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The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

Over the past few years, energy communities initiatives have gained traction in Europe as a means to achieve energy transition goals, allowing citizen involvement in energy production, consumption, and distribution. Following the 2018 European directive RED II, these projects have rapidly increased across the continent, showcasing their potential for creating sustainable local energy systems. While studies highlight economic, environmental, and social benefits, barriers such as regulatory challenges, finance access, and low public awareness remain. This paper presents the results of a systematic literature review that explores the state-of-the-art of the academic research on energy communities. The study adopts a systematic approach to examine the body of literature on the topic to provide a comprehensive analysis of the phenomenon on the basis of rigorous and replicable research criteria. The reviewed material consists of 140 articles collected through the Scopus database, evaluated using specific structural dimensions to group the literature into analytical categories. The analysis of scientific papers published between 2018 and 2022 shows a growing attention towards Renewable Energy Communities, particularly those based on solar energy for electricity production, often combined with storage systems and heat pumps, whereas attention towards energy communities based on thermal energy production is still limited. Furthermore, most scholars focused on analysing specific territorial contexts; comparisons between different regions allows to highlight common features and elements characterizing each model. It emerged that the development of energy communities depends mainly on regulatory, financial, and managerial barriers that require an adequate institutional and legal framework capable of promoting their diffusion. Most studies address the topic from an economic perspective, often in association with incentivizing community models. However, a social perspective should be included by introducing the concept of energy sharing and methodologies for its distribution among all members of the community. The scientific community is called upon to continue investigating and supporting the development of community energy initiatives by providing evidence-based recommendations to policymakers, industry stakeholders, and local communities. This can help to overcome existing barriers and facilitate the diffusion of energy communities models, contributing to the achievement of sustainable and inclusive energy transition goals. • Systematic literature review exploring academic research on energy communities in Europe. • Structural dimensions and related analytical categories as framework to review 140 journal articles. • Most studies concern ECs based on renewable energy sources, with solar power being the predominant source. • Most employed technologies are related to the production of electricity from photovoltaic with batteries and heat pumps. • Most studied focus on Renewable Energy Community, while Citizens Energy Community are less considered in academic studies. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

The power industry in China is the foremost emitter of carbon, necessitating a clean transition in its power generation segment for successful decarbonization. Identifying and quantitative analysis the critical factors that shape the transition and their operational mechanisms will help achieve a secure low-carbon transformation of the energy system. Nonetheless, the body of extensive research in this particular field remains notably limited. To bridge this gap, this paper initiates by developing a comprehensive mechanism framework (REEP) for the transition towards cleaner power generation including Resource endowment (abbreviated as R), Electricity market (abbreviated as E), Economic development (abbreviated as E) and Policy implementation (abbreviated as P) four fields. This framework is based on monthly power generation data derived from four categories of power generation sources at the provincial level in China, spanning the years 2014–2019. It dynamically and quantitative elucidates the multifaceted impacts of various influencing factors on the evolution of the power generation structure. Based on the REEP framework, we observe the power generation structure evolution trends. The study's findings underscore the significance of resource endowment as a paramount internal factor influencing the clean evolution of the power generation structure, while economic development emerges as a pivotal external driving force propelling its transformation. Despite wind and solar power generation accounting for a relatively small proportion at the end of the 14th Five Year Plan period, it is imperative for China to continue promoting the extensive development of renewable energy. While we should coordinate national power transmission and distribution on the foundation of incentivizing technological innovation in emission reduction and the development of energy storage technology. This approach is essential to comprehensively advance the low-carbon and stable development of the energy system, ultimately facilitating the realization of the dual-carbon goal. [ABSTRACT FROM AUTHOR]

• The self-assembly of supramolecular hydrogels and their physio-chemical properties are described. • The applications in stimuli responsiveness, energy storage and energy conversions, etc., are summarized. • For supramolecular hydrogels, none of the review papers provide comprehensive summaries of recent advancements and applications. This review article highlights the potential of supramolecular hydrogels as emerging energy materials. Supramolecular hydrogels are a class of soft materials with unique properties, such as self-healing, self-assembly, stimuli responsiveness, and biocompatibility, making them an attractive platform for various energy applications. The article discusses the definition, properties, and advantages of supramolecular hydrogels in energy materials. It further explores the recent advances in the field, such as self-healing properties, surface functionalization, and stimuli-responsive behavior. The article concludes by discussing the current challenges and future perspectives on using supramolecular hydrogels in energy materials. Overall, supramolecular hydrogels hold great potential as versatile and sustainable materials for energy storage and conversion, and further research in this area can lead to the development of novel energy solutions. [ABSTRACT FROM AUTHOR]

