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The purpose of this paper is to emphasize on the need for a fair and just economic system to achieve sustainability.

Using systems thinking approach it shows that climate change, ecological degradation, population growth, poverty and the resource scarcity, the problems of failing financial markets and economic recession are all intertwined with the present economic system, which has been responsible for transgressing the balance of nature.

The paper shows that all the reforms or alternatives to the contemporary economic system proposed in the literature do not really address the root cause of all the problems, which is the built-in usurious system, made possible with the paper currency, in the economy that creates great injustices. The paper argues that solutions of science and technology to ecological problems are limited because of ecological shortsightedness and corporate greed.

The paper introduces the concept of “fair and just” economic system for sustainable development of humanity and suggests future directions for its realization.

This study examines the carbon emissions of Australia’s agriculture, forestry and fishing sectors from a consumption perspective to develop effective policy frameworks. The objective is to identify key supply chains, industries and products contributing to these emissions and provide recommendations for sustainable development.

A multiregional input-output lifecycle assessment was conducted using the Australian Industrial Ecology Virtual Laboratory (IELab) platform to disaggregate sectors and enable benchmarking against other economic sectors.

In 2018, the “agriculture, forestry, and fishing” sector was responsible for 12.15% of Australia’s carbon footprint. Major contributors included the “electricity, gas, water, and waste” category (26.1%) and the sector’s activities (24.3%). The “transport, postal, and warehousing” sector also contributed 18.4%. Within the industry, the agriculture subsector had the highest impact (71.3%), followed by forestry and logging (15%) and fishing, hunting and trapping (7.6%). Aquaculture and supporting services contributed 6.1%.

The principal constraint encountered by the present study pertained to the availability of up-to-date data. The latest accessible data for quantifying the carbon footprint within Australia’s agriculture, forestry and fishing sector, utilizing the Input-Output analysis methodology through the Australian Industrial Ecology Virtual Laboratory (IELab) platform, about 2018.

The findings of this study provide policymakers with detailed insights into the carbon footprints of key sectors, highlighting the contributions from each subsector. This information can be directly used to develop effective emission-reduction policies, with a focus on reducing emissions in utility services, transport and warehousing.

The study, by raising public awareness of the significant role of industrial agricultural methods in Australia’s carbon footprint and emphasizing the importance of renewable energy and sustainable fuels for electricity generation and road transport, underscores the urgent need for action to mitigate climate change.

This study stands out by not only identifying the most impactful industries but also by providing specific strategies to reduce their emissions. It offers a comprehensive breakdown of specific agricultural activities and outlines mitigation strategies for utility services, agricultural operations and transport, thereby adding a unique perspective to the existing knowledge.

The study aims to empirically examine the effect of board industry expertise on environmental, social and governance (ESG) strategy, and the mediating role of environmental innovation.

Using an unbalanced sample of 341 publicly traded Italian non-financial firms and data collected from multiple sources over the period 2017–2021, this study applies single-mediator models via ordinary least squares regressions.

Results indicate that directors’ industry expertise improves the corporate orientation toward sustainability strategy that is reflected in ESG objectives. This effect is partly mediated by a greater level of environmental innovation.

The article suggests regulators to promote eco-innovation-friendly investment initiatives due to their value in advancing corporate sustainability strategies.

The research fills a gap in the literature that has never explored the effect of board industry expertise on sustainability-related outcomes. Moreover, it advances the debate on the implications of board human capital by assessing its influence on ESG strategy and environmental innovation.

This study aims to investigate the link between information technology capabilities (ITC) and organizational performance (OP) in the hospitality industry by focusing on the indirect effect of sustainability practices, service innovation (SINO), service improvement (SIMP) and the moderating role of top management support (TMS).

Time-lagged survey data from 488 hotel managers in the United Arab Emirates was used in this study to examine the hypotheses by the PROCESS Macro.

The authors found significant support for our framework, demonstrating that ITC are linked with OP. The study found that ITC and OP are sequentially mediated by sustainability practices, SINO and SIMP. Additionally, the influence of information technology (IT) capabilities on OP is moderated by TMS, whereas TMS also enhances the sequential mediating effect of sustainability practices, SINO and improvement, such that the sequential mediating effect is stronger when TMS is at a high level.

To the best of the authors’ knowledge, this paper is the first to examine the sequentially moderated mediating effect of sustainability practices and then SINO and SIMP between ITC and OP using a time-lagged design in the hospitality industry.

The purpose of current flow configuration is to spotlights the thermophysical aspects of magnetohydrodynamics (MHD) viscoinelastic fluid flow over a stretching surface.

