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With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

Sustainable development has become society's focal point. In this context, China's paper industry needs to be more concerned about a low‐carbon strategy. Therefore, a carbon performance evaluation system must be built to guide the low‐carbon enterprise transformation. Based on the carbon value flow analysis, this study takes JX Paper (a paper‐making enterprise) as an example to construct a carbon performance evaluation system consisting of carbon input, transfer, and output indicators. The quantitative and qualitative index weights were determined through a principal component analysis and an analytical hierarchy process. The overall carbon performance increased from 2012 to 2021, but room remains for improvement. These results show that the performance evaluation system provides a helpful reference for enterprises to identify the key factors affecting carbon emissions and evaluate their carbon performance. Moreover, objective criteria should be developed to improve sustainable enterprise carbon performance and low‐carbon competitiveness. [ABSTRACT FROM AUTHOR]

Driven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply. [ABSTRACT FROM AUTHOR]

Australia is unusual among developed countries both in lacking fleet‐wide vehicle carbon dioxide emission standards and in having a very low takeup of all‐electric vehicles. This paper outlines how fleet‐wide vehicle carbon dioxide emission standards operate and identifies mechanisms by which their absence might lead to a low takeup of electric vehicles in countries such as Australia. Although hard to verify one of those mechanisms in Australia (higher prices charged for all‐electric vehicles), it is easy to verify the unusually low number of models of all‐electric models made available for sale. The paper also outlines Australia's slow 15‐year journey towards standards, pointing to the possible role of political machinations and political caution in delaying their introduction. It identifies one group of employees with much to lose from a rapid uptake of electric vehicles—motor mechanics. [ABSTRACT FROM AUTHOR]

Existing researches lack systematic design of the operation mechanism and overall implementation process of park oriented multi‐state energy system; The existing researches on multi‐state energy systems ignored the dynamic response characteristics of load and the coupling effect of units considering the impact of carbon emissions, and did not go deep into the collaborative management of multiple multi state energy system parks. To this end, this paper first proposes a two‐stage multi‐energy system operation architecture for low carbon parks, and establishes a mathematical model considering carbon emissions, operating costs and other objectives. The response space of load is optimized by considering the dynamic interaction characteristics of load response and the coupling characteristics of unit operation. Then, considering the problems of data security and training efficiency, F‐DDPG technology is used to conduct collaborative training for multiple campus systems. Finally, an example is given to prove the effectiveness of the method, which is not only conducive to reducing the operating cost of the system and the carbon emissions on the supply side, but also improves the convergence of model training. In the case of collaborative training in multiple parks, the method in this paper converges faster and has better economic benefits. [ABSTRACT FROM AUTHOR]

The multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed. [ABSTRACT FROM AUTHOR]

Based on the carbon emissions trading pilot policy in China, this paper takes Chinese A‐share listed companies from 2012 to 2020 as a sample, and analyzes the role of company's cost transfer capability (ECTC) in the mechanism of market‐incentivized environmental regulation policy (MERP) influencing company green transformation. This paper finds the following four primary results. First, ECTC significantly strengthens the green transformation driving effect of MERP in general. However, results based on the division of ECTC by percentiles show that MERP‐induced green transformation is more pronounced in the top 25% of companies, which distinguishes them from companies in the top 25%–50%, whose cost transfer capacity hampers MERP's contribution to the green transformation. Second, the level of regional economic development, emission regulation, and business reputation have heterogeneous influences on the moderating effect of ECTC. Third, the results based on the moderated mediation model show that ECTC can influence the green transition driving effect of MERP by moderating the two paths of risk‐taking ability and financing constraints of companies. Finally, there is a diffusion effect on the green transformation driven by MERP through cost transfer among companies in non‐pilot industries and non‐pilot regions. [ABSTRACT FROM AUTHOR]

Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions. [ABSTRACT FROM AUTHOR]

An editorial is presented on advances in wave energy conversion systems that can be integrated into offshore structures to provide cost-effective power and form multifunction platforms. Topics include provide renewable and sustainable electricity without carbon dioxide emission, wave energy conversion technology has attracted extensive attention from multi-disciplinary communities; and levelised cost of energy (LCoE) from ocean waves is higher than that from wind and solar radiation.

