Energy Management and Sustainable Development from Economic, Social and Environmental Aspects

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability. Full article

This paper aims to develop a groundbreaking approach to fostering inclusive smart tourism destinations by integrating generative artificial intelligence (Gen AI) with natural language processing (NLP) and the Internet of Things (IoT) into an intelligent platform that supports tourism decision making and travel planning in smart tourism destinations. The acquisition of this new technology was conducted using Agile methodology through requirements analysis, system architecture analysis and design, implementation, and user evaluation. The results revealed that the synergistic combination of these technologies was organized into three tiers. The system provides information, including place names, images, descriptive text, and an audio option for users to listen to the information, supporting tourists with disabilities. Employing advanced AI algorithms alongside NLP, developed systems capable of generating predictive analytics, personalized recommendations, and conducting real-time, multilingual communication with tourists. This system was implemented and evaluated in Suphan Buri and Ayutthaya, UNESCO World Heritage sites in Thailand, with 416 users participating. The results showed that system satisfaction was influenced by (1) the tourism experience, (2) tourism planning and during-trip factors (attention, interest, and usage), and (3) emotion. The relative Chi-square (χ²/df) of 1.154 indicated that the model was suitable. The Comparative Fit Index (CFI) was 0.990, the Goodness-of-Fit Index (GFI) was 0.965, and the model based on the research hypothesis was consistent with the empirical data. This paper contributions significant advancements in the field of smart tourism by demonstrating the integration of Gen AI, NLP, and the IoT and offering practical solutions and theoretical insights that enhance accessibility, personalization, and environmental sustainability in tourism. Full article

This paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learning’s capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction. Full article

In the oil industry, there is a gap between the goals of sustainable development, the implementation of oil projects and its specific consequences. Oil projects are implemented in isolation from other variables, have an insufficiently targeted impact on the territory and often have a negative impact on the environment. The purpose of the study is to improve the efficiency of oil producing companies and increase their contribution to the development of the country’s economy as a whole. The methodology used in this article is based on the concept of sustainable development, systemic and integrated approaches, methodology of sub-potentials and modeling of business processes of a circular economy. The results of the study include a methodological approach to the formation of an effective business model for oil companies. We propose this methodological approach to select the projects of oil companies, taking into account economic, environmental and other factors, and the most promising prospects for Mexico. The significance of the study is that the proposed methodology makes it possible to increase the sustainability of the development of oil companies and integrate their business processes into the task of increasing the efficiency of operation and development of the territory. The novelty of the study lies in the application of the concept of sub-potentials and the calculation of critical indicator values for oil producing companies in Mexico to prevent the transition of sub-potentials of functioning and development into sub-potentials of threat and containment during project implementation. Full article

This study evaluated the suitability of two types of beech wood pellets as renewable, low-emission biofuel sources in order to combat the energy mix and poor air quality in Serbia. Key solid biofuel characteristics, including the heating values (18.5–18.7 MJ/kg), moisture content (5.54–7.16%), and volatile matter (82.4–84.4%) were assessed according to established standards. The elemental composition (mass fractions of 48.26–48.53% carbon, 6% hydrogen, 0.12–0.2% nitrogen, 0.02% sulfur, non-detected chlorine) and ash content (0.46–1.2%) demonstrated that the analyzed beech pellets met the criteria for high-quality classification, aligning with the ENplus A1 and ENplus A2 standards. The emissions of O₂, CO₂, CO, NO_x, SO₂, and TOC were quantified in the flue gas of an automatic residential pellet stove and compared with the existing literature. While combustion of the beech pellets yielded low emissions of SO₂ (6 mg/m³) and NO_x (188 mg/m³), the fluctuating CO (1456–2064 mg/m³) and TOC (26.75–61.46 mg/m³) levels were influenced by the appliance performance. These findings underscore the potential of beech wood pellets as a premium solid biofuel option for Serbian households, offering implications for both end-users and policymakers. Full article

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models’ accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN’s 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation. Full article

For high-quality growth to occur, new-type urbanization and environmental preservation must coexist and advance at the same time. The focus has shifted to maintain a balance between ecological quality and urbanization growth. This study focuses on the Yangtze River Delta (YRD) in China, utilizing panel data from 41 cities in the YRD spanning from 2009 to 2021 to construct evaluation index systems for new (type of) urbanization and ecological environment. To analyze spatial-temporal evolutionary aspects and determine the causes of the degree of coupling coordination between new-type urbanization and the ecological environment, methodologies such as the entropy weight method, coupled coordination degree model, and Tobit regression approach were used. The results show that (1) economic urbanization has experienced the most growth in the level of new-type urbanization in the YRD, which has been steadily increasing. Moreover, the ecological environment evaluation score increased from 0.581 in 2009 to 0.701 in 2021, revealing a cyclical pattern of increase and decrease in its evolutionary trajectory. (2) Within the scope of the study, the overall coupling coordination degree between new-type urbanization and ecological environment has increased, with the average value rising from 0.512 in 2009 to 0.540 in 2021. In comparison to Lishui, Huaibei, Huainan, Ningbo, Chuzhou, and Bozhou saw a greater increase in coupling and coordination degree, with pronounced variations and clustering patterns visible in their spatial distribution. (3) According to the Tobit regression analysis, the level of economic development, technological progress, industrial concentration, global openness, and educational investment had significant positive effects on the degree of coupled coordination between new-type urbanization and the ecological environment in the YRD, whereas the level of information technology did not reach the significance threshold. The findings of the study are crucial for establishing a regional framework for green and sustainable development, as well as for facilitating the coordinated growth of new-type urbanization and ecological environment. These findings hold great potential for driving positive change in both urban development and environmental conservation efforts. Full article

The high cost of centralized photovoltaic power generation projects is an important problem affecting industrial development, which needs to be solved urgently. It is particularly important to explore the influencing factors of cost control and the interaction between them. This paper takes a centralized photovoltaic power generation project as the research object, and determines the index system of influencing factors of cost control from the perspective of the life cycle. Secondly, the logical relationship between influencing factors is judged by the method of combining DEMATEL (decision-making trial and evaluation laboratory) and ISM (interpretive structural modelling). Finally, the multi-order recursive interpretation structure model is obtained, and the action mechanism between various factors is obtained. The results show that national policies and standards are the most profound influencing factors, and their cause degree reaches 2.155; the reason degree of market changes is the second, which is 1.586; bidding and contract management are the factors with the highest centrality, which is 7.120; and transmission and the storage of electricity and equipment repair and maintenance are the most direct factors affecting cost control. Finally, some suggestions are put forward for different types of influencing factors. The research results can better help photovoltaic power generation enterprises solve the problem of cost control. Full article

