binder slag-based geopolymer slurries on mechanical durability and microstructural properties of green prepacked composites

This study conducts a detailed investigation into the effects of slag-based quaternary binder geopolymer slurries on the physico-mechanical properties, high temperature resistance, and microstructural characteristics of prepacked composites. This research employs a strategic combination of aluminosilicate precursors, including metakaolin (MK), pumice powder (Pum), and perlite powder (Per), which are blended with slag in varying mix proportion to identify and optimizing the composite’s performance. In the key finding reveal that samples containing 20 % of MK achieved superior performance, displaying the lowest porosity and water absorption rates. The oven-dry density of the mixtures demonstrated a marked improvement with the inclusion of MK, increasing from a range of 2150–2230 kg/m3 in 0 % MK mixture to 2250–2350 kg/m3 in those with 20 % MK. Post heat curing, the compressive strength peaked at 31.12 MPa in the mixture comprising 20 % MK, 20 % pumice, and 10 % perlite; however, this strength reduced to 11.92 MPa when subjected to a temperature of 600℃. In high temperature resistance tests, the combination of 20 % MK and 10 % perlite resulted in a minimal mass loss of 4 % at 600℃, indicating a robust resistance to thermal degradation. Furthermore, the 24-h water capillary absorption displaying lowest water absorption rates. This study provides a critical advancement in the use of natural pozzolans for developing high-performance, slag based geopolymer slurries. The enhanced high-temperature strength and reduced water absorption underscore the potential of these materials to deliver durable and sustainable solution for the construction industry, particularly in application requiring superior thermal resistance and longevity.

Constructing Organic-Inorganic Woven Hybrid Membrane for Highly-Selective Catalytic Conversion of Lignin-based Phenolic Molecule to Value-added Products

Addressing the global need for sustainable chemical processes, this study introduces an innovative Woven Hybrid Membrane (WHM) specifically designed for the efficient oxidation of vanillyl alcohol (VAA) to vanillin (VLN). This transformation is crucial for converting lignocellulosic biomass into valuable chemical products, aligning with sustainable development goals. Utilizing a novel catalytic system that combines 2,2,6,6-tetramethylpiperidine 1-oxyl (TMP) and copper on a unique mesh-like structure, the WHM enhances both the economic and environmental viability of the process. This design not only overcomes the limitations of powdered catalysts, which are challenging to recover and reuse, but also promotes high catalytic efficiency and selectivity. By employing oxygen as the oxidant, our system maintains near 100% selectivity for VLN, demonstrating high conversion rates across multiple cycles without significant degradation in performance. Advanced analytical techniques, including electrochemical analysis and Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), were used to explore the catalyst’s functionality and the reaction mechanism. The results confirm the stability and effectiveness of the WHM, showcasing its potential as a reusable, highly efficient, and environmentally friendly catalyst. This research represents a significant step forward in the field of sustainable industrial chemistry, offering a robust solution for the bio-refinery industry's push towards greener processes.

Innovative heat sink design for enhanced cooling efficiency in Dual Active Bridge (DAB) topology

Abstract After in-depth research and analysis, aiming at the heat generation mechanism and limitations of the current heat dissipation method, this paper proposes an innovative heat dissipation design scheme. By optimizing the structural design of the heat sink, the heat dissipation surface area is increased, thus achieving more efficient heat emission and maintaining the optimal operating temperature of the equipment. Experimental data show that this new heat dissipation design significantly improves the cooling efficiency, an increase of about 60%, and provides support for the performance improvement of high-power power products, which brings a reliable and efficient cooling solution for related industries.

Economic Feasibility of 5G-Based Autonomous Mobile Robots Solutions for Industry 4.0

Economic Feasibility of 5G-Based Autonomous Mobile Robots Solutions for Industry 4.0

Machine learning techniques for enhancing security in financial technology systems

The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.

