Green Smart Research Articles

Rethinking the Smart Green City Project in South Korea: A Critical Juncture Approach

Over the past 10 years, the Korean government has declared its intention to achieve carbon neutrality to the international community and strived to create smart cities and green cities as a practice to achieve carbon neutrality. In the same context, the Smart Green City project, which began in earnest at the end of 2020, initially attracted national attention due to the strong will of the administration. However, limitations in its performance were revealed. This study aimed to identify the policy mechanisms that led the Korean government to pursue the Smart Green City project using a critical juncture approach and to derive useful implications for policy selection. A critical juncture framework was applied for in-depth analysis, and a descriptive case study based on critical thinking was conducted. The results confirmed that endogenous and exogenous environmental changes surrounding the government at critical junctures were mechanisms for establishing the Smart Green City project. In addition, the government’s attempt to differentiate itself from the policies of previous governments was confirmed to be a constraint that actually hindered the project’s progress. This study concludes that if a government facing a complex crisis makes hasty policy decisions, it is likely to repeat the policy pattern or institutional path of previous governments. It also argued that in this situation, policy choices that aim to cut a new path may cause the government to choose a wrong path, which may have irreversible consequences.

The Innovative Smart Green Campus as Life-Based Learning Characteristics of Future Learning Efforts to Complete the SDG's

Objective: This study examines the application of the Smart Green Campus idea in higher education, focusing on its contribution to sustainability and experiential learning. Theoretical Framework: The study is founded on theories of sustainability, intelligent campus design, and experiential learning. These frameworks thoroughly comprehend integrating green technologies and experiential learning within educational settings. Method: This research employs a qualitative methodology, utilizing case studies of colleges executing Smart Green Campus programs to collect data. Data were collected via literature analysis, campus observations, and interviews with principal stakeholders. Results and Discussion: The results indicate that Smart Green Campus programs enhance students' environmental awareness and skill acquisition. Green technologies, including energy-efficient structures and sustainable transportation, promote an interactive learning atmosphere. Nonetheless, problems like financial expenditures and opposition to change are acknowledged. Research Implications: This study presents practical and theoretical implications by illustrating how Smart Green Campus initiatives can serve as examples of education aligned with the Sustainable Development Goals (SDG 6). The study emphasizes how universities can implement sustainable methods to improve environmental and educational outcomes. Originality/Value: This study enhances the literature by offering novel insights into the Smart Green Campus idea and its practical implementation. Its significance resides in providing a novel educational framework that equips students to address sustainable development challenges and must ensure the availability of clean water and sanitation as stated in SDG 6.

Mapping the technological landscape of green smart buildings: A patent analytics of key topics, leading companies, and technology gaps

In this study, we investigate the technological landscape of green smart buildings, using patent data from the European Patent Office (EPO) from 2007 onwards. Latent Dirichlet Allocation identified five key technology topics, with the HVAC system (Topic 4) emerging as particularly promising. Technology life cycle analysis revealed a growth phase for leading companies' patents after the Kyoto Protocol's second commitment period, with a predicted shift towards maturity after the Paris Agreement. Self-Organizing Maps and Social Network Analysis further pinpointed eight vacant technology nodes and 39 high-centrality patents within them, predominantly held by Japanese companies. This research provides valuable insights into the current state and future trajectory of green smart building technologies, facilitating informed decision-making for industry stakeholders and contributing to a more sustainable built environment. The novelty lies in the combined application of topic modeling, technology life cycle analysis, and network analysis to comprehensively explore the green smart building patent landscape, offering a unique perspective on its evolution and potential future directions.

Smart Green Cotton Textiles with Hierarchically Responsive Conductive Network for Personal Healthcare and Thermal Management.

Whereas cotton as an abundant natural cellulose has been widely used for sustainable and skin-friendly textiles and clothes, developing cotton fabrics with smart functions that could respond to various stimuli is still eagerly desired while remaining a great challenge. Herein, smart multiresponsive cotton fabric with hierarchically copper nanowire interwoven MXene conductive networks that are seamlessly assembled along a 3D woven fabric template for efficient personal healthcare and thermal comfort regulation is successfully developed. The robust hierarchically interwoven conductive network was "glued" and protected by organic conductive polymer poly(3,4-ethylenedioxythiophene) along a 3D interconnected fabric template to enhance interfacial adherent and environmental stability. Benefiting from the robust multiresponsive hierarchically interwoven conductive network, smart cotton fabric exhibits real-time response to various external stimuli (light/electrical/heat/temperature/stress), and the details of human activities can be accurately recognized and monitored. Furthermore, the porous structure of 3D smart fabric induced strong capillary force and confinement to phase change materials PEG, which exhibits a wide range of phase transition temperatures for efficient thermal comfort regulation. After further encapsulation with transparent fluorosilicone resin, the smart cotton fabric exhibits excellent self-cleaning performance with water/oil repellent. The smart multiresponsive cotton fabrics hold great promise in next-generation wearable systems for efficient personal healthcare and thermal management.

