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The purpose of this paper is to propose a framework of green strategies as a combination of energy-efficiency measures and solutions towards environmental impact reduction for improving environmental sustainability at logistics sites. Such measures are examined by discussing the related impacts, motivations and barriers that could influence the measures' adoption. Starting from the framework, directions for future research in this field are outlined.

The proposed framework was developed starting from a systematic literature review (SLR) approach on 60 papers published from 2008 to 2022 in international peer-reviewed journals or conference proceedings.

The framework identifies six main areas of intervention (“green strategies”) towards green warehousing, namely Building, Utilities, Lighting, Material Handling and Automation, Materials and Operational Practices. For each strategy, specific energy-efficiency measures and solutions towards environmental impact reduction are further pinpointed. In most cases, “green-gold” measures emerge as the most appealing, entailing environmental and economic benefits at the same time. Finally, for each measure the relationship with the measures' primary impacts is discussed.

From an academic viewpoint, the framework fills a major gap in the scientific literature since, for the first time, this study elaborates the concept of green warehousing as a result of energy-efficiency measures and solutions towards environmental impact reduction. A classification of the main areas of intervention (“green strategies”) is proposed by adopting a holistic approach. From a managerial perspective, the paper addresses a compelling need of practitioners – e.g. logistics service providers (LSPs), manufacturers and retailers – for practices and solutions towards greener warehousing processes to increase energy efficiency and decrease the environmental impact of the practitioners' logistics facilities. In this sense, the proposed framework can provide valuable support for logistics managers that are about to approach the challenge of turning the managers' warehouses into greener nodes of the managers' supply chains.

This study aims to identify the various economic and non-economic determinants of renewable energy consumption (REC) in Brazil, Russia, India, China and South Africa (BRICS). Due to the adverse effect of carbon emission on the environment, every country is trying for a transition from fossil fuel towards renewable energy. Renewable energy plays a crucial role in reducing carbon emission and combating climate change. Understanding the determinants that influence REC helps to promote this transition.

The study is based on an unbalanced panel data over the period 2002–2019 for all five BRICS nations. The panel corrected standard error (PCSE) method has been adopted to examine the determinants of REC.

Industrialization, population growth and foreign direct investment (FDI) are found to be significant economic determinants of REC while patent on environmental technologies, political instability and industrial design are significant non-economic determinants of REC in the BRICS nations.

The findings imply that to increase REC in BRICS nations, policymakers should incentivize industries for investments in renewable energy, attract FDI aligned with environmental regulations, raise population awareness through training, enforce industrial design standards, establish fair technology transfer frameworks to overcome patent barriers and create stable, long-term renewable energy policies with risk mitigation instruments to address political instability.

The study captures the effect of patents on environmental technologies and industrial design on the consumption of renewable energy. Thus, the novelty lies in investigating unexplored variables in the previous literature likely to affect REC.

Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.

The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.

The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.

An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.

This paper aims to investigate the effect of partially blocking the cathode channel with the stair arrangement of obstacles on the performance of a proton exchange membrane fuel cell.

A numerical study is conducted by developing a three-dimensional computational fluid dynamics model.

As the angle of the stair arrangement increases, the performance of the fuel cell is reduced and the pressure drop is decreased. The use of four stair obstacles with an angle of 0.17° leads to higher power density and a lower pressure drop compared to the case with three rectangular obstacles of the same size and maximum height. The use of four stair obstacles with an angle of 0.34° results in higher power density and lower pressure drop compared to the case with two rectangular obstacles of the same size and maximum height.

Using the stair arrangement of obstacles as an innovation of the present work, in addition to improving the fuel cell’s performance, creates a lower pressure drop than the simple arrangement of obstacles.

The purpose of this paper is to investigate the performance of Passive Direct Methanol Fuel Cell (PDMFC) experimentally using various Membrane Electrode Assembly (MEA) shapes such as square, rectangle, rhombus, and circle with equal areas and equal perimeters. The variation in MEA shape/size is achieved by altering gasket openings in the dynamic regions.

In the equal areas of MEA shapes, gasket opening areas of 1963.5 (+/−0.2) mm2 are used. Whereas in the equal perimeters of shapes, gasket opening perimeters of 157.1 (+/−0.2) mm are used. In this experimentation, Nickel-201 current collectors with 45.3% of circular openings are used on both the anode and cathode sides. The experiment is carried out at a 5 molar methanol concentration to find out the highest power density of the cell.

In the equal areas, among the shapes that are chosen for investigation, the square shape opening consisting of a perimeter of 177.2 mm has developed a maximum power density of 6.344 mWcm⁻² and a maximum current density of 65.2 mAcm⁻². Similarly, in equal perimeters, the rhombus shape opening with an area of 1400 mm2 has developed a maximum power density of 7.714 mWcm⁻² and a maximum current density of 85.3 mAcm⁻².

The novelty of this research work is instead of fabricating various shapes and sizes of highly expensive MEAs, the desired shapes and sizes of the MEA are achieved by altering gasket openings over dynamic regions to find out the highest power density of the cell.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Combustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).

