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The escalating levels of greenhouse gas emissions have become a growing global concern, with household energy consumption emerging as a significant contributor. To develop effective public policies, it is crucial to understand the energy-saving behavior of households. This study delves into the determinants of energy-saving practices in a developing country.

The authors chose a multivariate probit model, as it allowed to look after possible correlations among seven energy-saving practices within households.

The findings underscore the significant influence of sociodemographic variables, such as gender, civil status, income and education, on energy-saving practices. Furthermore, the authors discovered that households where the head actively volunteers in social organizations are more likely to adopt energy-saving behaviors. Additionally, internet access positively contributes to pro-environmental behavior. This research reveals that certain energy-saving practices are interconnected, acting as complements or substitutes.

Recommendations for public policy include prioritizing education in rural areas to boost energy-saving practices, improving internet access in nonurban regions and promoting citizen involvement in social organizations to enhance environmental awareness and encourage energy-saving behavior. The authors contribute to literature evidencing that certain energy-saving practices are not independent of each other, they are rather complementary and, in some cases, substitutes.

Recommendations for public policy include prioritizing education in rural areas to boost energy-saving practices, improving Internet access in nonurban regions and promoting citizen involvement in social organizations to enhance environmental awareness and encourage energy-saving behavior.

Previous studies have overlooked these interdependencies, highlighting the necessity of a system of equations to yield more efficient estimates by considering correlations between error terms.

The green building (GB) market is maturing after years of practice; therefore, incentives for promoting GB should be adapted to reflect the market transformation. Adapting incentives can positively influence stakeholders' motives, which in turn change stakeholders' behavioural intention towards GBs. Hence, this study aims to examine the parameters influencing the decision to adopt GB and to ascertain how incentive mechanisms promote the uptake of GB in South Africa.

Using a scoping literature review and a sequential mixed research method, primary data were acquired from GB experts in four South African provinces. In analysing the quantitative data, the factors were ranked hierarchically using the mean ranking technique and factor analysis was computed to identify the underlying GB incentives. The qualitative data were analysed using content analysis.

The results indicate that incentive payment from a utility energy efficiency programme, rebates and discounts relating to environmentally friendly materials and products and providing grants to homeowners and developers to go towards certification were the most essential monetary incentives for promoting GB. In the category of non-monetary incentives, recognition of consultant team members and developers and free marketing/good publicity were the most important incentives. The incentive mechanisms were subsequently categorised as “Economic incentives” and “Reward scheme and technical support”.

This study was confined to four metropolitan areas in South Africa. Nevertheless, the outcomes have practical implications for GB projects in general and may serve as a good reference for other provinces in South Africa.

The findings of the study are a valuable resource for stakeholders such as the government, municipal assemblies, professional bodies and the Green Building Council of South Africa (GBCSA) in developing effective incentive mechanisms to promote GB adoption. This study adds to the body of knowledge relevant to GB incentivisation in South Africa.

While previous studies have highlighted the importance of incorporating environmental sustainability in building designs, there is a paucity of studies that assess the extent to which design teams in developing countries consider environmental sustainability at the building design stage. Therefore, using Zambia as a case study, this study examined the extent to which infrastructure design teams in a developing country consider environmental sustainability at the design stage.

The study used a qualitative research approach using structured interviews because there are hardly any studies which have explored the extent to which designers incorporate environmental sustainability in infrastructure designs in developing countries. The data is analysed thematically using the ATLAS.ti software.

The results show that environmental sustainability is not an important design consideration because it is secondary to functional, technical and aesthetic considerations. Environmental considerations are also made in an ad hoc manner and when it is cost-effective for the project. Regulatory requirements pertaining to environmental protection are adhered to without any cost considerations. It was, therefore, theorised that building design teams in developing countries make technical, functional and aesthetic consideration during the infrastructure design stage ahead of environmental considerations.

There is a paucity of studies that have investigated whether building infrastructure designers consider issues of environmental sustainability at the design stage in developing countries. The findings have practical implications on how developing countries can foster environmental sustainability at the design stage and avoid generating a building infrastructure stock that will require environmental resilience adaptation in the future.

This study aims to examine the historical development, present state and potential future directions of the integration between building information modeling (BIM) and life cycle assessment (LCA) in the field of construction. Additionally, this paper identifies current problems while offering insight into worldwide BIM research trends.

This study uses text mining on unstructured abstracts, a novel approach not previously documented in BIM research. By conducting a comprehensive systematic assessment of academic literature, this work uses advanced bibliometric approaches to examine the developmental trajectory of the integration of BIM and LCA. The research incorporates co-citation and keyword co-occurrence mapping, providing a complex visual depiction of the interconnectedness of information across different periods.

The results of this analysis reveal the historical development of the integration of BIM and LCA, including its roots and the initial research that established the foundation for further investigations. The aforementioned seminal works signify the inception of the discipline, serving as a source of inspiration for current scholarly investigations. Currently, there is a complex network of interdisciplinary cooperation that can be observed, combining knowledge and perspectives from the fields of design, engineering, construction and sustainability.

