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Review

The Sustainability Concept: A Review Focusing on Energy

by
Rafael Ninno Muniz
1,*,
Carlos Tavares da Costa Júnior
1,
William Gouvêa Buratto
2,
Ademir Nied
2 and
Gabriel Villarrubia González
3
1
Electrical Engineering Graduate Program, Department of Electrical Engineering, Federal University of Pará (UFPA), Belém 66075-110, Brazil
2
Electrical Engineering Graduate Program, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, Brazil
3
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(19), 14049; https://doi.org/10.3390/su151914049
Submission received: 4 September 2023 / Revised: 18 September 2023 / Accepted: 20 September 2023 / Published: 22 September 2023

Abstract

:
The concept of sustainability, with a focus on energy, has emerged as a central tenet in addressing the mounting global challenges of environmental degradation and resource depletion. Indicators of sustainability focusing on energy are crucial tools used to assess and monitor progress toward achieving a more sustainable energy system. These indicators provide valuable insights into the environmental, social, and economic dimensions of energy practices and their long-term impacts. By analyzing and understanding these indicators, policymakers, businesses, and communities can make informed decisions, formulate effective policies, and steer their efforts toward a more sustainable energy future. These indicators serve as navigational guides, steering the world toward energy practices that support both present needs and the well-being of future generations. In this paper, the concept of sustainability and measurement indexes used are reviewed, focusing on energy factors. The focus of the discussion presented here is related to an assessment of the possibilities for improving energy efficiency and evaluating the indicators that are used to measure whether the desired levels of sustainability are being achieved.

1. Introduction

It is increasingly important to include environmental, social, and governance considerations when evaluating results, especially in an industry as impacting as energy. Incorporating sustainability indicators assessments provides a more holistic evaluation of the long-term feasibility and harmony of deals with broader sustainable development objectives [1]. The sustainable principles serve as a unifying framework, promoting the responsible stewardship of resources, inclusivity, and intergenerational well-being. It further underscores how these principles provide a shared foundation for individuals, organizations, and societies to collaboratively contribute to a sustainable future [2].
A sustainability index implies providing information on the mechanisms and logic that operate in the area under analysis and quantifying the most important phenomena that occur in the system under study [3]. With this index, it is possible to understand how human activity affects the environment, alert people on the risks of survival, predict future situations, and offer sustainable alternatives in making better policy decisions [4].
Indicators make it possible to select the most relevant information, simplify complex phenomena, quantify qualitative information, and communicate information between collectors and users. They have applications for evaluating conditions and trends about objectives and goals, comparing results considering similar methodologies, and providing warning information to anticipate future conditions, among several other uses [5].
Sustainability is a balancing act between social, environmental, and economic dimensions [6]. The three dimensions of sustainability may imply conflicts due to the very concept of each part: environmental sustainability means the temporal maintenance of the fundamental characteristics of ecosystems under the use of their components and interactions; economic sustainability results in stable profitability over time; social sustainability associates the idea of systemic organization that must have cultural and ethical values of involved groups and society in a way that is acceptable to organizations over time [7].
These sustainability dimensions represent different aspects of human well-being and development that need to be balanced to ensure a sustainable and equitable future for both current and future generations [8]. These dimensions are interconnected and interdependent [9]. Achieving sustainability requires finding synergies and trade-offs among them, as actions in one dimension can have implications for the others [10].
The idea of the interconnections between sustainability dimensions is presented in Figure 1, especially focusing on energy; there are topics that are in more than one dimension, such as electricity access, which has social and economic importance. Each dimension of sustainability has indicators that measure whether the levels of sustainability are being satisfied, and the evaluation and discussion of these indicators are the focus of this paper.
Environmentally, indicators may include the share of renewable energy in the total energy mix, the reduction in greenhouse gas emissions, and improvements in energy efficiency. Social indicators might encompass access to affordable and reliable energy services for all, equitable distribution of energy resources, and the creation of green jobs in the renewable energy sector. On the economic front, indicators may assess the cost-effectiveness of sustainable energy solutions, investments in renewable energy infrastructure, and the overall resilience of energy systems to market fluctuations.
The social dimension focuses on the well-being and quality of life of people [11]. It includes aspects such as social equity, justice, human rights, health, education, cultural preservation, and community engagement [12]. In a sustainable society, social systems should ensure that all individuals have access to basic needs, opportunities, and a high quality of life without compromising the needs of future generations.
The environmental dimension relates to the health and resilience of ecosystems and the natural environment. It involves maintaining biodiversity, conserving resources, reducing pollution, addressing climate change, and promoting sustainable land use and resource management [13]. A sustainable approach considers the Earth’s finite resources and aims to minimize negative impacts on the environment.
The economic dimension concerns the long-term viability of economic systems. It involves promoting economic growth and development while ensuring that it does not come at the expense of social well-being or environmental health [14]. Sustainable economies strive to balance prosperity with the responsible use of resources, ethical practices, and consideration of the impacts of economic activities on society and the environment [15].
In recent years, discussions around sustainability have expanded to include additional dimensions such as cultural [16], political [17], and technological aspects [18], recognizing the complexity of achieving a truly sustainable future. In this case, energy is related to the technological aspect of sustainability, where there is a goal to provide energy with a lower environmental impact by improving the infrastructure of the system to make it reliable and efficient [19]. In this paper, the technological aspects are the focus, and the sustainability based on the energy is going to be covered.
The energy supply system consists of the physical infrastructure that defines the system configuration for resource input [20] and its expected response [21]. Resource inputs include primary energy inputs, such as crude oil, coal, natural gas, hydropower hydroelectric [22], nuclear [23], or renewable energy. The combination of resources and the configuration of infrastructure defines the production capacity of the energy system and, therefore, its ability to meet society’s needs at any given time [24].

1.1. Challenges

Renewable energies play a key role in reducing greenhouse gases as they do not come from burning fossil fuels, thus promoting energy sustainability and reducing pollutants. Achieving energy sustainability is a complex and multifaceted goal that involves addressing a range of challenges and barriers at both policy and technology levels. Here are some key challenges and potential strategies to overcome them (regarding policy and technology levels):
  • Dependency on Fossil Fuels. Policy Level: Implement policies that incentivize the transition to renewable energy sources through subsidies, carbon pricing, and phasing out fossil fuel subsidies. Technology Level: Invest in research and development to improve the efficiency and cost-effectiveness of renewable energy technologies [25].
  • Infrastructure and Investment. Policy Level: Develop financial mechanisms like tax incentives, grants, and public–private partnerships to attract investments in sustainable energy infrastructure. Technology Level: Focus on innovation and modular designs to lower the upfront costs of renewable energy installations and grid modernization [26].
  • Intermittency and Reliability of Renewable Energy. Policy Level: Encourage energy storage development through policies promoting the research, development, and deployment of energy storage technologies. Technology Level: Invest in advancements in energy storage technologies like batteries, pumped hydro, and grid integration solutions to mitigate the intermittency issue.
  • Lack of Energy Access. Policy Level: Prioritize policies that promote decentralized energy systems and microgrids, especially in underserved areas, to improve energy access. Technology Level: Develop affordable and scalable off-grid renewable energy solutions, such as solar home systems and mini-grids, and improving the electrical power system [20].
  • Environmental Impact. Policy Level: Enforce strict environmental regulations and carbon pricing to incentivize cleaner technologies and penalize high-emission energy sources. Technology Level: Invest in clean technologies like carbon capture and utilization and promote circular economy practices in energy production.
  • Technological and Knowledge Gaps. Policy Level: Invest in education and skill development programs to build a knowledgeable workforce capable of working with emerging clean technologies. Technology Level: Foster collaborations between academia, research institutions, and industry to bridge technological gaps and accelerate innovation.
  • Political and Socioeconomic Challenges. Policy Level: Engage in international cooperation and agreements to foster a global commitment to energy sustainability, encouraging countries to share knowledge and resources. Technology Level: Develop diplomatic relationships to facilitate the transfer of sustainable energy technologies between nations, especially to those in need.

1.2. Objectives

Considering the challenges presented here, this work has the following objectives:
  • Contextualize the concept of sustainability related to energy, based on the three classic dimensions (social, economic, and environmental), and propose a rereading with the insertion of two new dimensions (technical and institutional).
  • Review and propose energy sustainability indicators based on the five dimensions of sustainable development (environmental, economic, social, technical, and institutional).
  • Qualify the importance of energy sustainability for regional energy planning in line with public policies for sustainable development.
Addressing energy sustainability necessitates a holistic approach that combines policy interventions with technological advancements. Collaboration between governments, industries, research institutions, and communities is vital to overcoming the challenges and achieving a sustainable energy future [27]. Additionally, public awareness and engagement are crucial to garner support for sustainable energy initiatives and drive meaningful change. Given these challenges, the hypotheses/question arises:
Can harnessing renewable energy sources, implementing sustainable energy policies, and integrating energy efficiency measures lead to long-term viability and alignment with sustainable development goals in the energy sector?
Several researchers have presented reviews regarding sustainability, and some of these related works are discussed in Section 2. In Section 3, an evaluation of the methods available to save energy and improvements in power supply are presented. In Section 4, the indexes and indicators of sustainable development are explained, and finally, in Section 5, a conclusion is discussed.