Smart Green Concrete: Innovation of Concrete Materials from Fly ash Waste and Lapindo Mud Integrated Smart E. crassipes Coir Geotextile to Realize Indonesia's Sustainable Infrastructure

Pariaman City, situated near the Semangko fault and the Indo-Australian plate subduction zone, is highly prone to earthquakes and tsunamis. To address this, the Earthquake Buddy Application (EDY App) was developed as an innovative solution for disaster mitigation. EDY App utilizes Geographic Information Systems (GIS) to provide real-time earthquake vulnerability maps, early warnings, and evacuation route suggestions to Temporary Evacuation Sites (TES). It also offers education on earthquake preparedness and facilitates donations for relief efforts. The application’s main contribution lies in its integration of comprehensive disaster management tools into one platform. By providing critical information during emergencies, the app empowers users to make informed decisions, which can reduce casualties in high-risk areas. The app also encourages community preparedness by offering accessible education on earthquake mitigation. EDY App is designed to create a disaster-resilient society in Pariaman, ensuring that residents are better equipped to respond to natural disasters through real-time alerts and evacuation support, ultimately enhancing local preparedness and reducing the potential impact of future earthquakes.

The Influence of Smart Green Spaces on Environmental Awareness, Social Cohesion, and Life Satisfaction in High-Rise Residential Communities

The Influence of Smart Green Spaces on Environmental Awareness, Social Cohesion, and Life Satisfaction in High-Rise Residential Communities

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

In the context of increasing urban pollution and its adverse health effects, this study focuses on enhancing the air quality prediction model by forecasting future concentrations of various Air Quality Index (AQI) levels to support the development of green smart cities. The primary objective of this study is to develop a robust multivariate time series forecasting model using a combination of Convolutional Neural Networks (CNN) and Customized Stacked-based Long Short-Term Memory (CSLSTM) networks, namely C2SLSTM. The proposed methodology involved preprocessing AQI and meteorological data, scaling the features, and employing a hybrid CNN-LSTM architecture to capture spatial and temporal dependencies in the data. Dynamic AQI pattern changes are computed using online learning. The proposed model was trained on a big dataset containing AQIs such as particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2), along with meteorological factors like weather count, temperature, wind, humidity, and pressure. Experimental results demonstrate that the model achieved a mean absolute error (MAE) of 0.25, mean square error (MSE) of 0.083, root mean square error (RMSE) of 0.289 and a R2-Score of 0.84 on the test set. Also, compared to Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Stacked Long Short-Term Memory (SLSTM), and Multilayer Perceptron (MLP) models, the C2SLSTM model showcases a performance enhancement of 35 %, 50 %, 30 %, and 32 %. Notably, the CNN component effectively extracted spatial features, while the LSTM layers captured temporal patterns, leading to precise predictions. These results indicate that the proposed approach effectively captures temporal dependencies and improves forecasting performance, with R2-Score values tending towards 1 for 12-hour prediction intervals. This research demonstrates the potential of advanced SLSTM architectures in accurately predicting AQI metrics, which can aid in better environmental monitoring and management. Also, the spatiotemporal correlation analysis is done through the designed ir metric.

Internet of Things-Based Robust Green Smart Grid

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

그린스마트 미래학교의 저학년 교실공간 특성에 관한 연구

In July 2020, the Ministry of Education announced the 'Green Smart Future School' project plan as the representative task of the Korean version of the New Deal. The Green Smart Future School project is a project promoted in the form of 'smart classroom', 'green school', and 'connection with local communities' based on 'space innovation', and 'safety' was added in 'Green Smart Future School 2.0' in 2022. In this study, the characteristics of the changed classroom space were analyzed through the results of the spatial innovation project conducted by the Daegu City Office of Education. Based on these results, the direction of reorganization of classroom space for the lower grades of elementary school for Green Smart Future School was suggested. Therefore, this study is thought to be used as a guide for the creation of a green smart future school in the future.