• The proposed joint demand response and dynamic pricing strategies can make the net grid demand curve smoother and have practical significance. • The dynamic pricing strategy helps to realize the joint dispatch of multiple demand response resources in the region and alleviate the grid pressure during peak hours. • A two-layer optimization model is proposed to not only realize the economy of joint operation of multiple energy stations, but also make the net grid demand curve smoother through joint demand response. In recent years, countries around the world have made great efforts to coordinate energy green and low-carbon development and energy supply. In this context, it is of great significance to build energy stations that can greatly absorb renewable energy. The coordinated operation of multi-energy stations in the region can expand the role of energy stations and strengthen energy complementarity between regions. Aiming at the problem of energy interaction and coordinated operation of multi-energy stations in regional integrated energy system, this paper proposes a two-layer scheduling optimization model, which can effectively reduce the system operation cost and carbon emissions. At the same time, considering the joint demand response, it can effectively reduce the peak power supply pressure of the power grid. In addition, in order to ensure the accuracy and efficiency of the solution, this paper improves the traditional NSGA-II algorithm, greatly improving the overall performance of the algorithm. Finally, the simulation analysis is carried out through an example. The results show that the operation optimization model proposed in this paper can effectively reduce the total energy cost of the system, reduce carbon dioxide emissions, and reduce the peak pressure of the power grid. The coordinated operation optimization method is effective and feasible. [ABSTRACT FROM AUTHOR]

The changes in energy use and energy-mix are important in ensuring the implementation of the sustainability goals, especially those related to the climate change. Even though there have been attempts to analyse the energy-related greenhouse gas (GHG) emission in the European Union, there is still a lack of research that considers structural change, fossil energy consumption, and energy-mix simultaneously. In this context, the present paper addresses the growth in energy-related greenhouse gas (GHG) emissions in the European Union (EU) agriculture. The index decomposition analysis based on the logarithmic mean Divisia index is implemented over the period of 2004–2021. The change in the GHG intensity at the EU level is factorized with respect to the structural effect and those related to the energy intensity, the share of the fossil energy, and carbon factor for the fossil energy. The results suggest that a decline in the energy intensity appeared as the main driver pushing the energy-related GHG emission intensity down in the EU agriculture. This calls for further actions directed towards the increase of the use of renewables. [ABSTRACT FROM AUTHOR]

Clean energy plays an essential role in responding to environmental crises. Accurate forecasting of clean energy power generation can provide necessary references for the formulation of energy policy. This paper proposes a novel fractional discrete grey model with a dynamic time delay function (DTDFF-DGM (1,1)) to forecast clean energy power generation. This model introduces the fractional accumulation operator and the dynamic time-delay function into the discrete grey model, which ensures the priority of new information in the original data and improves the model's adaptability to different sample data. The optimal parameters of the model are calculated by the dynamic linkage between the fitting error and the test error, which effectively avoids the overfitting problem. Empirical studies have proved that the model has better prediction accuracy compared to other methods. Finally, the proposed model is employed to forecast clean energy power generation in China. The results show that from 2020 to 2025, the overall growth rate of hydropower, wind power and nuclear power in China will be 9.27%, 119.61% and 36.38%, respectively. Based on the discussion of the forecast results, relevant policy suggestions were made. This paper realizes the transformation from a static model to a dynamic model in methodology and promotes the sustainable development of clean energy power generation in application. [Display omitted] • A novel fractional discrete grey model is established. • The model is the general paradigm of the existing six discrete grey models. • The jump error is avoided in the transition from discrete to continuous. • The dynamic time-delay function is introduced. • China's clean energy power generation is effectively predicted by the model. [ABSTRACT FROM AUTHOR]

Clean energy has various environmental and economic benefits, including reducing air pollution. Also, due to its characteristics, it can be more compatible with the environment than fossil fuels. Industry leaders believe that high costs, technological shortcomings, and constraints on transmission infrastructure are hampering the growth of clean energy. In this paper, a new approach is presented to cover some of the drawbacks of the Failure Mode and Effects Analysis (FMEA) technique. The presented approach addresses the shortcomings of the FMEA method and applies complete prioritization for organizations. In this paper, a developed approach based on multi-criteria decision-making (MCDM) methods in a Spherical fuzzy set (SFS) to assess barriers to clean energy development has been stated. Three main criteria, severity, detection, and occurrence, are used to assess clean energy barriers and are weighed using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on SFS. In the next phase, clean energy development barriers in five categories: technical, economic, social and cultural, administrative, legal, and political, are prioritized using the Combinative Distance-based Assessment (CODAS) method in the SFS environment, which reduces uncertainty. The results show that incomplete markets and consecutive changes in the administrative system are critical barriers. Finally, to confirm the effectiveness of the results, the sensitivity analysis was performed with other common methods. The results show the performance of the developed approach and its ranking stability. Also, the results of the MOORA, COPRAS and CODAS methods were compared and CODAS was superior to the other methods with a correlation of 0.981. [ABSTRACT FROM AUTHOR]