Enhancing supply chain security and transparency with ai and blockchain integration for future-proof solutions

The objective of this review paper is to examine the integration of Artificial Intelligence (AI) and blockchain technology to enhance supply chain security and transparency, providing future-proof solutions for global industries. As the digital landscape evolves, traditional supply chain management methods face increasing vulnerabilities and inefficiencies. This paper reviews existing literature to assess the current challenges and potential benefits of integrating AI and blockchain in supply chains. The review highlights how AI enhances predictive analytics, demand forecasting, and operational efficiency, while blockchain ensures immutable, decentralized ledgers for real-time tracking and verification of goods. The synergy between these technologies offers unprecedented levels of transparency, enabling stakeholders to detect and respond to anomalies swiftly. Case studies across various industries demonstrate significant improvements in reducing fraud, counterfeiting, and human errors, alongside enhanced compliance with regulatory standards. Conclusions drawn from this review underscore the transformative potential of AI and blockchain in creating resilient, transparent, and secure supply chains. The integration of these technologies not only addresses current vulnerabilities but also lays the foundation for adaptive, intelligent supply chain networks capable of withstanding future disruptions. This paper provides actionable insights and practical frameworks for businesses and policymakers aiming to implement AI and blockchain solutions, emphasizing the importance of strategic planning, cross-industry collaboration, and continuous innovation in achieving long-term supply chain sustainability and security.

Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption

This paper introduces a novel Privacy-Preserving Verifiable Federated Learning (PPVFL) scheme that integrates blockchain technology and homomorphic encryption to address critical challenges in decentralized machine learning. The proposed scheme ensures data privacy, integrity, verifiability, robust security, and efficiency in collaborative learning environments, particularly in sensitive domains such as healthcare. By leveraging blockchain’s decentralized, immutable ledger and homomorphic encryption’s capability to perform computations on encrypted data, the model maintains the confidentiality of sensitive information throughout the learning process. The inclusion of Byzantine fault tolerance and Elliptic Curve Digital Signature Algorithm (ECDSA) further enhances the system’s security against malicious attacks and data tampering, while the optimization of computational processes ensures efficient model training and communication. The novelty of this work lies in the seamless integration of blockchain and homomorphic encryption within a federated learning framework, specifically tailored for post-quantum cryptography, a combination that has not been extensively explored in prior research. This research represents a significant advancement in secure and efficient federated learning, offering a promising solution for industries that prioritize data privacy, security, and trust in collaborative machine learning. The effectiveness, security, and efficiency of the PPVFL scheme were validated using the Glaucoma dataset. The proposed method outperformed baseline federated learning algorithms, achieving a Dice coefficient of 0.918 and a Hausdorff distance of 4.05 on Severe Glaucoma (SG) cases, compared to 0.905 and 5.27, respectively, with traditional FedAvg. Moreover, the integration of blockchain and homomorphic encryption ensured that data privacy was upheld without compromising model performance, while efficient computation and communication processes minimized latency and resource consumption. This study contributes a robust, privacy-preserving, secure, efficient, and verifiable federated learning framework that addresses the pressing need for secure and scalable data management in distributed machine learning environments.

Life cycle assessment of recycling waste crystalline silicon photovoltaic modules: A comparison between traditional and green solvent recycling processes

Crystalline silicon photovoltaic (PV) modules that have reached the end of their service life, if not effectively recycled, result in the loss of valuable resources such as silicon, silver, aluminum, and others. Moreover, improper disposal can lead to long-term environmental pollution. Therefore, the development of efficient and green recycling technologies for waste PV modules has become a key focus of current research. In this study, a life cycle assessment (LCA) of green solvent recycling (GSR) and traditional recycling technologies was conducted using the ReCiPe 2016 Mid-point evaluation method. The energy and resources consumed, along with the environmental impacts generated, were comparatively analysed, and their resource recovery benefits were calculated. Results show that GSR performs well in terms of environmental impact and even better in terms of resource recovery benefits. Leaching with 1,3-dimethyl-2-imidazolidinone (DMI) solvents reduces global warming potential by 487 kg CO2 eq compared to nitric acid used in chemical recycling. Deep eutectic solvents (DES) achieve higher leaching rates of up to 99 %, compared to metals recovered via mechanical separation, and reduce terrestrial ecotoxicity by 742 kg of 1,4-DCB. Of all energy and resource inputs, electricity consumption contributes the most to environmental impacts. In summary, this study offers a more environmentally friendly and effective recycling solution for the PV industry, supporting its green transformation and sustainable development.