Accelerating the transformation of the rural energy structure is an indispensable part of energy transformation in developing countries. In this novel study, the transformation effect of China’s rural energy structure from 2001 to 2020 was evaluated. Further, this paper also identified the decoupling state between the rural energy structure transition and carbon emissions, and decomposed the spatial–temporal effects of rural carbon decoupling through efficiency measures. According to the survey, the dual substitution index of the rural energy structure in China increased from 0.466 to 1.828, and showed a decreasing trend in spatial distribution from the east to the central and western regions. Economic development and climate characteristics have become important influencing factors for the dual substitution of the rural energy structure. The decoupling relationship between the dual substitution of the rural energy structure and carbon emissions was mainly characterized in the strong decoupling, expansion negative decoupling, and strong negative decoupling states. Regional imbalances have deepened as the efficiency of rural energy carbon decoupling has gradually increased. The annual average efficiency of rural energy carbon decoupling in a dynamic perspective has increased by 10.579%, and the dual substitution of the energy structure has a significant driving effect on rural carbon reduction. Full article

The eco-agricultural park is a new comprehensive agricultural technology system integrating agricultural production, rural economic development, ecological environment protection, and efficient resource utilization. Therefore, an in-depth analysis of the ecosystem structure of eco-agricultural parks will help achieve the goal of coordinated symbiosis between human development and environmental protection. This study takes the research area of the Eco-agricultural Park of Jinchuan Town, Huinan County, a typical town in the Changbai Mountains of Northeast China. Based on field surveys, market research, farmer consultation, and related data collection, emergy theory and methods are used to construct an emergy model for the park. The value evaluation index system integrates the unique emergy index of the agricultural ecosystem with the traditional emergy index system to conduct a targeted evaluation of the park’s functional structure and sustainable development capabilities in order to improve the efficiency of material and energy use and provide technical reference for ecological construction and comprehensive development of agricultural industry in mountainous areas in northern China. The research results show that: (1) The annual input total emergy of the eco-agricultural park is 4.04E+24 sej/a, and the emergy of labor input, electricity input, and topsoil loss is relatively high. The park is in a labor-intensive stage. The annual output total emergy is 5.09E+24 sej/a, the park is dominated by planting and forestry industries. (2) The park’s emergy utilization intensity is high—production efficiency is high, economic development is advanced, and the system’s self-control, adjustment, and feedback functions are vital—and plays a significant role in promoting the development of the regional economy. However, the park relies more on investment from external resources, and production in the park puts pressure on the environment. (3) The current sustainable development capability of the study area is weak, and the factors affecting the sustainable development capability are mainly energy loss and uneven distribution of industrial areas in the park. Effective measures to promote the transformation of the park to develop technology-intensive industries and improve the sustainable development performance of the park were proposed. These include: adjusting the proportion of industries in the park; reducing high-energy external input emergy, such as industrial auxiliary emergy; reducing the loss of non-renewable natural resources through ecological engineering measures, such as reducing the depth of slope runoff in the park; and combining modern resource-based production technology and environmentally sound management methods to reduce energy loss and rational use of natural resources. Full article

This article examines the relationship between macroeconomic variables and the share of renewable energy in Eurozone countries from 2006 to 2020. Using the Fully Modified Ordinary Least Squares (FMOLS) method, we analyze the impact of Gross Domestic Product (GDP) per capita, unemployment rate, Financial Development Index (FDI), inflation, government efficiency, and corruption control on the proportion of renewable energy. Focused on the Eurozone, our study fills a gap in existing research. We compile diverse findings from the literature review on this topic. Our analysis reveals that higher GDP per capita positively influences the proportion of renewable energy, while unemployment, lower financial development, higher inflation, inefficient governance, and corruption negatively impact renewable energy adoption. These findings underscore the importance of addressing economic development alongside sustainable energy initiatives. Policymakers should prioritize improving GDP per capita, and addressing barriers such as unemployment and corruption to facilitate the transition to a more sustainable energy landscape in the Eurozone. Full article

Digital innovation ecosystems are currently experiencing a period of growth and are navigating uncertain environments. Improving resilience is an important prerequisite for ensuring sustainable developments. This study, based on the technology, organization, and environment (TOE) framework, examines the impact of multilevel antecedent conditions on digital innovation ecosystem resilience using data from 31 Chinese provinces. By applying a necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), this study reveals complex causal relationships between five antecedents at the “technology–organization–environment” levels and digital innovation ecosystem resilience, along with the improvement paths of digital innovation ecosystem resilience. The results show the following: Firstly, individual antecedent conditions alone do not constitute necessary conditions for high or non-high digital innovation ecosystem resilience. Secondly, there are five configuration paths leading to high digital innovation ecosystem resilience, namely, a digital technology-enabled organization–environment-driven type (H1a), an organization–environment dual-wheel-driven type (H1b), a digital technology-led environment-driven type (H2), a technology–organization–environment trilateral type (H3), and a pressure–organization-driven type (H4). Thirdly, three configuration paths result in non-high digital innovation ecosystem resilience, exhibiting an asymmetric relationship with paths associated with the configuration paths of high digital innovation ecosystem resilience. Finally, potential substitution relationships exist among antecedent conditions at the technological, organizational, and environmental levels. Full article

The focus on the concept of upgrading in the study of global production networks has expanded from economic upgrading to encompass social and environmental upgrading. However, rare research pays attention to the complex interplay among these three aspects. This paper tries to integrate the economic, social, and environmental upgrading into an analytical framework through the lens of coupling coordination. Using the Granger causality test and panel regression model, it provides empirical evidence and an explanation of the triad’s interaction based on the Chinese case study. It is found that, over the past twenty-five years from 1996 to 2020, China has seen a significant improvement in the coupling coordination of economic, social, and environmental upgrading with the coordination degree rising from 0.35 to 0.51, though it remains at a low level of coordination. Regional disparities in economic upgrading are more pronounced than those in social and environmental upgrading, and the inter-group disparities between economic and environmental upgrading have widened following the economic crisis. Panel regression analysis shows that economic globalization, public governance, legal environment, and environmental regulation positively influence the coupling coordination of the three types of upgrading, while economic privatization and corporate violations of law tend to have a negative impact. Full article