In this paper, we suggest how India can become atmanirbhar (i.e. self-reliant) in development of green energy technologies- solar, battery storage and green hydrogen, given its stated commitments in clean energy. First, we introduce a generalized policy design – a set of ten policy measures: seven necessary and three sufficient- with the former being on the supply side and the latter on the demand side. The paper argues that while the ideal policy mix would be technology dependent, based on technology maturity and the strategic positioning of the country, it should draw both from supply side (i.e., for domestic development) and demand side (i.e., for scale deployment). Then, we suggest the required individual strategies for each of the three technologies- such as a manufacturing focused policy for solar solely to develop competence in manufacturing; a bottom-up, manufacturing-led policy for battery storage with focus on deployment in parallel with technology development; and traditional top-down, research-led strategy for green hydrogen by entering via development through supply side and complementing via deployment through demand side. Finally, we compare these with the existing domestic energy industrial policies as well as those prevalent in other jurisdictions such as the US and China. • Create self-reliance for India in green energy technologies. • Develop generalized policy design with supply and demand side measures. • For solar, manufacturing focused policy for competence solely in manufacturing. • For battery storage, bottom-up manufacturing-led policy with deployment alongside development. • For green hydrogen, top-down research-led strategy where deployment follows development. [ABSTRACT FROM AUTHOR]

Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints. [Display omitted] • Clarified China's short-term energy development path under carbon constraints. • Thermal power will not exit the market quickly in 2030. • Constructed an energy and power prediction model under carbon constraints. • Wind energy and photovoltaic energy will become the main development energy sources. • Support low-carbon power sector transformation under carbon policies. [ABSTRACT FROM AUTHOR]

The pursuit of sustainable development has become a prevalent goal in numerous countries. Therefore, renewable energy development is deemed the prime alternative, as it not only curtails CO2 emissions but also stimulates economic prosperity. Hence, there is a need to investigate rational options for renewable energy development. This paper utilises a research framework that combines linear and non-linear modelling. The specific results are as follows. The study's findings reveal that wind, solar and hydro energy can each promote economic growth and lower carbon emissions. Nevertheless, wind energy exhibits the most effective reduction in emissions, followed by solar energy, and lastly, hydro energy. When it comes to promoting economic development, hydro energy proves to be the most effective, followed by wind energy and then solar energy. Additionally, a panel threshold model is utilised to assess further the CO 2 reduction for solar, wind, and hydro energy across a range of economic levels. The results indicate a double threshold effect for wind energy, whereby the effectiveness of CO 2 suppression initially increases and then decreases as the economic level rises. In contrast, the use of solar power displays a sole threshold effect that incentivizes CO 2 emissions at sub-threshold economic levels, yet significantly curbs them beyond it. Similarly, hydro energy also demonstrates a single threshold effect, but lacks statistical significance below the established threshold and actually heightens carbon emissions above it. Our observations can assist in creating sustainable renewable energy development strategies. [Display omitted] • Investigating the impacts of solar, wind, and hydro on economic growth and carbon emission. • Using the linear and nonlinear panel data in 22 countries from 2001 to 2020. • Solar, wind, and hydro contribute to economic growth and carbon emissions reduction. • Countries with a good economy prioritize wind and solar, while others prioritize hydro and wind. [ABSTRACT FROM AUTHOR]

With its strong ties to the European Union, Germany is well-positioned to assist many countries achieve sustainability goals. Therefore, the main objective of this paper is to examine how renewable energy intensity and renewable energy share improve environmental sustainability, the load capacity factor (LCF) in Germany from 1970 to 2018. To this end, the study uses the autoregressive distributed lag model, with the effects of the Kyoto Protocol included as a dummy variable. The study contributes to the literature by considering and testing for the first time the impact of renewables on the LCF in terms of intensity and share for Germany. The empirical results show that (i) economic growth lowers the LCF faster in the early stages of economic development than in later periods; therefore, the environmental Kuznets curve hypothesis is valid in Germany. (ii) A 1% upsurge in renewable energy intensity has no statistically significant effect on the LCF, while a 1% rise in the share of renewables in the total energy mix increases LCF by 0.48%. (iii) A 1% rise in urbanization reduces the LCF by 6.52%. (iv) A 1% augment in human capital enhances the LCF by 3.49%. These outcomes suggest that the German government should use human capital development and increasing the share of renewable energy as policy instruments to achieve the sustainable development goals. [Display omitted] • The EKC hypothesis is valid for Germany. • Renewable energy intensity has no influence on the load capacity factor. • A higher renewable share in the energy mix is associated with an improvement in LCF. • Human capital plays a key role in solving environmental challenges. • The urbanization process has a devastating effect on the environment. [ABSTRACT FROM AUTHOR]