2. Sustainability Reviews

Feil et al. [28], through a literature review with 24 papers, in a historical series from 1998 to 2018, found that 31.4 sustainability indicators are used on average in industrial organizations. The frequency of factors in the environmental, social, and economic spheres was evaluated. In the environmental context, it was verified that the volume of atmospheric gases, electricity, and water consumption are the main indicators evaluated. Within the social context, the engagement with the community, health, safety, and ethical behavior stand out. And concerning the economic indicators, the gross resale value, net profit, and investment capital have the greatest relevance.
Muniz et al. [29] present a comprehensive examination of tools utilized for measuring energy sustainability, offering a comparative analysis of their features, methodologies, and applicability. This research investigates the diverse range of assessment tools available in energy sustainability, encompassing both quantitative and qualitative approaches. By critically evaluating the strengths and limitations of each tool, they aim to provide a nuanced understanding of their respective contributions to evaluating energy systems’ ecological, societal, and economic dimensions.
Brulé [30], employing qualitative analysis, proposed alternatives to the measurements of already-existing indexes. Their results show an interconnection capacity with other indexes and political relevance. Therefore, it is necessary to evaluate the quality and improvement in the adaptation of indexes that are already consolidated, bringing more effective and cognitive measures to the level of social interaction and the evaluated community.
When verifying the global development indexes, Guo et al. [31] proposed to determine the capacity for the development of green technologies and sustainability. They evaluated the carbon dioxide emissions, verifying that due to the capacity of larger countries to extract natural resources and have a larger industrial complex, they pollute more. Therefore, there is a need to introduce environmental taxes so that “green” technologies are adopted and prohibit subsidies that are harmful to the environment, to increase public investment in this sector, to monitor whether the indicators and action programs are effective in regulating environmental protection, and to reduce the risks of adherence of new technologies on the market that have the potential to reduce environmental impacts.
Wang et al. [32] made an assessment of the power system in the European community concerning sustainable development commitments. With the need to increase external commercialization and expand consumption by more than 60 percent in the continent’s energy matrix, there is a need to seek to minimize greenhouse gas emissions and maximize the energy efficiency of electricity generation. The improvement in generation efficiency can be in large power plants or even in microgeneration [33]. According to Yumashev et al. [34], researchers verified that besides being an important factor in the development of life expectancy and standard of human welfare, the volume and quality of energy consumed interfere with the improvement in sustainability in a country and the levels of socioeconomic development.
The volume and quality of energy consumed concerning human development in carbon dioxide emissions had its influence studied by Yumashev et al. [34], in which the researchers found that in addition to being an important factor in the development of life expectancy and standard of human well-being, it interferes in the improvement in sustainability in a country and the levels of socioeconomic development. Since energy is a parameter that can be evaluated by its efficiency, intensity of use, and sustainability, combined as a trilemma, Baloch et al. [35] found that in the BRICS block, Brazil and Russia are the countries that have achieved the best environmental performance index in these three aspects respectively among the five countries in a data collection that took place from 2011 to 2016.
Flores et al. [36] focused on determining the most vital indicators related to sustainability in bioenergy from natural resources, prioritizing the use of non-native biomass within the Amazon as an energy source in this region within the literature, determining the critical points of advancement, and individualizing each area whether, they are social, economic and/or environmental, in conjunction with the determination of correlated indicators in sampling about seven attributes of sustainability (AOS) of the Spanish framework of natural resource management, which are resilience, adaptability, productivity, equity, self-confidence, reliability, and stability, finding 29 relevant indicators including 11 environmental indicators, 11 social indicators, and 7 economic indicators, through 27 critical points in these seven AOSs of the applied framework.
The standardization of indicators that have greater use in different indices is determined by internal and external policies adopted by the leaders of the organization or country and also by the internal conjugations that can be subdivided by their capacity as sub-indices and in the relationship of the performance output according to the desired value metric together with the impact achieved and the importance of each sub-index in the process. Huovila et al. [37] presented a methodology in the context of international standards within the concept of smart cities and their impact according to sub-indices such as transport, economy, water and waste, education, culture, innovation, governance, and citizen engagement, among other parameters, allowing other researchers to have a synthesized content in the evaluation of various standards and determine which ones are best adapted concerning the focus that will be given in the development of indices.
In the management of sub-indices, the number of indicators of them in the composition of the intervals and linguistic rules should be evaluated in addition to their aggregation within the final index. Gunnarsdontir et al.’s [38] review evaluated several energy sustainability indicators from the literature, one of which was the metric developed by Sovacool and Mukherjee [39], which had 372 indicators among all. The transparency in this paper was evaluated through the selection and application of all the indicators analyzed in more than 50 indexes developed by world bodies such as the World Energy Council, the World Bank, and the World Economic Forum, which complement the obtainment of stakeholders and their engagement; provide verification with 320 simple and 52 complex indicators in energy security; contribute in new future studies synthesizing the criteria of quality, quantity, and context; andcontribute in the performance evaluation of policies that adopt metrics and suppress the obtainment of an index that can present if there are threats in the context of access to electricity in the analyzed area
Achieving energy sustainability depends on the implementation of tools and applications that increase energy efficiency in the electricity system, and globalized financing can allow the improvement in environmental impact reduction rates. Murshed et al. [40] verified the increase in this economic aspect and its importance in sub-Saharan African countries, in which a 1 percent increase in energy efficiency rates within the entire context of the electricity system contributes to up to 11 percent in the improvement in sustainability indexes in the seven global sustainable development goals and promotes the improvement in internal development policies in reducing barriers to foreign investments with partnerships of local companies and state support by reducing taxes in these financing models by countries with a high income concentration that could benefit from social advertisements and the capture of natural resources with an adequate percentage to the benefited countries and external countries.
Within these goals of improving the reduction in environmental impacts in the energy sector, regulation and energy policies are critical points in the densification of the application of renewable energies crucial in encouraging and accelerating business involving potentially more sustainable energy resources and greater financial risk, as seen by Drago and Gatto [41], who made an indicator composed of four factors: the access to transparency and monitoring standards of renewable energy utilities, the legalization structure of these, counterpart risks, and energy building codes showing that some countries have high performance, thus benefiting both internal and external investments, while other countries have limitations in accessing these data, reducing the scale of investments.
The interrelation of the parameters of regulation, sustainability, and energy policies when evaluating the relationship of electricity from renewable sources with the index of economic complexity to achieve carbon emission neutrality was reviewed by Li et al. [42], in which the researchers found that the greater use of renewable energy is linked to this reduction in air pollution. However, it must be in line with the reduction in imports of fossil fuels, headed by investments in the export of more sustainable technologies, and increased resources in innovations that improve energy efficiency in general, reducing the need for greater capture of natural resources.
When evaluating the context of a country or region with a large area, there is a high probability of the occurrence of energy poverty, which is the lack of access to current energy services and which can reduce the level of sustainability because there is usually the adherence to fossil fuels. When applying this index, which can occur in both developed countries and developing regions, its concepts and metrics must be evaluated so that there is equity in measurement and there are no unequivocal measurements; in this way, Sy and Mokaddem [43] noted that the scarcity of data is still an impediment in the comparison between regions and further increases the delay in the adoption of energy policies that achieve sustainability in conjunction with the wide access to electricity due to lower population and income in remote regions.
The review presented by Chenari, Carrilho, and da Silva [44] investigates the mastery of sustainable, energy-efficient, and health-conscious ventilation strategies in the context of building environments. The critical importance of ventilation in maintaining indoor air quality, thermal comfort, and overall occupant well-being is widely acknowledged. In the face of escalating energy demands and environmental concerns, the need for innovative approaches to ventilation becomes paramount. The analysis encompasses their respective advantages, drawbacks, and performance metrics in terms of energy consumption, indoor air quality enhancement, and potential health impacts. The intricate interplay between ventilation strategies, building designs, and local climate conditions is examined in depth.
Pacheco, Ordóñez, and Martínez [45] presented a study about energy-efficient building design, exploring its pivotal role in mitigating energy consumption and environmental impact within the construction sector. With escalating energy demands and heightened awareness of climate change, the imperative to revolutionize building design strategies has gained paramount importance. Their paper scrutinizes a spectrum of energy-efficient design principles and strategies, ranging from passive design techniques to cutting-edge technological innovations. It delves into their respective merits, limitations, and quantifiable outcomes in terms of energy conservation, reduced operational costs, and decreased carbon footprint.
As the global community grapples with escalating environmental concerns and the imperative to transition from fossil fuels becomes ever more pressing, policy frameworks aimed at advancing renewable energy sources have gained prominence. Lu et al. [27] examine a spectrum of sustainable energy policies implemented worldwide, analyzing their design, implementation, and outcomes. Through their research, they assess the multifaceted impacts of policy interventions on renewable energy adoption. By considering various dimensions such as economic viability, technological innovation, environmental impact, and social acceptability, this review provides a comprehensive assessment of the strengths and limitations of different policy approaches.
As concerns over resource depletion and climate change intensify, businesses are increasingly pressured to adopt strategies that minimize environmental impacts while maintaining efficient supply chain operations. Centobelli, Cerchione, and Esposito [46] examined the evolving landscape of research trends in this domain and proposed guidelines for effectively integrating environmental sustainability and energy efficiency within supply chain practices. They explored the intricate relationships between supply chain design, operations, transportation, and distribution in the context of reducing ecological footprints and optimizing energy usage.
Meng et al. [47] assessed the multifaceted impacts of incorporating smart technologies into factory environments. Their work scrutinizes various dimensions, including process optimization, resource utilization, waste reduction, and carbon footprint mitigation, to comprehensively evaluate how smart factories contribute to sustainable production. The review delves into the intricate web of technologies underpinning smart factories. Considering that there is a major effort to improve the efficiency and reliability of power systems, the next section presents a discussion about this topic based on other authors’ applications.

3. Technology and Efficiency Improvement

Hydrogen fuel cell buses represent a promising avenue for sustainable urban transportation, but their energy efficiency is contingent on the effective management of power distribution and consumption. The study presented by Shen et al. [48] delves into the utilization of deep reinforcement learning algorithms to enhance the energy efficiency of hydrogen fuel cell buses by dynamically adjusting their velocity profiles. As well evaluated nowadays, the energy supply has become more efficient with the use of time series forecasting [49] using hybrid models [50], classification (computer vision [51], convolutional neural networks [52], deep neural networks [53], multilayer perceptron, and k-nearest neighbors [54]), and Internet of Things (IoT) using embedded systems [55].
Measuring energy sustainability using fuzzy logic can be performed with biological inputs, which can have a highly polluting character when released inappropriately into the environment. Parameters are first defined in governance in order to reduce this impact as quickly as possible so that these two processes, being carried out by specialized teams, are later assigned importance in each social, economic, technical and environmental indicator in the form of numerical scores according to the scenario and location analyzed formulating technological priorities seeking planning and decisions that are more appropriate [56].
These strategies can be carried out from a small site to reach entire countries, and they can become more complex and assist energy policies by categorizing the selection of energy plant sites, structural decisions assessing global costs, and productivity gains due to increased exports by raising the renewable matrix with trading partners and the risks associated with this energy stance with existing and expected marketing possibilities [57,58,59].
Although artificial intelligence (AI) algorithms are complex and their combination with other statistical tools is required by technical criteria [60], at the end of the operation, the results must be easy to understand for legislators and decision-makers in the area of energy policy, clearly dealing with the recognition of qualitative and quantitative indicators and having robustness, that is, guaranteeing the conclusions obtained, and there is a need to minimize the consumption of time and financial resources compared to human choices experts presenting strategies that improve the investment environment and reduce risks in operational energy planning both micro and macro population environments [61].
Within the electricity sector, generation, transmission, and distribution systems are encompassed by specific protocols. It can be evaluated from the literature that these established standards facilitate the adoption of a certain method that will have greater adaptability in the iteration of the data that will be inserted in the proposed model and obtain an energy sustainability index [62], determining the degree of influence for each related indicator [63]. Focused on improving energy access, researchers have evaluated advanced methods to analyze the transmission lines’ performance, using models to determine how the leakage current, electric field, and partial discharges are affecting the power system supply. According to Klaar et al. [64] and Ribeiro et al. [65], in addition to the system performance analysis, the energy price is necessary to evaluate as a measure of social access.
Among the methodologies for creating and developing these indices in line with the objectives of sustainable development within the electricity sector, three methods stand out that are related to AI algorithms: multicriteria decision analysis, aggregate normalization in minimum and maximum discrepancies, and hierarchical analytical process. These are related in the three dimensions most evaluated in the literature: environmental, economic and social. In addition, the cultural, institutional, and technical aspects can also be added, depending on the complexity and need foreseen, as well as the size of each project [66,67,68]. Nowadays, AI-based models are applied considering their capacity to deal with nonlinear data [69,70,71].
To classify technologies that are similar in technical characteristics, fuzzy logic can be an excellent alternative for reducing the costs and time required for investor decisions, by establishing criteria and weights for the needs envisaged by this group using the technique of the order of preference using similarity in an ideal system solution (TOPSIS) [72], improving the consistency of comparisons by matrices and sub-criteria, and it is used more significantly and with less sensitivity in the classification of nuclear plants; however, it is not limited to this type of plant and can be applied to other energy transformation plants.
Through the vision of a hierarchical analytical process (AHP), weight criteria are determined, and the relative importance values of different parameters are calculated; when embedded in logic such as fuzzy logic, the consistency of data from human uncertainty can be improved [73]. This allows decisions to be made that are more sensitive, realistic, and concrete among the particularities from the perspectives of energy policy alternatives with conflicting criteria, both in the AHP tool and in the analytical network process (ANP), which is the most widely used technique in the approximate comparison of pairs within the context of multi-criteria decision methods; both complement each other, since the AHP methodology makes it more consistent and reduces the redundancy of the ANP, which has the disadvantage of disregarding mutual relationships between criteria, which the ANP tool adjusts for. However, these two tools are not recommended for high-dimensional multivariate data, also known as big data [74].
Formulating the sustainability index prior to and before applying the AHP and ANP tools, the authors in the literature carry out a SWOT analysis, which consists of assessing the strengths, weaknesses, opportunities, and threats in a matrix to which a project may be subjected at all stages from conception to operation [75]. Thus, by carrying out this characterization prior to execution, the aim is to strengthen the aspects that are most susceptible to external and internal interference and reduce the associated risks that could make the process unfeasible so that one or more AI techniques can then be configured and ranking can be applied, such as TOPSIS, according to the type of data and intended analysis [76].
Smart electric railway networks play a pivotal role in modernizing and optimizing public transportation systems while minimizing their environmental footprint. The research presented by [77] examines the methodologies utilized to design and implement energy-management systems in these networks to ensure efficient energy usage and reduced operational costs. Their study emphasizes the need for a holistic approach that integrates these technologies seamlessly into energy management systems.
Tung et al. [78] investigate the application of waste heat recovery technologies in the context of fishmeal production plants, focusing on their potential to enhance energy efficiency and mitigate environmental impacts. The case study demonstrates the significant energy efficiency improvements achieved through the adoption of waste heat recovery technologies. By capturing and reusing waste heat, the fishmeal production plants can reduce their overall energy consumption, leading to operational cost savings and decreased reliance on conventional energy sources.
Microgrids, which are localized energy systems that incorporate distributed energy resources, have gained prominence as a means to enhance energy resilience and reduce environmental impact. However, the dynamic and decentralized nature of microgrids poses complexities in managing energy supply and demand. To address these challenges, Hamidi, Raihani, and Bouattane [79] proposed a multi-agent system as an intelligent solution that enables autonomous decision making within the microgrid ecosystem. By utilizing the proposed system, the microgrid can optimize energy allocation, storage, and distribution in real time. This contributes to efficient load balancing, peak demand reduction, and integration of renewable energy sources.

