Spatiotemporal trajectory of energy efficiency in the Guangdong-Hong Kong-Macao Greater Bay Area and implications on the route of economic transformation.

The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China's transformation from a manufacturing to an innovation cluster. However, due to rapid economic expansion and rapid urbanization, the Great Bay Area still struggles with low energy efficiency and environmental degradation, which has slowed down the pace of development. Therefore, in order to alleviate energy pressure, promote the country's sustainable development and gain a competitive advantage in the global market, researching energy efficiency and improving energy utilization efficiency is crucial. In this study, macro-level energy efficiency indicators are constructed using energy consumption data from various cities in the Greater Bay Area for the period from 2000 to 2020, and the spatio-temporal evolution of energy efficiency is analysed. The results show that all cities in the Greater Bay Area experienced an increasing trend in energy efficiency from 2000 to 2019, with significant variation in growth rates and magnitudes between cities. Compared to the nine cities in Guangdong province, Hong Kong and Macao exhibited significantly superior energy efficiency, with Foshan recording the highest growth rate of 14%. In 2020, most cities experienced a decline in energy efficiency due to the COVID-19 pandemic, with Macao experiencing the greatest decrease at 57%. Hong Kong and Macao are both in the "low consumption and high efficiency" target region, while Guangzhou, Shenzhen and Zhuhai are consistently in the "both high" region. Changes in the industrial upgrading index correspond significantly with changes in energy efficiency trajectories, with the transition from primary to secondary and tertiary industries playing a more substantial role. There is no significant association found between the strength of environmental regulation and changes in energy efficiency. The study's findings indicate that the most effective way to achieve economic transformation in the majority of China's regions is to combine adequate environmental legislation with industrial structural adjustment.

The impact of energy poverty on the health and welfare of the middle-aged and older adults.

Drawing upon data from the 2018 CHARLS, this paper utilizes MEPI and a 10% threshold indicator to, respectively, assess the energy poverty (EP) status among middle-aged and older adults in China, focusing on the unavailability and unaffordability of energy services. Additionally, an econometric model is constructed to investigate the effects of EP on the health and welfare of middle-aged and older adults. Regression results indicate that EP exerts a significant negative impact on the health and welfare of middle-aged and older adults. This conclusion remains robust after conducting endogeneity and robustness tests, demonstrating its validity. Finally, based on the calculation results, we propose relevant policy recommendations including enhancing energy services for older adults in rural areas, integrating household energy alternatives with targeted poverty alleviation, enhancing monitoring mechanisms, and conducting energy education activities to alleviate EP and improve the quality of life of middle-aged and older adults.

Improving energy-related efficiency towards SDG7 in China: What role does energy poverty play?

The improvement of energy-related efficiency has promoted energy-sustainable development in China; however, it may be inhibited by energy poverty. This paper constructs an index system to measure China's energy poverty. Subsequently, we evaluate energy-related efficiency towards SDG7 comprised of energy production efficiency and energy sustainable utilization efficiency using a dynamic two-stage data envelopment analysis model. Furthermore, we adopt fixed effect models to quantify the impact of energy poverty on improving energy-related efficiency and the underlying mechanism. The findings indicate that: (1) China's energy poverty situation gradually improves from 2011 to 2020. The energy-related system's performance is medium due to the poor performance in its energy production stage in 30 provinces during 2011-2020. Energy-related efficiency is greater in areas with a higher response to SDG7. (2) The impact of energy poverty on improving energy-related efficiency is significantly negative. Addressing energy poverty significantly improves energy-related efficiency in regions characterized by a high energy poverty index and low energy-related efficiency. However, it bears no impact in regions with a low energy poverty index and high energy-related efficiency. (3) The moderating effect of technological innovation effectively improves energy-related efficiency, whereas the marketization level has an inverse effect. Consequently, this paper suggests several policy implications.

An empirical analysis to examine the role of institutions in bridging the gap between environmental policy stringency and energy poverty.