This research paper presents a comprehensive review of the literature on microgrid development in the UAE, focusing on the socio-economic costs and benefits, policy frameworks, market dynamics, and environmental implications. The analysis encompasses publications from 2011 to 2021, with a particular emphasis on the United Arab Emirates (UAE) and the Gulf Cooperation Council (GCC) countries. A total of 33 papers were identified and classified, revealing gaps in comprehensive valuation models, consideration of environmental and social governance factors, and research informing policy and investment decisions. The findings highlight the significance of microgrid technology in the UAE, its limited adoption and commercialization, and its predominant usage in remote areas and academic testbeds. The paper underscores the UAE’s vision for net-zero emissions by 2050 and the potential of microgrids in supporting its realization. Recommendations include the development of a comprehensive valuation framework to drive effective investments in microgrid technology, aligning with the UAE’s sustainable energy goals. The study contributes to the understanding of microgrid development in the UAE, offering insights into its socio-economic, policy, market, and environmental dimensions. Full article

Colombia has made different efforts to contribute to fulfilling its international commitments to curb climate change by reducing emissions and promoting technological development and project financing. However, the existing policies and regulatory framework primarily focus on promoting the photovoltaic industry for electricity production. Likewise, the energy sector has neglected the potential of solar thermal energy as a heat source. In this sense, it is necessary to redouble efforts through new public policies that integrate solar thermal energy in the residential and productive sectors. Using solar thermal energy for heating can contribute to the energy transition and meet its sustainable development goals. Therefore, the main objective of this work was to analyze Strengths, Weaknesses, Opportunities, and Threats to determine the potential application of thermal solar heat in Colombia while considering the local context. Factors such as their environmental conditions, policies, and regulations; the existence of international agreements; and their political status in general were analyzed. The analysis revealed Colombia’s significant solar heat potential, enabling over 1.3 million cold-climate households to access hot water or reduce firewood use. Industrially, applying solar heat in 5% of the current industry could decrease fossil fuel consumption by 13 PJ. The findings highlight that Colombia’s potential in thermal solar energy necessitates collaborative efforts, legislative reinforcement, climate-adaptive measures, and the resolution of political and social challenges. Full article

Alkaline electrolysis systems are currently considered to be suitable for large-scale hydrogen production. Previous research has primarily focused on integrating renewable energy sources such as solar and wind into water electrolysis systems. However, intermittent issues stemming from the sporadic nature of renewable energy sources have led to the introduction of energy storage systems (ESSs) to address these intermittent challenges. Extensive research has been conducted on the efficiency and operational aspects of these systems. In contrast to other renewable energy sources, hydropower offers the advantages of stable output and high utilization, making it a promising solution for overcoming intermittent issues. In this study, we propose the design of an optimized alkaline electrolysis system tailored for small-scale hydropower generation. This approach allowed us to confirm the efficiency of a small-scale hydropower-based hydrogen production facility and the analysis of hydrogen production costs under diverse scenarios. Notably, the optimal selling price per kilogram of hydrogen was determined to be USD 15.6 when the operational time exceeded 20 h, albeit indicating a challenging market supply. Under the consideration of various scenarios and government subsidies, this study revealed that a USD 10/kgH₂ subsidy or 24 h of continuous operation achieved break-even points in the sixth and eighth years, respectively. Ultimately, the findings underscore the necessity for essential measures, including government backing and technological advancements in small-scale hydropower facilities, to enhance the economic viability of the green hydrogen market in South Korea. Full article

A model for constructing sustainable Climate Change Haven communities in appropriate areas of the United States and globally is presented. The model proposes the construction of walkable communities of 20,000 to 30,000 residents with electricity provided by hydropower generators and biofuel combustion. The remediation of surface-mined areas using switchgrass and flood control dams to redirect excess rainfall will be required in some areas. This model also addresses the multiple social and cultural considerations required to resettle groups of migrants in Climate Change Haven communities, together with the preparation and preservation of nearby farmland for feeding the community. Full article

This study aims to compare the technological solutions that can contribute to more sustainable energy use in the residential sector. Specifically, the goal of the study is to evaluate the environmental impact of different energy (heat and electricity) supply technologies applicable for an average size single-family building in Latvia, a country known for climatic condition characterized by cold winters with frequent snowfall. The study applies the lifecycle assessment methodology of ISO 14040 and the impact assessment method known as ReCiPe 2016 v1.1, which has not been used before for the scope addressed in the study in the context of single-family building energy supply technologies for climatic conditions in Latvia. Thus, the results of the study will provide new information for more sustainable energy solutions in this area of study. The technologies included in the defined scenarios are conventional boiler, electricity from the grid, Stirling engine, and solar photovoltaics (PV). The results of the lifecycle impact assessment for damage categories revealed that all scenarios have a high impact on human health due to fine particulate matter formation followed by global warming. Regarding the damage to the ecosystem, the terrestrial ecotoxicity category has highest impact, followed by global warming. Sensitivity analyses affirmed the model’s validity and also showed that the impacts of conventional systems were most sensitive to changes in electricity consumption, and therefore, the scenarios with electricity supply from a Stirling engine or PV can be considered a more robust solution under changing electricity demands from an environmental perspective. Full article

Coal burning is a major contributor to air pollution. Selecting the optimal coal alternative path with economic feasibility and maximum environmental benefits is an important policy choice to mitigate air pollution. It could provide a basis for the design of energy transition policies and the green development of coal resource-based cities. This study designed a coal substitution policy based on the multi-objective optimization model, explored the optimal coal substitution path in coal resource-based cities with the goal of minimizing the costs and maximizing the benefits of coal substitution, and assessed the maximum emission reduction potential of air pollutants. The results show that: (1) by 2025, coal consumption in the study area must be reduced to 85%. The optimal coal substitution path is 90.00% coal-to-electricity and 10.00% coal-to-gas for civil emission sources and 83.94% coal-to-electricity and 16.06% coal-to-gas for industrial boiler emission sources. (2) by 2030, coal consumption must be reduced to 75%. The optimal coal substitution path is 90.00% coal-to-electricity and 10.00% coal-to-gas for civil sources and 78.80% coal-to-electricity and 21.20% coal-to-gas for industrial boiler sources. (3) by implementing the coal substitution policy, emissions of six key air pollutants such as SO₂, NO_X, CO, VOCs, PM₁₀, and PM_2.5 could decrease significantly. Full article