Power Grid Reliability

Focusing on reducing losses in electrical energy transmission, Klaar et al. [80] propose a hybrid machine learning approach to identify faults in insulators of power grids and alert the electric utility company about possible faults. This evaluation is based on the time series of leakage current, which is an indicator that a fault may occur [81]. In [82], the time series of ultrasound is evaluated, and in [83] the number of faults is evaluated, with the same goal.
According to Stefenon et al. [84], there is a major effort to identify faults in power grids before shutdowns happen since the maintenance becomes more costly and satisfaction levels are reduced with the lack of energy. Other strategies to keep the electricity running and reduce losses caused by shutdowns are visual inspection [85], the analysis of the contamination [86,87], the estimation of weather variation [88], and the optimization of the design of the equipment such as grid spacers [89] or insulators.
Accurate forecasting of renewable energy generation is essential for effective energy management and grid integration [90]. Zhou et al. [91] propose a novel forecasting model that combines the strengths of the dynamic accumulation method and grey seasonal model to enhance prediction accuracy. Their model showcases its efficacy in accurately predicting generation patterns of renewable energy across different time horizons, thus aiding in optimal energy resource allocation and grid stability.
Sauer et al. [92] presented research using historical energy-consumption data from residential buildings, considering variables such as weather conditions, occupancy patterns, and appliance usage. The XGBoost model, known for its robust predictive capabilities, is integrated to capture intricate relationships and patterns within the data. By effectively forecasting energy consumption in residential buildings, the proposed approach empowers homeowners, energy managers, and policymakers to make informed decisions regarding energy usage, demand response strategies, and overall energy efficiency improvements.
By amalgamating historical load data, meteorological conditions, and other pertinent variables, the cooperative ensemble model proposed by Ribeiro et al. [93] effectively optimizes the combination of individual forecasts. This model holds promise for facilitating improved energy-management strategies, bolstering grid stability, and enabling better-informed decision making in the field of electric power systems.
In the work of Silva et al. [94], an equivalent approach is proposed to multi-step short-term wind speed forecasting, holding promise for optimizing the integration of wind energy into power systems, facilitating energy resource allocation, and supporting informed decision making in the renewable energy sector. To determine whether sustainability is achievable, some indicators are used; these topics are discussed in the next section.

4. Discussions: Sustainable Development Index and Indicators

The objectives of sustainable development are interconnected and have as one of their fundamental requirements the elaboration of energy policies that enable the generation of sustainable electricity and that can be ranked from the formulation of national and/or international indexes generated from a series of data and indicators [95]. This can be carried out through operations with the statistical analysis and aggregation of an index composed of different indicators previously chosen [96].
There is a relationship between the dimensions of sustainability and their resources. In Figure 2, the environmental (ENV), economic (ECO), social (SOC), technical (TEC), and institutional (INS) dimensions are placed with their source, and the interconnection between the dimensions is presented in gray. For each of the dimensions, there are indicators that will be further discussed in this section.

Sustainability Indicators

The indicators have scores characterized by relevance, contextual sensitivity, and robustness, where they can combine and produce distinct indices at the municipal, state, or country scale concerning the quality of electricity, low carbon financing, and social effects such as job creation and acceptability of technology by society [97]. There are several indicators that are used to evaluate the levels of sustainability; some variables and indicators are shown in Table 1.
Regarding the relationship between consumption and quality of energy produced, the factor of the human development index (HDI) is considered an indispensable precedent and significantly related to the volume of energy consumption per person and is a determinant in the strategies that can be adopted according to socioeconomic development associated with government regulation; traditionally, in higher income countries, these strategies are the established investments and promotion of a safer and environmentally friendly energy market [103].
When the HDI is incorporated into the joint assessment of the management of the electricity matrix, equity in energy access, environmental sustainability, and energy security, the formation of the scope contained in the energy sustainability index provided by the World Energy Council of the analyzed region is obtained [103].
Considering the environmental, economic, social, technical, and institutional dimensions and giving the description of indicators to measure sustainability, the significance of each indicator is presented in Table 2.
These indicators are used to evaluate the level of sustainability for the considered dimensions. These indicators are calculated as follows:
T E C 1 = T N R T F C , T E C 2 = T R E T P E S ,
E C O 1 = T P E S G D P , E C O 2 = G D P P o p ,
S O C 1 = R E C P o p , S O C 2 = G I N I ,
E N V 1 = A R H T F C , E N V 2 = T C O 2 T P E S ,
I N S 1 = T P E S T F C , I N S 2 = G D P T F C .
To apply the indicators, it is necessary to normalize them to have a standard parameter for comparisons. There are some indicators that have used by the same authors, which is because there is a relationship between indicators. In Table 3, a matrix of these relationships is presented.
Any index of a regional, national, or international nature requires interpretation prior to its application in order to establish a reliable characterization, verifying that additional indicators can be included and that it meets the prerequisites designated by the targets previously established in governance plans. Subsidies and guarantees are critical factors within developing countries and can be considered as additional indicators to measure their importance quantitatively and qualitatively [113].
Besides all indicators having a relation to the indicators of the same dimension, some of them have an additional relationship with other dimensions, which highlights the statement previously discussed in the introduction section regarding the interconnection between the sustainability dimensions. A complete discussion about the relevance of each indicator and the ones that are related is presented in Table 4.
Following the evaluation, in Table 5, some limitations and possible solutions for each of the indicators are discussed. These limitations highlight how important it is to evaluate sustainability based on more indicators to have a wider view of how dimensions are needed to have sustainability.
The determination of the weights of each indicator, especially when there is a large number of them, becomes one of the biggest problems in the aggregation of multiple-criteria decision analysis, so it is recommended to use the criteria of the local energy policy within computational resources in the literature, such as statistical tools, data envelopment analysis, the method of organizing, and the ranking of preferences in order to carry out the enrichment of evaluations, which can collaborate in reducing the difficulty or subjectivity proposed in the creation and methodological development of an environmental performance index of the energy generated in each project. In addition, this computational and human analysis should be established in different scenarios proposed preliminarily, and sensitivity analyses will be designed after obtaining data [174].
When structuring different variables in the analysis of indices, one can seek to evaluate the correlation and dependence of two series via a cross-validation model that collaborates in the understanding of the established conditionals and in the analysis of the generated curves determining what the dependent and independent variables are [175]. By verifying different biomass electricity-generation technologies using this tool, aided by green total productivity factors through a hierarchical analytical process, it is possible to verify which of the alternatives are the most and least cost-effective in the economy, as well as which have the greatest and lowest emission of greenhouse gases or pollution at the local level and whether the technological innovation that has less polluting character is justified and what maximum investment limit must be met [176].
Next, we draw conclusions about which is the best technological alternative according to the local justification, if there is a need for environmental improvements to the technology with the best cost-effectiveness, or if the necessary burden of investment and fiscal benefit of technological innovation has the best benefit in the short, medium, and long term, so that both achieve the same relationship between the economy and the environment from different energy sustainability indexes. In addition, we establish the barriers that must be overcome and the reasons for the cost difference between the technical aspects that increase the cost of the innovation produced and analyze the cost-effective performance of each technological solution in different countries analyzed and different policy regiments [177].

5. Conclusions

The sustainability concept focusing on energy is a critical and multifaceted approach that recognizes the importance of preserving and efficiently utilizing our planet’s finite resources. As we continue to witness the escalating challenges posed by climate change and resource depletion, it has become increasingly evident that our current energy practices are not sustainable in the long term.
By shifting toward renewable and clean energy sources, such as solar, wind, hydro, geothermal, and bioenergy, we can significantly reduce greenhouse gas emissions and curb the detrimental impact of fossil fuels on our environment. Embracing sustainable energy solutions not only mitigates the effects of climate change but also fosters energy independence and resilience, as these sources are naturally replenished and less susceptible to geopolitical tensions.
Integrating sustainable energy practices into our daily lives and industries promotes economic growth and job creation. The transition to clean energy technologies and infrastructures opens up new avenues for innovation and investment, stimulating green industries and green-collar employment opportunities. Energy efficiency used as a daily practice, for instance, reducing the consumption of air conditioning, has an impact on the preservation of natural resources and a reduction in the emissions of pollutants. From a national perspective, this results in lower fuel prices (combined with public policies) and improved air quality. From an international perspective, energy sustainability reduces a country’s energy dependency and improves its ability to manage the value passed on to public or legal entities.
Based on the text and the research question presented in this paper, it can be concluded that the use of renewable energy sources, the application of sustainable energy policies, and the integration of energy efficiency measures are crucial factors in achieving long-term viability and alignment with sustainable development goals in the energy sector. This article highlights the importance of transitioning to renewable and clean energy sources, reducing environmental impacts, and promoting energy efficiency to create a more sustainable energy future. The indices and indicators presented serve as a basis for learning about society, the economy, the environment, institutions, technologies, and the interactions between these areas. When carefully chosen and communicated effectively, they can provide information in a politically neutral way and contribute to engaging government and citizens in a shared debate about the meaning of sustainability and energy, with the ultimate aim of developing public policies in favor of sustainable development.
The concept of sustainability in energy also entails energy efficiency, which involves optimizing our energy consumption across various sectors. Implementing energy-efficient technologies and practices, such as smart grids, energy-efficient buildings, and industrial processes, can significantly reduce energy wastage and enhance overall system performance. However, achieving a sustainable energy future requires collaborative efforts on a global scale. Governments, businesses, communities, and individuals must work together to prioritize and implement sustainable energy policies, promote research and development about innovative technologies, and engage in responsible energy consumption habits.
Embracing the sustainability concept focusing on energy is not merely an option but an imperative for the well-being of current and future generations. By committing to sustainable energy practices, we can create a cleaner, healthier, and more prosperous world while safeguarding the planet’s natural resources for the benefit of all life forms. It is a collective responsibility and a beacon of hope for a brighter and more sustainable future.