This paper is written in response to the paper "How green is blue hydrogen?" by R. W. Howarth and M. Z. Jacobson. It aims at highlighting and discussing the method and assumptions of that paper, and thereby providing a more balanced perspective on blue hydrogen, which is in line with current best available practices and future plant specifications aiming at low CO2 emissions. More specifically, in this paper, we show that: (i) the simplified method that Howarth and Jacobson used to compute the energy balance of blue hydrogen plants leads to significant overestimation of CO2 emissions and natural gas (NG) consumption and (ii) the assumed methane leakage rate is at the high end of the estimated emissions from current NG production in the United States and cannot be considered representative of all‐NG and blue hydrogen value chains globally. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. On the basis of our analysis, we show that it is possible for blue hydrogen to have significantly lower equivalent CO2 emissions than the direct use of NG, provided that hydrogen production processes and CO2 capture technologies are implemented that ensure a high CO2 capture rate, preferably above 90%, and a low‐emission NG supply chain. [ABSTRACT FROM AUTHOR]

Enterprises need to innovate business strategies to deal with the negative effects of low‐carbon development, among which digital transformation is an important breakthrough technology. This paper discusses the impact of low‐carbon development on enterprises' digital transformation. Theoretical analysis shows that environmental regulations driven by low‐carbon development have the compensatory effect of stimulating enterprise digital transformation to mitigate misallocation of capital and labor, especially for enterprises with higher productivity. In addition, both the construction of network infrastructure and the development of digital finance can strengthen the incentives for enterprises' digital transformation. The empirical tests based on Chinese A‐share listed enterprises show that the degree of enterprises' digital transformation increases by 0.0122 standard deviation for every one standard deviation reduction in regional carbon emission intensity. These results deepen the understanding of the relationship between green development and microenterprise digitalization. [ABSTRACT FROM AUTHOR]

This paper evaluates various modes of transport against the dual requirements of Net‐Zero carbon emissions and user convenience, in particular, speed of travel and cost of transportation. Results show that when operated across a whole country, battery‐powered ePlanes have the lowest energy use as measured by well‐to‐wing efficiency of other high‐speed transport systems such as the UKs HS2 and conventional diesel rail systems. This condition may not hold for extremely high passenger numbers per hour as seen in metropolitan areas with high density populations. Various proposed disruptive technologies lower cost of ownership when combined with changes in the transport paradigm that has rarely been explored in other papers. [ABSTRACT FROM AUTHOR]

Although a recent report by the U.S. Energy Information Administration indicates that CO2 emissions from power generation have now fallen below emissions from the transportation sector, electricity use remains a significant part of human's environmental footprint since fossil fuels are the major energy resource for electricity generation worldwide. This explains the continual effort by policy makers to develop a remedy to mitigate carbon emissions. It is then critically important to properly account for the emissions associated with electricity generation, delivery, and consumption. In particular, it is critical to be able to allocate emissions to the relevant electricity consumers creating the demand that results in a polluting emission. Therefore, in this paper a virtual carbon emission tracing method in power systems is thoroughly presented to facilitate allotting carbon obligation. The method is developed based on the proportional sharing principle that has been used in power system operation. The proposed methodology is implemented on the IEEE 5‐bus and 9‐bus systems, two widely used and recognized power grid benchmarks. [ABSTRACT FROM AUTHOR]

Digitalization and greening are key themes in global development, the manufacturing industry, as a major sector of energy consumption and carbon emissions, is in urgent need of transformation to realize energy conservation and emission reduction, digital transformation provides an opportunity for its green development. However, whether digital transformation can restrain the carbon emission intensity (CEI) of enterprises is still controversial. Based on the data of China's A‐share manufacturing listed companies from 2013 to 2020, this paper uses fixed effect model and intermediary effect model to explore the relationship between digital transformation and enterprise CEI and the influence path. the results show that: (1) Digital transformation has significantly reduced the CEI of manufacturing enterprises. (2) Mechanism analysis indicates that green technological innovation and resource allocation efficiency play a chain mediating role in this relationship. (3) Heterogeneity analysis shows that the carbon emission reduction effect of digital transformation is more significant when enterprises belong to an energy‐intensive manufacturing industry or face strong environmental regulations and industry competition. The analysis of this paper not only deepens enterprises' understanding of digital transformation and green development but also provides a policy basis for China to advocate the coordinated development of the digital economy and green economy. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions. [ABSTRACT FROM AUTHOR]