The demand for coffee in the local and global markets has encouraged massive production at upstream and downstream levels. The socioeconomic impact of coffee production still presents an issue, primarily related to the social benefit and economic value added for farmers. This study aims to identify the social impact of the coffee industry in rural areas in three different coffee industry management systems. Many coffee industries exist in rural areas, with various management systems: farmer group organizations, middlemen, and smallholder private coffee production. This study performed the social organization life cycle assessment to identify the social impact of the coffee industry in rural areas according to the management systems. The results indicated that the coffee industry managed by farmers is superior in providing a positive social impact to four stakeholders: workers, the local community, society, and suppliers, as indicated by the highest social impact scores of 0.46 for the workers, 0.8 for the local community, 0.54 for society, and 0.615 for the suppliers. The private coffee industry provides the highest social impact to consumers (0.43), and the middlemen were very loyal to the shareholders, with a total social impact score of 0.544. According to this social sustainability index analysis, the coffee industry managed by the farmer group has the highest endpoint of social impact at 0.64, which is categorized as the “sustainable” status. Meanwhile, the coffee industry managed by private companies and middlemen is categorized as “neutral or sufficient”. The coffee industry should implement improvement strategies to increase their social impact to all stakeholders in their business supply chain. Full article

The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide, including the energy sector due to lockdowns and restricted economic activity. This paper presents a comprehensive and analytical review of the impact of COVID-19 on the energy sector and explores the potential role of artificial intelligence (AI) in mitigating its effects. This review examines the changes in energy demand patterns during the pre-, mid-, and post-pandemic periods, analyzing their implications for the energy industries, including policymaking, communication, digital technology, energy conversion, the environment, energy markets, and power systems. Additionally, we explore how AI can enhance energy efficiency, optimize energy use, and reduce energy wastage. The potential of AI in developing sustainable energy systems is discussed, along with the challenges it poses in the energy sector’s response to the pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future, with examples drawn from previous successful studies, are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations. Full article

Geothermal energy has emerged as an alternative heating source that can replace fossil energy. This mature technology is already in use all over Europe, but there are significant differences in its use between European countries. One possible explanation for this phenomenon concerns societal differences directly related to geothermal energy, the topic that is investigated in this study. The present work proposes using the societal embeddedness level (SEL) method to analyze and compare the status of non-technical factors affecting geothermal energy use in Hungary, Iceland, Norway, Poland, and Slovakia. The method considers four dimensions: environment, stakeholder involvement, policy and regulations, and markets and financial resources. Only Iceland fully covers the four dimensions by reaching all the milestones in the SEL framework. Iceland has the advantage of a long history of active use of geothermal energy for domestic use. The other countries face challenges within several of the dimensions, while the form and cause of these challenges are specific to each country. The findings illustrate that to mitigate climate change and drive the energy transition forward, both technical and societal factors related to various renewable energy sources must be assessed. Full article

The current study investigates economic expectations and socio-psychological factors influencing individuals’ residential photovoltaic (RPV) adoption intentions in Thailand. The theory of planned behavior (TPB) and the diffusion of innovation theory provide a framework for our predictor selection. We obtained the data from a nationwide survey on electricity prosumer infrastructure. RPV non-users (N = 760) were asked to rate their RPV knowledge, attitudes, perceived behavioral controls (PBCs), norms, and innovativeness. They then read scenarios describing the current RPV installation cost and payback rate. They rated their adoption intention and specified their intended system capacity, affordable installation cost, and desirable payback period. The gaps between the actual and desired installation costs and the internal rate of return were calculated. These economic expectation gaps, attitudes based on financial benefits, PBC based on perceived financial barriers, social norms, and innovativeness significantly predicted the adoption intention. On the other hand, perceived knowledge, attitudes based on environmental and image benefits, and PBC based on anticipated troubles and inconveniences failed to predict intention. The implications of the TPB model for RPV adoption were discussed. Full article

In the context of global warming, the Wuhan Urban Agglomeration is actively responding to China’s carbon peak and carbon neutrality goals and striving to achieve a reduction in carbon sources and an increase in carbon sinks. Therefore, it is critical to investigate carbon emissions from land use. This study uses the carbon emission coefficient method to calculate carbon emissions from land use in the Wuhan Urban Agglomeration, analyzes its temporal and spatial changes and differences in urban structure, and couples with the Markov–PLUS model to simulate and predict the carbon emissions of four scenarios of land use in 2035. The research found the following: (1) during the Wuhan “1+8” City Circle stage, carbon sources and emissions increased steadily, with average annual growth rates of 1.92% and 1.99%, respectively. Carbon sinks remained stable and then decreased, with an average annual growth rate of −0.46%. (2) During the Wuhan Metropolitan Area stage—except for 2020 and 2021, which were affected by COVID-19—carbon sources, sinks, and emissions continued to grow in general, and the average annual growth rates increased to 4.46%, 1.58%, and 4.51%, respectively. (3) In terms of urban structure differences, Wuhan is a high-carbon optimization zone; Xianning, Huangshi, and Huanggang are ecological protection zones; other cities, such as Ezhou, Xiaogan, and Xiantao are comprehensive optimization zones; and there is no low-carbon development zone. (4) The multi-scenario simulation results show that carbon sources and emissions are the highest under the economic development scenario, with values of 100.2952 and 9858.83 million tons, respectively, followed by cropland protection, natural development, and low-carbon development scenarios. Under low-carbon development, carbon sinks were the highest, with values of 1.9709 million tons, followed by natural development, economic development, and cropland protection scenarios. The research results are conducive to the formulation of carbon peak and neutrality goals as well as low-carbon development plans for the Wuhan Urban Agglomeration. Full article

