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The research assessed waste-based briquettes consumption compared to conventional fuels in the Andes. Laboratory tests were conducted together with on-field analysis in Colquencha (Bolivia). The laboratory study shows that the performances of briquettes are better in terms of PM2.5 (933.4 ± 50.8 mg kg−1) and CO emissions (22.89 ± 2.40 g kg−1) compared to animal dung (6265.7 ± 1273.5 mgPM2.5 kg−1 and 48.10 ± 12.50 gCO kg−1), although the boiling time increased due to the lower fuel consumption rate and firepower compared to shrubs. The social survey organized with 150 Bolivian citizens suggested that low-income households are not able to pay for an alternative fuel: about 40% would pay less than 4 USD per month, while methane use for cooking is positively correlated with the income level (r = 0.244, p < 0.05). On field analysis suggested that local cookstoves are not appropriate for briquettes combustion since indoor air pollution overcomes 30 ppm of CO and 10 mgPM2.5 m-3. On balance, local small manufactures can be the main target for selling waste-based briquettes to reduce shrubs and wood consumption. However, briquettes production costs seem not yet competitive to natural easy-to-obtain fuels (i.e., animal dung). The research encourages the use of cellulosic and biomass waste-based briquettes in the Andean area for cooking, heating, or manufacturing and strongly advises policy-makers to introduce economic incentives for the recovery of secondary raw materials. • Analysis of waste-based briquettes use in Andean areas were conducted. • Laboratory analysis, social surveys, and on field evaluations are the methods. • Briquettes can be employed for reducing about 30% of the conventional fuels. • The research encourages the employment of waste-based briquettes in the Andes. • Incentives to briquettes production and consumption should be introduced. [ABSTRACT FROM AUTHOR]

The current study analyzed the high heating values (HHVs) of various waste biomass materials intending to the effective management and more sustainable consumption of waste as clean energy source. Various biomass waste samples including date leaves, date branches, coconut leaves, grass, cooked macaroni, salad, fruit and vegetable peels, vegetable scraps, cooked food waste, paper waste, tea waste, and cardboard were characterized for proximate analysis. The results revealed that all the waste biomass were rich in organic matter (OM). The total OM for all waste biomass ranged from 79.39% to 98.17%. Likewise, the results showed that all the waste biomass resulted in lower ash content and high fixed carbon content associated with high fuel quality. Based on proximate analysis, various empirical equations (HHV=28.296-0.2887(A)-656.2/VM, HHV=18.297-0.4128(A)+35.8/FC and HHV=22.3418-0.1136(FC)-0.3983(A)) have been tested to predict HHVs. It was observed that the heterogeneous nature of various biomass waste considerably affects the HHVs and hence has different fuel characteristics. Similarly, the HHVs of waste biomass were also determined experimentally using the bomb calorimeter, and it was observed that among all the selected waste biomass, the highest HHVs (21.19 MJ kg−1) resulted in cooked food waste followed by cooked macaroni (20.25 MJ kg−1). The comparison revealed that experimental HHVs for the selected waste biomass were slightly deviated from the predicted HHVs. Based on HHVs, various thermochemical and biochemical technologies were critically overviewed to assess the suitability of waste biomass to energy products. It has been emphasized that valorizing waste-to-energy technologies provides the dual benefits of sustainable management and production of cleaner energy to reduce fossil fuels dependency. However, the key bottleneck in commercializing waste-to-energy systems requires proper waste collection, sorting, and continuous feedstock supply. Moreover, related stakeholders should be involved in designing and executing the decision-making process to facilitate the global recognition of waste biorefinery concept. [Display omitted] • The high heating values (HHVs) of biomass waste were assessed with the aim for the sustainable consumption of waste as a source of clean energy. • All the biomass waste were rich in organic matter and fixed carbon content that ranged from 79.39% to 98.17% and 50.64%–54.26% respectively. • The maximum HHVs (21.19 MJkg−1) were recorded for cooked food waste. • The heterogeneous nature of various biomass waste considerably affects the HHVs and hence serve as suitable feedstock for various energy conversion technologies. [ABSTRACT FROM AUTHOR]

[Display omitted] • There is no reported work on 3D printed catalysts for biomass-biooil conversion applications. To fill this gap, this paper reviewed the key technologies that will be needed including 1) typical catalysts used for biomass conversion applications, 2) 3D printed catalysts, and 3) design and optimization of structured catalysts. • Desired catalysts performance in current biomass-biooil conversion research is mostly achieved by altering catalyst material composition rather than structural properties. • 3D printed catalysts research is still in its infancy and lacks full considerations of the design potentials of 3D printing, such as hierarchical complexity and material complexity. • Current work on design and optimization of structured catalysts shows deficiency in manufacturing constraints, for example, resolution of 3D printers, which may lead to coke formation issues in catalytic process. The devastative effects of traditional fossil fuel consumption have made biomass (e.g., plants, wood, and waste) a widely used renewable source of energy. The variety in biomass properties makes tunable catalysts an essential ingredient to promote reactions for desired molecule outputs. Catalysts with optimal catalytic performance are typically structured with complicated morphology, such as high porosity and sophistic pore network, which makes it challenging for catalyst fabrication. Recent progress in utilizing 3D printing as the fabrication method has demonstrated great potential of handling the complex structure while improving catalytic performance; however, rare work has focused on biomass-bio-oil conversion (BBC). With the aim of developing the optimal structured catalysts for BBC applications, this paper particularly reviewed 1) typical catalysts used for biomass conversion applications, 2) 3D printed catalysts, and 3) design and optimization of structured catalysts. It is found that the BBC is often isolated from manufacturing and focused more on different catalyst composition with transition metals to provide the desired catalyst architecture and further to improve upgrading of the BBC product. Current work on 3D-printed catalysts also show deficiency in several aspects including formation of coke and catalyst deactivation, lack of a comprehensive design for the catalyst structure (e.g., mostly 2D structures), absence of manufacturing constraints in design operation, and insufficient resolution of 3D printing methods. To solve these issues, several potential research directions and opportunities are discussed at the end of the study. [ABSTRACT FROM AUTHOR]

Supercritical water oxidation (SCWO) has been regarded as a new and efficient technology for the harmless treatment and energy utilization of organic wastes, resulting in the quickly homogeneous oxidation between organics and oxidizers and the former being wholly degraded into small environment-friendly green molecules such as H 2 O and N 2 and inorganic salts. This paper systematically analyzed the influencing behavior and mechanisms of the reaction factors, such as temperature, pressure, residence time, oxidant type, oxidation coefficient, and the concentration and pH values of the raw material, on the treatment effect of organic wastes. For most organic wastes, the SCWO conditions at 550 °C with a residence time of 1min and an oxidation coefficient of 100% can meet the removal rate of more than 99%. To further enhance the degradation rate of organics, the principles, implementation cases, and related equipment components of general enhancement technologies of supercritical water oxidation were discussed, such as fractional oxygen injection, auxiliary fuel co-oxidation, and hydrothermal flame-assisted degradation. This paper proposes a novel supercritical flame-assisted oxidation process in which the reactor performs preheating, corrosion protection, and desalination functions. The use of additive-enhanced oxidation, segmented oxidation, and supercritical hydrothermal flame-assisted oxidation has achieved good results in the complicated treatment process of brutal degradation of organic matter. Figure Schematics of supercritical water oxidation flowchart and focused process enhancement perspectives. [Display omitted] [ABSTRACT FROM AUTHOR]

