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• This paper systematically reviews the research progress of RS technology applied to various stages of PV system development. • We conclude that RS plays a significant role in PV potential assessment, large-scale data analysis and PV health monitoring. • We discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area. Developing solar photovoltaic (PV) systems is an effective way to address the problems of limited fossil fuel reserves, soaring world energy demand and global climate change. The earth observation information provides a promising perspective for estimating the PV energy potential and understanding the status of the PV system development, which is critical for making scientifically sound and cost-optimal sustainable planning strategies. Remote sensing (RS), a versatile technology that captures surface information at various temporal and spatial scales, is now widely applied in different fields of the PV development. However, despite the rapid growth of related research, there is still a lack of comprehensive review on the application of RS to different stages (i.e., planning, site selection, installation, maintenance, etc.) of the PV system development. This paper systematically reviews the research progress of RS technology applied throughout various stages of the PV system development. The reviewed literatures are organized as four major parts: i) PV potential estimation, ii) PV array detection, iii) PV fault monitoring and diagnosis, and iv) other cross-cutting areas where RS can facilitate PV development. We conclude that RS technology can bridge the gap caused by the traditional methods in effective assessment of resource potential, large-scale data analysis and PV health monitoring, which can provide strong support in assisting the planning, management, and decision-making of PV systems. Finally, we discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area. [ABSTRACT FROM AUTHOR]

The concept of eco-industrial park (EIP) has recently become the subject of a great deal of attention from industry and academic research groups. This paper proposes a series of systematic approaches for multi-level modelling and optimisation in EIPs. The novelties of this work include, (1) building a four-level modelling framework (from unit level to process level, plant level and industrial network level) for EIP research, (2) applying advanced mathematical modelling methods to describe each level operation, (3) developing efficient methodologies for solving optimisation problems at different EIP levels, (4) considering symbiotic relations amongst the three networks (material, water and energy networks) at the top EIP level with the boundary conditions of economic, social and legal requirements. For methodology demonstration, two cases at process level and industrial network level respectively are tested and solved with the developed modelling and optimisation strategies. Finally, the challenges and applications in future EIP research are also discussed, including data collection, the extension of the current networks to EIPs, and the feasibility of the proposed methodologies for complex EIP problems. The extended EIPs include the combination of material exchanges, energy systems and waste-water treatment networks. The aspects considered for future industrial ecology are carbon emission, by-product reuse, water consumption, and energy consumption. The main object of this paper is to explain the detailed model construction process and the development of optimisation approaches for a complex EIP system. In future work, this system is expected to share services, utility, and product resources amongst industrial plants to add value, reduce costs, improve environment, and consequently achieve sustainable development in a symbiosis community. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

Highlights • We estimate the energy rebound effect caused by technological progress in Shanghai. • We use the state space model with time-varying parameter based on the IPAT identity. • Shanghai has a high partial rebound effect on average at the overall-economy level. • Mitigating the energy rebound effect can significantly reduce carbon emissions. • Energy efficiency improvement should not be the only way to conserve energy. Abstract The energy rebound effect is regarded as an obstacle of achieving the expected target of energy-saving policies, especially under a rapid urbanization background in developing counties, such as China. This has become a substantial drag of sustainable development in some cities. Shanghai is the economic center of China, and it is also a typical energy import-dependent mega-city. Investigating the evolution of Shanghai's energy-saving performance and the energy rebound effect is significant for the implementation of energy-saving policies in other similar cities of China and other developing countries. Using the state space model with time-varying parameters and based on the IPAT identity and the Solow residual approach, this paper is the first study to present a specific estimation on Shanghai's energy rebound effect caused by technological progress. The results show that, during the period of 1991–2016, the average energy rebound effect of overall economy and secondary industry in Shanghai was 93.96% and 73.10%, respectively, indicating a high partial rebound effect. Most of expected energy saving caused by improved energy efficiency is offset by extra energy consumption caused by technological progress. Regarding tertiary industry, the average rebound effect was 146.61%, indicating a backfire effect. However, the average energy rebound amount of tertiary industry is less than that of secondary industry. In particular, there is an increasingly negative impact of the rebound effect of tertiary industry on energy conservation in recent years, with the sector's rapid expansion and corresponding increase in energy demand. Furthermore, we estimate the carbon rebound amount (i.e., carbon emissions caused by the energy rebound effect) and find that, on average, the energy rebound effect caused 13.1% and 0.41% increases in carbon emissions in Shanghai and China, respectively. Therefore, mitigating the energy rebound effect can significantly reduce carbon emissions. Due to the substantial impact of the rebound effect, technological progress and energy efficiency improvement should not be the only way to achieve energy-saving target, especially in energy import-dependent mega-cities like Shanghai. Some supporting policies should be implemented to ensure that the expected outcome of energy-saving effort can be realized as far as possible. [ABSTRACT FROM AUTHOR]