Nanochitosan-encapsulated melatonin: an eco-friendly strategy to delay petal senescence in cut gerbera flowers

BackgroundThe preservation of cut flowers, particularly Gerbera jamesonii, is crucial for maintaining their aesthetic value and extending vase life in the floriculture industry. To address this challenge, this study investigated the effects of melatonin (Mel) and encapsulated melatonin with nanochitosan (nCS-Mel) as preservative solutions on cut Gerbera jamesonii cv. ‘Terra kalina’ flowers. In research, we examined various physiological and biochemical parameters, including relative water content, membrane stability index, carbohydrate content, and antioxidant enzyme activities, to evaluate the efficacy of these treatments in prolonging the vase life and quality of cut gerbera flowers under controlled environmental conditions.ResultsOur results demonstrated that cut Gerbera jamesonii flowers maintained in vase solutions containing 0.1 and 0.5 mM nCS-Mel exhibited enhanced preservation of cell membrane integrity and anthocyanin content, while also maintaining higher levels of carbohydrates and total flavonoids in petals at the conclusion of their vase life. A decline in petal relative water content and protein levels was observed concomitantly with petal senescence, whereas total phenolic compounds showed an increase. The hydrogen peroxide (H2O2) content in petals exhibited an upward trend during vase life in control specimens, but this effect was mitigated in treatments containing melatonin. Although malondialdehyde (MDA) content generally increased throughout the vase life period, flowers subjected to either Mel or nCS-Mel treatments displayed reduced MDA accumulation. The activity of catalase (CAT) demonstrated an increasing trend during vase life, with the maximum activity observed in Gerbera flowers treated with 0.1 mM nCS-Mel. A similar upward trend was noted for superoxide dismutase (SOD) activity, with flowers in 0.5 mM nCS-Mel treatment exhibiting peak SOD values on day 12 relative to control and other treatments. Peroxidase (POD) activity also increased across all treatments, with particularly pronounced effects in vase solutions containing 0.1 mM Mel and nCS-Mel. Notably, flowers placed in vase solutions containing 0.1 mM nCS-Mel, followed by 0.5 mM nCS-Mel and 0.1 mM Mel, exhibited the most prolonged vase life, extending up to 12, 10.66, and 10.33 days, respectively, under room temperature conditions.ConclusionsThe application of nanoencapsulated melatonin as a vase solution for cut Gerbera jamesonii flowers demonstrates significant potential in extending vase life and maintaining flower quality through enhanced preservation of cellular integrity, antioxidant activity, and biochemical parameters. This innovative approach not only outperforms conventional treatments but also presents a more environmentally friendly alternative to traditional antimicrobial preservatives and sugars, offering a promising solution for the floriculture industry to improve cut flower longevity and reduce ecological impact.