Residents' gas-saving intention is of great significance to energy conservation, environmental protection, and sustainable development of clean energy. Based on micro household-level survey data from the city of Hefei, this paper uses logit and probit models to examine the relationship among residents' knowledge, acceptance of the increasing block tariffs policy for household gas (IBTsPHG), and their gas-saving intention; the consumption characteristics of residents were discussed. The results show that residents' knowledge of the IBTsPHG has a significant positive impact on its acceptance, which has a significant positive impact on residents' gas-saving intention; the higher the average gas price, the lower the willingness of residents to accept the IBTsPHG; compared with low-income households, high-income households have a lower willingness to accept the IBTsPHG; and households using wall-mounted gas boilers have a lower willingness to accept the IBTsPHG and stronger gas-saving intention but higher gas consumption. In addition, household characteristics, such as dwelling area, education, and occupation significantly affect residents' saving intention. Policy implications are derived from the analysis. [ABSTRACT FROM AUTHOR]

The rapidly increasing demand for electrical energy and the depletion of fossil fuels ignited research in renewable energy generation and management. Thermoelectric generation (TEG) systems as a green energy resource are designed to recover waste heat recovery. Concentrated solar tackles the disadvantages of low power generation efficiency. This paper investigates the feasibility of using machine learning (ML) based MPPT technique, to harvest maximum power of a centralized TEG system under various operating conditions. The environment-dependent issues such as the non-constant distribution of heat, loss to the heat transfer coating between sinks and sources, and mechanical faults create non-uniform current generation and impedance mismatch causing power loss. In this article, we have proposed a feed-forward neural network (FNN) trained by a novel flow direction algorithm (FDA) with a tuned PID controller to harvest the energy under non-uniform temperature gradient conditions. The non-uniformity of physical conditions generates multiple local maxima's (LMs) in electrical characteristics of TEG systems. Common gradient-based MPPT techniques are unlikely to track true GMPP. As a solution to this non-linear problem, novel FNN-FDA is tested under non uniformly distributed temperature (NTUD) over heat source surfaces under varying load and temperature conditions. In this study, certain contributions to the field of TEG systems were made by tackling the issues such as GMPP tracking, low efficiency, and oscillations around GMPP. The results are compared to the particle swarm optimization (PSO), Barnacle mating optimization (BMO), and grey wolf optimization (GWO) algorithm. Five experiments under different weather conditions are performed. Results are validated and proved with experiments and MATLAB/SIMULINK. The experimental results demonstrated that FNN-FDA performs significantly better when compared with other control techniques. • This paper presents Flow Direction Optimization (FDO) algorithm trained using Feed Forward Neural Network (FDA-FNN) based novel intelligent techniques for Maximum Power Point Tracking (MPPT) control of a centralized TEG systems. • FDA-FNN Results are compared against PSO, BMO and GWO based MPPT Techniques. • 99.9% power efficiency and 6% more energy under dynaic operating conditions is achieved. • Varying stochastic temperature, varying load, Nicholas-Zigler trained PID and experimental validation cases are studied. [ABSTRACT FROM AUTHOR]

In order to achieve global energy supply security, environment-friendly, coordinated configuration and efficient use, the development of clean energy is imperative. In this paper, a clean energy development index evaluation index system with four grades is established from the two aspects of clean energy installed capacity and clean energy generation. Based on the improved entropy method of the sub-section and sub-dimension comprehensive evaluation model and the clean energy installed capacity and clean energy power generation of the year of 2013 and 2014, the absolute, the relative and the incremental development index of various categories of clean energy in different countries is obtained, and then the overall clean energy absolute, the relative and the incremental development index of every country is calculated. Ultimately, the clean energy composite development index is obtained. According to the descriptive statistics analysis method and combined with the characteristics of clean energy development in studied countries, 231 countries are divided into four categories: A++, A+, A, A−. By changing the weight determination method, the efficiency coefficient entropy method is used to verify the level of clean energy development, and the Spearman correlation of the exponential results calculated by different methods is analyzed. The conclusion is drawn that the top 10 countries in the clean energy development index in 2014 are the United States, China, Germany, France, Spain, Italy, Kenya, the Philippines, Canada and New Zealand. In fully developed countries, the absolute, the relative and the comprehensive development index of clean energy are relatively high. In emerging countries, the incremental development indexes of clean energy are relatively high, and the emerging countries of the clean energy market have great potential for development. [ABSTRACT FROM AUTHOR]