Renewable energy resources are currently being deployed on a large scale to meet the requirements of increased energy demand, mitigate the environmental pollutants, and achieve socio-economic benefits for sustainable development. The integration of such distributed energy sources into utility grid paves the way for microgrids. The microgrid concept is introduced to have a self-sustained system consisting of distributed energy resources that can operate in an islanded mode during grid failures. In microgrid, an energy management system is essential for optimal use of these distributed energy resources in intelligent, secure, reliable, and coordinated ways. Therefore, this review paper presents a comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems. To manage the volatility and intermittency of renewable energy resources and load demand, various uncertainty quantification methods are summarized. A comparative analysis on communication technologies is also discussed for cost-effective implementation of microgrid energy management systems. Finally, insights into future directions and real world applications are provided. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

Energy sectors of all the countries around the world are faced with a big challenge: the energy transition under the imperative of continuous reduction of carbon dioxide emissions. The energy transition and the sustainability constraints pose a significant challenge for engineers, researcher and scientists developing new and clean energy-efficient technologies. Energy production and transformations, along with its storage, distribution and consumption, are achieved by utilising new and clean energy technologies, with the goal of the continuous increase of energy efficiency, the growth of renewable energy sources utilisation, the uninterrupted switch from fossil fuels to alternative energy sources, and the continued reduction of CO2 emissions. This editorial paper addresses the latest progress and the findings in research and developments in the sustainable energy technologies, energy and environment systems from selected papers of the conference on Sustainable Development of Energy, Water and Environment Systems held in 2019. [ABSTRACT FROM AUTHOR]

The paper analyzes consumption related household CO 2 equivalent (CO 2e ) emissions for the three Baltic States – Estonia, Latvia and Lithuania from 1995 to 2011. The analysis is based on a multi-regional input–output model, which allows us to estimate life-cycle emissions for all major household consumption items. Results demonstrate that household carbon footprints in all the Baltic States significantly increased by 47% in Estonia, 20% in Latvia and 52% in Lithuania during the study period. In 2011 in Estonia expenditures for housing and utility contributed the highest per capita life-cycle emissions – 43%. However, in Lithuania housing accounted only for 16% of per capita emissions, but food and transport were responsible for 31%; whereas in Estonia and Latvia transport accounted for 18% and 21%, respectively. Production processes related to food consumption are responsible for 18% of emissions in Estonia, 31% in Latvia and Lithuania. Most of the indirect emissions are related to imports from Russia and China followed by imports from other Baltic States. If consumption-based emissions are to decrease countries will have to (1) change household behavior, which requires relevant knowledge, infrastructure and resources to facilitate switching to lower carbon-intensive alternatives; (2) decarbonize their own energy and transport sectors; and (3) reduce lifecycle emissions associated with trade, by supporting imports from low carbon regions, including producing locally. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has been recognized as the most effective way to reduce the greenhouse effect and achieve sustainable development. From the perspective of air emissions, this paper adopts data envelopment analysis approach to evaluate the energy efficiency in China’s coastal areas over the period of 2000–2012. Carbon dioxide, sulfur dioxide and nitrogen oxide are treated as undesirable outputs of energy consumptions. The proposed global Epsilon-based measure is used to estimate the static energy efficiency with an annual cross-section of data. The weights of the three undesirable outputs are determined according to their treatment costs. A global Malmquist-Luenberger productivity index based on directional distance function is employed to dynamically evaluate the energy efficiency. The results indicate the following in China’s coastal areas: (1) the level of economic development is positively related to energy efficiency scores; (2) energy efficiency scores decrease when considering undesirable outputs except Beijing and Hainan; (3) the Circum-Bohai Sea Economic Region greatly improves energy efficiency and has great potential of air emission; (4) the annual growth rate of Malmquist-Luenberger productivity index change is overestimated; (5) energy efficiency improvement is mainly driven by technological improvement, and scale efficiency and management level are the main obstacles. [ABSTRACT FROM AUTHOR]