Cybersecurity Maintenance in the Automotive Industry Challenges and Solutions: A Technology Adoption Approach

Numerous attempts have been made to create a secure system that meets the criteria and requirements of the automotive vehicle development life cycle. However, a critical gap exists in the secure development lifecycle, particularly concerning the development and maintenance of software after the vehicle has been sold by the manufacturer. This step is often overlooked by original equipment manufacturers (OEMs), especially after the expiration of the vehicle warranty period, given the cost that it will require to update and test the software in their vehicles. This paper addresses the issues that affect current and future vehicle cybersecurity, during the maintenance of cybersecurity, and how the neglect of it could end up creating hazards for the vehicle owner or other road users. To accomplish this, we will employ the technology adoption model (TAM) as a theoretical framework, which is used to understand and predict how organizations adopt technology. Thus, through qualitative and quantitative research, including text mining, we identify the challenges in the adoption and diffusion of cybersecurity maintenance in the automotive sector and its supply chain. In addition, we propose possible solutions on how to maintain a level of security that will benefit road users, OEMs and regulators, covering the cybersecurity needs for the vehicle’s usable life, taking into account the vehicle’s heterogeneity of components and technology, connectivity, environmental impact and cost of production and maintenance of a vehicle.

Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification

Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production.

Flow Field Analysis within Industrial Quenching Tanks Operated with Aqueous Polymer Solutions in the Steel Industry

Abstract Quenching with polymer solutions for steel heat treatment offers adjustable performance and improved ecological impact compared to conventional water or oil quenching. The submersion cooling operation poses challenges due to the intense vapor/polymer film collapse on the component surface, potentially destabilizing batch components. Therefore, controlled recirculation is critical to preventing tank and specimen damage during cooling and ensuring effective steel hardening. However, flow structure analysis is often overlooked and disregarded in industrial tank design. Thus, this study evaluates the tank geometries of two partner industries to determine if they provide the necessary homogeneous flow for optimal quenching. The analysis combines experimental velocity measurements with a numerical model, with the identification of different flow intensity regions being the main outcome of this work. The results suggest that geometric modifications could improve flow recirculation, enhancing quenching performance.

Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry

AbstractTo optimize infection control and bolster productivity within the poultry industry, it is imperative to accurately classify Chicken Eimeria species. There are several methods for determining Eimeria disease in chickens. Traditional methods involve watching for clinical symptoms, and macroscopic lesions, and studying the parasite’s biology and oocyst morphology. These methods are frequently time-consuming and labor-intensive, necessitating the manual collection and analysis of samples, which can be especially difficult in large chicken farms. Deep learning algorithms, on the other hand, provide automated, accurate, and non-invasive methods for the detection of Eimeria. This paper proposed a classification model for the automatic classification of chicken Eimeria species. The proposed model is mainly based on integrating neutrosophic set theory and InceptionV3 deep-learning architecture. Three primary phases make up the proposed chicken Eimeria species classification model: the data preprocessing phase, the neutrosophic image conversion phase, and the image classification phase. To address the issue of class imbalance in the adopted dataset and enhance the model’s generalizability, the random oversampling method, and data augmentation techniques are employed during the data preprocessing phase. The preprocessed data is considered to feed the neutrosophic set-based segmentation algorithm, where true, false, and intermediate subsets are extracted. Finally, the true subset is utilized to feed the optimized InceptionV3. To determine the optimal hyperparameter values for InceptionV3, a modified version of the Brown Bear optimization algorithm is proposed in this paper. To evaluate the effectiveness of the proposed model, a real benchmark dataset comprising images of different Eimeria species is adopted. The experimental results revealed that the proposed model offers a more efficient and accurate alternative to traditional methods and state-of-the-art models, enabling faster and more effective diagnosis and treatment of Eimeria infections. It achieved an overall accuracy, specificity, sensitivity, and F1-score of nearly 100%. Additionally, the results showed that the high performance of the proposed model can reduce labor costs and boost throughput, thereby enhancing economic viability even more.