This research contributes novelty to the scholarly discourse by offering a holistic and up-to-date panorama of the dynamic BIM and LCA research landscape. It identifies emerging trends, influential contributors and uncharted territories, thus providing a foundation for scholars to contribute meaningfully to the advancement of knowledge in sustainable construction practices.

This study aims to investigate the interplay between environmental concerns, consumer attitudes and their influence on sustainable practices within the craft beer industry, exploring how consumers’ perception of environmental attributes impact their purchase intentions regarding craft beer.

The study used a quantitative approach, using a self-administered survey instrument comprised 28 Likert-scale items. Data was gathered from a convenience sample of 319 craft beer consumers. Confirmatory factor analysis was conducted to assess the measurement model’s validity and reliability. Subsequently, partial least squares structural equation modeling was used to test the hypothesized relationships.

This study’s findings challenge established notions within the field, demonstrating that water conservation, carbon footprint reduction, organic farming and local sourcing practices hold significant influence on consumers’ purchase intentions regarding sustainable craft beer. These findings depart from previous research that suggested a less pronounced impact of specific sustainability practices on consumer behavior.

This study contributes to the literature by revealing consumer insights into the environmental implications of craft beer choices, empowering them to make informed purchasing decisions aligned with their sustainability values. However, acknowledging potential limitations, sample size and demographic composition (young adults in Brazil) potentially impact the generalizability of the findings.

This study addresses the understanding of consumer attitudes and priorities regarding environmental sustainability within the craft beer industry. By focusing on the tangibility of specific sustainable attributes the research shows how these practices influence consumer perceptions and purchasing intentions.

The purpose of this paper is to ascertain the significant stimulating measures/enablers relating to the existing building regulations for promoting the adoption and overcoming the barriers to the uptake and implementation of the green building concept (GBC) in developing countries using Ghana as a case.

The quantitative research approach was used to attain the study’s goal. Purposive and snowball sampling techniques were found to be suitable for collecting data from 292 relevant stakeholders in Ghana’s construction industry. The mean score ranking technique, in conjunction with the relative importance index, was used to establish the relative ranking of, among other things, the stimulus measures for increasing green building uptake in Ghana. An exploratory factor analysis was also used to classify the most significant stimulation strategies for improving green building uptake.

“Educational programmes relevant to GBTs for developers, contractors, and policymakers,” “sufficient information on the cost and benefits of GBTs” and “mandated green building codes and regulations” were the top three listed stimulating measures to promote increasing use of green building technologies (GBTs). The enablers were classified as follows: government regulations and policies; commitment and GB research; education and publicity; and incentives and support.

The study was conducted in Ghana, a developing nation, and thus the findings and implications are peculiar to Ghana. However, the study’s findings have important practical implications for the adoption and marketing of GBCs and GBTs in other developing nations.

Prioritizing major stimulation initiatives may be beneficial in terms of overcoming the constraints to the adoption of GBCs and GBTs in developing countries.

The purpose of this study is to explore the determinants influencing industrial adoption of green hydrogen amidst the global transition towards sustainability. Recognizing green hydrogen as a pivotal clean energy alternative for industrial applications is critical for understanding its potential integration into sustainable practices.

This research examines the impact of factors such as innovativeness, perceived ease of use, user comfort, optimism and governmental policies on the industrial intention towards green hydrogen usage. Using responses from 227 Indian industry professionals and conducting analysis via the SmartPLS software, the study reveals a discernible discomfort among industrial workers pertaining to the daily application of green hydrogen.

The research presents an array of policy recommendations for stakeholders. Emphasized strategies include the introduction of green hydrogen certificates, sustainable public procurement mechanisms, tax incentives, green labelling protocols and the establishment of a dedicated hydrogen skill development council, all of which can significantly influence the trajectory of green hydrogen adoption within the industrial sector.

This research synthesizes various elements, from industry perception and challenges to policy implications, presenting a holistic view of green hydrogen’s potential role in industry decarbonization and SDG realization. In essence, this study deepens not only the empirical understanding but also pioneers fresh theoretical frameworks, setting a precedent for subsequent academic endeavours.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Design is an important construct in the retail environment literature. Yet, the measures used for design have not followed appropriate scale development procedures. The purpose of this study is to provide a conceptual definition and then develop a scale for retail environment design (RED).

Interviews with both consumers and marketing researchers are used to generate a potential list of items. Using four different studies, these items are refined, and the RED scale is offered.

This study develops and validates the four-dimensional RED scale to measure the design of retail environments. The dimensions are functional, aesthetic, lighting and signage.

The newly developed RED scale will allow retailing researchers to measure lighting and signage qualities as part of retail design, measure design of retail environments more accurately and allow different studies to be compared.

The newly developed RED scale will allow retailers to better understand customers’ perceptions of the four dimensions of design. Retailers spend significant time and money designing and redesigning retail environments. The RED scale will enable managers to ensure these significant investments create competitive advantages and an appropriate return on investment.

A scale to measure retail environment design is developed. The scale includes two dimensions (lighting and signage) that are not typically investigated.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.