Author Contributions

Writing—Original Draft Preparation, R.N.M. and W.G.B.; Writing—Review and Editing, Supervision, C.T.d.C.J., A.N. and G.V.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Junta De Castilla y León—Consejería De Economía Y Empleo: Characterization, analysis and intervention in the prevention of occupational risks in traditional work environments through the application of disruptive technologies. with ref. J125.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

In special thanks to the researchers Da Rocha, B.R.P.; De Sá, J.A.S. and Stefenon, S.F. for their fundamental contribution throughout the research, which had an assertive influence on the progress of the work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Andriuškevičius, K.; Štreimikienė, D.; Alebaitė, I. Convergence between Indicators for Measuring Sustainable Development and M&A Performance in the Energy Sector. Sustainability 2022, 14, 10360. [Google Scholar] [CrossRef]
  2. Lindsey, T.C. Sustainable principles: Common values for achieving sustainability. J. Clean. Prod. 2011, 19, 561–565. [Google Scholar] [CrossRef]
  3. Shah, S.; Zhou, P.; Walasai, G.; Mohsin, M. Energy security and environmental sustainability index of South Asian countries: A composite index approach. Ecol. Indic. 2019, 106, 105507. [Google Scholar] [CrossRef]
  4. Usubiaga-Liano, A.; Ekins, P. Monitoring the environmental sustainability of countries through the strong environmental sustainability index. Ecol. Indic. 2021, 132, 108281. [Google Scholar] [CrossRef]
  5. Rasoolimanesh, S.M.; Ramakrishna, S.; Hall, C.M.; Esfandiar, K.; Seyfi, S. A systematic scoping review of sustainable tourism indicators in relation to the sustainable development goals. J. Sustain. Tour. 2020, 31, 1497–1517. [Google Scholar] [CrossRef]
  6. Fischer, D.; Brettel, M.; Mauer, R. The three dimensions of sustainability: A delicate balancing act for entrepreneurs made more complex by stakeholder expectations. J. Bus. Ethics 2020, 163, 87–106. [Google Scholar] [CrossRef]
  7. Philippidis, G.; Sartori, M.; Ferrari, E.; M’Barek, R. Waste not, want not: A bio-economic impact assessment of household food waste reductions in the EU. Resour. Conserv. Recycl. 2019, 146, 514–522. [Google Scholar] [CrossRef]
  8. Schneider, F.; Kläy, A.; Zimmermann, A.B.; Buser, T.; Ingalls, M.; Messerli, P. How can science support the 2030 Agenda for Sustainable Development? Four tasks to tackle the normative dimension of sustainability. Sustain. Sci. 2019, 14, 1593–1604. [Google Scholar] [CrossRef]
  9. Kristensen, H.S.; Mosgaard, M.A. A review of micro level indicators for a circular economy–moving away from the three dimensions of sustainability? J. Clean. Prod. 2020, 243, 118531. [Google Scholar] [CrossRef]
  10. Terra dos Santos, L.C.; Giannetti, B.F.; Agostinho, F.; Liu, G.; Almeida, C.M. A multi-criteria approach to assess interconnections among the environmental, economic, and social dimensions of circular economy. J. Environ. Manag. 2023, 342, 118317. [Google Scholar] [CrossRef]
  11. Janker, J.; Mann, S. Understanding the social dimension of sustainability in agriculture: A critical review of sustainability assessment tools. Environ. Dev. Sustain. 2020, 22, 1671–1691. [Google Scholar] [CrossRef]
  12. Hutchins, M.J.; Richter, J.S.; Henry, M.L.; Sutherland, J.W. Development of indicators for the social dimension of sustainability in a U.S. business context. J. Clean. Prod. 2019, 212, 687–697. [Google Scholar] [CrossRef]
  13. Stanković, J.J.; Marjanović, I.; Papathanasiou, J.; Drezgić, S. Social, economic and environmental sustainability of port regions: Mcdm approach in composite index creation. J. Mar. Sci. Eng. 2021, 9, 74. [Google Scholar] [CrossRef]
  14. Sánchez-Flores, R.B.; Cruz-Sotelo, S.E.; Ojeda-Benitez, S.; Ramírez-Barreto, M.E. Sustainable supply chain management—A literature review on emerging economies. Sustainability 2020, 12, 6972. [Google Scholar] [CrossRef]
  15. Chen, C.; Chaudhary, A.; Mathys, A. Dietary change scenarios and implications for environmental, nutrition, human health and economic dimensions of food sustainability. Nutrients 2019, 11, 856. [Google Scholar] [CrossRef] [PubMed]
  16. Pizzi, S.; Del Baldo, M.; Caputo, F.; Venturelli, A. Voluntary disclosure of Sustainable Development Goals in mandatory non-financial reports: The moderating role of cultural dimension. J. Int. Financ. Manag. Account. 2022, 33, 83–106. [Google Scholar] [CrossRef]
  17. Biermann, F.; Hickmann, T.; Sénit, C.A.; Beisheim, M.; Bernstein, S.; Chasek, P.; Grob, L.; Kim, R.E.; Kotzé, L.J.; Nilsson, M.; et al. Scientific evidence on the political impact of the Sustainable Development Goals. Nat. Sustain. 2022, 5, 795–800. [Google Scholar] [CrossRef]
  18. Ch’ng, P.C.; Cheah, J.; Amran, A. Eco-innovation practices and sustainable business performance: The moderating effect of market turbulence in the Malaysian technology industry. J. Clean. Prod. 2021, 283, 124556. [Google Scholar] [CrossRef]
  19. Stefenon, S.F.; Singh, G.; Yow, K.C.; Cimatti, A. Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures. Sensors 2022, 22, 4859. [Google Scholar] [CrossRef]
  20. Corso, M.P.; Stefenon, S.F.; Singh, G.; Matsuo, M.V.; Perez, F.L.; Leithardt, V.R.Q. Evaluation of visible contamination on power grid insulators using convolutional neural networks. Electr. Eng. 2023, 1–14. [Google Scholar] [CrossRef]
  21. Souza, B.J.; Stefenon, S.F.; Singh, G.; Freire, R.Z. Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV. Int. J. Electr. Power Energy Syst. 2023, 148, 108982. [Google Scholar] [CrossRef]
  22. Stefenon, S.F.; Seman, L.O.; Aquino, L.S.; dos Santos Coelho, L. Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants. Energy 2023, 274, 127350. [Google Scholar] [CrossRef]
  23. Kartal, M.T.; Samour, A.; Adebayo, T.S.; Depren, S.K. Do nuclear energy and renewable energy surge environmental quality in the United States? New insights from novel bootstrap Fourier Granger causality in quantiles approach. Prog. Nucl. Energy 2023, 155, 104509. [Google Scholar] [CrossRef]
  24. Stefenon, S.F.; Seman, L.O.; Mariani, V.C.; Coelho, L.d.S. Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices. Energies 2023, 16, 1371. [Google Scholar] [CrossRef]
  25. Abas, N.; Kalair, A.; Khan, N. Review of fossil fuels and future energy technologies. Futures 2015, 69, 31–49. [Google Scholar] [CrossRef]
  26. Cabral, S.H.L.; Stefenon, S.F.; Ovejero, R.G.; Leithardt, V.R.Q. Practical Validation of a New Analytical Method for the Analysis of Power Transmission Lines at Steady State. IEEE Access 2023, 11, 87667–87675. [Google Scholar] [CrossRef]
  27. Lu, Y.; Khan, Z.A.; Alvarez-Alvarado, M.S.; Zhang, Y.; Huang, Z.; Imran, M. A critical review of sustainable energy policies for the promotion of renewable energy sources. Sustainability 2020, 12, 5078. [Google Scholar] [CrossRef]
  28. Feil, A.A.; Schreiber, D.; Haetinger, C.; Strasburg, V.J.; Barkert, C.L. Sustainability indicators for industrial organizations: Systematic review of literature. Sustainability 2019, 11, 854. [Google Scholar] [CrossRef]
  29. Muniz, R.N.; Stefenon, S.F.; Buratto, W.G.; Nied, A.; Meyer, L.H.; Finardi, E.C.; Kühl, R.M.; de Sa, J.A.S.; da Rocha, B.R.P. Tools for measuring energy sustainability: A comparative review. Energies 2020, 13, 2366. [Google Scholar] [CrossRef]
  30. Brulé, G. Evaluation of Existing Indexes of Sustainable Well-Being and Propositions for Improvement. Sustainability 2022, 14, 1027. [Google Scholar] [CrossRef]
  31. Guo, M.; Nowakowska-Grunt, J.; Gorbanyov, V.; Egorova, M. Green technology and sustainable development: Assessment and green growth frameworks. Sustainability 2020, 12, 6571. [Google Scholar] [CrossRef]
  32. Wang, D.; Gryshova, I.; Balian, A.; Kyzym, M.; Salashenko, T.; Khaustova, V.; Davidyuk, O. Assessment of Power System Sustainability and Compromises between the Development Goals. Sustainability 2022, 14, 2236. [Google Scholar] [CrossRef]
  33. Kasburg, C.; Stefenon, S.F. Deep Learning for Photovoltaic Generation Forecast in Active Solar Trackers. IEEE Lat. Am. Trans. 2019, 17, 2013–2019. [Google Scholar] [CrossRef]
  34. Yumashev, A.; Ślusarczyk, B.; Kondrashev, S.; Mikhaylov, A. Global indicators of sustainable development: Evaluation of the influence of the human development index on consumption and quality of energy. Energies 2020, 13, 2768. [Google Scholar] [CrossRef]
  35. Baloch, Z.A.; Tan, Q.; Iqbal, N.; Mohsin, M.; Abbas, Q.; Iqbal, W.; Chaudhry, I.S. Trilemma assessment of energy intensity, efficiency, and environmental index: Evidence from BRICS countries. Environ. Sci. Pollut. Res. 2020, 27, 34337–34347. [Google Scholar] [CrossRef] [PubMed]
  36. Flores, J.A.; Konrad, O.; Flores, C.R.; Schroder, N.T. Sustainability indicators for bioenergy generation from Amazon’s non-woody native biomass sources. Data Brief 2018, 21, 1900–1908. [Google Scholar] [CrossRef] [PubMed]
  37. Huovila, A.; Bosch, P.; Airaksinen, M. Comparative analysis of standardized indicators for Smart sustainable cities: What indicators and standards to use and when? Cities 2019, 89, 141–153. [Google Scholar] [CrossRef]
  38. Gunnarsdóttir, I.; Davidsdottir, B.; Worrell, E.; Sigurgeirsdóttir, S. Review of indicators for sustainable energy development. Renew. Sustain. Energy Rev. 2020, 133, 110294. [Google Scholar] [CrossRef]
  39. Sovacool, B.K.; Mukherjee, I. Conceptualizing and measuring energy security: A synthesized approach. Energy 2011, 36, 5343–5355. [Google Scholar] [CrossRef]
  40. Murshed, M.; Khan, S.; Rahman, A.A. Roadmap for achieving energy sustainability in Sub-Saharan Africa: The mediating role of energy use efficiency. Energy Rep. 2022, 8, 4535–4552. [Google Scholar] [CrossRef]
  41. Drago, C.; Gatto, A. Policy, regulation effectiveness, and sustainability in the energy sector: A worldwide interval-based composite indicator. Energy Policy 2022, 167, 112889. [Google Scholar] [CrossRef]
  42. Li, H.S.; Geng, Y.C.; Shinwari, R.; Yangjie, W.; Rjoub, H. Does renewable energy electricity and economic complexity index help to achieve carbon neutrality target of top exporting countries? J. Environ. Manag. 2021, 299, 113386. [Google Scholar] [CrossRef] [PubMed]
  43. Sy, S.A.; Mokaddem, L. Energy poverty in developing countries: A review of the concept and its measurements. Energy Res. Soc. Sci. 2022, 89, 102562. [Google Scholar] [CrossRef]
  44. Chenari, B.; Carrilho, J.D.; da Silva, M.G. Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review. Renew. Sustain. Energy Rev. 2016, 59, 1426–1447. [Google Scholar] [CrossRef]