Scraper Point Attitude Optimization in the Scraping Process of Aviation Composite Curved Surface

Aiming at the interference problem between the tool and the surface during the contact between the blade position and the composite material during the off-line programming scraping process of aviation composite parts, the contact effect of the blade during the scraping process of the composite surface is discussed, and a rotation projection attitude optimization strategy is proposed. Firstly, by constructing the mathematical model of the contact between the scraper and the surface, the geometric properties of the scraper and its contact on the surface are analyzed. Then, the non-uniform rational B-spline (NURBS) surface is used to reconstruct the surface, and the position information of the local area of the contact point is obtained to obtain the normal direction and curvature. In order to evaluate the contact effect of the scraper, an objective function is defined to minimize the contact error between the scraper linear trajectory and its projection on the surface, thereby quantifying the contact accuracy. By comparing the position and attitude of the scraper before and after optimization, the effectiveness of the proposed optimization method is verified. The optimized scraper posture significantly improves the contact accuracy with the composite surface, better adapts to the geometric characteristics of the complex surface, and ensures the efficiency and consistency of the scraping process. The research results provide a new perspective and method for the future development of automatic scraping process, and also provide an effective solution and technical means for other industries in the application scenario of curved scraping.

The Role of Business Incubators in Encouraging Maritime Technology Start-up Innovation

This study examines the critical role of business incubators in driving innovation in maritime technology start-ups. With the growing need for innovative technology solutions in the maritime industry, business incubators have emerged as a key catalyst in developing and introducing new ideas to the market. The study used a qualitative approach, involving in-depth interviews with 30 stakeholders, including start-up founders, incubator managers, and maritime industry experts. The results showed that business incubators provide critical support in the form of access to funding, expert mentoring, research and development facilities, and extensive industry networks. The findings also revealed that incubators that focus on the maritime sector are more effective in helping start-ups overcome industry-specific challenges. The study highlights the importance of collaboration between incubators, the maritime industry, and educational institutions to create an ecosystem that supports innovation. Implications of this study include recommendations for policymakers and industry players in designing more effective incubation programs to drive maritime technology innovation.

Controllable mechanism of hazardous jarosite transformation into recyclable hematite in the leaching solution of secondary zinc oxide powder

The controlled synthesis of recyclable hematite in the leaching solution of secondary zinc oxide powder is an urgent problem in the chemical industry and metallurgy fields. In this paper, the effects of the temperature, agitation speed, and seed addition on the contents of iron, sulfur, potassium, sodium, and zinc in the iron removal residues, as well as the iron concentration in the supernatant after iron removal were systematically studied. In addition to the temperature control already reported, we found that the agitation speed can also control the transformation between jarosite and hematite phases. The content of jarosite in the residues can be effectively controlled by adjusting the agitation speed, thereby significantly improving the quality of hematite product, as indicated by SEM-EDS and XRD results. Under temperature of 185 °C, an agitation speed of 500 rpm, and a seed addition of 15 g/L, the iron, sulfur and zinc contents in the filter residues and the iron concentration in the supernatant were 59%, 3.22%, 0.92%, and 4.182 g/L, respectively. Kinetic studies show that the rapid oxidation kinetics is not conducive to the formation of high-quality hematite products. These results can directly guide the process of transformation of harmful jarosite into recyclable hematite from the leaching solution of secondary zinc oxide powder, which is of great practical significance for the controlled and waste-free removal of iron from aqueous solutions in the chemical industry and metallurgy fields.

Decoupling Online Ride-Hailing Services: A Privacy Protection Scheme Based on Decentralized Identity

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry.

Problems And Solutions of Real Estate Industry in China

Since real estate industry is one of the national pillar industries in China, therefore, it has great contributions to China’s economy and society. for example, promoting the Chinese economic growth in the long-run, and it provide financial support to government and benefits for cities development. Secondly, real estate also helping the development of relevant business, creating large amount of job opportunities to society, for residents, they can enjoy higher living standard. However, at same time of rapid development of real estate industry and economic boost, they exist series of problems that harmful to society like real estate bubble, unfinished buildings and cooperates financing difficulties. aiming at these problems, this paper suggest that government can through promulgate some new policies, laws, and establish the compete financing channel mechanism to solve these kinds of problems. The target of this study is to help real estate industry in China keep healthy development.