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Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

The 17 Sustainable Development Goals (SDGs) defined by the United Nations represent a blueprint for the adoption of global practices and policies to foster sustainability in societies and increase efficiencies in resources uses. The Italian National Trust, FAI, has embraced these goals, developing a holistic and transdisciplinary methodology to promote a more sustainable approach to preserve its natural and built heritage. This methodology combines heritage protection with the SDGs in various macro-areas of interventions, including energy, water, biosphere, responsible consumption, and sustainable communities. The aim is to increase access to affordable and clean energy, reduce water, waste, and soil footprints, promote responsible consumption, and develop human capital. The paper proposes a set of strategies to achieve the SDGs in heritage, involving the collaboration of several stakeholders, ranging from institutions to citizens. The methodology is applied to a specific Italian case study, Casa Macchi in Morazzone, to highlight the challenges in achieving the dual objective of preserving the historic building and promoting sustainability in the built environment. [Display omitted] • We propose a trans-disciplinary methodology for sustainable heritage preservation. • The heritage preservation project is analyzed in five macro-areas of intervention. • Assessment of effects on energy, water, biosphere, resources, and sustainability. • Casa Macchi preservation project to apply methodology and prove effectiveness. [ABSTRACT FROM AUTHOR]

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal "black box" of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG 6 E) and SDG7 efficiency (SDG 7 E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG 7 E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper chooses 98 mining cities in China from 2007 to 2021 as research samples. It investigates the policy practice of green development in mining cities according to the point of view of policy combination. We establish a super-efficiency model considering undesirable output to measure the green development level of mining cities and further construct a dynamic panel model based on the Gaussian mixture model (GMM) to test the transmission mechanism and heterogeneous effect of different types of environmental regulation to promote the green development of mining cities. The findings demonstrate that environmental regulations about command control, market incentives, and public participation can all help mining cities develop more sustainably. Additionally, there is a nonlinear relationship of an inverted "U" shape between the degree of green development in mining cities and environmental regulations related to command control. Combining policies can encourage the green development of mining cities, but there are crowding-out and substitution effects when environmental regulations are combined with market forces. Environmental regulation promotes urban green development by improving the energy consumption structure of mining cities. In this process, the level of digital development positively regulates the impact of environmental regulation on urban green development. Therefore, the government should choose environmental regulation tools and combinations according to local needs to achieve a multi-pronged effect of energy structure improvement and urban green development and use digital technology to support the green development of mining cities. • Command-controlled, market-motivated, and public-participating environmental regulations can significantly promote urban green development by promoting the improvement of energy structure. • There is an inverted' U' -shaped nonlinear relationship between the intensity of command and control environmental regulation and the green level of mining cities. • The level of digitization can positively regulate the relationship between environmental regulation and urban green development. • The combination of environmental regulations have a significant positive impact on urban green development. • The combination of environmental regulations has heterogeneity in urban green development due to factors such as regional resource endowment. [ABSTRACT FROM AUTHOR]

The concept of NZEB (Nearly Zero Energy Building) has a promising share to fulfill the future of sustainable development goals. However, exploiting renewable-based THESs (Trigeneration Hybrid Energy Systems) for providing the energetic demands of the building sector needs further attention. In this regard, the present paper proposes a sustainable THES driven by a biogas-fueled gas engine. The bottoming systems comprise absorption/electrical chillers (AC and EC), organic Rankine cycle (ORC), solar collector (PTC), and an auxiliary boiler. A detailed sustainability evaluation framework (technical and eco-environment) is derived to approve the performance of the THES. Within this procedure, the best working fluid of the ORC is firstly selected and then the sensitivity of the system performance to the design variables is assessed by a comprehensive parametric study. To make the derived framework more applicable, a case study building is considered and the proposed THES is sized to meet its electricity, heating, and cooling energies. The analyses proved that R123 shows the best techno-economic performance among six investigated ORC working fluids. The parametric study illustrated that the highest and lowest sensitivity of the THES's performance is related to the outlet temperatures of EC condenser and AC evaporator. Furthermore, the energy, exergy, and economic efficiencies of the THES for the case study building are 61.78%, 25.24%, and 44.77%. In this condition, discounted payback period is 7 years, demonstrating the feasibility of this proposal. The maximum CO 2 emission is released in November, with a total electricity generation of 320 MWh. [Display omitted] • Renewable-based hybrid system is introduced to produce cold, heat, and power. • Comprehensive sensitivity analysis is done to assess impact of some decision variables. • A case study building is considered to boost NZEB concept based on the proposed system. • Energy efficiency and payback period is 61.78% and 7 years for the case study building. • Using renewables in energy systems to reduce fossil fuel share has great significance. [ABSTRACT FROM AUTHOR]