  45. Pacheco, R.; Ordóñez, J.; Martínez, G. Energy efficient design of building: A review. Renew. Sustain. Energy Rev. 2012, 16, 3559–3573. [Google Scholar] [CrossRef]
  46. Centobelli, P.; Cerchione, R.; Esposito, E. Environmental Sustainability and Energy-Efficient Supply Chain Management: A Review of Research Trends and Proposed Guidelines. Energies 2018, 11, 275. [Google Scholar] [CrossRef]
  47. Meng, Y.; Yang, Y.; Chung, H.; Lee, P.H.; Shao, C. Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability 2018, 10, 4779. [Google Scholar] [CrossRef]
  48. Shen, Y.; Zhou, J.; Zhang, J.; Yi, F.; Wang, G.; Pan, C.; Guo, W.; Shu, X. Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control. Sustainability 2023, 15, 12488. [Google Scholar] [CrossRef]
  49. Seman, L.O.; Stefenon, S.F.; Mariani, V.C.; dos Santos Coelho, L. Ensemble learning methods using the Hodrick–Prescott filter for fault forecasting in insulators of the electrical power grids. Int. J. Electr. Power Energy Syst. 2023, 152, 109269. [Google Scholar] [CrossRef]
  50. Borré, A.; Seman, L.O.; Camponogara, E.; Stefenon, S.F.; Mariani, V.C.; Coelho, L.S. Machine fault detection using a hybrid CNN-LSTM attention-based model. Sensors 2023, 23, 4512. [Google Scholar] [CrossRef]
  51. Vieira, J.C.; Sartori, A.; Stefenon, S.F.; Perez, F.L.; de Jesus, G.S.; Leithardt, V.R.Q. Low-Cost CNN for automatic violence recognition on embedded system. IEEE Access 2022, 10, 25190–25202. [Google Scholar] [CrossRef]
  52. dos Santos, G.H.; Seman, L.O.; Bezerra, E.A.; Leithardt, V.R.Q.; Mendes, A.S.; Stefenon, S.F. Static attitude determination using convolutional neural networks. Sensors 2021, 21, 6419. [Google Scholar] [CrossRef]
  53. Singh, G.; Stefenon, S.F.; Yow, K.C. Interpretable visual transmission lines inspections using pseudo-prototypical part network. Mach. Vis. Appl. 2023, 34, 41. [Google Scholar] [CrossRef]
  54. Corso, M.P.; Perez, F.L.; Stefenon, S.F.; Yow, K.C.; García Ovejero, R.; Leithardt, V.R.Q. Classification of contaminated insulators using k-nearest neighbors based on computer vision. Computers 2021, 10, 112. [Google Scholar] [CrossRef]
  55. Glasenapp, L.A.; Hoppe, A.F.; Wisintainer, M.A.; Sartori, A.; Stefenon, S.F. OCR applied for identification of vehicles with irregular documentation using IoT. Electronics 2023, 12, 1083. [Google Scholar] [CrossRef]
  56. Khishtandar, S.; Zandieh, M.; Dorri, B. A multi criteria decision making framework for sustainability assessment of bioenergy production technologies with hesitant fuzzy linguistic term sets: The case of Iran. Renew. Sustain. Energy Rev. 2017, 77, 1130–1145. [Google Scholar] [CrossRef]
  57. Kaya, I.; Colak, M.; Terzi, F. A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Rev. 2019, 24, 207–228. [Google Scholar] [CrossRef]
  58. Solangi, Y.A.; Tan, Q.; Mirjat, N.H.; Ali, S. Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. J. Clean. Prod. 2019, 236, 117655. [Google Scholar] [CrossRef]
  59. Hezam, I.M.; Mishra, A.R.; Rani, P.; Saha, A.; Smarandache, F.; Pamucar, D. An integrated decision support framework using single-valued neutrosophic-MASWIP-COPRAS for sustainability assessment of bioenergy production technologies. Expert Syst. Appl. 2023, 211, 118674. [Google Scholar] [CrossRef]
  60. Sahabuddin, M.; Khan, I. Multi-criteria decision analysis methods for energy sector’s sustainability assessment: Robustness analysis through criteria weight change. Sustain. Energy Technol. Assess. 2021, 47, 101380. [Google Scholar] [CrossRef]
  61. Ervural, B.C.; Zaim, S.; Demirel, O.F.; Aydin, Z.; Delen, D. An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renew. Sustain. Energy Rev. 2018, 82, 1538–1550. [Google Scholar] [CrossRef]
  62. Neofytou, H.; Nikas, A.; Doukas, H. Sustainable energy transition readiness: A multicriteria assessment index. Renew. Sustain. Energy Rev. 2020, 131, 109988. [Google Scholar] [CrossRef]
  63. Zhao, D.; Cai, J.; Shen, L.; Elshkaki, A.; Liu, J.; Varis, O. Delivery of energy sustainability: Applications of the “STAR” protocol to the Sustainable Development Goal 7 index and its interaction analysis. J. Clean. Prod. 2023, 389, 135884. [Google Scholar] [CrossRef]
  64. Klaar, A.C.R.; Stefenon, S.F.; Seman, L.O.; Mariani, V.C.; Coelho, L.S. Structure optimization of ensemble learning methods and seasonal decomposition approaches to energy price forecasting in Latin America: A case study about Mexico. Energies 2023, 16, 3184. [Google Scholar] [CrossRef]
  65. Ribeiro, M.H.D.M.; Stefenon, S.F.; de Lima, J.D.; Nied, A.; Mariani, V.C.; Coelho, L.S. Electricity price forecasting based on self-adaptive decomposition and heterogeneous ensemble learning. Energies 2020, 13, 5190. [Google Scholar] [CrossRef]
  66. Padilla-Rivera, A.; Paredes, M.G.; Güereca, L.P. A systematic review of the sustainability assessment of bioenergy: The case of gaseous biofuels. Biomass Bioenergy 2019, 125, 79–94. [Google Scholar] [CrossRef]
  67. Elavarasan, R.M.; Afridhis, S.; Vijayaraghavan, R.R.; Subramaniam, U.; Nurunnabi, M. SWOT analysis: A framework for comprehensive evaluation of drivers and barriers for renewable energy development in significant countries. Energy Rep. 2020, 6, 1838–1864. [Google Scholar] [CrossRef]
  68. Yin, C.; Zhao, W.; Ye, J.; Muroki, M.; Pereira, P. Ecosystem carbon sequestration service supports the Sustainable Development Goals progress. J. Environ. Manag. 2023, 330, 117155. [Google Scholar] [CrossRef]
  69. Fernandes, A.M.d.R.; Cassaniga, M.J.; Passos, B.T.; Comunello, E.; Stefenon, S.F.; Leithardt, V.R.Q. Detection and classification of cracks and potholes in road images using texture descriptors. J. Intell. Fuzzy Syst. 2023, 44, 10255–10274. [Google Scholar] [CrossRef]
  70. Westarb, G.; Stefenon, S.F.; Hoppe, A.F.; Sartori, A.; Klaar, A.C.R.; Leithardt, V.R.Q. Complex graph neural networks for medication interaction verification. J. Intell. Fuzzy Syst. 2023, 44, 10383–10395. [Google Scholar] [CrossRef]
  71. Nishant, R.; Kennedy, M.; Corbett, J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Inf. Manag. 2020, 53, 102104. [Google Scholar] [CrossRef]
  72. Luo, C.; Ju, Y.; Gonzalez, E.D.S.; Dong, P.; Wang, A. The waste-to-energy incineration plant site selection based on hesitant fuzzy linguistic Best-Worst method ANP and double parameters TOPSIS approach: A case study in China. Energy 2020, 211, 118564. [Google Scholar] [CrossRef]
  73. Altintas, K.; Vayvay, O.; Apak, S.; Cobanoglu, E. An extended GRA method integrated with fuzzy AHP to construct a multidimensional index for ranking overall energy sustainability performances. Sustainability 2020, 12, 1602. [Google Scholar] [CrossRef]
  74. Wang, Q.; Yang, X. Investigating the sustainability of renewable energy–An empirical analysis of European Union countries using a hybrid of projection pursuit fuzzy clustering model and accelerated genetic algorithm based on real coding. J. Clean. Prod. 2020, 268, 121940. [Google Scholar] [CrossRef]
  75. Bas, E. The integrated framework for analysis of electricity supply chain using an integrated SWOT-fuzzy TOPSIS methodology combined with AHP: The case of Turkey. Int. J. Electr. Power Energy Syst. 2013, 44, 897–907. [Google Scholar] [CrossRef]
  76. Palomares, I.; Martínez-Cámara, E.; Montes, R.; García-Moral, P.; Chiachio, M.; Chiachio, J.; Alonso, S.; Melero, F.J.; Molina, D.; Fernández, B.; et al. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Appl. Intell. 2021, 51, 6497–6527. [Google Scholar] [CrossRef]
  77. Davoodi, M.; Jafari Kaleybar, H.; Brenna, M.; Zaninelli, D. Energy Management Systems for Smart Electric Railway Networks: A Methodological Review. Sustainability 2023, 15, 12204. [Google Scholar] [CrossRef]
  78. Tung, T.V.; Nga, N.T.T.; Van, H.T.; Vu, T.H.; Kuligowski, K.; Cenian, A.; Tuan, N.Q.; Le, P.C.; Tran, Q.B. Energy Efficiency and Environmental Benefits of Waste Heat Recovery Technologies in Fishmeal Production Plants: A Case Study in Vietnam. Sustainability 2023, 15, 12712. [Google Scholar] [CrossRef]
  79. Hamidi, M.; Raihani, A.; Bouattane, O. Sustainable Intelligent Energy Management System for Microgrid Using Multi-Agent Systems: A Case Study. Sustainability 2023, 15, 12546. [Google Scholar] [CrossRef]
  80. Klaar, A.C.R.; Stefenon, S.F.; Seman, L.O.; Mariani, V.C.; Coelho, L.S. Optimized EWT-Seq2Seq-LSTM with attention mechanism to insulators fault prediction. Sensors 2023, 23, 3202. [Google Scholar] [CrossRef]
  81. Stefenon, S.F.; Seman, L.O.; Sopelsa Neto, N.F.; Meyer, L.H.; Mariani, V.C.; Coelho, L.d.S. Group method of data handling using Christiano-Fitzgerald random walk filter for insulator fault prediction. Sensors 2023, 23, 6118. [Google Scholar] [CrossRef] [PubMed]
  82. Stefenon, S.F.; Bruns, R.; Sartori, A.; Meyer, L.H.; Ovejero, R.G.; Leithardt, V.R.Q. Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods. IEEE Access 2022, 10, 33980–33991. [Google Scholar] [CrossRef]
  83. Branco, N.W.; Cavalca, M.S.M.; Stefenon, S.F.; Leithardt, V.R.Q. Wavelet LSTM for fault forecasting in electrical power grids. Sensors 2022, 22, 8323. [Google Scholar] [CrossRef] [PubMed]
  84. Stefenon, S.F.; Silva, M.C.; Bertol, D.W.; Meyer, L.H.; Nied, A. Fault diagnosis of insulators from ultrasound detection using neural networks. J. Intell. Fuzzy Syst. 2019, 37, 6655–6664. [Google Scholar] [CrossRef]
  85. Stefenon, S.F.; Singh, G.; Souza, B.J.; Freire, R.Z.; Yow, K.C. Optimized hybrid YOLOu-Quasi-ProtoPNet for insulators classification. IET Gener. Transm. Distrib. 2023, 17, 3501–3511. [Google Scholar] [CrossRef]
  86. Sopelsa Neto, N.F.; Stefenon, S.F.; Meyer, L.H.; Ovejero, R.G.; Leithardt, V.R.Q. Fault prediction based on leakage current in contaminated insulators using enhanced time series forecasting models. Sensors 2022, 22, 6121. [Google Scholar] [CrossRef]
  87. Stefenon, S.F.; Oliveira, J.R.; Coelho, A.S.; Meyer, L.H. Diagnostic of insulators of conventional grid through LabVIEW analysis of FFT signal generated from ultrasound detector. IEEE Lat. Am. Trans. 2017, 15, 884–889. [Google Scholar] [CrossRef]
  88. AlHaddad, U.; Basuhail, A.; Khemakhem, M.; Eassa, F.E.; Jambi, K. Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events. Sustainability 2023, 15, 16. [Google Scholar] [CrossRef]
  89. Stefenon, S.F.; Seman, L.O.; Pavan, B.A.; Ovejero, R.G.; Leithardt, V.R.Q. Optimal design of electrical power distribution grid spacers using finite element method. IET Gener. Transm. Distrib. 2022, 16, 1865–1876. [Google Scholar] [CrossRef]