A key aspect of greener network deployment is how to achieve sustainable growth of a telecommunications network, both in terms of operational and embodied energy. Hence, in this paper we investigate how the overall energy consumption and greenhouse gas emissions of a fast growing telecommunications network can be minimized. Due to the complexities in modeling the embodied energy of networks, this aspect of energy consumption has received limited attention by network operators. Here, we present the first model to evaluate the interdependencies of the four main contributing factors in managing the sustainable growth of a telecommunications network: (i) the network’s operational energy consumption; (ii) the embodied energy of network equipment; (iii) network traffic growth; and (iv) the expected energy efficiency improvements in both the operational and embodied phases. Using Monte Carlo techniques with real network data, our results demonstrate that under the current trends in overall energy efficiency improvements the network embodied energy will account for over 40% of the total network energy in 2025 compared to 20% in 2015. Further, we find that the optimum equipment replacement cycle, which will result in the lowest total network life cycle energy, is directly dependent on the technological progress in energy efficiency improvements of both operational and embodied phases. Our model and analysis highlight the need for a comprehensive approach to better understand the interactions between network growth, technological progress, equipment replacement lifetime, energy consumption, and the resulting carbon footprint. [ABSTRACT FROM AUTHOR]

Energy consumption has always been a central issue for sustainable urban assessment and planning. Different forms of energy analysis can provide various insights for energy policy making. This paper brought together three approaches for energy consumption accounting, i.e., energy flow analysis (EFA), input–output analysis (IOA) and ecological network analysis (ENA), and compared their different perspectives and the policy implications for urban energy use. Beijing was used to exemplify the different energy analysis processes, and the 42 economic sectors of the city were aggregated into seven components. It was determined that EFA quantifies both the primary and final energy consumption of the urban components by tracking the different types of fuel used by the urban economy. IOA accounts for the embodied energy consumption (direct and indirect) used to produce goods and services in the city, whereas the control analysis of ENA quantifies the specific embodied energy that is regulated by the activities within the city’s boundary. The network control analysis can also be applied to determining which economic sectors drive the energy consumption and to what extent these sectors are dependent on each other for energy. So-called “controlled energy” is a new concept that adds to the analysis of urban energy consumption, indicating the adjustable energy consumed by sectors. The integration of insights from all three accounting perspectives further our understanding of sustainable energy use in cities. [ABSTRACT FROM AUTHOR]

• An energy-oriented selective maintenance policy (ESMP) is developed. • ESMP policy is applied to series–parallel multi-unit systems under sustainable manufacturing. • Preventive maintenance and preventive replacement can be optimized comprehensively. • Optimal maintenance schemes are obtained under limited maintenance resources. • ESMP policy achieves a significant improvement of energy efficiency in batch production. With the industry sustainable development and increasing awareness of energy conservation, many manufacturing enterprises prefer to develop the operation and maintenance (O&M) with high production throughput and low energy consumption. In reality, many manufacturing systems are required to conduct a sequence of predefined missions with finite breaks between any two consecutive missions. To successfully complete the next mission production, maintenance actions are arranged and performed on machines during each scheduled break. In this paper, an energy-oriented selective maintenance policy (ESMP) for series–parallel systems is investigated. At each break, multiple maintenance actions with different impacts on machine degradation are available under limited maintenance resources. To obtain the throughput-and-energy based maintenance scheme, we first model the system energy efficiency based on system throughput and energy consumption. Then, we integer the energy efficiency modeling, production throughout analysis, and selective maintenance scheduling into an optimization model. And the model objective is to find the appropriate maintenance action for each machine at each break subject to cost and duration constraints. Numerical examples have been addressed to demonstrate the performance and adaptability of our proposed ESMP in long-term selective maintenance scheduling. Finally, a comparative analysis with traditional reliability-oriented policy shows the significant improvement of energy efficiency. [ABSTRACT FROM AUTHOR]