IETB framework for green clean hydrogen energy development in the low-carbon transition. [Display omitted] • Most of the countries agree to consider renewable energy-based hydrogen generation as their long-term strategy. • Primary driving reasons for the growth of hydrogen energy in BRICS-US are economic, institutional and behavioural factors. • Key aspects of hydrogen are reviewed i.e. production cost, application, production and delivery in BRICS-US. • This study focuses on the qualitative inflection point through IETB framework in the development of hydrogen energy. Carbon dioxide (CO 2) levels in the atmosphere have grown rapidly (by 50% since the 1750 industrial revolution) due to natural processes, the use of fossil fuels, deforestation, and other factors. Carbon dioxide contributes to global warming, which causes climate change and extreme weather events. Because of technological advancement and use, the world's energy consumption has skyrocketed. The novelty of the study as it focuses on the present state of carbon emissions (in terms of sectors and fuel) as a result of current hydrogen production projects, applications, cost, and delivery, as well as government policy and structural changes in the BRICS-US (Brazil, Russia, India, China, South Africa, and the United States) relationship throughout time. A conceptual model has been established for multi-dimensional debate for logical comparison analysis in institution-economic-technology-behavior (IETB). This paper has three main findings. First, in terms of horizontal comparison, each country has its advantages but distinct underlying driving causes. Second, in terms of vertical comparison, each country's hydrogen energy development is still in the quantitative stage, with the qualitative inflection point still to be reached. Third, in terms of a strategic goal, producing hydrogen energy benefits the energy transition from a primary mission aspect. However, studies have revealed that China is the leader in CO 2 emissions, followed by the US. China is the largest producer and consumer of hydrogen (24 million tonnes). Even though the world's largest green hydrogen plant is being developed in California (United States) (11,000 kg of green hydrogen per day and 3.8 million kilograms per year). This study highlights potential future development despite existing challenges and opens doors for enlightening the core researchers for establishing programs in transition (emerging and frontier) economies to strengthen state energy, efficiency, and conservation policies. [ABSTRACT FROM AUTHOR]

This paper, firstly, investigates the relationship between clean energy and economic growth using satellite remote sensing data to proxy economic activities. Then, this paper constructs a spatial autoregressive model, spatial error model, and spatial Durbin model to study the spatial spillover effects of financial development and clean energy. Further, this paper adopts the difference-in-differences (DID) model and the propensity score matching-DID (PSM-DID) method to conduct quasi-natural experiments on clean energy and examine whether China's energy policy can promote clean energy, thereby promoting economic growth. The analysis results reveal a positive correlation between clean energy and economic growth; local financial development promotes clean energy in the region but negatively impacts the clean energy in surrounding areas. Moreover, the findings confirm a significant positive relationship between the "policy treatment effect" and clean energy. Finally, we put forward relevant policy recommendations based on the empirical results. • Satellite remote sensing data acquired by DMSP/OLS is used to proxy GDP. • Clean energy has a significant role in promoting economic growth. • Spatial econometric models are constructed to study the spatial spillover effects. • The DID model and PSM-DID method are used to conduct quasi-natural experiments. • China's energy policy has a significant promoting effect on clean energy and GDP. [ABSTRACT FROM AUTHOR]

With the economic development of various countries in the world, the traditional energy supply has been unable to meet the demand of current economic development, and the environmental problems caused by the use of traditional energy are also a major issue that needs to be urgently solved in all countries in the world, so the development and use of clean energy is an important measure to alleviate these two problems. This paper mainly studies the coupling mechanism and development prospect of the innovation ecosystem of clean energy under the smart agriculture based on blockchain. This paper first analyzes the coupling mechanism between the blockchain smart agriculture and the clean energy innovation ecosystem; secondly, it establishes the evaluation index system of the clean energy innovation ecosystem, and measures the development efficiency of the clean innovation ecosystem by using the SBM model, super efficiency SBM model and the global principal component method; thirdly, it uses the development efficiency of the clean energy innovation ecosystem through coupling and coordination The model calculates the coupling coordination of clean energy innovation ecosystem in different regions, and analyzes the spatial-temporal differences, regional differences and spatial agglomeration of the coupling coordination degree. The calculation results show that the average coupling coordination degree of green innovation development in the East, the middle, the West and the whole country is consistent with the development trend of provinces, autonomous regions and cities, showing an upward trend. The results show that the eastern region increases from 0.62 to 0.70; the average coupling coordination degree of the central region is lower than the national average coupling coordination degree, and the distance is gradually increasing; the western region has been in a serious maladjustment level, the global Moran index I value of the coupling coordination degree of clean energy innovation ecosystem is greater than 0, and the Z statistics are greater than the critical value of normal distribution function at the level of 5 %, P values are all less than 0.05 Local Moran index I and Z statistics have been growing steadily, and the significance of spatial positive correlation is getting higher and higher. The research results of this paper can provide a useful reference for the development of clean energy innovation ecosystem under the smart agriculture based on blockchain. [ABSTRACT FROM AUTHOR]