  90. Ribeiro, M.H.D.M.; da Silva, R.G.; Moreno, S.R.; Mariani, V.C.; dos Santos Coelho, L. Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting. Int. J. Electr. Power Energy Syst. 2022, 136, 107712. [Google Scholar] [CrossRef]
  91. Zhou, W.; Jiang, H.; Chang, J. Forecasting Renewable Energy Generation Based on a Novel Dynamic Accumulation Grey Seasonal Model. Sustainability 2023, 15, 12188. [Google Scholar] [CrossRef]
  92. Sauer, J.; Mariani, V.C.; dos Santos Coelho, L.; Ribeiro, M.H.D.M.; Rampazzo, M. Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings. Evol. Syst. 2022, 13, 577–588. [Google Scholar] [CrossRef]
  93. Ribeiro, M.H.D.M.; da Silva, R.G.; Ribeiro, G.T.; Mariani, V.C.; dos Santos Coelho, L. Cooperative ensemble learning model improves electric short-term load forecasting. Chaos Solitons Fractals 2023, 166, 112982. [Google Scholar] [CrossRef]
  94. da Silva, R.G.; Moreno, S.R.; Ribeiro, M.H.D.M.; Larcher, J.H.K.; Mariani, V.C.; dos Santos Coelho, L. Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach. Int. J. Electr. Power Energy Syst. 2022, 143, 108504. [Google Scholar] [CrossRef]
  95. Mohsin, M.; Rasheed, A.; Sun, H.; Zhang, J.; Iram, R.; Iqbal, N.; Abbas, Q. Developing low carbon economies: An aggregated composite index based on carbon emissions. Sustain. Energy Technol. Assess. 2019, 35, 365–374. [Google Scholar] [CrossRef]
  96. Mohsin, M.; Taghizadeh-Hesary, F.; Panthamit, N.; Anwar, S.; Abbas, Q.; Vo, X.V. Developing low carbon finance index: Evidence from developed and developing economies. Financ. Res. Lett. 2021, 43, 101520. [Google Scholar] [CrossRef]
  97. Wang, J.; Chen, H.; Cao, Y.; Wang, C.; Li, J. An integrated optimization framework for regional energy planning with a sustainability assessment model. Sustain. Prod. Consum. 2023, 36, 526–539. [Google Scholar] [CrossRef]
  98. EPE. Generation Expansion. Available online: https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-432/EPE-DEE-088_2019_Repotencia%C3%A7%C3%A3o%20de%20Usinas%20Hidrel%C3%A9tricas.pdf (accessed on 18 September 2023).
  99. IBGE. PNAD—Continuous National Household Sample Survey. Available online: https://www.ibge.gov.br/estatisticas/sociais/trabalho/2511-np-pnad-continua/30980-pnadc-divulgacao-pnadc4.html (accessed on 18 September 2023).
  100. EPE. National Energy Balance. Available online: https://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/balanco-energetico-nacional-ben (accessed on 18 September 2023).
  101. SEEG. Emissions Map: Total Emissions CO2 (t). Available online: https://plataforma.seeg.eco.br/total_emission (accessed on 18 September 2023).
  102. EPE. Electrical Energy Statistical Yearbook. Available online: https://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/anuario-estatistico-de-energia-eletrica (accessed on 18 September 2023).
  103. Ponomarenko, T.; Reshneva, E.; Mosquera Urbano, A.P. Assessment of energy sustainability issues in the andean community: Additional indicators and their interpretation. Energies 2022, 15, 1077. [Google Scholar] [CrossRef]
  104. Grigoroudis, E.; Kouikoglou, V.S.; Phillis, Y.A.; Kanellos, F.D. Energy sustainability: A definition and assessment model. Oper. Res. 2021, 21, 1845–1885. [Google Scholar] [CrossRef]
  105. García-Álvarez, M.T.; Moreno, B.; Soares, I. Analyzing the sustainable energy development in the EU-15 by an aggregated synthetic index. Ecol. Indic. 2016, 60, 996–1007. [Google Scholar] [CrossRef]
  106. Sovacool, B.K. The methodological challenges of creating a comprehensive energy security index. Energy Policy 2012, 48, 835–840. [Google Scholar] [CrossRef]
  107. Iddrisu, I.; Bhattacharyya, S.C. Sustainable Energy Development Index: A multi-dimensional indicator for measuring sustainable energy development. Renew. Sustain. Energy Rev. 2015, 50, 513–530. [Google Scholar] [CrossRef]
  108. IBGE. Territorial Organization (Structure): Legal Amazon. Available online: https://geoftp.ibge.gov.br/organizacao_do_territorio/estrutura_territorial/amazonia_legal/2022/Mapa_da_Amazonia_Legal_2022_sem_sedes.pdf (accessed on 31 August 2023).
  109. Brown, M.A.; Sovacool, B.K. Developing an’energy sustainability index’to evaluate energy policy. Interdiscip. Sci. Rev. 2007, 32, 335–349. [Google Scholar] [CrossRef]
  110. Liu, G. Development of a general sustainability indicator for renewable energy systems: A review. Renew. Sustain. Energy Rev. 2014, 31, 611–621. [Google Scholar] [CrossRef]
  111. German Agency for Technical Cooperation; NU. CEPAL; OLADE. Energy and Sustainable Development in Latin America and the Caribbean: Approaches to Energy Policy. 1997. Available online: https://repositorio.cepal.org/items/8ace52db-c1e0-4b71-b7a9-7ddbb98b69d4 (accessed on 31 August 2023).
  112. Vera, I.A.; Langlois, L.M.; Rogner, H.H.; Jalal, A.; Toth, F.L. Indicators for sustainable energy development: An initiative by the International Atomic Energy Agency. Nat. Resour. Forum 2005, 29, 274–283. [Google Scholar] [CrossRef]
  113. Shanmugam, K.; Gadhamshetty, V.; Tysklind, M.; Bhattacharyya, D.; Upadhyayula, V.K. A sustainable performance assessment framework for circular management of municipal wastewater treatment plants. J. Clean. Prod. 2022, 339, 130657. [Google Scholar] [CrossRef]
  114. Holechek, J.L.; Geli, H.M.; Sawalhah, M.N.; Valdez, R. A global assessment: Can renewable energy replace fossil fuels by 2050? Sustainability 2022, 14, 4792. [Google Scholar] [CrossRef]
  115. Achuo, E.D.; Miamo, C.W.; Nchofoung, T.N. Energy consumption and environmental sustainability: What lessons for posterity? Energy Rep. 2022, 8, 12491–12502. [Google Scholar] [CrossRef]
  116. Sanchez, S.F.; Segovia, M.A.F.; López, L.C.R. Estimating a national energy security index in Mexico: A quantitative approach and public policy implications. Energy Strategy Rev. 2023, 45, 101019. [Google Scholar] [CrossRef]
  117. Luiz-Silva, W.; Garcia, K.C. Sustainable future and water resources: A synthesis of the Brazilian hydroelectricity sector in face of climate change. Sustain. Water Resour. Manag. 2022, 8, 120. [Google Scholar] [CrossRef]
  118. Li, M.; He, N. Carbon intensity of global existing and future hydropower reservoirs. Renew. Sustain. Energy Rev. 2022, 162, 112433. [Google Scholar] [CrossRef]
  119. Caglar, A.E.; Ulug, M. The role of government spending on energy efficiency R&D budgets in the green transformation process: Insight from the top-five countries. Environ. Sci. Pollut. Res. 2022, 29, 76472–76484. [Google Scholar] [CrossRef]
  120. Chen, H.; Shi, Y.; Zhao, X. Investment in renewable energy resources, sustainable financial inclusion and energy efficiency: A case of US economy. Resour. Policy 2022, 77, 102680. [Google Scholar] [CrossRef]
  121. Zhang, Y. Analysis of China’s energy efficiency and influencing factors under carbon peaking and carbon neutrality goals. J. Clean. Prod. 2022, 370, 133604. [Google Scholar] [CrossRef]
  122. Wei, X.; Mohsin, M.; Zhang, Q. Role of foreign direct investment and economic growth in renewable energy development. Renew. Energy 2022, 192, 828–837. [Google Scholar] [CrossRef]
  123. Zhao, J.; Sinha, A.; Inuwa, N.; Wang, Y.; Murshed, M.; Abbasi, K.R. Does structural transformation in economy impact inequality in renewable energy productivity? Implications for sustainable development. Renew. Energy 2022, 189, 853–864. [Google Scholar] [CrossRef]
  124. Murshed, M.; Apergis, N.; Alam, M.S.; Khan, U.; Mahmud, S. The impacts of renewable energy, financial inclusivity, globalization, economic growth, and urbanization on carbon productivity: Evidence from net moderation and mediation effects of energy efficiency gains. Renew. Energy 2022, 196, 824–838. [Google Scholar] [CrossRef]
  125. Zhao, X.; Mahendru, M.; Ma, X.; Rao, A.; Shang, Y. Impacts of environmental regulations on green economic growth in China: New guidelines regarding renewable energy and energy efficiency. Renew. Energy 2022, 187, 728–742. [Google Scholar] [CrossRef]
  126. Liu, J.; Jain, V.; Sharma, P.; Ali, S.A.; Shabbir, M.S.; Ramos-Meza, C.S. The role of Sustainable Development Goals to eradicate the multidimensional energy poverty and improve social Wellbeing’s. Energy Strategy Rev. 2022, 42, 100885. [Google Scholar] [CrossRef]
  127. Bianchi, M.; Cordella, M. Does circular economy mitigate the extraction of natural resources? Empirical evidence based on analysis of 28 European economies over the past decade. Ecol. Econ. 2023, 203, 107607. [Google Scholar] [CrossRef]
  128. Khan, Z.; Badeeb, R.A.; Nawaz, K. Natural resources and economic performance: Evaluating the role of political risk and renewable energy consumption. Resour. Policy 2022, 78, 102890. [Google Scholar] [CrossRef]
  129. Yu, C.; Moslehpour, M.; Tran, T.K.; Trung, L.M.; Ou, J.P.; Tien, N.H. Impact of non-renewable energy and natural resources on economic recovery: Empirical evidence from selected developing economies. Resour. Policy 2023, 80, 103221. [Google Scholar] [CrossRef]
  130. Khan, Z.; Hossain, M.R.; Badeeb, R.A.; Zhang, C. Aggregate and disaggregate impact of natural resources on economic performance: Role of green growth and human capital. Resour. Policy 2023, 80, 103103. [Google Scholar] [CrossRef]
  131. Eras, J.J.C.; Fandino, J.M.M.; Gutiérrez, A.S.; Bayona, J.G.R.; German, S.J.S. The inequality of electricity consumption in Colombia. Projections and implications. Energy 2022, 249, 123711. [Google Scholar] [CrossRef]
  132. Li, H.; Liu, X.; Wang, S.; Wang, Z. Impacts of international trade on global inequality of energy and water use. J. Environ. Manag. 2022, 315, 115156. [Google Scholar] [CrossRef]
  133. Overland, I.; Juraev, J.; Vakulchuk, R. Are renewable energy sources more evenly distributed than fossil fuels? Renew. Energy 2022, 200, 379–386. [Google Scholar] [CrossRef]
  134. Asiedu, M.; Effah, N.A.A.; Aboagye, E.M. Finance, poverty-income inequality, energy consumption and the CO2 emissions nexus in Africa. J. Bus. Socio-Econ. Dev. 2023, 3, 214–236. [Google Scholar] [CrossRef]
  135. Zhang, R.; Wei, Q.; Li, A.; Ren, L. Measuring efficiency and technology inequality of China’s electricity generation and transmission system: A new approach of network Data Envelopment Analysis prospect cross-efficiency models. Energy 2022, 246, 123274. [Google Scholar] [CrossRef]
  136. Condé, T.M.; Tonini, H.; Higuchi, N.; Higuchi, F.G.; Lima, A.J.N.; Barbosa, R.I.; dos Santos Pereira, T.; Haas, M.A. Effects of sustainable forest management on tree diversity, timber volumes, and carbon stocks in an ecotone forest in the northern Brazilian Amazon. Land Use Policy 2022, 119, 106145. [Google Scholar] [CrossRef]
