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Renewable energy sources are likely to play a major role in meeting the future energy requirement of a developing country like India. Among the various renewable energy sources, the bio‐energy plays a key role for the power generation. In this paper, an attempt is made to develop a fuzzy based linear programming optimal electricity allocation model (OEAM) that minimizes the cost and determines the optimum allocation of different energy sources for the centralized and decentralized power generation in India with special emphasis to bio‐energy.

The OEAM model optimizes and selects the appropriate energy options for the power generation on the factors such as cost, potential, demand, efficiency, emission and carbon tax. The objective function of the model is minimizing the cost of power generation. The other factors are used as constraints in the model. The fuzzy linear programming optimization approach is used in the model.

The extents of energy sources distribution for the power generation in the year 2020 would be 15,800 GWh (4 per cent) from the coal based plants, 85,400 GWh (20 per cent) from the nuclear plants, 191,100 GWh (44 per cent) from the hydro plants, 22,400 GWh (5 per cent) from the wind mills, 45,520 GWh (11 per cent) from the biomass gasifier plants, 14,112 GWh (3 per cent) from the biogas plants, 8,400 GWh (2 per cent) from the solid waste, 33,600 GWh (8 per cent) from the cogeneration plants and 11,970 GWh (3 per cent) from the mini hydel plants, respectively.

The OEAM has been developed for the electricity demand allocation for the year 2020. An extensive literature survey revealed that carbon tax and emission constraints were never used in the previous models and they are considered in the present model.

This study aims to examine the impacts of board chairman characteristics on the decision to finance with debts.

Based on historical data from 173 active nonfinancial firms listed on Gulf Cooperation Council (GCC) Stock Exchange Markets during 2012–2019, this research uses ordinary least squares (OLS) and dynamic system-generalized methods of moments to test its hypotheses. The final dataset comprises 1,384 firm-year observations from 10 major nonfinancial industry classifications.

Results indicate a negative impact of board chairman ownership on the decision to finance with retained earnings (RE). Negative effects of the chairman and chief executive officer (CEO) from the same family on the decision to finance with RE, whereas positive effects of the chairman and CEO from the same family on the decision to finance with debts are observed. In addition, a negative effect of the chairman from a royal family on the decision to invest with debts is found.

Many board chairmen characteristics, such as age, gender, experience, education level, periodic change and ethnicity, are unaddressed. Financial decisions (FDs) are also limited to two decisions (internal financing with RE and external financing with debts).

Findings of this study provide an improved understanding of the role of chairman characteristics in FDs in GCC. Investors and lenders dealing with companies in GCC markets benefit from the authors' results because of the effects of chairman characteristics on FDs when making investment decisions in company stocks.

The study clarifies how each of the three board chairman characteristics (i.e. chairman ownership, chairman and CEO from the same family and the chairman from the royal family) affects FDs, especially the decisions to finance with debts and RE.

The clean development mechanism (CDM) provides industrialized countries with an incentive to invest in emission reduction projects in developing countries to achieve a reduction in CO₂ emissions at lowest cost that also promotes sustainable development in the host country. Windmill pumps could be of interest under the CDM because they directly displace greenhouse gas emissions while contributing to sustainable rural development. This study aims to assess the maximum theoretical as well as the realistically achievable CDM potential of windmill pumps in India.

To estimate the CDM potential of windmill pumps in case of electricity replacement (by the substitution of electric motor pumps) regional baseline(s) has been used whereas in case of diesel replacement (by the substitution of diesel engine pumps) the IPCC emission factor(s) has been used. The authors have analyzed the financial feasibility of five designs of windmill pumps at ten select locations in India to justify the financial additionality of windmill pump‐based CDM projects. To analyze the future dissemination levels of windmill pumps logistic growth model has been used.

The annual CO₂ emissions mitigation potential through windmill pumps in India could theoretically reach five million tones. Under more realistic assumptions about diffusion of windmill pumps based on past experiences with the government‐run programmes, annual CER volumes by 2012 could reach 13,000‐46,000 and 0.07‐0.3 million by 2020. This would require that the government sets the subsidy level for windmill pumps at a level that allows them to become viable with the CER revenue. From a macro‐economic point of view, this makes sense if the sustainability benefits are deemed sufficiently high to warrant promotion of this project type.