Green smart multifunctional wooden roofs enabled by single-step hydrophobic laser-induced graphene fabrication

Global warming, contributing to the worsening climate crisis, has led to more frequent heavy snowfall and rainfall. This causes corrosion, mold, and structural damage to roofs, which shortens the roofs' lifespan and potentially creates safety hazards. Addressing these challenges, we propose an approach to develop a multifunctional roof crucial for proactive adaptation to extreme weather. In this article, we present hydrophobic laser-induced graphene (LIG) onto wood in a single-step process using femtosecond-laser-direct-writing (FsLDW) technology in ambient air without additional chemical treatment. This hydrophobic LIG, showcasing a high electrical conductivity of 10.0 Ω·sq⁻1 and a high contact angle of 148.8°, seamlessly integrates onto roofs, creating cost-effective, fast, eco-friendly, and smart roofing solutions. The hydrophobic surface of these LIG electrodes incorporates a heating function of LIG showcasing their versatility in providing waterproofing, drying wood, and facilitating de-icing functions. This eco-friendly invention, reliant solely on laser patterning on wood, not only extends roof lifespan but also relieves concerns about electronic waste and recycling, promising the integration of green, smart, and sustainable roofing solutions.

A Local-First Approach for Green Smart Contracts

Shared code in blockchains, known as smart contracts , stands to replace important parts of our digital governance and financial infrastructure. The permissionless execution of smart contracts is tightly coupled to cryptocurrencies and Proof-of-Work blockchains. As a result, smart contracts inherit the environmental impact of Proof-of-Work blockchains, such as its energy consumption, carbon footprint, and electronic waste. The four concepts of relaxed consistency, strong identities, probabilistic consensus, and the use of liabilities instead of assets may change the status quo. This work explores the integration of these concepts to decouple smart contracts from Proof-of-Work blockchains. By means of a local-first approach, which may expose users to inconsistent ephemeral contract states, the architecture of smart contracts can be transformed to become green. Because such contract states may be dropped, we base the interactions between users on liabilities. We propose a novel paradigm for smart contract architectures, named Green Smart Contracts, that is based on a local-first approach. Furthermore, we present and implement a prototype solution for this paradigm. We validate the need for a mechanism to resolve consistency violations by replaying the contract calls of a real smart contract. Our simulation shows that violations occur more often (13% of contract invocations) when using liabilities than when using a traditional blockchain (3% of contract invocations). However, we additionally validate that they can be avoided using a consensus mechanism, and our experiments show that a publish-subscribe messaging pattern uses the fewest messages to do so, though it may not be applicable for use cases that disallow the inherent imbalance in the messaging between peers. Our carbon emission estimation shows that a Green Smart Contract approach lowers carbon emissions by 52.31% when compared with the messaging behavior of a typical peer-to-peer blockchain with 1000 nodes.

D2A Performance with SSK Modulation and SWIPT in Green Smart Cities

D2A Performance with SSK Modulation and SWIPT in Green Smart Cities

The analysis of nutritive value, vitamin, and minerals content of natural isotonic beverages formulated from Smart Green House Melon

The development of isotonic beverages on the market is very rapid because many types of isotonic beverages meet the standards for consumption. One of the local food ingredients that has the potential to be developed as a natural isotonic beverage is melon because it has good nutritional and energy content. This study aims to determine the nutritional and mineral content of three isotonic beverage formulations made from Smart Green House (SGH) melon. The method of this study is an experimental laboratory. There are three formulations of isotonic beverages, P1 contains 89.5% of melon, P1 contains 84.5% of melon, and P3 contains 74.5% of melon. The Laboratory analysis showed that formula P1 has higher nutritional content than formula P2 and P3. The nutrition contents of P1 are potassium 149 + 2.83 mg/100g, sodium 132 + 4.24 mg/100g, pH 4.35 + 0.01, vitamin C 11.3 + 0.28%, calories 44.99 + 0.06 kcal/100 g, carbohydrates 10.23 + 0.04%, and sucrose 8.78 + 0.04%. Based on the results of this research, it can be concluded that SGH melon has the potential to be developed into a healthy isotonic beverage.

미래학교 사전기획 보고서에 반영된 학교안전요소분석에 관한 연구

The Green Smart Future School project, launched in 2021, aims to ensure structural stability and enhance educational functionalities in school buildings over 40 years old. The Ministry of Education has established a pre-planning system required before the design competition phase, which includes stages of current state analysis, educational planning, and user participatory design. This study aimed to propose directions for improving pre-planning to create safer schools by categorizing and analyzing the frequency and key considerations within the area of school safety. It was possible to identify necessary improvements related to safety considerations, including step-by-step safety considerations in pre-planning reports, features of safety discussions by school level, and the main issues in the five areas of school safety. The analysis revealed that the evaluations in pre-planning reports, which aim to anticipate and prepare for safety-related issues, were generally insufficient. In particular, the initial safety evaluations that take into account age-specific characteristics during the current state analysis stage were found to be lacking. Additionally, it was suggested that the decision-making process on the type of project should include assessments of both structural safety and overall school safety, necessitating more detailed evaluations and enhanced efforts towards improving safety.