Combined heat and power (CHP) plants fueled by biomass can collect and utilize waste straw resources in a productive way. This paper considers the impact of regional factors on biomass energy potential and the energy needs of the population, so as to study the differences in construction of biomass CHP plants and the collection scope of raw materials, and proposes evaluating suitability for biomass energy development based on scope of resource collection. Taking five counties in China as its study areas, this paper assesses biomass energy potential. A topology system of biomass CHP plants has been reasonably established in different counties through ArcGIS, the required installed capacity has been calculated according to the number of persons served by such plants. Finally, the collection length and corresponding value range of raw materials of CHP plants along roads has been obtained based on biomass energy potential and energy demand. The result shows that the differences in area, straw yield and biomass fuelization rate depending on regions have a great impact on biomass energy potential, while the residue-to-product ratio of straw and biomass calorific value have less of an impact. When the biomass energy per capita of a region reaches 9.75GJ/person, it is suitable for biomass energy development. The installed capacity in the biomass CHP plant system of each study area is mostly within the scope of 3–59 MW, and the collection length of corresponding biomass resources of such plants along roads is mostly within the scope of 5.09–25.23 km. • Assessment of biomass energy potential based on regional biomass differences. • Suitability for biomass energy development based on scope of resource collection. • Strategies for biomass plant system construction in different regions are proposed. [ABSTRACT FROM AUTHOR]

Economic transformation and development serve as effective strategies for resource-based regions to avoid the "resource curse." By optimizing industrial structure and enhancing energy efficiency through government macro-regulation, these regions can achieve sustainable and environment-friendly growth. The general equilibrium model is widely employed to assess the effectiveness of economic transformation and development. In this study, we constructed a provincial social accounting matrix based on macro data from Shanxi, China, to examine the impact of different environmental protection tax rates on the macro economy, industrial structure, and energy consumption. The findings reveal that the implementation of environmental protection tax has successfully optimized the industrial structure and improved energy utilization efficiency. However, it has also led to a slight decline in total output. Hence, it is imperative to establish a more balanced and reasonable environmental protection tax rate to further promote economic transformation and development in resource-based areas. Consequently, this paper puts forth policy recommendations aimed at achieving a harmonious balance between ecological preservation and economic progress in resource-based regions, ultimately enhancing the overall quality of economic development in such areas. • Economic transformation and development are effective ways for resource-based regions to avoid the "resource curse". • This paper is based on macro data from Shanxi, and constructs a provincial social accounting matrix to study the impact of different environmental protection tax rates on macroeconomic, industrial structure, and energy consumption. • Setting a reasonable environmental protection tax rate can promote the economic transformation and development of resource-based areas [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

• Novel queueing theory method to deal with free riding problem in P2P network. • Sleep/wakeup mechanism and asynchronous vacation strategy aimed at reducing energy consumption of system. • Quantified energy consumption of peers in each state. • Nash equilibrium between the arrival rate and the net profit of a single node. • Social optimal strategy. This paper proposes a penalty strategy with differentiated service rate based on the free riding phenomenon in P2P networks, and establishes an M/M/ c + d queueing model. Based on this model, a sleep/wakeup mechanism is introduced for the peers at the service end, and a single asynchronous vacation strategy is adopted to reduce the energy consumption of the system. In addition, the energy consumption of peers in each state is quantified, and the relationship between the energy consumption and parameters of the system is analyzed. In order to avoid excessive requests for unnecessary services from requesting nodes and increasing energy consumption of the system, this paper analyzes the Nash equilibrium between the arrival rate and the net profit of a single node, and then studies the optimization of social profit. The stationary distribution of queueing model is obtained by the method of matrix geometric solution, the performance indicators of the system are constructed, and the system performance is analyzed by numerical experiments. Experimental results show that the model developed in this paper has a significant penalty effect on free riding behavior, and that the single asynchronous vacation strategy not only saves more than 10% of the total energy consumption compared with the single synchronous vacation strategy, but also makes the hybrid P2P networks more flexible and efficient. [ABSTRACT FROM AUTHOR]

In recent years, the increase in the use of services in cloud, fog, edge, and IoT ecosystems has been very notable. On the one hand, environmental sustainability is affected by this type of ecosystem since it can produce a large amount of energy consumption which translates into CO2 emissions into the atmosphere. On the other hand, due to the COVID-19 pandemic, the use of these ecosystems has increased considerably. Thus, it is necessary to apply policies and techniques to maximize sustainability within these ecosystems. Some of these policies and techniques are those based on artificial intelligence. However, the current processing of these policies and techniques can also consume a lot of resources. From this perspective, this article aims to clarify whether the sustainability of cloud/fog/edge/IoT ecosystems is improved by the application of artificial intelligence. To do this, a systematic literature review is developed in this paper. In addition, a set of classifications of the analyzed works is proposed based on the different aspects related to these ecosystems, their sustainability, and the applicability of artificial intelligence to improve them. • The use of cloud, fog, edge, and IoT has increased drastically in the last few years. • It is necessary to apply policies to maximize the sustainability of AI. • This work clarifies whether the sustainability of these environments is improved by AI. • A SLR is performed to achieve this aim. • The application of AI to these environments is a newborn topic. [ABSTRACT FROM AUTHOR]

Capital and energy are essential input factors in the production and operation of firms, and they are closely interconnected. Nudging firms to improve energy performance during capital investment is crucial for attaining green competitiveness. Nonetheless, little is known about how capital-biased tax incentives affect firm energy performance in the process of encouraging firms to update or expand fixed assets. To fill this critical gap, this paper leverages the 2014 and 2015 accelerated depreciation policy for fixed assets as quasi-natural experiments to investigate the impact of capital-biased tax incentives on firm energy intensity. This study uses information from a unique dataset of Chinese firms, and the staggered difference-in-difference strategy is constructed to address identification challenges. The findings of this paper are as follows: (1) The accelerated depreciation policy for fixed assets significantly increases firm energy intensity by approximately 11.2%. A series of validations reinforce the robustness of this result. (2) Restructuring energy use and the factor substitution of energy for labour are the main channels through which the accelerated depreciation policy for fixed assets increases firm energy intensity. (3) The accelerated depreciation policy for fixed assets has a more remarkable effect on the energy intensity enhancement of small-scale firms, capital-intensive firms, and firms in energy-endowed regions. These conclusions support shaping policy options moderately by coordinating tax incentives and government regulation as key factors in promoting sustainable firm development. Overall, this research provides empirical evidence regarding the micro-environmental consequences of capital-biased tax incentives and offers valuable insights for enhancing corporate energy performance. • The staggered difference-in-difference method is employed in this paper. • This paper treats the 2014 and 2015 ADPFA as quasi-natural experiments. • ADPFA significantly increases firm energy intensity by about 11.2%. • Energy restructuring and factor substitution of energy for labor are main channels. • Firm and regional heterogeneity is further considered in the analysis. [ABSTRACT FROM AUTHOR]