Climate change threatens economic and environmental stability and requires immediate action to prevent and counteract its impacts. As large investments are already going into mitigation efforts, it is crucial to know how to best allocate them in time and among the alternatives. In this work, we tackle this problem using optimal control methods to obtain the temporal profiles of investments and their allocation to either clean energy development or carbon removal technologies expansion. The optimal allocation aims to minimize both the abatement and damage costs for various scenarios and mitigation policies, considering the optimization time horizon. The results show that early investments and a larger share of demand satisfied by clean energy should be priorities for any economically successful mitigation plan. Moreover, less stringent constraints on abatement budgets and reduced discounting of future utility are needed for a more economically and environmentally sustainable mitigation pathway. Full article

The clean utilization of traditional energy and renewable, clean energy utilization are the key points of the energy structure transition in the Yellow River Basin. This paper constructs an evolutionary game model, with the participation of local governments and energy companies, to analyze the dynamic evolution of each game subject. The results from the study highlight three important facts about the energy mix transformation in the Yellow River Basin: (1) the high ratio of traditional clean energy utilization and the low ratio of renewable, clean energy utilization align with the actual energy use in the Yellow River Basin, which can better promote the inclusive development of both types of energy; (2) increasing the capacity to utilize both energy sources can improve the energy system resilience gains of game players, for example, at the immature stage of renewable, clean energy utilization technologies, local government’s willingness to subsidize renewable clean energy utilization is positively related to their energy system resilience gains; and (3) under the premise of ensuring the energy supply, the introduction of penalty parameters can ensure a reasonable share of both types of energy utilization, and an increase in the penalty parameters makes the game participants increase their willingness to implement energy structure transformation policies. Full article

Reducing energy demand and greenhouse gas emissions in the construction industry is one of the daunting challenges to be addressed in the context of global warming. The purpose of these analyses was to examine how the energy class of a multi-family residential building regarding thermal insulation and type of ventilation affects the usable energy demand for heating and ventilation purposes, the length of the heating season, and the amount of demand for energy consumed by auxiliary devices. This article presents the energy analyses of multi-family residential buildings with identical technical parameters located in different locations in Poland. For research purposes, a total of 354 energy balances were compiled, covering 59 meteorological stations, 3 types of ventilation systems, and 2 building insulation standards. This article presents the ways in which the location and energy class of buildings affect the length of the heating season and the demand for energy required for heating and ventilation purposes. The results of the analyses carried out in this article show that the location and the energy class of the building have a significant impact on the demand for primary energy (EP). As a result, it was concluded that when designating a reference building for the energy rating system, its location should be taken into account and reference buildings should be designated considering climate zones. Full article

The Sloping Land Conversion Program (SLCP) is a significant measure to achieve the Sustainable Development Goals (SDGs) proposed by the United Nations in 2015. SLCP plays an important role in poverty alleviation and income increase for farmers in poor areas. The purpose of this study is to analyze whether the income of farmers has increased after participating in SLCP, and whether SLCP has released the agricultural labor force to obtain non-agricultural income by participating in non-agricultural work. Based on the field investigation in Luocheng County and Longsheng County of Guangxi, Libo County, and Dushan county of Guizhou, this paper uses the method of propensity score matching (PSM) to explore the impact of SLCP on the income of farmers in rocky desertification areas. According to our research, it is found that: (1) SLCP has a positive effect of 5.2% on the average annual net income of farmers, a positive effect of 43.2% on agricultural income, and a negative effect of 9.8% on non-agricultural income, but all of the effects are insignificant. Selective deviation will overestimate the impact of SLCP on farmers’ total income and agricultural income and underestimate the impact on non-agricultural income. SLCP failed to promote the transformation of farmers into secondary and tertiary industries. The mechanism of SLCP to increase farmers’ income is complex. (2) Farmers’ participation in SLCP is influenced by work experience and education level in human capital, participation in skills training in social capital, and owning durable consumer goods in physical capital. Although SLCP will promote economic development under the condition of improving the ecological environment in the future, it is not advisable to exchange farmers’ livelihood for ecological construction at present. The implementation of SLCP should consider not only the overall ecological benefits, but also the short-term social and economic benefits. Full article

Human activities have been limited by coronavirus disease 2019 (COVID-19), and the normal conditions of our lifestyles have changed, particularly in terms of electricity usage. The aim of this study was to investigate the impact of COVID-19 on the power sector in the Lao PDR in 2020, as well as the challenge of using solar energy to supply power to the network using an optimal approach. The returns on investment of network extension and the purchase of solar energy were also evaluated. Furthermore, load conditions caused by the country’s lockdown policy were analyzed. We analyzed the optimal sizing and location of solar energy using a particle swarm optimization method based on the main objective functions, with the system’s power loss decreasing and its reliability improved. The results demonstrated that the suddenly reduced load from industry and commercial business did not have a large impact on its operations; however, revenue was reduced. The optimal method for connecting solar energy to a network can reduce power loss and improve system reliability. In addition, we discovered that the location and capacity of solar generation can reduce the investment costs of extensions for new lines, with the surplus power being exported. Full article

As the largest carbon emitter in the world, China aims to reach its peak carbon emissions goal by the year 2030, while the construction industry makes a significant contribution to carbon emissions, directly affecting the country’s commitment to meet its target. The present paper investigates the dynamic characteristics of carbon emissions released by China’s construction industry under single- and multiple-scenario settings with altering economic growth rates, optimizing energy structures, adjusting industrial structures, and modifying carbon emission policy factors. The research results show that the total carbon emissions generally present a steady increase from the year 2000 and will reach 12,880.40 million tons (MT) by 2030 under a scenario without any intervention. Indirect carbon emissions released from associated industries account for over 96% of the total carbon emissions, while direct carbon emissions make a minor contribution to the total. Single and comprehensive scenarios have positive effects on reducing emissions; it was also observed that only under energy structure scenario III and comprehensive scenario III could carbon emissions released from the construction sector reach a peak value by 2030. The effects of emissions reductions as a result of single policies can be presented in the following order: energy structure, economic growth, carbon emissions policy factor, and industrial structure. All of the emissions reduction effects of multiple scenarios are superior to the single scenarios. The research results provide a basis and guidance for policymakers to adopt the correct steps to fulfill China’s aim of achieving peak carbon emissions by the projected date. Full article