  137. Zaman, K. Environmental cost of deforestation in Brazil’s Amazon Rainforest: Controlling biocapacity deficit and renewable wastes for conserving forest resources. For. Ecol. Manag. 2022, 504, 119854. [Google Scholar] [CrossRef]
  138. Raihan, A.; Muhtasim, D.A.; Farhana, S.; Pavel, M.I.; Faruk, O.; Rahman, M.; Mahmood, A. Nexus between carbon emissions, economic growth, renewable energy use, urbanization, industrialization, technological innovation, and forest area towards achieving environmental sustainability in Bangladesh. Energy Clim. Chang. 2022, 3, 100080. [Google Scholar] [CrossRef]
  139. Alsaleh, M.; Abdulwakil, M.M.; Abdul-Rahim, A.S. Land-use change impacts from sustainable hydropower production in EU28 region: An empirical analysis. Sustainability 2021, 13, 4599. [Google Scholar] [CrossRef]
  140. Anh, L.H.; Thanh Truc, N.T.; Tuyen, N.T.K.; Bang, H.Q.; Son, N.P.; Schneider, P.; Lee, B.K.; Moustakas, K. Site-specific determination of methane generation potential and estimation of landfill gas emissions from municipal solid waste landfill: A case study in Nam Binh Duong, Vietnam. Biomass Convers. Biorefinery 2022, 12, 3491–3502. [Google Scholar] [CrossRef]
  141. Li, H.; Meng, B.; Yue, B.; Gao, Q.; Ma, Z.; Zhang, W.; Li, T.; Yu, L. Seasonal CH4 and CO2 effluxes in a final covered landfill site in Beijing, China. Sci. Total Environ. 2020, 725, 138355. [Google Scholar] [CrossRef] [PubMed]
  142. Aldhafeeri, T.; Tran, M.K.; Vrolyk, R.; Pope, M.; Fowler, M. A review of methane gas detection sensors: Recent developments and future perspectives. Inventions 2020, 5, 28. [Google Scholar] [CrossRef]
  143. Montoya, M.A.; Allegretti, G.; Bertussi, L.A.S.; Talamini, E. Renewable and Non-renewable in the energy-emissions-climate nexus: Brazilian contributions to climate change via international trade. J. Clean. Prod. 2021, 312, 127700. [Google Scholar] [CrossRef]
  144. Wada, I.; Faizulayev, A.; Bekun, F.V. Exploring the role of conventional energy consumption on environmental quality in Brazil: Evidence from cointegration and conditional causality. Gondwana Res. 2021, 98, 244–256. [Google Scholar] [CrossRef]
  145. Naimoğlu, M. The impact of nuclear energy use, energy prices and energy imports on CO2 emissions: Evidence from energy importer emerging economies which use nuclear energy. J. Clean. Prod. 2022, 373, 133937. [Google Scholar] [CrossRef]
  146. Lazaro, L.L.B.; Soares, R.S.; Bermann, C.; Collaço, F.M.d.A.; Giatti, L.; Abram, S. Energy transition in Brazil: Is there a role for multilevel governance in a centralized energy regime? Energy Res. Soc. Sci. 2022, 85, 102404. [Google Scholar] [CrossRef]
  147. Wang, Y.; Chen, X. Natural resource endowment and ecological efficiency in China: Revisiting resource curse in the context of ecological efficiency. Resour. Policy 2020, 66, 101610. [Google Scholar] [CrossRef]
  148. Awosusi, A.A.; Adebayo, T.S.; Altuntaş, M.; Agyekum, E.B.; Zawbaa, H.M.; Kamel, S. The dynamic impact of biomass and natural resources on ecological footprint in BRICS economies: A quantile regression evidence. Energy Rep. 2022, 8, 1979–1994. [Google Scholar] [CrossRef]
  149. Jahanger, A.; Usman, M.; Murshed, M.; Mahmood, H.; Balsalobre-Lorente, D. The linkages between natural resources, human capital, globalization, economic growth, financial development, and ecological footprint: The moderating role of technological innovations. Resour. Policy 2022, 76, 102569. [Google Scholar] [CrossRef]
  150. Ma, L.; Long, H.; Chen, K.; Tu, S.; Zhang, Y.; Liao, L. Green growth efficiency of Chinese cities and its spatio-temporal pattern. Resour. Conserv. Recycl. 2019, 146, 441–451. [Google Scholar] [CrossRef]
  151. Song, M.; Xie, Q.; Shen, Z. Impact of green credit on high-efficiency utilization of energy in China considering environmental constraints. Energy Policy 2021, 153, 112267. [Google Scholar] [CrossRef]
  152. Usman, M.; Balsalobre-Lorente, D.; Jahanger, A.; Ahmad, P. Pollution concern during globalization mode in financially resource-rich countries: Do financial development, natural resources, and renewable energy consumption matter? Renew. Energy 2022, 183, 90–102. [Google Scholar] [CrossRef]
  153. Vandeninden, F.; Grun, R.; Fecher, F. Energy subsidies and poverty: The case of fossil fuel subsidies in Burkina Faso. Energy Sustain. Dev. 2022, 70, 581–591. [Google Scholar] [CrossRef]
  154. Thellufsen, J.Z.; Lund, H.; Sorknæs, P.; Østergaard, P.; Chang, M.; Drysdale, D.; Nielsen, S.; Djørup, S.; Sperling, K. Smart energy cities in a 100% renewable energy context. Renew. Sustain. Energy Rev. 2020, 129, 109922. [Google Scholar] [CrossRef]
  155. Catolico, A.; Maestrini, M.; Strauch, J.; Giusti, F.; Hunt, J. Socioeconomic impacts of large hydroelectric power plants in Brazil: A synthetic control assessment of Estreito hydropower plant. Renew. Sustain. Energy Rev. 2021, 151, 111508. [Google Scholar] [CrossRef]
  156. Thaler, P.; Hofmann, B. The impossible energy trinity: Energy security, sustainability, and sovereignty in cross-border electricity systems. Political Geogr. 2022, 94, 102579. [Google Scholar] [CrossRef]
  157. Nangia, R. Securing Asia’s energy future with regional integration. Energy Policy 2019, 132, 1262–1273. [Google Scholar] [CrossRef]
  158. Martins, J.C.; Morandi, M.I.W.M.; Lacerda, D.P.; Andrade, B.P.B. Energy efficiency decision-making in non-energy intensive industries: Content and social network analysis. Production 2022, 32, e20210065. [Google Scholar] [CrossRef]
  159. Bank, W. The Changing Wealth of Nations 2021: Managing Assets for the Future; The World Bank: Washington, DC, USA, 2021. [Google Scholar]
  160. Dědeček, R.; Dudzich, V. Exploring the limitations of GDP per capita as an indicator of economic development: A cross-country perspective. Rev. Econ. Perspect. 2022, 22, 193–217. [Google Scholar] [CrossRef]
  161. de Lima, L.M.; Bacchi, M.R.P. Assessing the impact of Brazilian economic growth on demand for electricity. Energy 2019, 172, 861–873. [Google Scholar] [CrossRef]
  162. de Mello Delgado, D.B.; de Lima, K.M.; de Camargo Cancela, M.; dos Santos Siqueira, C.A.; Carvalho, M.; de Souza, D.L.B. Trend analyses of electricity load changes in Brazil due to COVID-19 shutdowns. Electr. Power Syst. Res. 2021, 193, 107009. [Google Scholar] [CrossRef]
  163. Sitthiyot, T.; Holasut, K. A simple method for measuring inequality. Palgrave Commun. 2020, 6, 112. [Google Scholar] [CrossRef]
  164. Couto, L.C.; Campos, L.C.; da Fonseca-Zang, W.; Zang, J.; Bleischwitz, R. Water, waste, energy and food nexus in Brazil: Identifying a resource interlinkage research agenda through a systematic review. Renew. Sustain. Energy Rev. 2021, 138, 110554. [Google Scholar] [CrossRef]
  165. Fang, T.; Fang, D.; Yu, B. Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants. Energy Policy 2022, 165, 112955. [Google Scholar] [CrossRef]
  166. Yemelyanov, O.; Symak, A.; Petrushka, T.; Vovk, O.; Ivanytska, O.; Symak, D.; Havryliak, A.; Danylovych, T.; Lesyk, L. Criteria, indicators, and factors of the sustainable energy-saving economic development: The case of natural gas consumption. Energies 2021, 14, 5999. [Google Scholar] [CrossRef]
  167. Thoma, D.P.; Tercek, M.T.; Schweiger, E.W.; Munson, S.M.; Gross, J.E.; Olliff, S.T. Water balance as an indicator of natural resource condition: Case studies from Great Sand Dunes National Park and Preserve. Glob. Ecol. Conserv. 2020, 24, e01300. [Google Scholar] [CrossRef]
  168. Reyes, S.R.C.; Miyazaki, A.; Yiu, E.; Saito, O. Enhancing sustainability in traditional agriculture: Indicators for monitoring the conservation of globally important agricultural heritage systems (GIAHS) in Japan. Sustainability 2020, 12, 5656. [Google Scholar] [CrossRef]
  169. Ali, Q.; Yaseen, M.R.; Anwar, S.; Makhdum, M.S.A.; Khan, M.T.I. The impact of tourism, renewable energy, and economic growth on ecological footprint and natural resources: A panel data analysis. Resour. Policy 2021, 74, 102365. [Google Scholar] [CrossRef]
  170. Maynard, D.d.C.; Vidigal, M.D.; Farage, P.; Zandonadi, R.P.; Nakano, E.Y.; Botelho, R.B.A. Environmental, social and economic sustainability indicators applied to food services: A systematic review. Sustainability 2020, 12, 1804. [Google Scholar] [CrossRef]
  171. Sun, H.; Edziah, B.K.; Sun, C.; Kporsu, A.K. Institutional quality, green innovation and energy efficiency. Energy Policy 2019, 135, 111002. [Google Scholar] [CrossRef]
  172. Ding, Q.; Khattak, S.I.; Ahmad, M. Towards sustainable production and consumption: Assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations. Sustain. Prod. Consum. 2021, 27, 254–268. [Google Scholar] [CrossRef]
  173. Dabbous, A.; Tarhini, A. Does sharing economy promote sustainable economic development and energy efficiency? Evidence from OECD countries. J. Innov. Knowl. 2021, 6, 58–68. [Google Scholar] [CrossRef]
  174. Bompard, E.; Ciocia, A.; Grosso, D.; Huang, T.; Spertino, F.; Jafari, M.; Botterud, A. Assessing the role of fluctuating renewables in energy transition: Methodologies and tools. Appl. Energy 2022, 314, 118968. [Google Scholar] [CrossRef]
  175. Tiwari, A.K.; Abakah, E.J.A.; Shao, X.; Le, T.L.; Gyamfi, M.N. Financial technology stocks, green financial assets, and energy markets: A quantile causality and dependence analysis. Energy Econ. 2023, 118, 106498. [Google Scholar] [CrossRef]
  176. Wang, J.; Dong, X.; Dong, K. Does renewable energy technological innovation matter for green total factor productivity? Empirical evidence from Chinese provinces. Sustain. Energy Technol. Assess. 2023, 55, 102966. [Google Scholar] [CrossRef]
  177. Yang, T.; Li, F.; Du, M.; Huang, M.; Li, Y. Impacts of alternative energy production innovation on reducing CO2 emissions: Evidence from China. Energy 2023, 268, 126684. [Google Scholar] [CrossRef]
Figure 1. Interconnection of sustainability dimensions.
Figure 1. Interconnection of sustainability dimensions.
Sustainability 15 14049 g001
Figure 2. Dimensions and indicators of sustainability.
Figure 2. Dimensions and indicators of sustainability.
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Table 1. Variables and indicators used to assess sustainability.
Table 1. Variables and indicators used to assess sustainability.
DataAcronymUnitSource
Area of reservoir of hydroelectric plantsARHkm 2 [98]
Distribution of income across a populationGINI-[99]
Gross domestic productGDPCurrency[99]
PopulationPopInhabitants[99]
Residential energy consumptionRECGWh[100]
