Search results

Multinational enterprises (MNEs) in the agri-food sector are continuously transforming their global value chains (GVCs) to address sustainable development challenges of food security (SDG2) and climate change (SDG13). However, the central role of non-governmental organisations (NGOs) in (re)creating GVCs across multilevel stakeholders through innovative approaches to solve sustainability challenges remains under investigated. This explorative study investigates how international NGOs influence the transformation of large-scale industrial animal agriculture to a more sustainable (cell-based) agri-food GVC. The authors conducted a case study on the Good Food Institute (GFI), an international NGO, that has been an active player in the transition to alternative sources of protein to solve animal-based agriculture sustainability issues. The results show that an international NGO can contribute to the transition to a more sustainable GVC and can enhance the GVC’s innovation capabilities.

This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy, single-family homes that meet affordable housing criteria in diverse locations.

The framework is developed and applied in a case example of a TEA of four designs for achieving net zero-water and energy in an affordable home in Saint Lucie County, Florida.

Homes built and sold at current market prices, using combinations of well versus rainwater harvesting (RWH) systems and grid-tied versus hybrid solar photovoltaic (PV) systems, can meet affordable housing criteria for moderate-income families, when 30-year fixed-rate mortgages are at 2%–3%. As rates rise to 6%, unless battery costs drop by 40% and 60%, respectively, homes using hybrid solar PV systems combined with well versus RWH systems cease to meet affordable housing criteria. For studied water and electricity usage and 6% interest rates, only well and grid-tied solar PV systems provide water and electricity at costs below current public supply prices.

This article provides a highly adaptable framework for conducting TEAs in diverse locations for designs of individual net-zero water and energy affordable homes and whole subdivisions of such homes. The framework includes a new technique for sizing storage tanks for residential RWH systems and provides a foundation for future research at the intersection of affordable housing development and residential net-zero water and energy systems design.

This paper aims to assess the economic, environmental, policy-related and social implications of establishing green hydrogen production in Jordan.

The comprehensive analysis has been investigated, including economic assessments, environmental impact evaluations, policy examinations and social considerations. Furthermore, the research methodology encompasses energy demand, sector, security and supply analysis, as well as an assessment of the availability of renewable energy resources.

The results indicate substantial economic benefits associated with green hydrogen production, including job creation, increased tax revenue and a reduction in energy imports. Additionally, the study identifies positive environmental impacts, such as decreased greenhouse gas emissions and air pollution. Noteworthy, two methods could be used to produce hydrogen, namely: electrolysis and thermochemical water splitting. As a recommendation, the study proposes that Jordan, particularly Aqaba, take proactive measures to foster the development of a green hydrogen industry and collaborate with international partners to exchange best practices and establish the necessary infrastructure.

To the best of the authors’ knowledge, this paper is among the first to provide a comprehensive perspective on the potential of green hydrogen production as a driving force for Jordan’s economy, while also benefiting the environment and society. However, the research recognizes several challenges that must be addressed to materialize green hydrogen production in Jordan, encompassing high renewable energy costs, infrastructure development requirements and community concerns. Despite these obstacles, the study asserts that the potential advantages of green hydrogen production outweigh the associated risks.

Blockchain for energy trading is a trending research area in the current context. However, a noticeable gap exists in the review articles focussing on solar energy trading with blockchain technology. Thus, this study aims to systematically examine and synthesise the existing research on implementing blockchain technology in sustainable solar energy trading.

The study pursued a systematic literature review to achieve its aim. The data extraction process focussed on the Scopus and Web of Science (WoS) databases, yielding an initial set of 129 articles. Subsequent screening and removal of duplicates led to 87 articles for bibliometric analysis, utilising VOSviewer software to discern evolutionary progress in the field. Following the establishment of inclusion and exclusion criteria, a manual content analysis was conducted on a subset of 19 articles.

The results indicated a rising interest in publications on solar energy trading with blockchain technology. Some studies are exploring the integration of new technologies like machine learning and artificial intelligence in this domain. However, challenges and limitations were identified, such as the absence of real-world solar energy trading projects.

This study offers a distinctive approach by integrating bibliometric and manual content analyses, a methodology seldom explored. It provides valuable recommendations for academia and industry, influencing future research and industry practices. Insights include integrating blockchain into solar energy trading and addressing knowledge gaps. These findings advance societal goals, such as transitioning to renewable energy sources (RES) and mitigating carbon emissions, fostering a sustainable future.

The purpose of this study is to assess the effect of supply chain (SC) partnerships on sustainable performance (SP) and investigate the potential mediating role of total quality management (TQM).

A total of 185 responses were collected from pharmaceutical industry employees. The research data were analyzed using the partial least squares structural equation modeling approach.

The results reveal that relationships with suppliers (RS), distributors (RD) and intermediaries (RI) have a direct impact on SP. In addition, this study found that TQM serves as a mediator between RS, RD, RI and SP. This study enhances the understanding of the significance of TQM, SC and SP in business environment development. The findings suggest that organizations in the Jordanian pharmaceutical industry should prioritize the enhancement of their RS, intermediaries and distributors to improve their SP.

By providing decision-makers with valuable information, this study enables them to identify and implement TQM and SC practices to enhance the SP of pharmaceutical companies in Jordan.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

Although the significance of technological progress in economic development is well-established in theory and policy, it has remained challenging to agree upon shared priorities for strategies and policies. This paper aims to develop a model of how policymakers can develop effective and easy to communicate strategies for science, technology and economic development.

By integrating insights from economic complexity, competitiveness and foresight literature, a replicable research framework for analysing the opportunities and challenges of technological revolutions for small catching-up countries is developed. The authors highlight key lessons from piloting this framework for informing the strategy and policies for bioeconomy in Estonia towards 2030–2050.

The integration of economic complexity research with traditional foresight methods establishes a solid analytical basis for a data-driven analysis of the opportunities for industrial upgrading. The increase in the importance of regional alliances in the global economy calls for further advancement of the analytical toolbox. Integration of complexity, global value chains and export potential assessment approaches offers valuable direction for further research, as it enables discussion of the opportunities of moving towards more knowledge-intensive economic activities along with the opportunities for winning international market share.

The research merges insights from the economic complexity, competitiveness and foresight literature in a novel way and illustrates the applicability and priority-setting in a real-life setting.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.