The paper illustrates the vast theoretical potential of CO₂ mitigation by using windmill pumps for irrigation water pumping in India.

This paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.

The analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.

The authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.

If the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.

This study provides value in providing a forecast for expected future growth for renewable energy sources.

The purpose of this paper is to address the question of how a developing country, like Mexico, can reform its electricity industry at the same time as addressing climate change issues.

The objective is to provide a tool that policy makers could use to make better and more informed decisions if they decide to liberalise the power sector in Mexico. The problems they would face are difficult to address in an analytically tractable way using conventional economic models. Also, these problems are too idiosyncratic to solve by translating empirical experience from other markets. In response, a system dynamics model has been developed in order to test the impact of a range of different environmental and energy policies.

The paper finds that a reform setting where competition is allowed in fossil fuel technologies; while the public company (CFE) keeps control of hydro and nuclear capacity produce the most well rounded scores in terms of efficiency, CO2 emission reductions and political feasibility. The results could improve the policy implementation process by shedding light on the circumstances and policy choices that can exacerbate or minimise effects.

A simulation of this kind would increase the understanding of these kinds of policies by providing structured insights into key uncertainties. They can also foster new ideas – in the energy sector have underpinned major policy initiatives. But results should be taken with caution, as complete validation of models is impossible.

These results add to the policy implementation literature. For the case of Mexico that has not engaged in a specific model of deregulation, the use of simulation model would be very useful to predict flaws in the design of the new regulation and to prevent unwanted scenarios to happen.

A judicious implementation of an electricity reform can help achieve lower carbon emissions reductions which would be beneficial against the climate change problem.

To the author's knowledge, the approach of analysing ex ante the environmental outcome of electricity reform using a simulation model in a developing country has not been treated in literature.

This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA.

The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model.

The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India.

This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.

Sustainability is an accepted measure of business performance, with reductions in energy demand a commonly practised sustainability initiative by multinational corporations (MNCs). Traditional energy models have limited scope when applied to the entire MNC as the models exhibit high data and time intensity, high technical proficiency, specificity of application and omission of non-manufacturing activities. The purpose of this paper is to propose a process centric energy model (PCEM), which adopts a novel approach of applying business processes for business energy assessment and optimisation. Business processes are a fundamental requirement of MNCs across all sectors. The defining features of the proposed model are genericity, reproducibility, minimum user input data, reduced modelling time and energy evaluation of non-manufacturing activities. The approach forwards the adoption of Industry 4.0, a subset of which focuses on business process automation or part thereof.

A quantitative approach is applied in development of the PCEM. The methodology is demonstrated by application to the procure to pay and electroplating business processes.

The PCEM quantifies and optimises the business energy demand and associated carbon dioxide emissions of the procure to pay and electroplating business processes, validating the application of business processes. The application demonstrates minimum user inputs as only equipment operational parameters are required and minimum modelling time as business process models and optimisation options are pre-defined requiring only user modification. As MNCs have common business processes across multiple sites, once a business process energy demand is quantified, its inputs are applied as the default in the proceeding sites, only requiring updating. The model has no specialist skills requirement enabling business wide use and eliminating costs associated with training and expert’s services. The business processes applied in the evaluation are developed by the researchers and are not as comprehensive as those in actual MNCs, but is sufficiently detailed to accurately calculate an MNC energy demand. The model databases are not exhaustive of all resources found in MNCs.

This paper provides a new approach to MNC business energy assessment and optimisation. The model can be applied to MNEs across all sectors. The model allows the integration of manufacturing and non-manufacturing activities, as it occurs in practice, providing holistic business energy assessment and optimisation. The model analyses the impacts of the adoption of Industry 4.0 technologies on business energy demand, CO₂ emission and personnel hours.

The academic literature and research lines exploring the effect of quality improvement methods on environmental performance still remain in early stages. The purpose of this paper is therefore to investigate, through a systematic review of the existing academic literature, the environmental (green) impact of using quality and operations improvement methods such as Lean, Six Sigma and Lean Six Sigma. This includes the impact on energy saving and the usage of natural resources.