An emergy analysis is used to assess the sustainability of urban agglomerations’ eco-economic systems, which are generally measured by emergy–value sustainability indicators using a combination of several system indicators. However, this assessment approach is not applicable to economically developed high-density urban agglomerations. The application of the traditional entropy value evaluation method needs to be expanded to further strengthen the sustainability of the complex eco-economic–social relationships in megacity cluster regions. In this study, taking the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) as a case study, we study a new evaluation method for evaluating the sustainable development capacity of cities. This method is based on the entropy power method and is used to construct the evaluation system of all indicators of the social–economic–natural subsystems of the eco-economic system, and it couples the development degree with the coordination degree. (1) This study shows that the new method is applicable for the sustainability assessment of high-density megacity clusters and is more accurate and comprehensive. The sustainability rankings are provided for Zhaoqing, Jiangmen, Huizhou, Guangzhou, Macau, Foshan, Zhongshan, Dongguan, Zhuhai, and Shenzhen. Hong Kong is the most representative, with a high sustainability index, but has the lowest level of coordination and a clear incoherence within the system. (2) The current emergy structure of the GBA city cluster is extremely unreasonable. The GBA city cluster is a resource-consuming city with a common characteristic of a low level of coordinated development. Although urban clusters have some potential in terms of renewable emergy and resources, the recycling rate of waste is low, and the consumption rate of nonrenewable resources is high. The effective use of land resources has become an important factor in the bottlenecking of sustainable development, and all other cities face such problems, except Zhaoqing, Jiangmen, and Huizhou. (3) The GBA city cluster can be divided into three categories according to the new method. Category 1 mainly includes Hong Kong, Shenzhen, Dongguan, and Zhuhai, which have coordinated development degrees ranging between 0.0 and 0.135 and the highest emergy density (ED) values but are extremely dependent on external emergy. They have high levels of emergy use per capita (EUC), high living standards, and high quality of life. The effective use of land resources severely restricts sustainable economic development, resulting in extreme ecological and environmental carrying pressure. Category 2 includes Guangzhou, Macau, Foshan, and Zhongshan, whose coordinated development degrees range from 0.143 to 0.179. The sustainable development capacity of these cities is at the middle level amongst the whole GBA. Their main emergy characteristics are emergy flow and subsystem evaluation indices that are between category 1 and category 3, but each has its own characteristics. The category 3 cities include Zhaoqing, Jiangmen, and Huizhou, whose coordinated development degrees are between 0.192 and 0.369. These cities are characterized by relatively low ED and EUC values, living standards, and quality, but their land resources have certain potential. These cities have a high emergy self-sufficiency rate (ESR) and natural environmental support capacity, but their environmental loading ratio (ELR) is still much higher than the national average. In terms of the economic development and innovation development levels, these cities are ranked as category 1 > category 2 > category 3. In terms of the ecological and environmental conditions and blue–green space protection, these cities are ranked as category 1 < category 2 < category 3. The results of this study can provide cities in the GBA with more scientific and consistent directions for the coordinated development of their ecological–economic–social systems to provide sustainable development decision-making services for megacity cluster systems. Full article

Various human activities emit greenhouse gasses (GHGs) that contribute to global climate change. These include the burning of fossil fuels for energy production, transportation, and industrial uses, and the clearing of forests to create farmland and pasture, all for urban and industrial development. As a result, temperatures around the world are rising, extreme weather events are occurring more frequently, and human health is suffering because of these changes. As a result of massive traffic, agriculture, and urbanization, the natural environment is being destroyed, negatively affecting humans and other living things. Humanity plans to live in smart cities within this ecosystem as the world evolves around these mutations. A smart city uses technology and data to improve the quality of life of its citizens and the efficiency of its urban systems. Smart cities have the potential to be more sustainable because they use technology and data to improve the efficiency of urban systems and reduce the negative impact of human activities on the environment. Smart cities can also use technology to improve green transportation and waste management and reduce water consumption, which can help conserve natural resources and protect the environment. Smart cities can create livable, efficient, and sustainable urban environments using technology and data. This paper presents a new Enterprise Architecture Framework for reducing carbon emissions for environmental sustainability that combines gamification and green behavior with blockchain architecture to ensure a system that is trustworthy, secure, and scalable for shareholders, citizens, service providers, and technology vendors. In order to achieve this, the hyper-integrated framework approach explains a roadmap for how sustainability for reducing carbon emissions from transportation is based on an optimized MaaS approach improved by gamification. As part of this study, a computational model and a formulation are proposed to calculate the activity exchange values in the MaaS ecosystem for swapping, changing, and bartering for assets within the integrated system. This paper aims to propose the framework and a module interoperability approach, so numerical values for computation parameters are not included as they may belong to other research studies. In spite of this, a case study section has been provided as an example of a calculation approach. Full article

Applying the circular economy (CE) concept is crucial for achieving sustainable development goals. A transition towards a CE requires new tools to clarify the interdependency among systems and assist policy-makers in their decisions, particularly in the risk assessment field. This paper analyzes the systemic effects and interdependencies of several risks in the context of a CE. The developed tool helps adopt proactive strategies that consider the four aspects of sustainability (economic, environmental, social, and technological). The adopted tool improves strategic thinking for a circular economy concept and supports organizations with respect to assessing risks. This paper aims to provide a comprehensive and novel model to quantify the priority weights of the sustainability risk indicators to provide guidelines for supporting the policy formulation process for decision-makers. In this paper, the taxonomy of various risk indicators has been proposed, and we have identified and adopted 40 risk indicators for the CE. This paper focuses on understanding how risks can be constructed and how they affect the performance of power plants over time in terms of availability, efficiency, and operational and maintenance cost. The causal loop diagram (CLD) model is built by deploying various risk quantifications, and the adopted tool was tested and validated to assess the CE risks relevant to the environmental perspective in power plants in the Middle East. The risk indicators under the concept of the CE model and the system thinking approach can help policy-makers in their strategic and operational decision-making process for achieving a better understanding of the risk assessment process. The taxonomy of risk categories and its linking with the system thinking approach will help in the successful and effective implementation of a CE in the energy sector in the long-term. The proposed model offers a tool for policy-makers to design policies when planning a CE. Full article