Simultaneous Localization And Mapping (SLAM) is crucial for autonomous navigation. ORB-SLAM is a state-of-the-art Visual SLAM system based on cameras used for self-driving cars. In this paper, we propose a high-performance, energy-efficient, and functionally accurate hardware accelerator for ORB-SLAM, focusing on its most time-consuming stage: Oriented FAST and Rotated BRIEF (ORB) feature extraction. The Rotated BRIEF (rBRIEF) descriptor generation is the main bottleneck in ORB computation, as it exhibits highly irregular access patterns to local on-chip memories causing a high-performance penalty due to bank conflicts. We introduce a technique to find an optimal static pattern to perform parallel accesses to banks based on a genetic algorithm. Furthermore, we propose the combination of an rBRIEF pixel duplication cache, selective ports replication, and pipelining to reduce latency without compromising cost. The accelerator achieves a reduction in energy consumption of 14597× and 9609×, with respect to high-end CPU and GPU platforms, respectively. • Feature extraction takes 60% of the localization execution time in current solutions. • Streaming architecture in which the bottleneck is the computation of rBRIEF. • A genetic algorithm can statically reorder the rBRIEF pattern to reduce the latency. • Optimizations include: pixel cache, selective replication of ports and pipelining. • Search of the most energy efficient factors that fulfill the self-drivings requirements. [ABSTRACT FROM AUTHOR]

Wireless network-on-chip architectures (WNoCs), by combining wired and wireless modules and links, provide fast and efficient communication infrastructures for complex on-chip systems such as various IoT and intelligent systems. The most challenging step of designing high-performance and energy-efficient WNoCs is the structure of transceivers. In fact, transceivers and wired routers are the most power-hungry elements in WNoCs; therefore, any cut-down of power consumption in transceivers and routers significantly reduce the power consumption of WNoCs. In this paper, a low-power transceiver for WNoC is designed and then, a high-performance and configurable energy consumption management scheme for the transceiver is proposed. In the designed transceiver, various strategies are employed at both circuit-level and also at architectural-level for reducing static and dynamic power consumption. The proposed energy management scheme controls the activity and tunes the voltage level of the power supply of each module, in a distributed and dynamic manner. The evaluation results demonstrate that a WNoC utilizing the proposed scheme provides lower energy consumption, less latency, and higher reliability and higher network throughput compared to both of relevant single-chip and multi-chip WNoC architectures, at the cost of negligible impact on silicon area. • Simulation of a low-power WNoC transceiver at circuit-level. • Designing a novel energy consumption management scheme for the WNoC transceiver. • Conducting cycle-accurate, architecture-level and network-level simulations in both single and multi-chip WNoCs. • Performing various experimental evaluations considering synthetic and real traffic patterns. [ABSTRACT FROM AUTHOR]

Emerging Internet-of-Everything (IoE) services require connected devices to respond instantly and operate for long durations. As smart mobile devices (SMDs) are often powered by batteries of limited capacity, offloading some computational tasks to nearby edge servers is a promising solution to reduce the latency and energy consumption of SMDs in operation. However, a challenge of computation offloading in the IoE system is the large number of SMDs that need to be handled. To address this problem, in this paper, we propose an intelligent two-stage computation offloading scheme with multiple optimization objectives. In the first stage, we categorize the computation tasks into classes (e.g., computation-intensive, data-intensive) and make early offloading decisions on some classes of tasks with offloading preferences. In the second stage, we make offloading decisions on the remaining tasks by solving a multi-objective optimization problem using the powerful Non-dominated Sorting Genetic Algorithm (NSGA-II). This two-stage design can help reduce the size of task instances in the second optimization stage, thus accelerating the convergence of the NSGA-II algorithm. The extensive simulation results show that, compared to the existing NSGA-II algorithm-based optimization methods, the proposed offloading scheme improves the performance by 10% in terms of latency and energy consumption. • The data transmission and task execution processes are modeled as queueing systems. • A model is proposed to balance the data-intensive and computation-intensive tasks. • An algorithm is proposed to make offloading decisions in advance for heavy tasks using preferences. • The modified NSGA-II algorithm is applied to the remaining computation tasks to obtain the optimal offloading decisions. • Comprehensive experiments are conducted for comparisons of the proposed method with some benchmark methods. [ABSTRACT FROM AUTHOR]

Membrane distillation crystallization (MDC) is an emerging hybrid thermal membrane technology that synergizes membrane distillation (MD) and crystallization, which can achieve both freshwater and minerals recovery from high concentrated solutions. Due to the outstanding hydrophobic nature of the membranes, MDC has been widely used in numerous fields such as seawater desalination, valuable minerals recovery, industrial wastewater treatment and pharmaceutical applications, where the separation of dissolved solids is required. Despite the fact that MDC has shown great promise in producing both high-purity crystals and freshwater, most studies on MDC remain limited to laboratory scale, and industrializing MDC processes is currently impractical. This paper summarizes the current state of MDC research, focusing on the mechanisms of MDC, the controls for membrane distillation (MD), and the controls for crystallization. Additionally, this paper categorizes the obstacles hindering the industrialization of MDC into various aspects, including energy consumption, membrane wetting, flux reduction, crystal yield and purity, and crystallizer design. Furthermore, this study also indicates the direction for future development of the industrialization of MDC. [ABSTRACT FROM AUTHOR]

This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While a significant number of related works explore machine learning-based NIDS for the IoT edge, they generally lack considering the issue of the required computational and energy resources. The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular, the computational and energy efficiency. In particular, the paper studies Google's Edge TPU as a hardware platform and considers the following three key metrics: computation (inference) time, energy efficiency and traffic classification performance. Various scaled model sizes of two major deep neural network architectures are used to investigate these three metrics. The performance of the Edge TPU-based implementation is compared with that of an energy-efficient embedded CPU (quad-core ARM Cortex-A53). • This is the first paper that explores Edge TPU for NIDS in IoT, to the best of our knowledge. • This paper investigates various feed-forward and convolutional neural network structures, with different hyperparameters, on Edge TPU and the quad-core ARM cortex-A53 hardware platforms. • The performance is compared on the hardware platforms, across multiple performance metrics including the attack detection/classification performance, the inference time, and energy efficiency. • This study evaluated the energy efficiency of explored neural network structures versus the model size on Edge TPU and Cortex-A53 platforms. [ABSTRACT FROM AUTHOR]

Establishing an accurate edge server power model is helpful for resource providers to predict and optimize power consumption within edge data centers. Considering the fact that the accuracy of the previous energy consumption model is easily affected by the workload types, this paper develops an edge server power model based on BP (back propagation) neural network and feature selection, which is denoted by DSBF. For different task types, DSBF leverages "principal component analysis (PCA)" to analyze the contribution of each energy consumption parameter and selects "representative parameter", and then builds a power model based on BP neural network. In contrast to other power models, DSBF can effectively handle the variable workload. To measure the effectiveness of the DSBF model, a series of experiments were conducted. The results suggest that compared with other energy consumption models, DSBF can better adapt to the changing workload and has advantages in predicting the accuracy of the energy consumption model. • Designing the power modeling process, including parameter selection, parameter analysis and screening, model training and evaluation. • Using the "principal component analysis (PCA)" to analyze the factor contribution of each parameter. • Building a new power model named DSBF based on BP neural network and feature selection. [ABSTRACT FROM AUTHOR]

Improvements in data storage may be constrained by the lower performance of hard disk drives (HDD) and the higher cost per gigabyte of solid-state drives (SSD). To mitigate these issues, hybrid storage architectures have been conceived. Some works evaluate the performance of storage architectures, but energy consumption is usually neglected and not simultaneously evaluated with performance. This paper presents an approach based on generalized stochastic Petri nets (GSPN) for performance and energy consumption evaluation of individual and hybrid storage systems. The proposed models can represent distinct workloads and also estimate throughput, response time and energy consumption of storage systems. Experiments based on industry-standard benchmarks are adopted to demonstrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development. • China's Coastal region is the major geographical unit for the future development of China's industrial. • In high-quality economic growth and climate change, clean energy use is vital for sustainable development. • The utilization of wind energy and it's efficiency is predicted using Deep Belief Network (DBN) model. • Empirical results link economic growth with carbon emissions, showing growth promotes emissions offset. • Green innovation changes the economic growth-carbon link, aiding China's carbon reduction efforts. [ABSTRACT FROM AUTHOR]

