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Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

In this study, the author intend to investigate the impacts of renewable energy use and environmental taxation on sustainable development measured by the adjusted net savings (ANS).

This study employs the quantile regression (QR) for a set of 24 Organization for Cooperation and Economic Development (OECD) countries over the period 1994–2018.

The main empirical findings of estimates show that access to renewable energy and environmental taxation generate positive and significant effects in increasing the ANS for most quantiles. Hence, they are practical tools for achieving sustainable development goals (SDGs).

This study has important implications for governments and policymakers of the OECD countries. Therefore, governments can use subsidies and incentives to promote the adoption of renewable energy sources, energy-efficient technologies and sustainable practices. Similarly, by imposing taxes on pollution and resource use, governments can encourage the adoption of cleaner technologies and practices toward more sustainable behavior.

This paper is based on a novel measure of sustainable development (ANS) and a novel econometric method (QR).

Green transition is a long-term direction of corporate development that can achieve sustainable corporate development. This study aims to investigate whether state ownership promotes corporate green transition by mitigating managerial myopia and the impact of environmental regulations, internal controls and ownership on this pathway.

Using data from 2,608 Chinese listed companies for 2010–2019, the authors investigate the relationship between state ownership, managerial myopia and corporate green transition by using fixed-effects and moderated mediation models.

State ownership can boost green transitions and alleviate managerial myopia. Managerial myopia mediates the relationship between state ownership and corporate green transition. Furthermore, environmental regulations, internal controls and ownership moderate the mediating effects of managerial myopia.

The authors construct a multidimensional green transition index to examine the influence of state ownership on corporate green transition behavior and reveal the underlying mechanism by which state ownership promotes green transition by “mitigating managerial myopia.” This study enriches the literature on state ownership, management myopia and green transition and provides important evidence for the promotion of mixed ownership reforms.

This study is intended to analyze the impact of information and communication technology (ICT) infrastructure, technological innovation, renewable energy consumption and financial development on carbon dioxide emissions in emerging economies.

The present study adopts the autoregressive distributed lag (ARDL) cointegration technique for the annual data collection of Vietnam from 1990 to 2020.

The results of the study unveil that renewable energy consumption, the interaction between renewable energy consumption and ICT infrastructure and financial development have significant predictive power for carbon dioxide emissions. In the long term, renewable energy consumption, export and population growth reduce CO2 emissions, whereas the interaction between renewable energy consumption and ICT infrastructure and financial development increases CO2 emissions, while ICT infrastructure does not affect emissions. In the short run, changes in ICT infrastructure contribute to carbon dioxide emissions in Vietnam. In addition, changes in renewable energy consumption, financial development, the interaction between ICT infrastructure and renewable energy consumption and population growth have a significant effect on CO2 emissions. Notably, technological innovation has no impact on CO2 emissions in both the short and long run.

The current study provides new insights into the environmental effects of ICT infrastructure, technological innovation, renewable energy consumption and financial development. The interaction between renewable energy consumption and ICT infrastructure has a significant effect on carbon dioxide emissions. The paper suggests important implications for setting long-run policies to boost the effects of financial development, renewable energy consumption and ICT infrastructure on environmental quality in emerging countries like Vietnam in the coming time.

Manufacturing companies continue to encounter a diverse set of obstacles while embracing sustainable development goals. Accordingly, the purpose of this study is to explore critical sustainable development-related barriers to flexible packaging manufacturing companies in the New Zealand context.

Drawing on a qualitative multiple case studies approach, the authors collected data from the New Zealand flexible packaging industry. Semistructured interviews were conducted with the senior corporate managers in two large flexible packaging companies. Following the thematic analysis approach, the authors analyzed the information collected from the participants and synthesized our findings under the key dimensions of internal and external barriers to sustainable development.

The findings revealed that internal barriers to sustainable flexible packaging are linked to economic, operational and technical issues. Conversely, external barriers include global crises and disruption, customer behavior and preferences and institutional and infrastructural-related aspects. Based on the analysis of empirical findings, the authors further identified the underlying reasons for sustainable flexible packaging barriers and recommended guidelines that could assist corporate managers and policymakers in addressing obstacles inhibiting the flexible packaging industry from adopting sustainable business practices.