The fluid momentum problem is mathematically formulated by using the Prandtl–Eyring constitutive law. Also, the non-Fourier heat flux model is considered to disclose the heat transfer characteristics. The governing problem contains the nonlinear partial differential equations with appropriate boundary conditions. To facilitate the computation process, the governing problem is transmuted into dimensionless form via appropriate group of scaling transforms. The numerical technique shooting method is used to solve dimensionless boundary value problem.

The expressions for dimensionless velocity and temperature are found and investigated under different parametric conditions. The important features of fluid flow near the wall, i.e. wall friction factor and wall heat flux, are deliberated by altering the pertinent parameters. The impacts of governing parameters are highlighted in graphical as well as tabular manner against focused physical quantities (velocity, temperature, wall friction factor and wall heat flux). A comparison is presented to justify the computed results, it can be noticed that present results have quite resemblance with previous literature which led to confidence on the present computations.

The computed results are quite useful for researchers working in theoretical physics. Additionally, computed results are very useful in industry and daily-use processes.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.

To achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.

The paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.

This research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.

This study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.

The present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.

The purpose of the current flow configurations is to bring to attention the thermophysical aspects of magnetohydrodynamics (MHD) Williamson nanofluid flow under the effects of Joule heating, nonlinear thermal radiation, variable thermal coefficient and activation energy past a rotating stretchable surface.

A mathematical model is examined to study the heat and mass transport analysis of steady MHD Williamson fluid flow past a rotating stretchable surface. Impact of activation energy with newly introduced variable diffusion coefficient at the mass equation is considered. The transport phenomenon is modeled by using highly nonlinear PDEs which are then reduced into dimensionless form by using similarity transformation. The resulting equations are then solved with the aid of fifth-order Fehlberg method.

The rotating fluid, heat and mass transport effects are analyzed for different values of parameters on velocity, energy and diffusion distributions. Parameters like the rotation parameter, Hartmann number and Weissenberg number control the flow field. In addition, the solar radiation, Joule heating, Prandtl number, thermal conductivity, concentration diffusion coefficient and activation energy control the temperature and concentration profiles inside the stretching surface. It can be analyzed that for higher values of thermal conductivity, Eckret number and solar radiation parameter the temperature profile increases, whereas opposite behavior is noticed for Prandtl number. Moreover, for increasing values of temperature difference parameter and thermal diffusion coefficient, the concentration profile shows reducing behavior.

This paper is useful for researchers working in mathematical and theoretical physics. Moreover, numerical results are very useful in industry and daily-use processes.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

The COVID-19 pandemic disrupted healthcare systems globally, including antenatal care coverage. Pregnant women, who were considered “at risk” during the pandemic, replaced in-person antenatal visits with telemedicine and accessed health information online. However, little is known about pregnant women’s online information-seeking behaviour during the COVID-19 pandemic in Nigeria. Hence, the purpose of this paper is to investigate the information seeking behaviour of pregnant women online during the COVID-19 pandemic.

This research uses qualitative study and in-depth interviews to obtain data from eight pregnant women during the pandemic. Data were thematically analysed, with responses presented verbatim to illustrate themes.

Findings show that during the COVID-19 pandemic, the unavailability of health professionals and the fear of contracting the COVID-19 virus influenced pregnant women’s information-seeking behaviour online. Pregnant women accessed online sources as alternatives to consultations with health professionals, searched for drug prescriptions and asked pregnancy-related questions online. Findings also revealed that pregnant women conceptualised these online sources and platforms as safe spaces for sharing and dealing with pregnancy-related anxieties and difficulties during the pandemic.

The number of participants sampled in the study is considered satisfactory since data saturation was achieved. However, considering the generalisation and transferability of the research findings, note that the study focused on a limited number of pregnant women in one state in Nigeria (Lagos State). Hence, the design and sample do not provide adequate generalisation to a larger population of pregnant women in Nigeria. Future research may generalise more broadly to other states in Nigeria. Another limitation of the study was using telephone interviews to collect data. Therefore, this paper could not analyse body language and facial expressions, which prevented us from gaining insights into participants’ descriptions of health information-seeking behaviour online. Therefore, further studies should use alternative data collection methods, such as face-to-face or online video interviews, instead of telephone interviews.

This study has implications for health policy interventions. The study’s findings can guide policies on designing digital health systems for pregnant women during health crises.

This study contributes to existing literature on health information-seeking behaviour online among a vulnerable population – pregnant women in a developing country. Specifically, the study contributes to knowledge on how pregnant women’s health information-seeking behaviour can change online within a health-crisis context like the COVID-19 pandemic and its implications for their overall well-being.