This research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C₇H₁₆) for diesel and methyl-oleate (C₁₉H₃₆O₂) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm³ swept volume with inlet and exhaust valves closed.

The pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.

This work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

In recent times, there has been a growing interest in buoyancy-induced heat transfer within confined enclosures due to its frequent occurrence in heat transfer processes across diverse engineering disciplines, including electronic cooling, solar technologies, nuclear reactor systems, heat exchangers and energy storage systems. Moreover, the reduction of entropy generation holds significant importance in engineering applications, as it contributes to enhancing thermal system performance. This study, a numerical investigation, aims to analyze entropy generation and natural convection flow in an inclined square enclosure filled with Ag–MgO/water and Ag–TiO2/water hybrid nanofluids under the influence of a magnetic field. The enclosure features heated slits along its bottom and left walls. Following the Boussinesq approximation, the convective flow arises from a horizontal temperature difference between the partially heated walls and the cold right wall.

The governing equations for laminar unsteady natural convection flow in a Newtonian, incompressible mixture is solved using a Marker-and-Cell-based finite difference method within a customized MATLAB code. The hybrid nanofluid’s effective thermal conductivity and viscosity are determined using spherical nanoparticle correlations.

The numerical investigations cover various parameters, including nanoparticle volume concentration, Hartmann number, Rayleigh number, heat source/sink effects and inclination angle. As the Hartmann and Rayleigh numbers increase, there is a significant enhancement in entropy generation. The average Nusselt number experiences a substantial increase at extremely high values of the Rayleigh number and inclination.

This numerical investigation explores advanced applications involving various combinations of influential parameters, different nanoparticles, enclosure inclinations and improved designs. The goal is to control fluid flow and enhance heat transfer rates to meet the demands of the Fourth Industrial Revolution.

In a 90° tilted enclosure, the addition of 5% hybrid nanoparticles to the base fluid resulted in a 17.139% increase in the heat transfer rate for Ag–MgO nanoparticles and a 16.4185% increase for Ag–TiO2 nanoparticles compared to the base fluid. It is observed that a 5% nanoparticle volume fraction results in an increased heat transfer rate, influenced by variations in both the Darcy and Rayleigh numbers. The study demonstrates that the Ag–MgO hybrid nanofluid exhibits superior heat transfer and fluid transport performance compared to the Ag–TiO2 hybrid nanofluid. The simulations pertain to the use of hybrid magnetic nanofluids in fuel cells, solar cavity receivers and the processing of electromagnetic nanomaterials in enclosed environments.

The building and construction industry has a significant potential to reduce adverse climate change effects. There are plans to improve the natural resource use and greenhouse gas emissions caused by the buildings by choosing energy-efficient technologies, renewable energy sources and sustainable architectural and constructional elements. This study systematically reviews the patent data for climate change mitigation technologies related to buildings, aiming to detect their relative importance and evaluate each technology in the Y02B network.

The applied approach covers the process of (1) selecting high-impact technology, (2) collecting patent data from the USPTO database, (3) creating a citation frequency matrix using cooperative patent classification codes, (4) linking high-impact patents with analytical network process method, (5) limiting centrality of identifying core technologies from indicators and (6) creating a technology network map with social network analysis.

The study results show that energy-saving control techniques, energy-efficient lighting devices, end-user electricity consumption, management technologies and systems that convert solar energy into electrical energy are core solutions that reduce the effects of climate change. In addition, solutions that will support core technologies and whose effects are expected to increase in the coming years are energy-efficient heating, ventilating and air conditioning systems, smart grid integration, hybrid renewable energy systems, fuel cells, free cooling and heat recovery units and glazing technologies.

Most of the studies on patent analysis have failed to demonstrate any convincing evidence down to the lowest component groups of an entire technology network. The applied approach considers and evaluates each component included in a technology network from a holistic perspective.

The case is based on the data collected from various secondary sources only.

Godi India, a lithium-ion cell manufacturing company in India, was working to design e-cell for electric vehicles (EV) which would be compatible with Indian conditions and reduce the cost of battery to the extent possible because e-cell contributes half of the electric vehicle’s price. Godi India was set up in January 2020 by Mahesh Godi. Looking for opportunities in India after having worked in the USA for 17 years, Mahesh found that even with the rise in EV the lithium-ion cell manufacturing in India was almost zero. Using innovation as its main strategy, the start-up started its operation with a team of 30 scientists. The start-up already registered 25 patents under its name with few awaiting. Most of the EV companies relied on Chinese lithium-ion cell. Local lithium-ion cell manufacturing was believed to be the key for EV industry growth in a country. Central government production linked schemes worth INR 18,100 crore were signed by major players like Ola electric, Reliance new energy and Rajesh exports to develop locally manufactured advance cells. The push from the government for locally manufacturing the cells was a major trigger for the rise in the EV industry. The case provides the analysis of the strategies applied by the company to grow in the lithium-ion cell manufacturing industry.

This case can be used in strategic management, entrepreneurship and general management courses/modules at the Undergraduate and Postgraduate level.