Smart Green Electric Vehicle Charging Station

Smart Green Electric Vehicle Charging Station

Resilience Development Approach for Smart Power Distribution System Coupled with Smart Green Transportation System on Cloud Computing Platform

Resilience Development Approach for Smart Power Distribution System Coupled with Smart Green Transportation System on Cloud Computing Platform

How different can smart cities be? A typology of smart cities in China

China, renowned for being one of the world's largest smart city testing fields, has witnessed the emergence of numerous smart cities. Existing publications highlight the diversity in smart city development, wherein cities with different contexts and institutional factors exhibit distinct characteristics. To delve deeper into this subject, we employed a mixed methods approach to classify 49 Chinese smart cities and subsequently developed a typology for Chinese smart cities in this study. Our methodology involved three steps. Initially, we devised a classification framework based on the input-throughput-output model and conducted content analysis to identify the characteristics of the 49 cities. Subsequently, we utilized a combination of principal component analysis (PCA) and K-means clustering analysis to categorize these cases. Finally, we formulated a typology consisting of five types of Chinese smart cities: knowledge-technocratic smart cities, holistic smart cities, green smart cities, equipment-technocratic smart cities, and emerging smart cities. The findings reveal that different smart city types are characterized by distinct features and priorities in input, throughput, and output. The development of smart cities should comprehensively consider and respect the local urban contexts and the challenges they present. The insights from this study hold relevance for both policymakers and academic researchers.

Laser‐induced graphene formation on recycled woods for green smart furniture

AbstractIn the pursuit of carbon neutrality policies, the development of eco‐friendly and intelligent furniture commands a significant role. However, the integration of non‐biodegradable electronic components in smart furniture fabrication has led to substantial electronic waste. Here, we report a straightforward approach, the rapid production of Laser‐Induced Graphene (LIG) on medium‐density fiberboard (MDF), a prevalent recycled wood in furniture production. This LIG electrode is crafted with negligible material ablation in ambient air with the aid of femtosecond laser pulses, without requiring any additional materials, showcasing the highest electrical conductivity (2.781 Ω sq−1) among previously reported lignocellulosic materials‐based LIG. The application of this LIG electrode for lighting, heating, and touch sensors displays sufficient performance for smart furniture implementation. For eco‐conscious furniture, LIG‐based human‐machine interfaces are demonstrated on recycled woods for the facile control of smart devices, which will readily enable IoT‐oriented smart sustainable furniture.image

Unpacking the Role of Green Smart Technologies Adoption, Green Ambidextrous Leadership, and Green Innovation Behaviour on Green Innovation Performance in Malaysian Manufacturing Companies

Integrating the natural resource-based view theory, contingency theory and paradox theory, the current study investigates the impact of the green smart technologies adoption (GSTA) and green ambidextrous leadership (GAL) on green innovation performance (GIP), along with the mediating role of Green Innovation Behaviour (GIB). The research design for this study consists of a quantitative method, a positivism paradigm, a cross-sectional time horizon and a structured questionnaire survey. A total of 313 responses from Malaysian manufacturing firms were analysed using partial least squares-structural equation modelling (PLS-SEM). The findings reveal that GSTA, GAL and GIB positively impact GIP. Moreover, GIB mediates the association between GSTA and GIP, but there is no mediation observed in the GAL–GIP association. The results of this study provide valuable insights for policymakers, management and relevant stakeholders in developing countries on how to strike the right balance of exploitation and exploration leadership in uncertain and volatile market settings, while fostering GSTA and GIP. The adoption of GSTA and GAL improves manufacturing companies’ sustainability. Furthermore, these findings contribute to the Malaysian government’s commitment to the Nationally Determined Contribution towards the Paris Agreement, the Shared Prosperity 2030 vision and the proposed 15 key economic growth activities.

Research on Regional Industrial Layout and Structural Adjustment and Upgrading under Digital Economy

Digital transformation promotes the change of urban governance. Based on the concept of "system", this paper focuses on exploring the influencing factors and evaluation indicators of the industrial structure adjustment and energy production and consumption structure adjustment of green smart cities in the new era under the promotion of digital economy, providing a strong theoretical basis for the comprehensive evaluation of the influencing factors in the later period.