Economic growth has been crucial in contributing to carbon dioxide (CO2) emissions from the Industrial Revolution, and it affects CO2 emissions heterogeneously with different income levels. Therefore, studying the role of economic growth on inequality in carbon emissions is imperative. This paper analyses the determinants of CO2 emissions inequality in the panel dataset of 37 Organisation for Economic Co-operation and Development (OECD) countries from 1990 to 2019. Age dependency, globalisation, and institutional quality reduce CO2 inequality in the OECD economies. However, gross domestic product per capita increases CO2 inequality. The results are robust to utilise different panel data estimation techniques. This paper provides the first evidence in the literature of determinants of CO2 inequality across the OECD countries. It is suggested that governments in the OECD economies offer a blueprint for a sustainable society of green economic growth. Other potential policy implications are also discussed. [ABSTRACT FROM AUTHOR]

This paper aims to update our understanding of the carbon cycle in the Himalayas, the most intense collisional orogeny globally, by providing new insight into its impact on Cenozoic climate cooling through the use of isotopic variations in both organic and inorganic carbon and an isotopic mass balance model. Our results from 20 selected hot springs show that the relative contributions of dissolved carbon from the mantle, metamorphic decarbonization, aqueous dissolution, and soil organic matter are approximately 2%, 82%, 6%, and 10%, respectively. Approximately 87% ± 5% of CO2 generated in the deep crust precipitates as calcite, while approximately 5.5% ± 1% of this carbon is converted to biomass through microbial chemosynthesis at depths less than 2 km. Our random forests approach yielded a metamorphic carbon flux from the entire Himalayan orogenic belt of approximately 2.7 ∼ 4.5 × 1012 mol/yr. The minor CO2 released into the atmosphere (2.5 ∼ 4.2 × 1011 mol/yr) is comparable to the carbon consumption driven by Himalayan weathering. This paper provides new insights into deep carbon cycling, notably that approximately 93% of deeply sourced carbon is trapped in the shallow crust, rendering orogenic processes carbon neutral and possibly acting as one of the major triggers of long‐term climate cooling in the Cenozoic. Plain Language Summary: Large‐scale deep metamorphic decarbonization and CO2 release into the atmosphere during continental collision orogenesis worldwide have had significant impacts on Cenozoic global climate change. However, the carbon sources and sinks and the mechanisms underlying their impacts on climate change are not fully understood. This paper reports the chemistry of aqueous solutions, carbon isotopic compositions of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC), and helium and carbon isotopic compositions of free gases from hot springs in southern Tibet. The carbon isotope fractionation model between gas and aqueous solutions revealed that the carbon released from depth underwent six different stages. Based on the random forests machine learning algorithm, we estimated the metamorphic carbon flux across the Himalayan orogenic belt. Furthermore, only a small fraction (7%) of deep carbon is eventually released into the atmosphere, and this volume is the same magnitude as the amount of carbon absorbed by silicate weathering on the Tibetan Plateau. This paper emphasizes that the carbon sequestration process in the upper crust may have resulted in nearly carbon‐neutral geological bodies in the Himalayan orogenic belt, thus playing a critical role in Cenozoic climate cooling. Key Points: Approximately 87% of deeply sourced carbon precipitates as calcite, while approximately 5.5% is sequestered as biomass in the shallow crustThe carbon flux across the Himalayan orogenic belt was estimated by the random forests approachCrustal thickening has attenuated long‐term deep carbon emissions from the Himalayas [ABSTRACT FROM AUTHOR]

In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods. [ABSTRACT FROM AUTHOR]

In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components. [ABSTRACT FROM AUTHOR]

One of the ways to increase the participation and penetration of renewable energy resources is to bring down the cost of these abundant resources for easy implementation and affordability. In this paper, a generalized reduced gradient (GRG) non‐linear optimization algorithm is implemented to solve a tri‐objective optimal design and sizing of a low‐cost hybrid mix consisting of a photovoltaic (PV) power plant, biomass power plant (BPP), and battery energy system for water pumping load applications in the University of Johannesburg, South Africa considering four different hybrids of biomass‐battery, PV‐battery, PV‐biomass, and PV‐biomass‐battery. The optimization model considers available energy and battery state of charge while minimizing least cost of energy (LCOE), carbon dioxide emission (tCO2eq), and loss of power supply probability (LPSP) including carbon tax incentive and penalty. The results when compared against particle swarm optimization (PSO) show the superiority of GRG over PSO with an optimal combination of PV‐biomass‐battery mix with optimal size of the PV power plant as 360.50 kW, the BPP 181.08 kW, and the battery size of 6,553.60 kWh giving a minimal optimal LCOE, CO2 emission and LPSP of 0.018 $/kWhr (with carbon tax), and 0.016 $/kWhr (without carbon tax), 28,067.73tCO2eq tCO2eq, and 1.7%, respectively. These values give a competitive advantage compared to the unit cost and values of CO2 emission and LPSP currently in the literature. [ABSTRACT FROM AUTHOR]