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

Biomass residue is one of the focus areas for green energy in Indonesia, elaborated in Government Regulation no 79 the year 2014 on national energy development. The assessment of residual resources is an important way to show the availability distribution to realize the target of biofuel is more than 5% of the energy supply in 2025. Thus, this paper will present a method to assess the energy production from biomass residue as a primary consideration in its development. Statistical data and field observations have been used to estimate the total availability of residue. Furthermore, to calculate the energy potential of biomass residue, the minimum and maximum value parameters, such as residue to product ratio, moisture content, and heating value, were obtained from works of literature. The power potential was also analyzed through three scenarios of the conversion factor efficiency, i.e., low, medium, and high, to provide comprehensive results. The proposed methods allow the annual Indonesian biomass residue production from agriculture and forest residue to be nearly 155,271 and 2554 tonnes, respectively. It can be generated an energy potential around 1261 PJ, covering 22.12% of national energy consumption. Given the high potential for sustainability in biomass residue supply and utilization, strategic factor in the areas of the government regulation and policy, greenhouse gasses emission, land-use change, and conversion technology were identified to support residue valorization in the context of planning, organizing and operating. The challenges and barriers that can be minimized by integrated collaboration and synergy among policymakers, entrepreneurs, academics, and community to assist the sustainability of green energy production in the future, and evaluation of environmental, economic, and social analysis as a framework to improve the quality and quantity of the production processes. [Display omitted] • A mapping analysis is a model used to estimate the energy potential of the biomass residue. • Biomass residue reached 1097.77 PJ per year, covering 33.5% of national energy demand. • The spatial distribution represents the location spread of the availability of energy potential. • The analysis may be a helpful tool for policymakers to develop bio-energy sustainability. • It is necessary for the integrated collaboration of implementing bio-energy sustainability. [ABSTRACT FROM AUTHOR]

Under the downward pressure of the economy, China's extensive economic growth that relies on factor input is difficult to sustain. Scientific analysis of the industrial restructuring and energy consumption has become the key to realizing China's economic growth from high-speed to high-quality. Based on the fully-modified least square method and panel vector error correction model, this paper explores the equilibrium relationship and Granger causality among the industrial restructuring, energy consumption and economic growth at aggregated and disaggregated levels. The results show that: (1) The effect of industrial structure rationalization on economic growth is higher than that of industrial structure upgrading. When emphasizing industrial structure upgrade, the more reliable industrial policy to achieve China's sustainable economic growth is to pay attention to the industrial structure rationalization, especially in western China. (2) The impacts of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption; more energy policies should be designed to encourage clean energy development. • The industrial structure rationalization has highest contribution to the economic growth of eastern China, followed by central and western China. • There exists unidirectional causality from industrial structure upgrading to structure rationalizing in western China and bidirectional causality between them in central China. • Effects of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption. • Unidirectional long-term causality from renewable energy to economic growth exist in each sub-region. [ABSTRACT FROM AUTHOR]

• The total-factor carbon performance (TFCP) of 72 economies during 2000–2018 is calculated to indicate low carbon development. • The mediation analysis and the moderation analysis are conducted, and the robustness of the results is checked by the Sobel Test and the Bootstrap Test. • Clean energy use is the mediator of the positive impact of national affluence and R&D on TFCP. • Clean energy use mitigates the negative impact of industrialization on TFCP. The world seeks for a more sustainable and low carbon development (LCD) and the application of clean energy forms a critical solution in this process. However, there are few studies about how clean energy utilization works as a mediator between national income level and LCD as well as between R&D and LCD or a moderator adjusting carbon emission activities in the world's low-carbon development. We hypothesize that clean energy use is a mediating variable for national income levels and R&D investment and positively affect low-carbon development. In contrast, clean energy acts as a moderating variable that mitigates the negative impact of industrialization on low-carbon development. To test the hypothesis, this study uses panel data of 72 economies from 2000 to 2019. Total-factor carbon performance (TFCP) is calculated by fixed-effects stochastic frontier analysis and indicates LCD level. The hypothesis is supported by the results of mediation and moderation analysis. The Sobel mediation test and Bootstrap Test are applied to verify the robustness of the results. This paper deeply reveals the importance of clean energy utilization as a mediator and a moderator. Under the pressure of industrialization and increasing energy need for economic growth, clean energy should have a larger share. Investments and R&D should pay more attention to clean energy development. [ABSTRACT FROM AUTHOR]