• Pyramidal approach for the analysis and decomposition of energy intensity. • Definition of structural and efficiency indicators for the whole energy system. • Impact of transformation processes and fuel types on supply side energy efficiency. • Emerging nations needed to worsen the efficiency of the supply side to thrive. • Renewable electrification simultaneously benefits energy and carbon intensities. Energy efficiency remains as the main mitigation factor to slow down the growth of energy consumption and related CO 2 emissions, undoubtedly the major responsible for climate change. Gaining insights into the driving forces that make efficiency change is a keystone to define energy policies and examine pathways to sustainable development. To this aim, this paper proposes a pyramidal approach for the analysis and decomposition of energy intensity, the main global efficiency indicator, using the LMDI method. First, the effects related to supply and demand sides of the energy system are separated in Primary Energy Factor and final energy intensity, respectively. Then, supply side is further decomposed to progressively reveal structural effects associated to transformation processes and fuel types. The approach is applied to the most emitting and consuming nations (China, United States, European Union, India, Russia, Japan) to provide a meaningful cross-country analysis over the period 1995–2017. Results show that energy intensity gains have been mainly driven by widespread demand side efficiency improvements from 25% to 61%. Regarding the supply side, unfavourable structural changes due to electrification, up to 12% in China, have only been offset by transformation efficiency gains about 6% in developed countries. Consequently, emerging economies have worsened their energy sector efficiency as they thrive. Changes in fuel mixes have generally contributed to energy intensity reductions (up to 4%) mainly due to shifts from coal and nuclear power towards gas and renewables plants. The proposed methodology could help stakeholders to effectively analyse the energy system and to develop policies to reduce its environmental impact. [ABSTRACT FROM AUTHOR]

• Method for measuring progress towards accessible, green and efficient energy. • Evaluation relative to idiosyncratic parameters (targets and thresholds). • Multidimensional and flexible approach to evaluate access and use of energy. • Empirical analysis of 157 countries relative to Sustainable Development Goal 7. • Positive dynamics across the world but still much to be done. This paper presents an index to evaluate progress in achieving the key dimensions of the Sustainable Development Goal 7, which entails ensuring "access to affordable, sustainable and modern energy for all." The key aspects of this index, called the Inclusive Green Energy index, are: (i) it focuses on changes, rather than on levels, of the access, greenness and use of energy; (ii) it exhibits a decomposability feature that permits integrating several dimensions, both positive and negative, in a friendly way; and (iii) the evaluation of progress is made relative to some reference values (targets and thresholds) that can differ between countries. We calculate the Inclusive Green Energy index of progress for 157 countries using data from 2004 to 2014 on three indicators intended to capture inclusiveness, greenness, and efficiency of energy use. The results show that progress has, on average, been positive across the world, with more than 87 per cent of the sample of countries experiencing some degree of progress. However, progress is smaller for the Middle East and North African and Sub-Saharan African countries and it is negative for most of the countries that exhibit low levels of human development, as measured by the Human Development Index. Furthermore, fewer than one in four of the countries in the sample have an Inclusive Green Energy index commensurate with having met their targets. This suggests that much remains to be done globally with regard to being on track towards meeting their Sustainable Development Goal 7 by 2030. [ABSTRACT FROM AUTHOR]