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Electricity plays an essential role in nations' economic development. However, coal and renewables currently play an important part in electricity production in major world economies. The current study aims to forecast the electricity production from coal and renewables in the USA, China and Japan.

Two intelligent grey forecasting models – optimized discrete grey forecasting model DGM (1,1,α), and optimized even grey forecasting model EGM (1,1,α,θ) – are used to forecast electricity production. Also, the accuracy of the forecasts is measured through the mean absolute percentage error (MAPE).

Coal-powered electricity production is decreasing, while renewable energy production is increasing in the major economies (MEs). China's coal-fired electricity production continues to grow. The forecasts generated by the two grey models are more accurate than that by the classical models EGM (1,1) and DGM (1,1) and the exponential triple smoothing (ETS).

The study confirms the reliability and validity of grey forecasting models to predict electricity production in the MEs.

This study aims to investigate the interrelationships and elasticities between the production of renewable energy (RE) and three key variables: oil prices, gross domestic product (GDP) and carbon dioxide (CO₂) emissions.

The research uses panel data and time-series analyses for 10 developed and 16 emerging countries for the period 1976–2018, to identify panel and country-specific elasticity of RE production and dynamic causal relationships between these variables. The study uses an autoregressive distributed lag model to determine the long- and short-run dynamics between RE production and the three variables in each country.

Results show a long-run elasticity between RE and GDP, and short-run dynamics between RE and oil prices and CO₂ emissions in the developed countries. Whereas in the emerging countries category, there were long-run relationships between RE and GDP, CO₂ emissions and oil prices.

Results of this study are in fact crucial and can be applied in the drafting of resilience policies to tackle energy vulnerability as well as sustainable growth. The study results will inform and guide governments on the right policies to stimulate RE production in their own countries in the interests of both their national security and sustainable development globally.

This paper attempts to contribute to the literature in at least two ways. First, research on identifying common determining factors, including socioeconomic factors, in both emerging and advanced economies is considerably scarce. Most of the previous research in this field has focused only on the absolute value of RE production in a particular geographical area. Second, many studies have focused on RE consumption. This research differs from them by focusing on the production of RE. Thus, the main contribution of this study is to fill these gaps. The study also presents novel empirical evidence to determine RE production elasticity from 26 countries.

This study aims to examine the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries.

This study uses semi-annual panel data covering the period 2000–2020 among 152 developing and middle-income countries and Cox proportional hazard model for the analysis.

Estimates indicate that effective operations of environmental institutions, investment in research and development, subsidizing the production of renewable energy, government investment in producing renewable energy and investment in renewable energy production made by the private sector will contribute immensely toward achieving sustainability of renewable energy production.

This study recommends that governments should rationalize their expenditures to mobilize enough resources for investment in renewable energy production. Again, operations of environmental institutions should be enhanced through giving their managers’ performance contracts and licensing its employees. Enabling environment should be created for private sector to increase their investment in renewable energy production.

Empirical studies have been carried out exploring measures to deal with climate change. Nonetheless, the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries have not been explored in existing empirical studies. Hence, this study fills the gap in existing empirical studies.

The purpose of this paper is to undertake comparative analysis examining the effect of renewable energy production on employment focusing on European and Asian Countries.

The study utilizes panel data from the period 2000 to 2018 and System Generalized Method of Moments (System GMM) for the analysis. This study focuses on 50 European and 40 Asian countries data used for the analysis.

The result of the study indicates that, renewal energy production has positively affected employment in both European and Asian countries. However, the positive effect result of European countries is stronger than that of Asian countries.

The study recommends that, governments among these countries should continue to show strong commitment towards investing in renewable energy production as stated in Paris Agreement (2015). This will have a strong effect towards increasing further employment creation among these countries.

Numerous empirical studies have been carried out examining the effect of renewable energy production on employment. This study contributes to existing empirical studies by undertaking comparative analysis to examine the subject matter focusing on European and Asian countries.

This study aims to explore the diverse sources of electricity generation (coal, natural gas, oil and hydroelectricity) and their respective associations with economic growth and environmental quality.

This study uses static panel data analysis with a random effects model for six selected ASEAN countries (Indonesia, Malaysia, Filipina, Thailand, Vietnam and Myanmar) from 1994 to 2014.

This study reveals that economic growth in six selected ASEAN countries is enhanced by electricity generation from all sources, while the contribution of electricity production from hydroelectricity remains the largest and strongest. There is no environmental impact of electricity production from hydroelectric, whereas fossil fuel-based electricity production emits carbon dioxide, with coal sources being the largest contributor, followed by natural gas and oil.

Based on the results, these six ASEAN countries should invest more in hydropower projects, reduce the coal mix in power generation and promote clean coal technology to improve economic efficiency and environmental sustainability.

To the best of the authors’ knowledge, no research has examined the relationship between electricity production, environmental quality and economic growth in Southeast Asian nations. Therefore, the outcome of this study is expected to provide insightful results to supplement the framing and implementation of national and collective regional strategies for sustainable electricity generation in ASEAN countries.

In the case of Iran, about 19 years have passed since the announcement of general energy policies. Policies adopted in the past included too many approaches and goals for the energy sector that some of them are in conflict with each other and their simultaneous implementation was impossible. Therefore, many of the expected goals of these policies have not been achieved. It is very important to adopt an appropriate approach that is compatible with the country’s characteristics and can steer energy policies. Therefore, this study aims to adopt an appropriate approach for the country’s energy sector according to the conditions and needs of the country.

A participatory scenario technique is designed and used to develop plausible projections in the case of Iran’s energy futures. Based on the proposed methodology, the research process starts with data gathering through interviews and national plans analysis. Then, findings were used as inputs to a simulation-based scenario development process. Scenarios are developed based on Monte Carlo simulation via cross-impact analysis technique purified based on expert judgments.

In this paper, to recognize Iran’s general approach to energy in the next 20 years, driving forces of change were introduced and used as input for the scenario development phase. Results showed sanctions play a significant role in Iran’s energy future and determine the directions of other driving forces. For renewable energy, it was proposed to increase the renewable share in Iran’s electricity generation mix to 5% of the total installed capacity. In the case of fossil fuel extraction, the maximum efficient rate was proposed for both oil and natural gas national production strategy.

This research is novel both in terms of application and theory. A new participatory scenario development method is used using simulations that are equipped based on experts’ judgments. Also from the practical perspective, this research targeted a future-oriented challenging problem to initialize national policy in a resource-rich developing economy (i.e. Iran).

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

This study aims to identify sustainability indicators in the energy sector through a literature review and validate them from the perspective and context of professionals working in the sector in an emerging economy country, Brazil, considering the relationship of these indicators with the achievement of the targets set by the United Nations sustainable development goals (UN SDGs).

To accomplish this, a literature review on sustainability indicators specific to the energy sector was conducted. Subsequently, a research instrument (questionnaire) based on the identified indicators was developed and a survey was administered to professionals in the field. The collected data were analyzed using the Lawshe method.

The results revealed 20 indicators, distributed across environmental, economic and social dimensions. Among these, nine indicators were validated, including global impacts, local impacts, renewable energy production as a percentage of total production, greenhouse gas emissions, access to electricity, investment in the energy sector, installed capacity in the electricity sector, energy prices in the end-use sector and energy distribution and conversion efficiency.

Consequently, it was possible to determine which SDGs are directly impacted and provide a foundation for future actions that can contribute to the sustainable advancement of the energy sector in emerging countries.