• Energy Research
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Ensuring sustainability through green supply chain management practices has become challenging for the textiles and garments industry. Organizations need to examine the factors of the firm’s sustainability performance and how to manage them strategically. Hence, the strategic organizational orientation can be the best approach for implementing green supply chain management (GSCM) practices to improve firm sustainability performance. This study aims to assess the impact of strategic orientation in three dimensions, such as green entrepreneurial orientation (GEO), market orientation (MO), and knowledge management orientation (KMO) on the implementation of green supply chain management (GSCM) practices and the subsequently sustainable firm performance. Data were gathered from an extensive scale survey of 266 respondents of textile manufacturing firms in Bangladesh. Data were analyzed in the structural equation model (SEM) with partial least squares techniques to justify the proposed hypotheses. The results reveal that GEO and MO have a significant positive effect on GSCM practices, affecting sustainable firm performance. Surprisingly, KMO does not have a positive impact on GSCM practices. Further, this study reveals that GSCM practices partially mediate the relationship between GEO and sustainable firm performance while MO and KMO partially mediate the relationship between GEO and GSCM practices. Overall, findings help textiles firm management comprehensively understand the implementation strategies of GSCM practices in operations and reconfigure accordingly in the competitive business environment while improving firm performances. This study is the first to investigate the effect of strategic orientation on GSCM practices implementation in the textiles industry from the context of an upstream operation with a comprehensive understanding of the factors while reducing environmental impact.

Recently, the integrated landscape approach has gained increasing interest of the scientific community, as well as of organizations active in the field of sustainable development. However, the enthusiastic welcome is challenged by little consensus on theory, terminology and definitions. Moreover, the operationalization of the approach into practice is a major challenge. In this paper, we present a framework to operationalize the integrated landscape approach in practice by putting a long-term collaboration between scientists and various stakeholder at center stage. Based on encompassing understanding of landscape-level processes and interactions, four pillars addressing different steps of a joint-learning circle are described and illustrated with examples. We consider the integrated landscape approach to be a prime way of targeting the Sustainable Development Goals (SDGs), but novel forms of collaboration between scientists and other stakeholders based on long-term commitments will be needed for operationalization in practice.

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

This paper explores the energy management of a natural gas-based thermal power plant, with a focus on improving its efficiency, sustainability, and economic feasibility. The study uses the Ashuganj Power Station Company Limited (APSCL) in Bangladesh as a case study. To evaluate the efficiency of the APSCL, both energy and exergy perspectives are considered, and sustainability is assessed through exergetic parameters. The plant’s economic feasibility is analyzed based on its fuel costs and operational losses. The results of the study indicate that the energy and exergy efficiencies of the APSCL vary from 32.97% to 33.21% and from 32.63% to 32.87%, respectively, for steam turbines and from 39.77% to 56.98% and from 39.36% to 56.40%, respectively, for combined cycle power plants (CCPP) during 2016–2021. The slightly lower efficiency in exergy calculations accounts for the system loss incurred over time, which is often omitted in efficiency calculations using energy parameters. To measure the sustainability of the APSCL, eight key indicators are used: the depletion number, exergy sustainable index, cumulative exergy loss, relative irreversibility, lack of production, the wasted exergy ratio, environmental effect factor, and improvement potential. The results indicate that APSCL still has opportunities for improvement in terms of sustainability, with 50% of the fuel being depleted in the plant, contributing to a sustainable index of 2.00 and cumulative exergy loss varying between 48.59 and 21.17. Regarding financial costs and losses, APSCL has experienced an increasing trend in the price of electricity generated. However, by implementing intelligent maintenance practices and upgrading equipment in a timely manner, it is possible to minimize costs and increase exergy output, reducing the per-unit fuel requirement for electricity production and the global carbon footprint significantly.

A hybrid solar photovoltaic (PV)/biomass generator (BG) energy-trading framework between grid supply and base stations (BSs) is proposed in this article to address the power crisis of the utility grid, to enhance energy self-reliance, and to downsize the cost. The optimal size, technical criteria, energy generation, and different types of costs have been evaluated considering the dynamic behavior of solar radiation, traffic arrival intensity, and average biomass energy potential. Additionally, the wireless network performance in terms of total achievable throughput, spectral efficiency (SE), and energy efficiency (EE) are extensively examined using the MATLAB-based Monte-Carlo simulations taking multipath fading, system bandwidth, transmission power, and inter-cell interference (ICI) into consideration. The numerical results demonstrate that the energy-trading facility can achieve net present cost (NPC) and greenhouse gas saving up to 3.20% and 65.8%, respectively. In the end, the performance of the hybrid solar PV/BG system has been thoroughly compared with the standalone solar PV, hybrid PV/wind turbine (WT), and hybrid PV/diesel generator (DG) systems under on-grid and off-grid configurations for benchmarking.

In this paper, we have examined the effects of COVID-19 on the socio-economic condition of the three-wheeled electric vehicle drivers in some selected areas of Bangladesh from the cross-sectional data (September–November 2020). The results of linear regression indicate that under COVID-19 conditions, age (p = 0.022) and hardship (p = 0.000) positively, and education (p = 0.036), driving duration (p = 0.023), COVID consciousness (p = 0.086) and easy bike vehicle (p = 0.000) negatively affects income of the respondents. The deaths of COVID in the district (p = 0.003), income (p = 0.000), age (p = 0.037), easy bike vehicle (p = 0.018), debt (p = 0.059) and sufferings of diseases (p = 0.044) positively, and property holdings (p = 0.028), residence in urban areas (p = 0.004) and COVID consciousness (p = 0.082) negatively affect the family expenditure. The results from binary logistics regressions show that diseases sufferings (adjusted p = <0.001; unadjusted p = <0.001), corona fear (unadjusted p = 0.005; adjusted p = <0.001) have positive, and income (unadjusted p = <0.001; adjusted p = <0.001), cooking fuel (unadjusted p = 0.003; adjusted p = 0.091) and easy bike vehicle (unadjusted p = <0.001; adjusted p = 0.288) have negative association with hardship or misery due to COVID-19; death of COVID-19 in the district (unadjusted p = 0.008; adjusted p = 0.037), hardship or misery (adjusted p = 0.005; adjusted p = 0.001), and urban dwelling area (unadjusted p = 0.002; adjusted p = 0.004) have positive, and access to pure drinking water (unadjusted p = 0.005; adjusted p = 0.011) has negative link with corona fear; and, family savings (unadjusted p = 0.001; adjusted p = 0.013), satisfaction in the current job (unadjusted p = <0.001; adjusted p = <0.001), and government medical service (unadjusted p = 0.065; adjusted p = 0.012) have positive affiliation, and household size (unadjusted p = 0.007; adjusted p = 0.020) has negative affiliation with the continuation desire of the current job of respondents. All the obtained results are consistent and have significant policy implications.