Excessive energy-related carbon emission intensity will lead to deterioration of environmental quality and hinder green economic growth. This paper proposes a novel self-adaptive fractional order grey generalized Verhulst model (SAFGGVM) to predict energy-related carbon emission intensity in China, America, India, Russia, and Japan with nonlinear and complex characteristics effectively. Firstly, self-adaptive fractional order and dynamic background value coefficient are introduced to capture the nonlinear evolutionary trends. Subsequently, the optimal nonlinear parameters are determined using Grey Wolf Optimization algorithm through comprehensive comparison. Furthermore, the flexibility of SAFGGVM is verified by presenting its relevance to existing models. Finally, the validity, applicability, and robustness of SAFGGVM are confirmed by comparing with two machine learning models and four grey prediction models. The empirical results exhibit that the overall precision of SAFGGVM model significantly prevails over the others in five cases, with MAPE values less than 6% in both the simulation and prediction intervals. The out-of-sample forecast results indicate that the energy-related carbon emission intensity of five countries will fall into 5.7024, 1.6030, 7.3668, 8.1633, 2.0399 tons/10,000 USD by 2025, and the decreasing rate of developing countries tends to catch up with, or even exceed, developed countries. Projection results can serve as a reference point for countries to achieve green and sustainable development. • A novel self-adaptive grey generalized Verhulst model is proposed. • Energy-related carbon emission intensity (ECEI) of five countries is predicted. • Optimal nonlinear parameters are determined by Grey Wolf Optimization algorithm. • The novel model is superior to other competing models in accuracy and roubutness. • Decline rate of ECEI in developing countries will catch up with developed countries. [ABSTRACT FROM AUTHOR]

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of "double carbon target". The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is "high in the east and low in the west, high in the south and low in the north," showing obviously regional differences. [Display omitted] • Factors about production technology are taken into account, reflecting the characteristics of the construction industry. • Developed regions have better decoupling potential and more responsibility for CO 2. • The spatial distribution is "high in the east and low in the west, high in the south and low in the north". • The provinces in a region have similar decoupling status and carbon emission distribution. [ABSTRACT FROM AUTHOR]