This study follows a systematic literature review approach through which it analyses research papers published in top 16 operations and quality management journals. No specific time frame was established, but a set of keywords were used to short-list the articles. A sample of 70 articles was finally short-listed and analysed to provide a discussion on environmental concerns related to Lean, Six Sigma and Lean Six Sigma.

The comprehensive review of short-listed articles indicates that both Lean and Six Sigma can be considered effective methods to support the conservation of resources, combat global warming and saving energy. Various scholars provide evidence of this, and as such, organisations should not only consider these methods to manage quality and improve operational performance but also meet environmental regulations. A set of research questions that demands further investigation has also been proposed based on the findings of this research.

This study is limited to a sample of 70 articles collected from top 16 operations and quality management journals. The search of journals is also limited to a set of key words (“Lean”, “Green”, “Six Sigma”, “environment”, “sustainable” and “sustainability”) used to short-list the sample size.

The study shows that organisations can consider the adoption of Lean, Six Sigma and Lean Six Sigma to meet environmental regulations, save costs and also meet quality management standards. This will contribute in helping organisations to formulate more effective and inclusive strategies which do not only consider the quality and operational dimensions but also the environmental dimension.

Literature exploring the environmental/green impact of quality management methods commonly used in industry is limited. There is also a lack of studies aiming to investigate the green impact of Lean and Six Sigma in top operations and quality management journals. The study focusing on investigating the green impact of Lean, Six Sigma and Lean Six Sigma methods altogether is also a research first of its kind.

This paper aims to evaluate the effect of biodiesel additive in fuel system of diesel engines to reduce wear characteristics. Biofuels are environmentally friendly and renewable alternatives to mineral-based fuels and cause low pollution; thus, they can be used to comply with future emission regulations to safeguard environmental and human health.

Two types of diesel fuel, pure petrodiesel and soybean oil, were compared for their fuel properties and tribological performance. The ball-on-disk wear testing method was used as an analytical tool for this purpose. The lubricating efficiency of the fuels was estimated using a photomicroscope to measure the average diameter of the wear scar produced on the test ball.

The wear experiments showed that the wear scar diameters were 1.13 and 0.94 mm for lubrication of the pure petro-diesel and soybean oil, respectively. However, fatty acids containing biodiesel typically have thicker molecular layers than mineral pure petro-diesel, and thus can reduce the wear rate of the sliding metals. This improved the boundary lubrication conditions and the lubricity of the fuel. Biodiesel fuels are effective lubricity enhancers and have greater lubricity enhancing properties than petro-diesel.

The ability of biodiesel to be highly biodegradable and its superior lubricating property when used in compression ignition engines make it an excellent fuel. Biofuel is an attractive alternative fuel to various energy sectors, particularly the transportation sector. Biofuel has immense potential for use in a sustainable energy mixture in the future.

Engine component endurance is related to fuel properties. Decreasing the sulfur content of a fuel reduces its lubricity, thus damaging engines and fuel systems. Therefore, promoting the use of a biofuel must involve assessing the functionality and lubricity of the fuel.

The ball-on-ring (BOR) wear tester was applied to determine the optimal additive concentration and the mechanism of reduction of the wear and friction of the diesel engine fuel injection system. The lubricating efficiency of the fuels was estimated by using a photomicroscope to measure the average diameter of the wear scar produced on the test ball. An optical microscope and scanning electronic microscope were used for wear surface examinations.

The wear test revealed that the wear diameter of the steel ball lubricated with either the pure petrodiesel or 20 Wt.per cent Jatropha curcas biodiesel blends was 1.36 or 1.05 mm, respectively. The experimental results indicated that when Jatropha curcas biodiesel was added into petrodiesels to reduce friction, the wear resistance of the fuel blends increased concurrently with increasing Jatropha curcas biodiesel concentration. This was attributed to the presence of stearic acid in Jatropha curcas biodiesel blends. Stearic acid has a strong affinity for metal surfaces; therefore, a chemical coating was formed between the two motion surfaces to protect the two contacted surfaces from wear. Therefore, the proposed Jatropha curcas biodiesel can be used to effectively enhance the lubricity of a petrodiesel under the condition of boundary lubrication.

Using biofuels as the fuels for diesel engines can assist developed and developing countries in reducing the impacts of their fossil fuel consumption on the environment.