The findings related to strict environmental policies and energy poverty have been found contradictory. Strict environmental regulations may protect the environment by enhancing renewable energy resources but at the same time, major dependent sectors and consumers reliance on non-renewable energy resources face the problem of energy poverty. Moreover, environmental policies either soft are strict and depend on stakeholders' preferences, and such policies are implemented through institutions. Considering these aspects, the current study examines the impact of environmental policy stringency on energy poverty and further examines the role of institutions in bridging the gap between environmental policy stringency and energy poverty for the selected 31 countries from 1996 to 2020. For empirical analysis, Pooled OLS, random effect model, and system generalized methods of moments (GMM) are applied. To check the robustness of baseline models, spatial lag, spatial error, and feasible generalized methods of moments are applied. Furthermore, to examine the mediating role of institutions, we applied the structural equation modeling technique. Empirical analysis shows that an increase in environmental policy stringency significantly increases energy poverty, while institutional proxies significantly decrease energy poverty. The interactive effects of institutional proxies indicate declining effects on energy poverty. More importantly, institutions act as important mediators between environmental policies and energy poverty. Based on the findings, this study recommends soft environmental policies to maintain a balance between a sustainable environment and minimum energy poverty. This study further recommends authorizing and strengthening the institutions to formulate and regulate balanced environmental policies for environmental safety along with reduced energy poverty. This study further recommends increases in urbanization, personal remittances, and enhanced energy efficiency to minimize energy poverty.

Analysis of energy consumption and change structure in major economic sectors of Pakistan.

Studying and analyzing energy consumption and structural changes in Pakistan's major economic sectors is crucial for developing targeted strategies to improve energy efficiency, support sustainable economic growth, and enhance energy security. The logarithmic mean Divisia index (LMDI) method is applied to find the factors' effects that change sector-wise energy consumption from 1990 to 2019. The results show that: (1) the change in mixed energy and sectorial income shows a negative influence, while energy intensity (EI) and population have an increasing trend over the study period. (2) The EI effects of the industrial, agriculture and transport sectors are continuously rising, which is lowering the income potential of each sector. (3) The cumulative values for the industrial, agricultural, and transport sectors increased by 57.3, 5.3, and 79.7 during 2019. Finally, predicted outcomes show that until 2035, the industrial, agriculture, and transport incomes would change by -0.97%, 13%, and 65% if the energy situation remained the same. Moreover, this sector effect is the most crucial contributor to increasing or decreasing energy consumption, and the EI effect plays the dominant role in boosting economic output. Renewable energy technologies and indigenous energy sources can be used to conserve energy and sectorial productivity.

Reassessing determinants of urban energy intensity in China: insights from controllable and uncontrollable factors.

This study adopts a new approach to reassess the factors influencing urban energy intensity in China. Initially, the factors impacting energy intensity are classified into controllable and uncontrollable categories. Subsequently, employing a single-factor multi-stage method combined with the Adaboost method, 289 Chinese cities are categorized based on uncontrollable factors to eliminate the influence of inherent differences on energy intensity. Finally, panel data regression analyses are conducted using data from 289 Chinese cities between 2005 and 2016, individually for each city type, to evaluate the extent to which controllable factors contribute to energy intensity. The findings indicate that (1) heightened energy prices, an increased share of electricity consumption, and a greater proportion of centralized heating significantly influence the reduction of energy intensity across all city types; (2) to optimize energy consumption, each city type should adopt specific strategies. For instance, cities located in resource-rich heating regions with low economic outputs can reduce their energy intensity by increasing electricity consumption, while cities with high economic outputs can decrease their energy intensity by increasing natural gas consumption. The findings of this study carry substantial implications for the Chinese government in shaping targeted energy policies tailored to different city types.

Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix.

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

Renewable Energy Potential: Second-Generation Biomass as Feedstock for Bioethanol Production.