The world is experiencing an unprecedented transition towards a low-carbon and renewable-based energy system, driven by the imperatives of mitigating climate changes, building climate resilience and ensuring sustainable development. Recently, polygeneration systems based on renewable energies have emerged as a viable solution to support the energy transition since they can improve resource utilization and energy efficiency, thus reducing the dependency on fossil fuels and the environmental impacts on ecosystems. Renewable polygeneration systems can play a significant role in the decarbonization of the food processing industry due to its high thermal energy demand at low and medium temperature levels. This paper presents a detailed analysis of a renewable polygeneration system integrating solar, geothermal and biomass energies and able to produce electricity, heat and cold water. The novelty of this study is the analysis of polygeneration system configurations relying solely on renewable energies and the optimization of the strategy to control the energy exchange between power systems, storage facilities and utilities. Two system configurations based on different power technologies and control strategies are investigated and compared, considering as end-user a milk production process with hot and cold water requirements at different temperature levels and load profiles. To this end, a dynamic model in Matlab/Simulink has been developed to simulate the behaviour of the polygeneration system over time and evaluate its overall energy performance parameters. For each system configuration, the optimal operating strategy has been identified by maximizing the energy conversion efficiency and minimizing the unmet thermal and cooling loads. Simulation results highlighted that, compared to polygeneration systems relying only on fluctuating renewable energies, those including a backup heat source (i.e., a biomass boiler) were able to meet the thermal energy needs of larger and more complex milk production processes. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the impact of renewable/non-renewable energy consumption on ecological quality and economic growth can serve to the sustainability of energy, ecology, and economy. This paper provides a new perspective of the comprehensive system of the 3E model, and introduces indicator of social factor to examine the non-linear effects of urbanization on energy, economy, and the environment. A threshold panel regression model is developed using the data of 120 countries in the last 20 years. In the proposed model, renewable energy and non-renewable energy are explanatory variables, economic growth and ecological footprint are explained variables, and urbanization are threshold variable. Non-linear relationships are concretized and quantified in the research of global and different income groups. The results show that global renewable energy can promote economic growth while improving the environment. As the urbanization rate increases, the negative effect of renewable energy on the ecological footprint first weakens and then increases, and the positive coefficient on the economy maintains a growth trend. Non-renewable energy has a more obvious positive effect on economic growth, but it increases the ecological footprint. The development of urbanization strengthens its promotion of the economy and reduces its pressure on the environment. From the perspective of different income groups, renewable energy does not always suppress the ecological footprint. The energy transition reduces economic growth in the initial stages of urbanization in certain regions. After crossing the threshold, the coefficient changes of different income groups have similarities and differences. Based on the conclusions, some recommendations are put forward, including promoting the transformation of energy consumption structure, improving energy efficiency and accelerating the process of urbanization. The model in this paper is suitable for investigating the nonlinear impact of urbanization on the 3E system through panel data, and can provide theoretical support for studying the threshold effect of a country or economy. This study also shows the importance of renewable energy and the indirect effects of urbanization, which is helpful for formulating global sustainable and coordinated development policies. [Display omitted] • A new perspective of the energy-economy-environment (3E) model is proposed. • Threshold models enable the non-linear relationships to be concretized and quantified. • Global renewable energy improves the environment while promoting economic growth. • Urbanization reduces the pressure on the environment from energy consumption. • Urbanization strengthens the positive effect of energy on economic growth. [ABSTRACT FROM AUTHOR]

Military expenditures in the Asia-Pacific region have grown rapidly in tandem with economic growth over the past few decades. The military sector's impact on environmental change has not been given enough attention. Renewable energy utilization in the Asia-Pacific region also has increased significantly. This paper examines the relationship between militarization, economic growth, renewable energy utilization, and environmental change in the 15 countries participating in the Regional Comprehensive Economic Cooperation (RCEP) agreement using data that covers 1990–2018. The panel cointegration and cross section autoregressive distributed lag (CS-ARDL) models are used to examine the long- and short-term relationships among variables. The empirical results show that economic growth and rising levels of militarization have a positive impact on a country's ecological footprint. The increase in military capital intensity has aggravated the environmental harm caused by the military. Improving the utilization rate of renewable energy can alleviate detrimental environmental effects in the long term. This study also puts forward policy recommendations from the policy perspective of sustainable development to reduce the level of regional militarization and improve the utilization of renewable energy. • Militarization is the cause of environmental degradation. • The increase of military capital intensity will aggravate its damage to the environment. • Renewable energy use can ease ecological footprint expansion. [ABSTRACT FROM AUTHOR]

The sustainable development of port operation management is strongly related to the energy consumption of production at automated container terminals (ACTs). This paper focuses on the production activities at a container yard, which is the primary facility of ACTs. A digital twin-based approach is proposed to optimize the operation of an automatic stacking crane (ASC) handling containers in terms of energy consumption. A virtual container yard that syncs with a physical container yard in the ACT digital twin system for observation and validation is developed. A mathematical model is established to minimize the total energy consumption of completing all tasks. Then, the Q-learning algorithm is adapted to optimize a solution based on the operating data from the ACT digital twin system. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach by comparing it with two other solution algorithms, viz., genetic algorithm (GA) and particle swarm optimization (PSO). The total energy consumption of two operation strategies (i.e., centralized and decentralized) are also compared using the proposed digital twin-based approach. With digital twin, the operational environment and energy consumption are visualized to support optimization and management of ASCs. Managers and operators can choose an appropriate strategy according to the designated sustainable goal. [Display omitted] • A digital twin-based approach is proposed to study the port operations. • Digital twin and Q-learning are used to observe operation and optimize production. • The total energy consumption of handling containers is minimized. • The proposed approach is superior to 2 well established methods based on case studies. • The centralized and decentralized strategies are analyzed for the sustainability. [ABSTRACT FROM AUTHOR]