To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network. [ABSTRACT FROM AUTHOR]

Under the promotion of low‐carbon policies, the coordinated operation of integrated electricity‐gas distribution system (IEGDS) considering CO2 emission is of great significance to accelerate the process of energy conservation and could improve the economy of IEGDS. The paper proposes a low‐carbon coordinated operation of the IEGDS considering hybrid AC/DC distribution network, carbon capture and carbon storage. Firstly, the coordinated scheduling model of the IEGDS is discussed, with coupling constraints of gas turbines and power‐to‐gas. Secondly, the paper presents the model of hybrid AC/DC power distribution system, where non‐linear network constraints are relaxed into second‐order‐cone programming forms. Regarding carbon emission, the CO2 produced by fossil fuel burning can be partly captured by carbon capture plant, stored in carbon storage tank, and transported by trucks to carbon storage warehouse that trades with the carbon market. With the cooperation of various components, a comprehensive model considering carbon flow and trading in the distribution system is innovatively presented here. Numerical experiments validate the effectiveness of proposed low‐carbon coordinated scheduling model for the integrated electricity and natural gas distribution system. [ABSTRACT FROM AUTHOR]

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Since the "double carbon" target was proposed in 2020, emissions reduction has become the focus of the Chinese government's work. Industrial development is a crucial factor contributing to the increase in carbon emissions. Therefore, implementing carbon reduction in the industrial sector is an important part of China's goal of "double carbon." In this context, this paper uses a three‐stage data envelopment analysis model to calculate the green total factor productivity (GTFP) of 18 industry sectors in Shanxi Province. The results show that: (1) the external environment has influence on the GTFP of energy revolution reform pilot area industrial sectors, and the efficiency value of two industries is overestimated before excluding environmental factors; (2) the industry difference of GTFP is significant, among which pharmaceutical manufacturing industry has the greatest promoting effect on GTFP, and ferrous metal mining and dressing industry has the greatest inhibiting effect on GTFP; (3) there is a serious redundancy in R&D investment of energy revolution reform pilot area in China, and only six industries belong to zero redundancy. (4) To reflect the GTFP of various industrial sectors more clearly, this paper divides four industries: "Double‐high" industry, "High‐low" industry, "Low‐high" industry, and "Double‐low" industry. [ABSTRACT FROM AUTHOR]

The quantity of carbon dioxide in the atmosphere rises yearly because human activities emit more of it than the earth's natural processes can absorb. The overall carbon dioxide emissions continue to rise due to development demands. In a panel setting, this paper investigates the link dynamics between gross national spending, energy consumption, information and communications technology, and CO2 emissions in advanced nations from 2000 to 2020. We employ quantitative sequential approaches, such as DOLS, unit root test, and cointegration techniques, to ensure the coherence and feasibility of the study. Both versions of the estimators show statistically significant effects of gross national expenditure, energy consumption, and Information & Communications Technology on CO2 emissions. The findings of this paper show that there is a long‐run relationship between independent and dependent variables. The study also shows that rising exports of ICT services, energy consumption, and population need environmental protection and highlight the necessity for environmental regulations that can minimize emissions throughout the country's expansion. Even in advanced nations, climate vulnerability is not automatically reduced by development status but is instead reduced by the right sort of growth. [ABSTRACT FROM AUTHOR]

Today's world is characterized by the extensive production of data in different scenarios that everyday citizens need to understand for their informed participation in society. With the increase in the availability of data in a society defined by the industrious production of data, the educational system needs to think of possibilities to bring young children closer to the world of data science. This paper presents a nontraditional data exploration experience with an 8‐year‐old participant helped by a data visualization tool. A task‐based interview was conducted while the participant explored a carbon dioxide emission dataset. This paper studied how the participant interrogates the data, draws inferences and exhibits dispositions. At the end, some reflections are presented when introducing the exploration of nontraditional data in teaching. [ABSTRACT FROM AUTHOR]