Biofuels are clean and renewable energy resources gaining increased attention as a potential replacement for non-renewable petroleum-based fuels. They are derived from biomass that could either be animal-based or belong to any of the three generations of plant biomass (agricultural crops, lignocellulosic materials, or algae). Over 130 studies including experimental research, case studies, literature reviews, and website publications related to bioethanol production were evaluated; different methods and techniques have been tested by scientists and researchers in this field, and the most optimal conditions have been adopted for the generation of biofuels from biomass. This has ultimately led to a subsequent scale-up of procedures and the establishment of pilot, demo, and large-scale plants/biorefineries in some regions of the world. Nevertheless, there are still challenges associated with the production of bioethanol from lignocellulosic biomass, such as recalcitrance of the cell wall, multiple pretreatment steps, prolonged hydrolysis time, degradation product formation, cost, etc., which have impeded the implementation of its large-scale production, which needs to be addressed. This review gives an overview of biomass and bioenergy, the structure and composition of lignocellulosic biomass, biofuel classification, bioethanol as an energy source, bioethanol production processes, different pretreatment and hydrolysis techniques, inhibitory product formation, fermentation strategies/process, the microorganisms used for fermentation, distillation, legislation in support of advanced biofuel, and industrial projects on advanced bioethanol. The ultimate objective is still to find the best conditions and technology possible to sustainably and inexpensively produce a high bioethanol yield.

Operationalizing a fisheries social-ecological system through a Bayesian belief network reveals hotspots for its adaptive capacity in the southern North sea.

Fisheries social-ecological systems (SES) in the North Sea region confront multifaceted challenges stemming from environmental changes, offshore wind farm expansion, and marine protected area establishment. In this paper, we demonstrate the utility of a Bayesian Belief Network (BN) approach in comprehensively capturing and assessing the intricate spatial dynamics within the German plaice-related fisheries SES. The BN integrates ecological, economic, and socio-cultural factors to generate high-resolution maps of profitability and adaptive capacity potential (ACP) as prospective management targets. Our analysis of future scenarios, delineating changes in spatial constraints, economics, and socio-cultural aspects, identifies factors that will exert significant influence on this fisheries SES in the near future. These include the loss of fishing grounds due to the installation of offshore wind farms and marine protected areas, as well as reduced plaice landings due to climate change. The identified ACP hotspots hold the potential to guide the development of localized management strategies and sustainable planning efforts by highlighting the consequences of management decisions. Our findings emphasize the need to consider detailed spatial dynamics of fisheries SES within marine spatial planning (MSP) and illustrate how this information may assist decision-makers and practitioners in area prioritization. We, therefore, propose adopting the concept of fisheries SES within broader integrated management approaches to foster sustainable development of inherently dynamic SES in a rapidly evolving marine environment.

Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

Stationarity and cycles in the energy consumption in the United States.

The purpose of this paper is twofold: analyzing stationarity of energy consumption by source in the United States and studying their cycles and pairwise synchronization. We study a panel of nine time series of monthly energy consumption for the period 1973-2022. Four of the series (namely coal, natural gas, petroleum, and nuclear electric power consumption) are non-renewables, whereas the remaining ones (hydroelectric power, geothermal, biomass, solar, and wind energy consumption) are renewable energy sources. We employ a nonparametric, panel stationarity testing approach. The results indicate that most of the series may be trend-stationarity, with nuclear and geothermal energy consumption being the only exceptions. Additionally, a study on potential cycles in the series of energy consumption by source is carried out, and subsequently we analyze pairwise concordance between states of different energy sources and between states of energy sources and the business cycle. Significant correlations are detected in the latter analysis, which are positive in the case of fossil fuel sources and negative for two renewable sources, namely geothermal and biomass energy consumption.

Techno-environmental analysis to valorize the secondary energy resources from refuse-derived fuel-based waste to energy plant.