In the global effort to reduce CO 2 emissions, the concurrent enhancement of pollutant degradation and reductions in fossil fuel consumption are pivotal aspects of microalgae-mediated wastewater treatment. Clarifying the degradation mechanisms of bacteria and microalgae during pollutant treatment, as well as regulatory biolipid production, could enhance process sustainability. The synergistic and inhibitory relationships between microalgae and bacteria are introduced in this paper. The different stimulators that can regulate microalgal biolipid accumulation are also reviewed. Wastewater treatment technologies that utilize microalgae and bacteria in laboratories and open ponds are described to outline their application in treating heavy metal-containing wastewater, animal husbandry wastewater, pharmaceutical wastewater, and textile dye wastewater. Finally, the major requirements to scale up the cascade utilization of biomass and energy recovery are summarized to improve the development of biological wastewater treatment. [Display omitted] • The synergistic and antagonistic interactions between bacteria and microalgae are summarized. • Different stimulators that could regulate microalgal biolipid accumulation are also reviewed. • Wastewater treatment technologies that utilize microalgae and bacteria in the laboratory and open ponds are described. • The trends in microalgae-bacterial consortium in multistage energy production are proposed. [ABSTRACT FROM AUTHOR]

A cloud computing environment processes user workloads or tasks by exploiting its high performance computational, storage, of reducing and network resources. The virtual machines in the cloud environment are allocated to tasks with the aim of reducing overall execution time. The use of high performance resources incurs monetary costs, as well as high power consumption. The heuristic based approaches implemented for scheduling tasks are unable to cope with the complexity of optimizing multiple parameters. In this paper, we propose a multi-objective grey-wolf optimization based algorithm for scheduling tasks on cloud platforms. The proposed algorithm targets to minimize schedule length (overall execution time), energy consumption, and monetary cost required for executing tasks. For optimization, the algorithm incorporates steps that are performed iteratively for mimicking the behavior of grey wolves attacking their prey. It uses discrete values for positioning wolves for encircling and attacking the prey. The assignment of tasks to virtual machines is performed using the solution found after multi-objective optimization that incorporates weighted sorting for arranging solutions. Our experimentation performed using the CloudSim framework shows that the proposed algorithm outperforms other algorithms with performance improvement ranging from 3.98% to 16.07%, while considering the schedule length, monetary cost, and energy consumption. • For cloud computing environments, we present a novel task scheduling approach, termed as GWOTS. • Using the proposed approach, the schedule length, monetary cost, and energy consumption are optimized. • The GWOTS algorithm uses multi-objective optimization and incorporates weighted sorting for arranging solutions. • The proposed GWOTS algorithm outperforms other state-of-the-art algorithms by producing optimized schedules. [ABSTRACT FROM AUTHOR]

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms. • Propose a novel energy-aware VM placement approach in cloud data centers. • Introduce renewable energy for managing energy consumption and carbon footprint. • Design renewable energy generation prediction method based on EEMD and TCN. • Demonstrate the superiority of the proposed algorithm with five baseline algorithms. [ABSTRACT FROM AUTHOR]

• GPUs may be safely undervoltage, allowing for non-conventional DVFS configurations. • A benchmark suit characterizes GPU components regarding undervoltage limitations. • ALU and DRAM-Cache controller are the most sensitive components to voltage drops. • High energy efficiency gains are obtained with decoupled frequency-voltage pairs. • Accuracy of Deep Neural Networks (DNN) applications is not compromised. The use of GPUs to accelerate DNN training and inference is already widely adopted, allowing for a significant performance increase. However, this performance is usually obtained at the cost of a consequent increase in energy consumption. While several solutions have been proposed to perform voltage-frequency scaling on GPUs, these are still one-dimensional, by simply adjusting the frequency while relying on default voltage settings. To overcome this limitation, this paper introduces a new methodology to fully characterize the impact of non-conventional DVFS on GPUs. The proposed approach was evaluated on two devices, an AMD Vega 10 Frontier Edition and an AMD Radeon 5700XT. When applying this non-conventional DVFS scheme to DNN training, the obtained results show that it is possible to safely decrease the GPU voltage, allowing for a significant reduction of the energy consumption (up to 38%) and of the EDP (up to 41%) on the training procedure of CNN models, with no degradation of the networks accuracy. [ABSTRACT FROM AUTHOR]

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening—energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process. • Green innovation resilience can promote energy efficiency. • Early digital economy development can lead to resource grabbing and technology constraints effects. • Pursuing the policy orientation of "digital greening—energy efficient". [ABSTRACT FROM AUTHOR]

The world is currently facing urgent climate and environmental issues, such as global warming, ecosystem collapse, and energy shortages. In this context, this study selected data from 2000 to 2021 and employed the Method of Moment Quantile Regression (MMQR) to thoroughly investigate the impact of renewable energy consumption, economic complexity, and geopolitical risks on the ecological footprint of the Group of Twenty (G20) countries. The results indicate that in countries with lower quantiles, renewable energy consumption significantly reduces the ecological footprint, whereas its effect is not prominent in countries with higher quantiles. Economic complexity has a negative impact on the ecological footprint, and this impact becomes stronger as the quantile of the ecological footprint rises. Additionally, economic complexity moderates the effect of renewable energy on the ecological footprint. Geopolitical risks facilitate the growth of the ecological footprint. Likewise, robustness tests such as DOLS, FMOLS, and quantile regression confirm these estimates in the same framework. This study has conducted a profound analysis of global environmental issues, offering innovative perspectives and recommendations for achieving goals related to sustainable energy utilization, mitigating climate change, and improving the ecological environment. The findings of this research will guide policymakers in G20 countries to adopt more effective environmental protection measures, thereby contributing to the construction of a sustainable future. • This paper investigates the factors influencing the ecological footprint of G20 countries. • The study utilized a novel Method of Moment Quantile Regression to explore nexus among the study variables. • Renewable energy consumption has no significant effect on the ecological footprint in higher quantiles. • Economic complexity moderates the effect of renewable energy on the ecological footprint. • Geopolitical risks exhibit a unidirectional causality with the ecological footprint. [ABSTRACT FROM AUTHOR]

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO 2) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO 2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO 2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies. • The impact of considered economic sectors on CO 2 emissions were analyzed in 80 Russian constituent entities in 2005–2019. • Weighted regression and panel data methods were used to catch the specifics of Federal districts and constituent entities. • Population size and electricity consumption have the highest impact on CO 2 emissions. • Increasing energy efficiency and modernization of energy facilities are the most promising solutions to reduce the emissions. [ABSTRACT FROM AUTHOR]