Total CO 2 equivalent emissionTCO 2 tCO 2 eq[101]
Total final consumption of energyTFCGWh[102]
Total non renewable energy generationTNRGWh[100]
Total primary energy sourcesTPESGWh[100]
Total renewable energy generationTREGWh[100]
Table 2. Indicators purposes and your references.
Table 2. Indicators purposes and your references.
IndicatorSignificancePurposeReferences
TEC1Dependence on fossil fuelsMeasures the rate of consumption of sources in relation to total final consumption.[104].
TEC2Renewable generationMeasures the rate of renewable generation to total primary generation.[105,106].
ECO1Energy intensityMeasures the use of primary energy needed to generate one unit of GDP.[107,108].
ECO2Economic productivityMeasures productivity per inhabitant.[104,108].
SOC1Social use of energyMeasures residential energy consumption per inhabitant.[107,109,110].
SOC2Social inequalityConsiderated GINI Income Inequality Index.[107,111,112].
ENV1Energy deforestationMeasures deforestation due to energy generation generation.[104,107].
ENV2Carbon intensityMeasures carbon emissions from generation and waste disposal.[107].
INS1Energy securityMeasures the rate of dependence on the import/export of energy.[105].
INS2Energy productivityMeasures the rate of energy consumption to produce one unit of GDP.[104].
Table 3. Relationship matrix between indicators.
Table 3. Relationship matrix between indicators.
IndicatorTEC1TEC2ECO1ECO2SOC1SOC2ENV1ENV2INS1INS2
TEC1 xx xxxx
TEC2x xx x
ECO1xx xx x x
ECO2 x xxx x
SOC1 xxx xxx x
SOC2 xxx x
ENV1xx xx xx
ENV2x x x x x
INS1xxxx xx x
INS2 xxxx xx
Table 4. Relevance to the sustainability of indicators.
Table 4. Relevance to the sustainability of indicators.
IndicatorRelevance to SustainabilityRelated IndicatorsRef.
TEC1The greater the dependence on fossil fuels for energy generation and consumption, the lower the region’s degree of sustainability, because the burning of fossil fuels has a direct impact on atmospheric greenhouse gas emissions.ENV1, ENV2, INS1, TEC2, ECO1, SOC2[114,115].
TEC2Having a large share of renewable energy sources in its energy matrix points to greater sustainability due to the advantages of using renewables, promoting a lower carbon intensity in energy generation. There is greater institutional energy security if the renewable source is hydroelectric (a non-intermittent source) and lower energy intensity in final energy consumption.ENV1, ENV2, INS1, TEC1, ECO2, SOC2[116,117,118].
ECO1Increasing energy efficiency in the economy, via the reduction in intensity, results in an increase in the useful life of energy resources with the promotion of the profitability of productive sectors. It becomes possible to produce more resources with the same amount of GDP. This makes a given region/state more productive and consequently more energy-sustainable, which can result in a postponement of investments to expand the energy supply.ECO2, ENV2, TEC1, TEC2, SOC1, INS2[119,120,121].
ECO2Increases in the production of goods and services are a basic indicator of an economy’s behavior correlated with sustainable development. The economic productivity of a nation, region, or state has a direct influence on energy generation since an increase in production requires an increase in the availability of energy. Extracting natural resources to transform them into consumer goods involves intensive use of energy, which is why it has a direct connection with energy intensity.ENV1, ENV2, ECO1, SOC1, SOC2, INS2[122,123,124,125].
SOC1Energy consumption per inhabitant is associated with a country’s level of economic and social development of a region. A higher per capita consumption generates more social development. However, this puts greater pressure on the environment with natural resources due to the extraction of raw materials. On the other hand, limiting the use of energy causes a major institutional risk, especially in developing countries, which need to increase energy consumption to elevate social productivity.ENV1, ENV2, ECO1, ECO2, INS2, TEC2[126,127,128,129,130].
SOC2This is an important indicator for policies to combat and reduce social inequalities, and it measures the differentiated appropriation of income by individuals and social groups. It is also used to monitor the social acceptance of access to energy as electricity is lacking in societies with low development and standard of living.SOC1, ECO2, ECO1, INS2[131,132,133,134,135].
ENV1Any damage to the forest certainly compromises its environmental sustainability. In the case of hydroelectric power generation, which is a renewable source, it nonetheless causes a socio-environmental impact at the time of installation and start-up. On the other hand, it has the advantage of not causing further plant extraction over the years of its operation, which is equivalent to a fixed deforestation rate.ENV2, ECO2, SOC1, INS1, TEC1, TEC2[136,137,138,139].
ENV2This final disposal includes solid waste sent to sanitary landfills, controlled landfills, and open dumps. Basic sanitation in Brazil is still very precarious, with a large number of municipalities still operating open dumps, which contributes considerably to emissions of greenhouse gases such as methane. Methane has an impact of up to 28 times more carbon equivalent compared to carbon dioxide.ENV1, ECO1, SOC1, INS2, TEC1[140,141,142].
INS1Increasing energy security implies diversifying energy sources and reducing dependence on energy imports. Regions with low energy self-sufficiency rely heavily on imports, which leads to low energy sustainability in the institutional dimension, as they are not able to guarantee the supply of energy demand.INS2, TEC1, TEC2, ENV1, ECO1, ECO2, SOC2[143,144,145,146].
INS2The more productivity a region has, the more sustainability, as it will need to consume fewer natural and energy resources to produce the same amount of GDP units. The base indicator shows the efficiency of a given region’s primary energy-conversion technologies. Low conversion efficiency means that more natural resources are needed to meet the same level of useful energy demand, which is required for high efficiency.INS1, ENV2, ECO1, ECO2, SOC1, TEC2[147,148,149,150,151,152].
Table 5. Indicators, limitations, and solutions found.
Table 5. Indicators, limitations, and solutions found.
IndicatorLimitationsSolutions FoundRef.
TEC1Localities have low consumption of fossils at the same time that they do not have renewable generation, due to the fact that they are importing states’ power.The energy security indicator, which measures dependence on energy import/export, was used together.[153,154].
TEC2Increase in the rate of deforestation due to hydroelectric reservoirs. Institutional energy insecurity in the case of renewable generation, having a large share of intermittent sources such as solar and wind.No solution was found for these limitations.[155,156,157].
ECO1Affected by economic cycles, production structure, and energy-intensive economic activities such as aluminum production. This would lead to an increase in the indicator, even with improvements in energy consumption in each sector.No solution was found for these limitations.[158].
ECO2Even if it has a good application to measure the development level of a region, it is insufficient to express the degree of social well-being, particularly with regard to inequality in income distribution.This indicator is used in association with the GINI index, correlated with energy consumption for GDP production (energy intensity) and the social use of energy.[159,160].
SOC1Commercial and industrial uses of energy are not included in the calculation of this indicator. The focus of its measurement is social energy consumption.This indicator is integrated with the increase in energy productivity and promotes the use of renewable sources., reducing the pressure impact on ecosystems used for energy generation.[161,162].
SOC2As a measure of inequality calculated through a ratio, it has some limitations regarding interpretations of what is measured. When comparing poor or rich regions or states, it can both measure inequality in the material quality of life and the distribution of luxury beyond basic needs. It gives rise to different results.Associated with this indicator is the social use of energy, measuring the energy consumption of the population of each state/region.[163].
ENV1One limitation is when thermal energy generation is for fossil resources, because this indicator calculates biomass deforestation for energy.Results are associated with greenhouse gas emissions due to energy generation, which shows the equivalent carbon emissions from reservoirs and fossil thermal generation.[104,164].
ENV2Equivalent carbon intensity is limited to power generation and the disposal of solid waste. Emissions are not included in the calculation of the base indicator due to industrial, commercial, and other activities of the productive chain outside energy generation and solid waste disposal.No solution was found for these limitations.[165].
INS1It does not consider other important factors such as the conservation of natural resources. This can cause a region, state, or nation that has high energy security but still has low sustainability.This indicator needs to be integrated with others in order to minimize these limitations.[166,167,168,169,170].
INS2Energy productivity does not consider the quality of the product or service generated, nor what makes a region, state, or nation have a high energy productivity. Even if it produces low-quality consumer goods, it can lead to a long-term decrease in sustainability.No solution was found for these limitations.[171,172,173].
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Muniz, R.N.; da Costa Júnior, C.T.; Buratto, W.G.; Nied, A.; González, G.V. The Sustainability Concept: A Review Focusing on Energy. Sustainability 2023, 15, 14049. https://doi.org/10.3390/su151914049

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Muniz RN, da Costa Júnior CT, Buratto WG, Nied A, González GV. The Sustainability Concept: A Review Focusing on Energy. Sustainability. 2023; 15(19):14049. https://doi.org/10.3390/su151914049

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Muniz, Rafael Ninno, Carlos Tavares da Costa Júnior, William Gouvêa Buratto, Ademir Nied, and Gabriel Villarrubia González. 2023. "The Sustainability Concept: A Review Focusing on Energy" Sustainability 15, no. 19: 14049. https://doi.org/10.3390/su151914049

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