The present study quantifies the environmental and sustainability impacts associated with municipal solid waste management (MSWM) in India which plays a vital environmental issue in recent times. The upsurge in population has resulted in massive waste generation, leading to a concerning rise in the level of greenhouse gas (GHG) emissions. Therefore, the sustainable management of MSW has been discussed and highlights the conversion of MSW into refuse-derived fuel (RDF) to identify its potential for generating electricity in waste-to-energy (WtE) plants. The life cycle assessment (LCA) study has been done to identify and compare the environmental impacts associated with different scenarios (SC) as SC1: landfilling without energy recovery, SC2: open burning and SC3: processing of RDF in WtE plant by considering the nine impact categories from the inventory data obtained over a period of 12 consecutive months (Jan 2021-Jan 2022). The results exhibited that the global warming potential caused by emissions of GHG are in the order of SC1 (1188 kg CO2 eq) > SC2 (752 kg CO2 eq) > SC3 (332 kg CO2 eq), respectively from 1 t of MSW. It is concluded that the WtE plant can help in the reduction of environmental issues, strengthening the capacity of electricity generation and improving the aesthetic view of the city which is socially acceptable as well. Thus, WtE technology can help in achieving sustainable development goal 12 to regenerate the sustainable secondary resources for the twenty-first century and minimize global climate change.

Human-wildlife conflicts in the aerial habitat: Wind farms are just the beginning.

The aerial habitat occupies an enormous three-dimensional space around Earth and is inhabited by trillions of animals. Humans have been encroaching on the aerial habitat since the time of the pyramids, but the last century ushered in unprecedented threats to aerial wildlife. Skyscrapers, jet-age transportation and recently huge wind turbines kill millions of flying animals annually and despite substantial efforts, our detection and mitigation capabilities are lagging far behind. Given the situation, our readiness to handle the impact of millions of drones buzzing through the sky carrying batteries, payloads and soon also people, is questionable at best. In radar aero-ecology, radars are used to document and analyse animal movement high above the ground, opening a hatch to ecological processes in the aerial habitat. Differentiating bats from birds, a simple task at ground level, was impossible aloft, which limited our ability to study and characterise high-altitude bat behaviour. Many high-altitude infrastructure developments around the world were thus planned and executed with no regard to possible impacts on bats and caused millions of bat fatalities. BATScan, the first automatic bat identifier for radar, demonstrates how artificial intelligence can be implemented together with ecological insight to solve basic scientific questions and minimise negative human impact on natural habitats. We demonstrate a facet of the complexity of bat aero-ecology using the Israeli BATScan database and substantiate the claim that activities taken by the wind energy industry to minimise bat mortality may prove limited and leave bats unprotected. We further discuss upcoming challenges in the face of a forthcoming transportation revolution that will change the human-aerial wildlife conflict from a conservation concern to a major human safety issue.

Development of Liquid Organic Hydrogen Carriers for Hydrogen Storage and Transport.

The storage and transfer of energy require a safe technology to mitigate the global environmental issues resulting from the massive application of fossil fuels. Fuel cells have used hydrogen as a clean and efficient energy source. Nevertheless, the storage and transport of hydrogen have presented longstanding problems. Recently, liquid organic hydrogen carriers (LOHCs) have emerged as a solution to these issues. The hydrogen storage technique in LOHCs is more attractive than those of conventional energy storage systems like liquefaction, compression at high pressure, and methods of adsorption and absorption. The release and acceptance of hydrogen should be reversible by LOHC molecules following favourable reaction kinetics. LOHCs comprise liquid and semi-liquid organic compounds that are hydrogenated to store hydrogen. These hydrogenated molecules are stored and transported and finally dehydrogenated to release the required hydrogen for supplying energy. Hydrogenation and dehydrogenation are conducted catalytically for multiple cycles. This review elaborates on the characteristics of different LOHC molecules, based on their efficacy as energy generators. Additionally, different catalysts used for both hydrogenation and dehydrogenation are discussed.

Bidding against the wind: A choice experiment in green energy, green jobs and offshore views in North Carolina, USA.