This paper constructs a carbon quota allocation index that takes into account equity, efficiency and ecological construction, and calculates carbon emissions and energy consumption data in important periods based on the expected carbon emission targets and economic and social development indicators of the Chinese government. Based on the calculated carbon emissions, the zero-sum game data Envelopment model (ZSG-DEA) is used to discuss the initial allocation of regional quotas and the optimal carbon quota scheme. The results show that:(1) there is a large gap between the optimal carbon quota and the initial carbon quota allocation in Shandong, Guangdong, Jiangsu and other provinces in 2025, and the implementation of emission reduction measures should be accelerated. (2) By 2030, the final allocation of Beijing, Tianjin, Inner Mongolia, Qinghai, Shanghai, Ningxia, Liaoning and Xinjiang will be positive. The provinces with negative final allocation should carry out the work of carbon peak as soon as possible to avoid increasing the pressure of emission reduction in the future. (3) The central region faces greater pressure of emission reduction, while the western region can accept the transfer of carbon emissions from other regions over time. The research conclusions have important policy implications for establishing a fair and effective carbon quota allocation mechanism, achieving the national total carbon emission control target, stimulating the vitality of the unified carbon market, and promoting regional coordinated emission reduction. • There exist differences in carbon quota allocation among provinces in the future. • There are changes in the initial and optimal carbon quotas obtained by each province. • The role of carbon emissions between regions is changing over time. [ABSTRACT FROM AUTHOR]

• The Sigmoid algorithm applies the well-known sigmoid function to load balancing. • The algorithm uses all the resources of a system to their fullest requiring no effort. • It adapts to different hardware configurations and application behaviours. • Sigmoid successfully scales to adapt to the systems of the future. A challenge that heterogeneous system programmers face is leveraging the performance of all the devices that integrate the system. This paper presents Sigmoid, a new load balancing algorithm that efficiently co-executes a single OpenCL data-parallel kernel on all the devices of heterogeneous systems. Sigmoid splits the workload proportionally to the capabilities of the devices, drastically reducing response time and energy consumption. It is designed around several features; it is dynamic, adaptive, guided and effortless, as it does not require the user to give any parameter, adapting to the behaviour of each kernel at runtime. To evaluate Sigmoid's performance, it has been implemented in Maat, a system abstraction library. Experimental results with different kernel types show that Sigmoid exhibits excellent performance, reaching a utilization of 90%, together with energy savings up to 20%, always reducing programming effort compared to OpenCL, and facilitating the portability to other heterogeneous machines. [ABSTRACT FROM AUTHOR]

In the twenty-first century, there has been an increase in energy demand and waste production, due to the rising population of the world. One good approach for satisfying the energy demand and overcoming the waste management issues is to convert waste to energy. Additionally, using waste biomass as the feedstock of waste-to-energy (WtE) conversion methods makes them renewable and green and also helps the environmental challenges and reduces the emission of greenhouse gases (GHGs). Gasification is a thermochemical WtE route, which can produce hydrogen-rich gaseous biofuel called synthetic gas (syngas), from wastes. In this paper, different aspects of gasification process are reviewed with greater focus on catalyst usage. Syngas processing steps, which increase the quality and H 2 content of the syngas to form bio-hydrogen, are discussed. Solid oxide fuel cell (SOFC) technology is one of the most promising techniques of renewable energy production due to their environmental cleanness characteristics and high efficiencies. Thus, one of the best ways to exploit the energy content of the bio-hydrogen product of gasification is to employ it in a SOFC. Therefore, waste biomass gasification process can be integrated with SOFCs to build high efficiency systems for production of clean and renewable energy from waste, which are called integrated gasification fuel cell (IGFC) systems. These systems provide the opportunity of further upgrading of syngas inside the SOFC. In this paper, we are going to briefly discuss fuel cell technology (especially SOFCs) and review SOFC applications from the aspect of integration with gasification process (IGFC system). Finally, the impacts and issues of gasification process and SOFC technology are considered. • Different concepts related to waste and waste management are discussed. • Waste-to-Energy conversion methods, are presented. • Gasification process is discussed in detail with focus on its catalyst usage. • Syngas processing for bio-hydrogen production is considered. • Integrated gasification solid oxide fuel cells are considered as an efficient system. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

The existence of various pollutants in water environment contributes to global pollution and poses significant threats to humans, wildlife, and other living beings. The emergence of an effective, realistic, cost-effective, and environmentally acceptable technique to treat wastewater generated from different sectors is critical for reducing pollutant accumulation in the environment. The electrochemical advanced oxidation method is a productive technology for treating hazardous effluents because of its potential benefits such as lack of secondary pollutant and high oxidation efficiency. Recent researches on advanced oxidation processes (AOPs) in the period of 2018–2022 are highlighted in this paper. This review emphasizes on recent advances in electro-oxidation (EO), ozone oxidation, sonolysis, radiation, electro-Fenton (EF), photolysis and photocatalysis targeted at treating pharmaceuticals, dyes and pesticides polluted effluents. In the first half of the review, the concept of the AOPs are discussed briefly. Later, the influence of increasing current density, pH, electrode, electrolyte and initial concentration of effluents on degradation are discussed. Lastly, previously reported designs of electrochemical reactors, as well as data on intermediates generated and energy consumption during the electro oxidation and Fenton processes are discussed. According to the literature study, the electro-oxidation technique is more appropriate for organic compounds, whilst the electro-Fenton technique appear to be more appropriate for more complex molecules. [ABSTRACT FROM AUTHOR]

Due to high material density, high energy consumption density and CO 2 emission density, it is not only difficult but significant to clarify the relationship between energy consumption, the CO 2 emission and the production cost in different conditions. However, the previous researches rarely refer how to balance the energy consumption, the CO 2 emission and the production cost after the fluctuation of material, energy and carbon price as well as what will happen to them if production structure changes. Therefore, based on the conservation law of mass and energy, to study iron and steel manufacturing process (ISMP), this paper, taking carbon price into consideration, establishes a muti-optimization model of energy consumption, CO 2 emission and cost. After optimization with different objectives, the production cost per tonne of crude steel is reduced by 192.03 CNY (7.71%), the CO 2 emission per tonne of crude steel is reduced by 224.22 kg (13.37%), and the energy consumption per tonne of steel is reduced by 51.20 kgce (9.10%). Moreover, based on the optimization results under different objectives, it is ironmaking process (coal ratio and ore ratio) and steelmaking process (amount of scrap steel) that has more impact on three above as well as ore blending and coal blending have a great influence on production cost but little effect on energy consumption and CO 2 emission. • The muti-objective optimization model of iron and steel industry is established. • The carbon price is considered during model establishment and analyzing. • The objective with energy consumption, CO 2 emissions and cost are optimized. • The factors affecting cost, energy consumption and CO 2 emission are discussed. [ABSTRACT FROM AUTHOR]

Remanufactured mechanical products with high-added value are generally claimed to gain environmental benefits. These claims were made based on different products and assessment methodologies. The variability of life cycle assessment (LCA) results precludes a meaningful comparison across products and studies. This paper aims to critically and systematically evaluate the lifecycle environmental performance of remanufactured products compared with their new counterparts and to identify the key factors, strengths, and limitations in the assessment procedure. Faced with the noteworthy variations, we closely examined and harmonized the unit function, allocation approach, system boundary, impact assessment method, and the underlying assumptions in screened 20 papers regarding 11 types of products. The environmental indicators adopted in this study were global warming potential (GWP) and primary energy consumption (PEC). In terms of these two indicators, the environmental burdens of remanufactured products relative to newly manufactured alternatives were harmonized to the comparison ratios. With these harmonized samples, descriptive statistics were calculated using Monte Carlo Simulation to disclose the variations of comparison results and identify the general tendency. Results of this meta-study showed that remanufacturing could contribute to over 50% reduction for GWP when usage and end-of-life stages were excluded from the life cycle. The GWP and PEC of remanufactured mechanical products account for 28.5% and 25.9% of the new counterparts, respectively, on average. This meta-analysis of comparative LCAs on new and remanufactured products would advance the understanding of the environmental advantages of remanufacturing. • Meta analysis was performed on the comparison of new and remanufactured products. • Variation in the calculations of life cycle impact was harmonized. • Remanufactured products cause over 50% reduction in Global Warming Potential. • The key factors and limitations in the screened papers were identified. [ABSTRACT FROM AUTHOR]