Topics related to sustainable economic development are currently important issues in the modern world. However, the implementation of this concept and related operational strategies raises many controversies. On the one hand, it offers hope for ecological, safe, and independent economic development, while on the other hand, it raises public concerns about the costs of such changes. These problems are widely appreciated in the EU, which is the undoubted leader in implementing the concept of sustainable economic development. With regard to this issue, this paper presents the developed methodology for assessing the sustainable energy and climate development of the EU-27 countries. The basis of this assessment is 17 selected indicators characterizing the most important areas related to this development. Their selection was conditioned by the assumptions of the Europe 2020 Strategy and the goals (7 and 13) of the UN Agenda for Sustainable Development 2030. Five widely used methods for multi-criteria analysis supporting management processes (CODAS, EDAS, TOPSIS, VIKOR, and WASPAS) were used for the study. In order to carry out an unambiguous assessment and determine the final ranking of countries in terms of energy and climate sustainability, a methodology was developed to specify the normalized value of the Final Assessment Score ($A{s}_{final}$). Based on it, the sustainability of individual EU-27 countries in 2010, 2015, and 2020 was assessed, and this assessment formed the basis for dividing these countries into four classes (levels) in terms of sustainability. The results confirmed the high differentiation of the EU-27 countries in terms of sustainability, indicating leaders as well as countries with low levels of sustainability. The countries with the highest and most stable levels of sustainable development of the economy are Sweden and Denmark. The results provide opportunities for their interpretation, both in terms of analyzing changes in individual indicators and in terms of the global assessment of sustainable development in individual countries. These results should be used when developing an energy and climate strategy for the next few years for the EU as a whole and for individual countries. Full article

Solar energy is considered one of the most hopeful alternative sources to avoiding dependence on fossil fuels, and it does not cause any air pollution. GIS-based solar energy potential evaluation is mainly focused on regional scale; further, more solar energy potential evaluation with building scale is calculated through observation data and mathematical model. Therefore, in this paper, a GIS-based joint solar energy potential evaluation is developed to evaluate the distributed photovoltaic potential and centralized photovoltaic potential. Shanxi province in China, which has abundant coal resources, is used as the study area. The raster grid scale is used as the minimum research scale, which could not only deal with the distributed photovoltaic potential but could also calculate the centralized photovoltaic potential. The obtained results indicate that the developed method could effectively deal with problems associated with the distributed photovoltaic potential and centralized photovoltaic potential in the raster grid scale. Full article

To obtain the early realization of carbon peak and carbon neutrality in China, this study explores the cooperative relationship of inter-regional energy power-generation substitution between regions dominated by traditional thermal power and renewable energy sources (RES). By taking a regional government as the decision-making subject, focused on interest and environmental factors, an evolutionary game model of inter-regional energy cooperation is structured, and a simulation platform of the two different power-generation replacement cooperative patterns/strategies is constructed by using system dynamics. Then, the influences of the sensitive parameters on the cooperative evolutionary path under symmetric and asymmetric sharing cost cases have been discussed based on practical example in the regions of China. The results imply that agents can only select the favorable cooperative strategies unilaterally, by choosing a strategy of sharing the environmental revenues rather than the cooperative costs. When the failure cost of the opportunity revenues is less than or equal to the RES power-generation cost, a traditional thermal power regional government adopts a cooperative no-sharing strategy, while an RES regional government selects the opposite strategy. However, under the optimized dynamic proportional allocation schema, it is more likely that the traditional thermal power regional government will prefer cooperative sharing strategies, which can promote the social value of RES. This study provides beneficial inspiration for the Chinese government to further improve its RPS policy. The RES consumption fulfilled by direct or indirect trans-regional energy cooperation can be included in the RPS index framework assigned to traditional thermal power energy regions, and the added environmental value should be regarded as being as crucial as the economic and energy factors are in the cooperative process. In addition, RES regions that contribute more to clean energy absorption should raise the weight of the RPS rewards. Full article

In the context of international carbon neutrality, energy prices are affected by several nonlinear and nonstationary factors, making it challenging for traditional forecasting models to predict energy prices effectively. The existing literature mainly uses linear models or a combination of multiple models to forecast energy prices. For the nonlinear relationship between variables and the mining of historical data information, the prediction strategy and accuracy of the existing literature need to be improved. Thus, this paper improves the prediction accuracy of energy prices by developing a “decomposition-reconstruction-integration” thinking strategy that affords medium- and short-term energy price prediction based on carbon constraint, eigenvalue transformation and deep learning neural networks. Considering 2011–2020 as the research period, the prices for traditional energy resources and polysilicon in clean photovoltaic energy raw materials are selected as representatives. Based on energy price decomposition using the Singular Spectrum Analysis (SSA) method, and combining it with Learning Vector Quantization (LVQ) cluster technology, the decomposed quantities are aggregated into price sequences with different characteristics. Additionally, the carbon intensity is considered the leading market’s overall constraint, which is input with the processed price data into a Long Short-Term Memory network (LSTM) model for training. Thus, the SSA-LSTM combined forecasting model is developed to predict the energy price under carbon neutrality. Four indices are employed to evaluate the prediction accuracy: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-squared. The results highlight the following observations. (1) Using a sequence decomposition clustering strategy significantly improves the model’s prediction accuracy. This strategy enhances predicting the overall trend of the price series and the changes in different periods. For coal price, the RMSE value decreased from 0.135 to 0.098, the MAE value decreased from 0.087 to 0.054, the MAPE value decreased from 0.072 to 0.064, and the R-squared value increased from 0.643 to 0.725. Regarding the polysilicon price, the RMSE value decreased from 0.121 to 0.096, the MAE value decreased from 0.068 to 0.064, the MAPE value decreased from 0.069 to 0.048, and the R-squared value increased from 0.718 to 0.764. (2) The prediction effect is better in the case of carbon constraint. Considering “carbon emission intensity” as the overall constraint of the leading market, it can effectively explore the typical characteristics of energy price information. Four evaluation indicators show that the accuracy of the model prediction can be improved by more than 3%. (3) When the proposed SSA-LSTM model is used to predict both prices, the results show that the evaluation index of the prediction error remained at about 1%, while the model’s accuracy was high. This also proves that the proposed model can predict traditional energy prices and new energy sources such as solar energy. Full article