Abstract: An innovative idea on extracting clean energy from man-made wind resources with micro wind turbine system for power generation is introduced in this paper. This system generates on-site clean energy using a micro wind generation system. A vertical axis wind turbine (VAWT) with an enclosure is mounted above a cooling tower’s exhaust fan to harness the wind energy for producing electricity. The VAWT is positioned at a specific position at the cooling tower outlet to avoid a negative impact on the performance of the cooling tower. The enclosure can act as a safety cover and also enhance the performance of the VAWT. It is designed with several guide-vanes positioned at the up-stream side of the wind turbine to create a venturi effect and guide the wind before it interacts with the turbine blades. Moreover, the enclosure design is comprised of diffuser-plates that can draw more wind and accelerate the flow. Laboratory test conducted on a scaled model shows no measurable difference in the air intake speed and current consumption of the power-driven fan when the turbine was spinning above the cooling tower. Field test on an actual induced-draft cooling tower shows no significant difference on the outlet air speed of the cooling tower. A small difference was observed on the power consumption by the fan motor which is 0.39% higher with the presence of the VAWT. This system is retrofit-able to existing cooling towers and has very high market potential due to abundant cooling towers and other unnatural exhaust air resources globally. [Copyright &y& Elsevier]

• Performance analysis of earth air heat exchanger has been done in Lower Himalayan Region. • Air outlet temperature decreases with increase in pipe length during summers and vice-versa for winters. • The maximum average cooling and heating potential is 14.81 kWh & 27.7 kWh. Energy demand in India at present is fulfilled by non-renewable sources despite the prime geographical location of some states. Geothermal energy is the renewable energy within the earth's interior will be used for various purposes. In this paper, an attempt has been made to use the renewable source of energy for sustainable development. The thermal performance of earth to air tunnel heat exchanger for passive winter preheating and summer cooling has been evaluated. The effect of an operating parameter such as pipe/tube material, soil thermal conductivity and air velocity on the thermal performance of earth to air heat exchanger (GEAHE) to be used in Lower Himalayan Region has been analyzed. The results indicate that the exit temperature drops with an increase in underground pipe/tube length during summers and decreases during winters. However, the mean efficiency increases with increase in underground pipe/tube length. Maximum efficiency of 85.6% and 87.7% is achieved during summer and winter respectively at an air velocity of 0.5 m/s for an underground pipe of 60 m length. Also, the maximum average cooling potential is 14.81 kWh during summer for earth air tunnel heat exchanger at underground pipe length of 60 m and at a velocity of 0.5 m/s. Similarly, the maximum average heating potential is 27.7 kWh during winter for same operating parameters; which is higher as compared to summers. Thus the results indicate that earth air tunnel heat exchanger is more effective during winter in Lower Himalayan Region. The results demonstrate that geothermal energy stored in a Lower Himalayan Region of India can be a driving force for sustainable development. [ABSTRACT FROM AUTHOR]

Integrated Energy Systems (IES) become more and more important for the sustainability in energy system development and for promoting the application of clean energy technologies. System complexity and various uncertainties interaction make it difficult to maintain a reliable energy supply. In this paper, a systematic method is proposed for energy supply reliability analysis in complex Integrated Energy Systems (IES). The developed method integrates stochastic modeling, supply capacity analysis and reliability evaluation, which is able to give more comprehensive knowledge of the ability of the IES for satisfying the energy needs under different, coupled uncertainties. Firstly, stochastic models are developed for each unit in IES, including renewable energy, customer demands and components in natural gas pipeline networks and electric power grids, according to their characteristics. Then, a two-stage optimization model is developed for simulating the operation strategies of IESs, and calculating the supply capacities under randomly generated scenarios. Finally, the reliability of supply is evaluated based on the results of the random simulations. The developed method is used to analyze the supply reliability of an assumed IES, to test its effectiveness. The results show the ability to provide valuable information from multiple perspectives including system, individual customer and resource allocation, for design, extension and management of IES. • A systematic method is developed for reliability of energy supply in IES. • Different uncertainties can be coupled and analyzed in the proposed method. • The complexities of system structure and dynamics are modeled in this framework. • Supply reliability is analyzed from the aspects of system, customers and resources. [ABSTRACT FROM AUTHOR]