Circular Economy Model (CEM) holds the promise to provide an economic benefit while not compromising environmental balance, growth, and competitiveness. The paper presents the proposal of Circular Economy Competitiveness Index (CECI) which measure the development of CEM implementation on country level in areas of social behaviours, business operations, resource management, and innovativeness. CECI combines a catastrophe progression method based on catastrophe theory and principial component analysis into composite index. The paper presents analysis of CECI state and dynamics in EU countries. The model supported with Eurostat data allow to classify the EU countries into four groups based on CEMI dynamic as well as analyse EU traditional regions' competitiveness in context of CE implementation. Additionally, correlation to Global Competitiveness Index (GCI) has been analysed. The paper presents a quantitative reference for evaluating CE competitiveness and offers relevant implications for EU countries' policymakers in relation to the factors determining that competitiveness. • The paper explores impact of Circular Economy implementation on countries' competitiveness. • Circular Economy Competitiveness Index (CECI) based on catastrophe progression method is proposed. • UK, Italy, Sweden, Netherlands are leading EU countries according to CECI, because they balanced energy and material consumption. • CECI can be useful indicator for government to stimulate economy transformation while not losing competitiveness. [ABSTRACT FROM AUTHOR]

Exploiting Chongqing's promotion to a province-level municipality, this paper investigates the causal effect of political hierarchy on corporate SO 2 emissions using a spatial regression discontinuity (RD) design. In 1997, the prefecture-level Chongqing was promoted to a province-level municipality, obtaining a substantial increase in decision-making power over fiscal, personnel and administrative affairs. Given the fact that the areas around the Chongqing-Sichuan border shared the same social, economic, and geographical conditions prior to the treatment, the novel findings show that political hierarchy negatively affects corporate SO 2 emissions and Chongqing's enterprises adjacent to the border have a 26% (1-e−0.301) reduction of SO 2 emissions comparatively. The effects are robust enough using alternative model specifications and bandwidths. Besides, this paper provides empirical evidence that enterprises of Chongqing have resource-receiving and external-financing advantages owing to its higher political status. These interwoven advantages help enterprises in Chongqing exhibit better financial performance and enable them to conduct energy substitution, improve energy efficiency and adopt more end-of-pipe treatment, thereby mitigating SO 2 emissions. Based on these findings, this study provides policy implications by shedding light on the effects of political hierarchy on corporate pollution emissions. • Political hierarchy can shape pollution geography. • Different positions in the political hierarchy lead to disparities in resource-receiving and external financing for firms. • Political hierarchy has negative impacts on corporate SO 2 emissions. • The influencing channels include clean energy substitution, improved energy efficiency and end-of-pipe treatment. [ABSTRACT FROM AUTHOR]

Electrocoagulation (ECoag) technique has shown considerable potential as an effective method in separating different types of pollutants (including inorganic pollutants) from various sources of water at a lower cost, and that is environmentally friendly. The EC method's performance depends on several significant parameters, including current density, reactor geometry, pH, operation time, the gap between electrodes, and agitation speed. There are some challenges related to the ECoag technique, for example, energy consumption, and electrode passivation as well as its implementation at a larger scale. This review highlights the recent studies published about ECoag capacity to remove inorganic pollutants (including salts), the emerging reactors, and the effect of reactor geometry designs. In addition, this paper highlights the integration of the ECoag technique with other advanced technologies such as microwave and ultrasonic to achieve higher removal efficiencies. This paper also presents a critical discussion of the major and minor reactions of the electrocoagulation technique with several significant operational parameters, emerging designs of the ECoag cell, operating conditions, and techno-economic analysis. Our review concluded that optimizing the operating parameters significantly enhanced the efficiency of the ECoag technique and reduced overall operating costs. Electrodes geometry has been recommended to minimize the passivation phenomenon, promote the conductivity of the cell, and reduce energy consumption. In this review, several challenges and gaps were identified, and insights for future development were discussed. We recommend that future studies investigate the effect of other emerging parameters like perforated and ball electrodes on the ECoag technique. [Display omitted] • Several applications of ECoag technique for inorganic removal were reviewed. • ECoag mechanisms, emerging application, and sludge production were discussed. • Emerging designs for ECoag technique were compared. • The effects of reactor design on efficiency and cost were assessed. [ABSTRACT FROM AUTHOR]

In 2019, aviation was responsible for 2.6% of world CO 2 emissions as well as additional climate impacts such as contrails. Like all industrial sectors, the aviation sector must implement measures to reduce its climate impact. This paper focuses on the simulation and evaluation of climate scenarios for air transport. For this purpose, a specific tool (CAST for "Climate and Aviation - Sustainable Trajectories") has been developed at ISAE-SUPAERO. This tool follows a methodology for the assessment of climate impacts adapted to aviation. Firstly, models for the main levers of action, such as air traffic, aircraft energy consumption and energy decarbonization, are provided using trend projections from historical data or assumptions from the literature. Second, the evaluation of scenarios is based on aviation carbon budgets, which are also extended to non- CO 2 effects using the concept of GWP*. Several scenario analyses are performed in this paper using CAST allowing different conclusions to be drawn. For instance, the modelling of the scenarios based on the more recent ATAG (Air Transport Action Group) commitments shows that aviation would consume 6.5% of the world carbon budget for +1.5 °C. Some illustrative scenarios are also proposed. By allocating 2.6% of the world carbon budget to aviation, it is shown that air transport is compatible with a +2 °C trajectory when the annual growth rate of air traffic varies between −1.8% and +2.9%, depending on the technological improvements considered. However, using the same methodology for a +1.5 °C trajectory shows that a drastic decrease in air traffic is necessary. Lastly, analyses including non- CO 2 effects emphasize the importance of implementing specific strategies for mitigating contrails. • Development of the open-access tool CAST to simulate aviation climate trajectories. • Modelling of different levers of action to decarbonise aviation. • Use of carbon budgets to assess the sustainability of scenarios. • Analysis of industrial scenarios with regard to the Paris Agreement. • Investigation of sustainable scenarios for different technological forecasts. [ABSTRACT FROM AUTHOR]

Carbon neutrality is an important environmental management tool to deal with the global greenhouse effect. Addressing climate change is an important starting point for promoting the high-quality development of China's economy and the construction of ecological civilization, as well as an important area for participating in global governance and adhering to multilateralism. In this paper, from the perspective of carbon neutrality, taking China's Jiangsu Province as an example, the super-efficiency DEA model is selected to measure the level of ecological efficiency in Jiangsu, and on this basis, the Tobit model is used to verify the main factors affecting regional ecological efficiency. The results are as follows: (1) The regional eco-efficiency in Jiangsu shows a trend of decreasing from south to north, with obvious phenomenon of "club convergence", with significant spatial correlation and agglomeration. (2) The industrial structure is negatively related to the regional eco-efficiency of Jiangsu regions; Energy consumption structure has a positively impact in southern Jiangsu,but negatively in central and northern Jiangsu; FDI has a positively impact in central and northern Jiangsu, but negatively in southern Jiangsu; Regional economic development has a positively impact in southern and northern Jiangsu, but negatively in central Jiangsu; The progress in the technological level has positive effects on the eco-efficiency of all regions of Jiangsu. The final paper puts forward some suggestions based on the carbon neutral target Provide theoretical reference for achieving the goal of carbon neutrality. • Construct an regional ecological efficiency evaluation index system. • Use super-DEAmethod to measure ecological efficiency level. • Study from the perspective of carbon neutrality. • Propose a reference for achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