Offshore wind development is in its nascent stages in the United States. Recent research indicates that the visual impacts of offshore wind farms are viewed negatively by the general population. This North Carolina application is the first US-focused discrete choice experiment that explicitly asks respondents to consider the positive local and global benefits from offshore wind development, such as job creation and greenhouse gas emission reductions, simultaneously with their visual impacts. We find significant willingness to pay (WTP) for reducing the visual impacts of offshore wind farms, and that the extent of disamenity varies in the population and with placement along developed tourist towns (as much as $783/year for three years) or preserved coastlines (as much as $451/year for three years). We also find that some preference classes value projects that create permanent jobs and reduce carbon emissions. We use our estimates of preferences for the positive and negative attributes to explore specific wind farm configurations and locations that could achieve positive consensus in a heterogenous population.

Satellite mapping reveals extensive industrial activity at sea.

The world's population increasingly relies on the ocean for food, energy production and global trade1-3, yet human activities at sea are not well quantified4,5. We combine satellite imagery, vessel GPS data and deep-learning models to map industrial vessel activities and offshore energy infrastructure across the world's coastal waters from 2017 to 2021. We find that 72-76% of the world's industrial fishing vessels are not publicly tracked, with much of that fishing taking place around South Asia, Southeast Asia and Africa. We also find that 21-30% of transport and energy vessel activity is missing from public tracking systems. Globally, fishing decreased by 12 ± 1% at the onset of the COVID-19 pandemic in 2020 and had not recovered to pre-pandemic levels by 2021. By contrast, transport and energy vessel activities were relatively unaffected during the same period. Offshore wind is growing rapidly, with most wind turbines confined to small areas of the ocean but surpassing the number of oil structures in 2021. Our map of ocean industrialization reveals changes in some of the most extensive and economically important human activities at sea.

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.

Optimal allocation of distributed energy storage systems to enhance voltage stability and minimize total cost.

The enhancement of energy efficiency in a distribution network can be attained through the adding of energy storage systems (ESSs). The strategic placement and appropriate sizing of these systems have the potential to significantly enhance the overall performance of the network. An appropriately dimensioned and strategically located energy storage system has the potential to effectively address peak energy demand, optimize the addition of renewable and distributed energy sources, assist in managing the power quality and reduce the expenses associated with expanding distribution networks. This study proposes an efficient approach utilizing the Dandelion Optimizer (DO) to find the optimal placement and sizing of ESSs in a distribution network. The goal is to reduce the overall annual cost of the system, which includes expenses related to power losses, voltage deviation, and peak load damand. The methods outlined in this study is implemented on the IEEE 33 bus distribution system. The outcomes obtained from the proposed DO are contrasted with those of the original system so as to illustrate the impact of ESSs location on both the overall cost and voltage profile. Furthermore, a comparison is made between the outcomes of the Ant Lion Optimizer (ALO) and the intended Design of Experiment DO, revealing that the DO has obtained greater savings in comparison to the ALO. The recommended methodology's simplicity and efficacy in resolving the researched optimization issue make the acquired locations and sizes of ESSs favorable for implementation within the system.

Assessment of pyrolysis potential of Indian municipal solid waste and legacy waste via physicochemical and thermochemical characterization.

This study explores the viability of utilizing municipal solid waste (MSW) and legacy waste as a renewable energy source through pyrolysis, akin to solid fuels. The heating value of MSW and legacy waste were found to be 37737.89 and 40432.84 kJ/kg, respectively. Proximate analysis shows that MSW fits within Tanner diagram parameters, eliminating the need for auxiliary fuel in pyrolysis. With 47.6 % and 44.16 % lignin content in MSW and legacy waste were deemed suitable for char production. Thermal degradation resulted in mass losses of 68 % for MSW and 82 % for legacy waste. The kinetic and thermodynamic assessment indicates lower activation energy (Ea) and Gibbs free energy (ΔG) for MSW (5.72 kJ/mol and 170.37 kJ/mol, respectively) compared to fossil fuels, suggesting faster reactions without additional energy requirement. MSW emerges as a promising alternative to fossil fuels, aligning with the United Nations' 2030 Sustainable Development Goals.