This research examines the potential economic rebirth of the Central Appalachian Region as persons from the southwestern and southeastern parts of the United States seek a safe and livable environment for their families and businesses. Central Appalachia is projected to be the largest habitable area in the continental United States by the year 2050. However, mined land remediation, water control efforts, and hydro-electric energy generation units will be required to support the incoming population. Full article

Shanghai, as an international metropolis, has an ever-growing population and ongoing economic development, so the pressure on the natural resources and the environment is continually increased. How to ease the tension among economy, resources and the environment? The sustainable green development of Shanghai has been the focus of the public and the government. Urban carrying capacity involves complex interactions among population, the economy and the environment. Understanding the balance between these elements is an important scientific issue for sustainable green development in Shanghai. For this purpose, the balance between urban development and ecological resources was emphasized, and population carrying capacity, GDP (Gross Domestic Product), green ecological index and added value of secondary industry were investigated to measure urban carrying capacity. The dynamic changes of the carrying population, GDP, green ecological index and the added value of the secondary industry in Shanghai during 2018–2035 were simulated using a system dynamics model including three subsystems and 66 variables from a macroscopic perspective. Five development scenarios were employed during the simulation, namely a status-quo scenario, an economic-centric scenario, a high-tech-centric scenario, an environment-centric scenario and a coordinated equilibrium scenario. The simulation results indicated that the potential of carrying population will decline by 2035, and the economic and ecological indicators will also be at a low level under the status-quo scenario, which is an inferior option, while the under coordinated equilibrium scenario, the ecological environment, population growth and economic development will all perform excellently, which is the best option. Therefore, the urban carrying capacity of population, economy and resources in Shanghai may be improved by increasing investment in scientific research, increasing the expenditure on environmental protection and improving the recycling efficiency of waste solid and water. The results provide insights into the urban carrying capacity of Shanghai city. Full article

Unstable factors such as international relations, geopolitics, and transportation routes make natural gas trade complex and changeable. Diversified and flexible sources of liquefied natural gas (LNG) can guarantee the energy supply security of natural gas-consuming countries. Therefore, it is very important to find potential natural gas trade links to help the government find potential partners and prepare strategically in advance. In this paper, the global LNG network is taken as the research object. In order to fully consider the importance of nodes and the influence of economic and political factors, the “centrality degree” and “node attraction degree” are added into the link prediction algorithm, and multifactor coupling is carried out. The reliability of the improved algorithm is verified using the area under the curve (AUC) evaluation index, and the prediction results are analyzed. The results are as follows: Trinidad, Russia, Algeria, Nigeria, Angola, and Equatorial Guinea (Eq. Guinea) are more likely to establish new LNG trading relationships with other countries. For all potential trade relationships, potential relations involving the above countries are more likely to be realized within 5 years, while potential relations involving China, India, Japan, and South Korea are more likely to be realized within 2 years. China, India, and South Korea are more likely to import LNG from Algeria, and Taiwan Province is more likely to import LNG from Algeria, Angola, Eq. Guinea, and America. On the basis of the above study, states and governments can give priority to the above countries and regions when dealing with the possible LNG supply crisis. Full article

According to sustainability transitions theory, socio-technical change requires a convergence of politics, social change, technology, and niche innovations. Recently, a circular economy has been proposed as the engine of such change in the EU New Green Deal and Germany. Mainstream circular economy emphasizes the closing of material loops as the way to ensure green growth, and there is a key role for design to achieve such change. According to reports, however, the global appetite for a circular economy remains limited and critics have pointed to several contradictions between the rhetoric and reality of the circular economy and sustainable development. In addition, current formulations of circular economy misrepresent the plurality of discourses for a sustainable circular economy and the role of expert and diffuse circular design. In this study, we employ the recently articulated ten principles for a sustainable circular economy and society to analyze two contrasting circular roadmap projects in Germany, which reflect two contrasting technical and reformist circular discourses, and understandings of the role of design. We find that there are narrow and broad interpretations of design inherent in these circular policies as well as the exemplification of the difference between a technical circular economy and reformist circular society discourses. The practical applied value of this analysis is that the framework can be employed to analyze other policies. Full article

In 2020, China put forward the goals of “peak carbon dioxide emissions” and “carbon neutrality” (“double-carbon”) and it is urgent for the energy industry to achieve green transformation. Aiming at the rigid requirements of the carbon-peaking and carbon-neutrality goals (“double-carbon”), this study established a performance evaluation index system for an energy supply chain of a four-tier structure based on the “double-carbon” goals, calculating its weight by the analytic hierarchy process (AHP). On this basis, a three-stage data envelopment analysis (DEA) evaluation model was established to evaluate the performance of the energy supply chain in 2010–2019. According to the three-stage DEA evaluation mode, the initial input–output efficiency value of the energy supply chain was calculated by the DEA-BCC (extended by Banker, Charnes and Cooper) model and DEA-CCR (proposed by Charnes, Cooper and Rhodes) model and the influence of environmental noise was eliminated by stochastic frontier analysis (SFA) regression; we then obtained the adjusted efficiency value for the energy supply chain. At the same time, taking 2015 as the dividing point, the advantages and disadvantages between the traditional energy supply chain and new energy supply chain were analyzed and summarized. Further analysis and suggestions are provided to consumers, enterprises and countries from four aspects: energy supply, energy production and processing, energy transmission and distribution and energy consumption. Full article

The synergistic development of economic construction and low-carbon transformation of energy systems must be promoted for building a green, low-carbon, and cyclic economic system and achieving the “double carbon” goal in China. Based on the panel data of 30 provincial-level administrative regions in China from 2015 to 2019, the global entropy method, coupling coordination degree model, and spatial statistical analysis methods are applied to analyse the factors affecting the coupling effect. The coordination degree increased in the study period, with Beijing, Shanghai, Tianjin, Jiangsu, and Zhejiang being the regions with the highest values. The spatial distribution of the coupling coordination degree is strongly positively correlated with the eastern provincial-level administrative regions located in the high–high concentration area of the Moran scatterplot and western provincial-level administrative regions concentrated in the low–low concentration area. The spatial association pattern is stable in the study period, with only two provinces exhibiting a transition: Shandong province made the transition to high–high agglomeration areas, and Liaoning province made the transition to low–low agglomeration areas. The level of regional economy, urbanization process, energy consumption structure, and level of investment in science and innovation enhance the coupling coordination degree, whereas the industrial structure deteriorates this degree. Full article