After the Paris Climate Conference (COP21), many countries start progressing towards carbon neutrality targets. In doing so, green technology innovations (GTIs) and clean energy are the essential factors that can help to achieve the carbon neutrality goal. Therefore, this paper examines the linkages between green technology innovation and renewable energy and carbon dioxide emissions based on the STIRPAT model in Turkey during the time of 1990–2018. The study used testing like "unit-root" to verify the variables' integrative properties containing the information for structural breaks. Also, the bootstrapping ARDL-bound testing technique is used to analyze the relationship between the variables. The causal relationship between green technology innovation, energy consumption, renewable energy, population, income per capita, and carbon dioxide emissions is tested through a Granger causality test. The empirical findings show that green technology innovation, renewable energy, energy consumption, population, income per capita, and carbon dioxide emissions are co-integrated for the long-term association. Additionally, green technology innovation and renewable energy decline carbon dioxide emissions, whereas energy consumption, population, and per capita enhance carbon emissions. This paper helps the policymakers design a comprehensive policy for strengthening environmental sustainability through green technology innovation and renewable energy, specifically in the region of Turkey. • Examine the impact of GTIs and renewable energy on CO 2 emissions in Turkey. • We employed bootstrapping ARDL-bound testing technique. • GTIs and renewable energy are lowering CO 2 emissions. • Non-renewable energy consumption boosts CO2 emissions. • Population and personal income are also determinantal to carbon emissions. [ABSTRACT FROM AUTHOR]

The transition to sustainable energy requires an assessment of drivers of the use of clean and dirty fuels for cooking. Literature highlights the importance of access to clean fuel for switching from dirty fuels to clean fuels. Though access to cleaner fuels, such as electricity promotes clean fuel use, it does not necessarily lead to a complete transition to the use of clean fuels. Households continue using traditional fuels in addition to the clean fuels. The main objective of this paper is to explain the choice of dirty cooking fuels even when access to electricity is provided. We use nationally representative household survey data to study the household energy use decisions in three middle-income countries, namely, India, Kazakhstan, and the Kyrgyz Republic. The study discusses the role of access to natural gas, free fuel, convenience or multi-use of fuels featured by the heating system installed, built-in environment, and other socio-economic factors in household fuel choice for cooking. The results show that access to natural gas increases the likelihood of opting for clean fuel, while the availability of free fuel in rural areas and the coal-based heating system promote the use of solid fuels. • Households continue using traditional fuels in addition to modern fuels;. • This paper explains the choice of solid cooking fuels;. • Access to natural gas increases the likelihood of opting for natural gas;. • The availability of free fuel in rural areas and the coal-based heating system promotes solid fuels.. [ABSTRACT FROM AUTHOR]

The International Maritime Organization has set a goal to achieve a 50% reduction of total annual greenhouse gas emission related to the international shipping by 2050 compared to the 2008 baseline emissions. Malaysia government has taken an initiative to investigate the assessment (cost-effectiveness) of this International Maritime Organization's short-term measure on Malaysian-registered domestic ships although this measure is only for international merchant ship. To achieve this, this paper collected the ship's data from the shipowners from 25 sample ships. Engine power limitation is the most cost-effective option, but low power limits can lead to substantially increased sailing times. Based on cost-efficiency analysis, it creates for the purpose of compliance with the operational carbon intensity indicator. It found that even if it is possible to bring an asset back into service, it may not be possible to do so in a manner that generates a profit or complies with applicable regulations. In these situations, it may be more prudent to scrap the asset rather than run the risk of having it become a stranded asset. This is especially true for older tankers. Alternatives with lengthy payback periods are not desirable for the domestic tanker fleet that is already in operation. [Display omitted] • International Maritime Organization cut greenhouse gas emissions. • This report collects shipowner data from 25 sample ships. • Low engine power limitation can increase sailing times. • It found that reactivating an asset may not be profitable or compliant. • Instead of risking stranding, it may be better to scrap the asset. [ABSTRACT FROM AUTHOR]

Identifying key drivers of manufacturing CO 2 emissions is critical to carbon reduction practices. For manufacturing, CO 2 emissions are mainly determined by production capacity and production scale. However, traditional production-theoretical decomposition analysis (PDA) fails to consider production-scale-related drivers. To better support policy development and implementation, this paper improves PDA based on industrial linkage theory. The improved model can identify seven production-capacity-related drivers and five production-scale-related drivers, allowing a comprehensive understanding of CO 2 emission drivers. Then this model is implemented to investigate CO 2 emission changes in 18 manufacturing sectors in Hubei Province, China, from 2012 to 2017. Results show that manufacturing CO 2 reduction efforts in Hubei Province have yielded some achievements, with reduced potential energy intensity and improved CO 2 emission technical efficiency in most sectors. Changes in external market demand and final demand structure have contributed to CO 2 reduction in most sectors. Results also reveal some problems in manufacturing in Hubei Province, such as the inability to improve CO 2 emission technical efficiency and CO 2 emission technology strength, the slow improvement of energy utilization technical efficiency and energy utilization technology strength, and the reduction of value-added rate. • A new decomposition analysis model is built to decompose CO 2 emission changes. • The new model integrates industrial linkage theory into PDA. • Seven of twelve drivers relate to production capacity and five to production scale. • CO 2 emission changes in 18 manufacturing sectors in Hubei Province are decomposed. • Several important implications for manufacturing CO 2 reduction are proposed. [ABSTRACT FROM AUTHOR]

To achieve sustainable development, local governments are under enormous pressure to simultaneously achieve the three assessment tasks (TATs) of energy saving, CO₂ reduction, and pollution reduction. However, the TATs are often managed by different authorities and have three types of measures (TTMs) that correspond to them. The lack of adequate cooperation between these authorities has led to the inefficient investment of policy resources and even to policy conflicts, and the interactions among the TTMs are not yet known. To this end, this paper uses the MCEE model to assess the interactions among the TTMs quantitatively using Guangzhou, China, as a case study. The results showed the following. (1) According to the current development trend, if the authorities managing the TATs continue to work alone, they will not be able to fulfill the corresponding assessment tasks in the future. (2) The TTMs have interactions with each other, and their synergies are far greater than their trade-offs. From 2015 to 2035, it is expected that energy-saving measures can accomplish 54.39% of the CO₂ reduction tasks and 32.74% of the pollution reduction tasks indirectly, and low-carbon measures can accomplish 55.53% of the energy saving tasks and 27.20% of the pollution reduction tasks indirectly, However, environmental-protection measures will cause fewer trade-offs (energy demand and CO 2 emissions increase by 3.76% and 2.88%, respectively). (3) In some years, the contribution of interactions among the TTMs are even higher than their direct contribution to the TATs. Our findings suggest that intensive cooperation between authorities is necessary, and that the benefits of such cooperation will outweigh the disadvantages. • An integrated energy-economic-environmental assessment model for multiple indicators is established. • An index evaluation method for multiple synergies and trade-offs is established. • It reveals three types of measures do have interactions and their synergies are far greater than the trade-offs. • Intensive cooperation among authorities is necessary and the benefits of such cooperation will outweigh the disadvantages. [ABSTRACT FROM AUTHOR]