The authors argue that this study is one of the early studies to consider inhibiting factors to incorporate sustainable development into the New Zealand flexible packaging industry context. Building on a range of theoretical perspectives, the authors extend the current body of knowledge seeking to advance the sustainable development agenda in the New Zealand flexible packaging industry and offer recommended pathways fostering sustainable development in a distinctive manufacturing context.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

This study examines the appropriate measures that need to be intensified among African countries to achieve sustainable environment to mitigate climate change.

The study employs panel data covering the period 2000 to 2020 among 54 African countries and Cox proportional hazard model for the analysis.

Estimates indicate that the practice of carbon farming, the development of rooftop gardens, renewable energy production and consumption contribute positively toward achieving sustainable environment, while governance adversely affects this objective of achieving sustainable environment.

The study recommends that governments should enforce the constant practice of carbon farming among these countries through passing laws to enforce its application among farmers and allocate 2% of ministry of agriculture's budget toward financing carbon farming for poor farmers.

Empirical studies have been carried out exploring measures to deal with climate change. Nonetheless, the appropriate measures of achieving sustainable environment to mitigate climate change have less been explored in literature on Africa. Hence, this study fills the gap in existing empirical studies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0290.

The present work highlights the outstanding properties of Cannabis sativa that can be harnessed for various utilitarian functions and its climate friendly properties.

In this paper, the authors reviewed current research on all possible utilities from household work to manufacturing of various products that are environmentally sustainable. The authors have presented some of their research on this materials and also exploration of hemp as an archaeological material based on the findings from wall paintings of Ellora caves.

There are references of hemp use in mixing with earthen/lime plaster of western Indian monuments. Around 1,500 years of Ellora’s earthen plaster, despite harsh climatic conditions, survived due to the presence of hemp in the plaster that adds durability, fibrosity and its capacity to ward off insects and control humidity. Furthermore, the outstanding quality of Cannabis as carbon sequestrant was harnessed by Indians of ancient times in Ellora mural paintings.

This work discusses some relevant literature on the potential use of hempcrete aligned with Agenda 2030 of sustainable development goals.

There are several research going on in producing sustainable materials using hemp that have the least environmental impact and can provide eco-friendly solutions.

The authors impress upon the readers about multifarious utility of the hemp and advices for exploration of this material to address many environmental issues.

This paper presents both review of the existing papers and some components coming directly from their laboratory investigations.

Accelerating the green economy’s transition is a practical means of lowering emissions and conserving energy, and its effects on the greenhouse effect merit careful consideration. Growing environmental deterioration has compelled decision-makers to prioritize sustainability alongside economic growth. Policymakers and the business community are interested in green investment (GRE), but its effects on social and environmental sustainability are still unknown. Based on this, this study aims at looking into the time-frequency interplay between GRE and carbon dioxide emissions and assessing the impacts of economic growth, financial globalization and fossil fuel energy (FUE) usage on this nexus in Vietnam across different time and frequency domains.

The authors employ continuous wavelets, cross wavelet transforms, wavelet coherence, Rua’s wavelet correlation and wavelet-based Granger causality tests to capture how the domestic variance and covariance of two-time series co-vary as well as the co-movement interdependence between two variables in the time-frequency domain.

The results shed new light on the fact that GRE will increase the levels of environmental quality in Vietnam in the short and medium run and there is a bidirectional causality between the two indicators across different time and frequencies. In addition, when the authors observe the effect of economic growth, financial globalization and fossil fuel energy consumption on this interplay, the findings suggest that, in different time and frequencies, any joined positive change in these indicators will move the CO₂ emissions-GRE nexus.

Policymakers and governments can greatly benefit from this topic by utilizing the function of economic institutions in capital control of GRE and CO₂ emissions and modifying the impact of GRE on the greenhouse effect by accelerating the green growth of economic industries.

The current work contributes to the current literature on GRE and CO₂ emissions in several dimensions: (1) considering the sustainable development in Vietnam, by employing a new single-country dataset of GRE index, this paper aims to contribute to the growing body of research on the factors that influence CO₂ emissions, as well as to provide a detailed explanation for the relationship between GRE and CO₂ emissions; (2) localized oscillatory components in the time-domain region have been used to evaluate the interplay between GRE and CO₂ emission in the frequency domain, overcoming the limitations of the fundamental time-series analysis; (3) the mediation role of economic growth, financial globalization and FUE in affecting the GRE-CO₂ relationship is empirically explored in the study.