Better digging the sustainable development path of the top five energy consumers (China, the United States, India, Japan and Russia) can serve both domestic economy and global economy to achieve coordinated development with the ever-increasing energy demand. This work has made the following efforts: (1) the Tapio decoupling model was employed to determine the decoupling states between energy consumption and economy; (2) based on the extended Kaya formula and the LMDI method, this work focused on measuring the impact of technological factors and urbanization factor on energy consumption; (3) the LMDI method and the Tapio decoupling model were combined to measure the impact degree of influencing factors on decoupling indicators. The main findings show that from 2001 to 2018: (1) Japan was the country with the most year of strong decoupling, followed by the United States, which indicates that the decoupling status of developed countries was better than that of developing countries. (2) The driving forces behind the energy consumption growth are mainly the effects of R&D scale and urbanization, while the main factors that hinder energy consumption growth were technological factors, including energy intensity effect and R&D efficiency effect. (3) Energy intensity effect was the main driving force for the decoupling process, and the R&D efficiency effect was the biggest obstacle to decoupling energy consumption from economic growth. Finally, this paper proposes that the government can give priority to relying on technological innovation to accelerate the development and promotion of energy-saving and emission-reduction technologies. • Exploring the coordinated development path of energy consumption and economic growth in top five energy consumers. • Extending the traditional LMDI method to measure the impact of technological progress and urbanization on energy consumption. • R&D scale and urbanization promote energy consumption growth, while technological progress inhibits energy consumption growth. • Governments can prioritize technological innovation to speed up energy conservation and emissions reduction. [ABSTRACT FROM AUTHOR]

The paper deals with the reuse of fibers from textile waste for the development of innovative and sustainable materials applied in the construction field. Currently, managing the high volumes of textile waste produced, and reducing the damage this waste does to the ecosystem, involves finding solutions for its reuse. Meanwhile, the construction sector has a great impact on the environment, and there is an urgent need to design innovative, sustainable building materials to reduce energy consumption and prevent the depletion of resources. In this context, the objective of the MOBIOTEX project is to develop new materials to replace the petrochemical-made vapor-barrier (VB) and rain-screen (RS) generally used in timber-frame construction (TFC). For that, nonwoven (NW) fabrics from textile waste were manufactured. Then, measurements based on standards for VB and RS were carried out on untreated and functionalized NW fabrics, to test their sealing and mechanical properties. Lamination of NW gives satisfactory results and some modified materials meet the requirements for both VB and RS applications and have similar properties as commercial products. These first results are quite promising as they show that, with some treatment, nonwoven from textile waste can be as technically efficient as commercial products. The different processes used during the MOBIOTEX project made it possible to build up the system boundary for the production of functionalized nonwoven in order to carry out future Life Cycle Assessment (LCA) and to compare with commercial products' LCA. In a circular economy approach, other characteristics such as durability, fire resistance and recyclability of such materials should be studied and optimized in order to reduce energy consumption and environmental impacts. Still, the main benefit of the developed materials, compared to commercial petrochemical rain-screens and vapor barriers, is on the production of raw materials, because it involves less exploitation of non-renewable resources, as an important part of materials come from waste, and the application in timber-framed construction can be a positive way in which to reuse a non-negligible volume of waste from the textile industry. [ABSTRACT FROM AUTHOR]