In the current "Internet+" era, the integration of energy and the Internet is creating crucial opportunities as well as challenges for China. This study aims to provide an overview of "Energy + Internet" in China, by clarifying its advantages and disadvantages, sorting out its practical business modes, and pointing out the future directions of its management changes. It is found that (1) the traditional energy system dominated by fossil energy has been unable to meet the sustainable development needs of China's economy. The integration of energy and the Internet has become an important opportunity for the upgrading of China's energy industry. (2) Compared with traditional single-energy, integrated-energy supply chain management requires more changes. During this process, full coverage of the micro-grid could help realize the coordinated development of integrated-energy supply and demand. (3) In recent years, smart park demonstration pilot projects have gradually been implemented in China, which is driving by policy support and technological innovations. The structure of integrated-energy service plays a key role in the sustainable operation of these smart parks. China's practices show that the construction of the pilot projects promotes the exploration of energy development paths in areas with different resource endowments. Based on the conclusions, corresponding policy recommendations are provided at the end of the paper. • The environments of "Energy + Internet" development are analyzed through PEST-SWOT. • Social-economic changes brought by "Energy + Internet" have been discussed. • Suggestions for the development of "Energy + Internet" are provided. • "Energy + Internet" is expected to help China obtain clean and efficient energy. [ABSTRACT FROM AUTHOR]

Due to high consumption of energy, its associated concerns such as energy security and demand, wastage of resources, and material-energy recovery are leading to the importance of sustainable energy system development. This is a high time to assess the sustainability in energy systems for meeting the requirements of energy with an enhanced economic, ecological, and social performance from a nation context. The energy system plays a significant role in deciding the economic progress of emerging economies such as India, China, Brazil, and Africa. In this paper, an original attempt has been made to list and evaluate important indicators for sustainability assessment of energy systems development and management in an emerging economy especially India. Firstly, based on the analysis of the extant literature and then followed by expert opinion, potential key sustainability assessment indicators for energy systems development and management were identified. Further, grey based Decision-Making Trial and Evaluation Laboratory technique to understand the causal interactions amongst indicators and segregate them into cause and effect groups, is used. This work can provide useful aids to decision making bodies, sustainability practitioners and business organisations in selective implementation, monitoring and control of sustainable strategies in energy systems development and management and meeting sustainable development goals of clean energy in a nation context. Image 1 • Clean energy is a major challenge for sustainable development. • The energy system contributes to a significant extent to a nation economy. • This work evaluated the indicators for sustainability of energy systems in an emerging economy. • Grey-DEMATEL is applied to understand the causal interactions amongst indicators. • 'Adoption of energy management systems (ISO 50001:2011) and 'Developing system capabilities' are significant indicators. [ABSTRACT FROM AUTHOR]

• Detailed energy cost structures are integrated into a recursive dynamic CGE model. • Investment support and electricity import price changes are considered. • Consistent Lithuanian energy development scenarios are analysed. • Domestic electricity does not necessarily provide the net economic benefits. • The advantages of international cooperation are highlighted. The development of domestic energy generation is often expected to provide benefits to the economy through, for example, improved energy trade balance and jobs created. However, the economic impacts of energy development scenarios are not limited to the energy sector, and the decision makers must consider the impacts on the entire economy. In this paper, we analyse the full impacts of increased domestic electricity generation in a computable general equilibrium (CGE) framework. Realistic energy development scenarios are modelled using the MESSAGE energy system model and considered to be exogenously described energy pathways that generate responses from the remaining economy. These technically and economically consistent energy scenarios are incorporated into the computable general equilibrium model through the creation of fixed, variable, and investment cost structures for the energy forms considered. Model application in Lithuanian case revealed the interchangeable character of electricity imports and local generation based on a relatively significant share of imported resources. Depending on electricity market conditions, scenarios with higher domestic electricity generation levels might fail to provide substantial economic benefits: although increasing electricity import prices would create preconditions to increase domestic electricity production and turn Lithuania to the net electricity exporter, gross domestic product (GDP) might decrease due to the negative impacts on other economic activities. International cooperation for least-cost clean energy supply might be economically beneficial to energy importing countries and increasing domestic electricity generation should not be set as the ultimate goal. [ABSTRACT FROM AUTHOR]