The rapidly expanding global energy demand is forcing a release of regulated pollutants into water that is threatening human health. Among various wastewater remediating processes, electrocoagulation (EC) has scored a monumental success over conventional processes because it combines coagulation, sedimentation, floatation and electrochemical oxidation processes that can effectively decimate numerous stubborn pollutants. The EC processes have gained some attention through various academic and industrial publications, however critical evaluation of EC processes, choices of EC processes for various pollutants, process parameters, mechanisms, commercial EC technologies and performance enhancement via other degradation processes (DPs) integration have not been comprehensively covered to date. Therefore, the major objective of this paper is to provide a comprehensive review of 20 years of literature covering EC fundamentals, key process factors for a reactor design, process implementation, current challenges and performance enhancement by coupling EC with pivotal pollutant DPs including, electro/photo-Fenton (E/P–F), photocatalysis, sono-chemical treatment, ozonation, indirect electrochemical/advanced oxidation (AO), and biosorption that have substantially reduced metals, pathogens, toxic compound BOD, COD, colors in wastewater. The results suggest that the optimum treatment time, current density, pulse frequency, shaking speed and spaced electrode improve the pollutants removal efficiency. An elegant process design can prevent electrode passivation which is a critical limitation of EC technology. EC coupling (up or downstream) with other DPs has resulted in the removal of organic pollutants and heavy metals with a 20% improved efficiency by EC-EF, removal of 85.5% suspended solid, 76.2% turbidity, 88.9% BOD, 79.7% COD and 93% color by EC-electroflotation, 100% decolorization by EC-electrochemical-AO, reduction of 78% COD, 81% BOD, 97% color by EC-ozonation and removal of 94% ammonia, 94% BOD, 95% turbidity, >98% phosphorus by aerated EC and peroxicoagulation. The major wastewater purification achievements, future potential and challenges are described to model the future EC integrated systems. [ABSTRACT FROM AUTHOR]

Research on the ability of environmental regulations to achieve an innovation-offset effect by increasing clean energy use is required. This study aims to verify this by analysing the impact of market-incentive environmental regulation on enterprises' clean energy consumption. Using China's sulfur dioxide (SO 2) emission trading system (ETS) for 2007 as a quasi-natural experiment, the difference-in-differences model and data obtained from the Chinese Industrial Enterprises Database and Chinese Industrial Enterprises Pollution Database were used to determine whether an ETS affects enterprises' clean energy consumption. The results show that an ETS encourages enterprises to utilise clean energy and has a significantly positive impact on enterprises' clean energy consumption. Moreover, this study finds that an ETS promotes clean energy consumption by improving the production of enterprises. This study verifies the rationality of China's SO 2 ETS design. • This paper is the first to examine the impact of emission trading system (ETS) on enterprises' clean energy consumption. • We adopt the difference-in-differences model in the empirical section. • The ETS increases enterprise's clean energy consumption. • The improvement of enterprises' production structure is an important mechanism. [ABSTRACT FROM AUTHOR]

Based on the panel data of 20 countries in EU during the period of 2007–2019, this paper study the effect of energy market integration (EMI) on renewable energy development (RED). We develop a general equilibrium model to explain how EMI affect the RED and the role of different mechanisms. The empirical results reports that the European EMI increased both the consumption and power generation of renewable energy, which proves a significant positive effect of EMI on the RED. In line with our expectations of theoretical model, our estimates show that the increase of renewable energy consumption is mainly due to the fossil energy cost increased, technology advancement and regional environmental regulation strengthening. And the fossil energy cost is the main driven force which plays a completely mediating role between EMI and RED. Furthermore, we also observe a negative effect of FDI and industry structure on RED. • We establish a general equilibrium model to explain how EMI affect the RED and the role of different mechanisms. • The European EMI increased both the consumption and power generation of renewable energy. • The increase of renewable energy consumption is mainly due to the fossil energy price increased and technology advancement. • A negative effect of FDI and industry structure on RED is observed. [ABSTRACT FROM AUTHOR]

Protecting our environment while maintaining economic growth, requires a delicate balance among interlinked sustainable development policies. In this paper, we examine China's economic industries, including a high-resolution of the country's electricity sector during 2020–2030, using a multi-objective optimization model based on Input-Output analysis. This model, investigates the synergy and trade-offs of sustainable development goals in maximizing employment and GDP while minimizing energy and water consumption, CO 2 emissions, and five major pollutants to advance a sustainable industrial structure adjustment pathway for China. Our results reveal that there exists both synergies and trade-offs among multiple objectives, e.g., synergy among goals of minimizing air pollutant emissions and trade-offs between minimizing energy consumption and maximizing employment. Through the planned industrial restructuring period (2020–2030), the GDP, employment, carbon emission, and energy consumption will increase respectively by, 96.1%, 7.2%, 16.8%, 16.8%, and 6.3%, while pollutant emissions would decrease. Moreover, our research indicates that energy and water conservation should be prioritized in industrial structure adjustment strategies and policies. Our model demonstrates how the synergies and trade-offs among multiple policy targets can empower policy-makers, especially in developing nations, to make more informed and optimized industrial structure adjustment policies for sustainable development. • A multi-objective optimization model based on the Input-Output model is proposed. • Synergies and trade-offs among policy targets are examined. • The structural adjustment of economic sectors with fine-resolution power sectors is investigated. • The compromise solution and the most recommended policy priority are explored. [ABSTRACT FROM AUTHOR]

Impaired bioenergetic capacity of the nervous system is thought to contribute to the pathogenesis of many neurodegenerative diseases (NDD). Since neuronal synapses are believed to be the major energy consumers in the nervous system, synaptic derangements resulting from energy deficits have been suggested to play a central role for the development of many of these disorders. However, long axons constitute the largest compartment of the neuronal network, require large amounts of energy, are metabolically and structurally highly vulnerable, and undergo early injurious stresses in many NDD. These stresses likely impose additional energy demands for continuous adaptations and repair processes, and may eventually overwhelm axonal maintenance mechanisms. Indeed, pathological axon degeneration (pAxD) is now recognized as an etiological focus in a wide array of NDD associated with bioenergetic abnormalities. In this paper I first discuss the recognition that a simple experimental model for pAxD is regulated by an auto-destruction program that exhausts distressed axons energetically. Provision of the energy substrate pyruvate robustly counteracts this axonal breakdown. Importantly, energy decline in axons is not only a consequence but also an initiator of this program. This opens the intriguing possibility that axon dysfunction and pAxD can be suppressed by preemptively energizing distressed axons. Second, I focus on the emerging concept that axons communicate energetically with their flanking glia. This axoglial metabolic coupling can help offset the axonal energy decline that activates the pAxD program but also jeopardize axon integrity as a result of perturbed glial metabolism. Third, I present compelling evidence that abnormal axonal energetics and compromised axoglial metabolic coupling accompany the activation of the pAxD auto-destruction pathway in models of glaucoma, a widespread neurodegenerative condition with pathogenic overlap to other common NDD. In conclusion, I propose a novel conceptual framework suggesting that therapeutic interventions focused on bioenergetic support of the nervous system should also address axons and their metabolic interactions with glia. [ABSTRACT FROM AUTHOR]