Despite the substantial effort on almost all levels during the last decades, the buildings' renovation rate needs to be at least doubled from the current 1% to meet ambitious energy efficiency goals. In the same way, the energy-intensive material replacement did not reach yet the desired grade in terms of sustainability measures and outlined goals heading to a low-carbon economy. This paper summarizes principles of sustainable development together with the current methodological framework relevant to the civil engineering and construction industry. The main part is devoted to the identification and understanding of principal factors preventing faster adoption of energy efficiency measures. High initial financial costs, investment risk over a long-term period, poor acceptance of sustainable measures, lack of information, and limited methodological framework for a reliable evaluation of environmental projects with the intergeneration context or understanding of externalities of human activities are identified as the main barriers to the sustainable building retrofits. These barriers are closely related each other and can be merged into several groups according to the stakeholders or scientific disciplines to amplify their primary impact. Notwithstanding, solution strategies based on narrow boundary conditions and limited multidisciplinary approach prevent substantial advances towards the sustainable building sector. In this regard, the major obstacles preventing the achievement of energy efficiency goals can be remedied by interdisciplinary cooperation. • Lacking multidisciplinary approach hinders sustainable measures adoption. • Limited public acceptance prevents a stronger political commitment. • Methods of social measures need to be studied for decision support. • Sustainable building industry transition is not a technical issue only. [ABSTRACT FROM AUTHOR]

The stamping industry is critical in the automotive and aerospace industries, and the environmental impacts and sustainability of stamping systems have attracted considerable attention. To analyze the environmental impact and sustainability of stamping systems quantitatively, this paper presents an emergy-based evaluation method considering all the associated environmental impact factors of a pressing workshop including material and energy consumption, waste flows, time and mass losses, and processing costs. On the basis of analyzing the stamping processes and sustainability, this method comprises data collection, emergy based sustainability evaluation model and the development of sustainable evaluation indexes and systems. Based on the proposed method, the environmental impact of an industrial vehicle stamping workshop was evaluated. The results show that the net emergy yield ratio of the stamping system was greater than 2, and the waste emergy output ratio was very low, indicating that the system had high productivity levels, high yields, and low pollution discharge rates. However, its sustainability index was less than one owing to its high energy and material consumption during the production processes. To reduce the environmental impact of this stamping system and improve its sustainability, we investigated the potential improvements that could be achieved in energy consumption, productivity, and environmental impact by implementing energy-saving scheduling in the stamping workshop. The results show that the corresponding net emergy yield ratio and sustainability index of the system were both improved. The proposed emergy-based method could be used to quantitatively evaluate the environmental impacts and sustainability of stamping systems and facilitate efforts to reduce the environmental impacts of pressing workshops while improving their productivity and economic profitability. [ABSTRACT FROM AUTHOR]

Among the main components of a smart city, the energy system plays a vital and core role in the transition towards a sustainable urban life. Furthermore, the utilization of renewable energy sources has been demonstrated as a significant contribution to reducing pollutant emissions and enhancing the quality of the living environment. Therefore, designing the energy systems based on clean and renewable criteria is considered a sustainable solution for smart cities. Indeed, the deep and rapid penetration of renewable energy-based technologies have been believed to very well fit into a smart city under various scales, this could supply a secure basis for a modern society with a low-carbon economy. In this review paper, the main components and roles of renewable energy resources (such as solar, wind, geothermal, hydropower, ocean, and biofuels) for the smart city were fully introduced. Besides, integrating the renewable sources form into the energy systems of smart cities was thoroughly analyzed on the basis of technical and economic criteria. Finally, existing challenges and future scenarios were also discussed in detail to clarify the progress and perspective of smart renewable energy systems for the smart city. In general, the integration of renewables into energy systems of the smart city is a sagacious perspective and solution aiming to achieve cleaner process and more sustainable development. However, the optimization issues of the energy system for integrating of renewable components, ensuring good stability, maximizing the operating range, and minimizing the investment costs should be critically evaluated in the future works. • Designing tools and key criteria for smart renewable energy systems. • Efficiency and prospect of integrating renewables into energy system for smart city. • Existing problems and challenges for renewable-based energy system in smart city. • Future scenarios for the sustainable development of energy system in smart city. [ABSTRACT FROM AUTHOR]