As the United Kingdom reduces its CO2 emissions in order to meet its 2050 net zero greenhouse gas targets, there will be a significant evolution of the UK's energy mix. The reliance on hydrocarbons will decrease while there is predicted to be an increase in low carbon energy sources such as renewables and nuclear. In order to decarbonise and achieve the net zero emissions targets while concurrently producing enough energy to provide for national energy needs, large‐scale, low carbon energy generation projects need to be developed alongside energy storage facilities to provide flexibility within a low carbon energy supply. Robust CCUS programmes will need be developed in order to capture and store unavoidable carbon dioxide emissions. The subsurface geology of the UK provides opportunities for the development of low carbon energy generation, energy storage and CCS, and the Upper Permian Zechstein Supergroup deposited in eastern England and offshore in the Southern North Sea is a potential host for these new developments. In NE England, salt cavern gas storage sites have been developed in thick Zechstein evaporites since the mid 20th centrury. In this paper we present new isopach maps and well correlation panels which will help to outline optimal locations for the development of additional salt caverns for gas storage. A review of the Zechstein Supergroup indicates that it does not exhibit great potential for the development of CCS, due both to its complex reservoir characteristics and to difficulties with both subsurface imaging and monitoring. However thick Zechstein evaporites could provide an excellent seal for CO2 storage in the underlying Lower Permian Rotliegend Group. [ABSTRACT FROM AUTHOR]

Self‐organized industrial symbiosis (IS) starts with one actor's decision to invest in a waste recovery plant and the other actors' decision to buy the recovered flow. Technical and institutional conditions of the cluster influence actors' decisions. This paper explores the emergence of IS collaborations in industrial clusters under different techno‐economic conditions in the long term. We propose a mixed‐integer linear programming model that incorporates costs and constraints associated with waste recovery and exchange to study actors' investment decisions and investigate shaped symbiotic exchanges under rising energy prices and limited electricity supply. The approach is implemented in Iran's Persian Gulf Mining and Metals Special Economic Zone as a case study. The results revealed that changes in internal or external condition simultaneously influence the industrial and waste recovery plants. For instance, increasing energy prices without raising product prices significantly decreased the production level of industrial plants and, consequently, heat recovery potential. Furthermore, the waste heat recovery plants' contribution to improving the cluster's economic and environmental performance was not the same. Electricity recovery from a power plant's waste heat can result in 55 PJ grid electricity intake reduction and 720 M€ cluster cash flow increase. Recovered cooling or electricity from the steelmaking plant waste heat was consumed internally rather than shaping IS. These model outcomes show its capability to study IS within the socio‐technical structure of the cluster, not a standalone phenomenon. Implemented conceptualization offers a novel system‐level approach, which could be adjusted to assess other industrial development strategies. [ABSTRACT FROM AUTHOR]

BEST PAPER OF 2021 WINNER OF THE 2021 BEST PAPER AWARD Extrapolating Acute Contact Bee Sensitivity to Insecticides Based on Body Weight Using a Phylogenetically Informed Interspecies Scaling Framework I Tobias Pamminger i https://doi.org/10.1002/etc.5045 Assessing environmental risks of chemicals is to a significant extent based on testing surrogate species for their sensitivity toward the chemical of interest. Weber, A., Jeckel, N., Weil, C., Umbach, S., Brennholt, N., Reifferscheid, G., & Wagner, M. (2021). Fenton, S. E., Ducatman, A., Boobis, A., DeWitt, J. C., Lau, C., Ng, C., Smith, J. S., & Roberts, S. M. (2021). [Extracted from the article]

In near future, the main challenge is to reduce the use of fossil fuels that currently emit the bulk of global CO2 emissions, responsible of the global warming and climate change. Hydrogen, as a future fuel candidate, would play a significant role for net‐zero emission requirements. As a matter of fact, new hydrogen tank design is needed to bring this technology to the readiness level. This paper aims to investigate on the impact resistance of a hybrid aluminum‐composite hydrogen tank. In particular, the paper presents the development of two finite element (FE) models for the simulation of two impact scenarios experimentally investigated and proposed in literature. The models provided acceptable levels of accuracy with consequent benefits in supporting designers in the certification process of such device type. [ABSTRACT FROM AUTHOR]

As global environmental problems are becoming more severe, how to efficiently reduce carbon emissions has become an urgent problem to be solved. Manufacturers and retailers can effectively reduce carbon emissions through low‐carbon efforts, and doing so is important for the sustainable development of low‐carbon supply chains. This paper develops a general framework that considers how bounded rationality and fairness concerns affect the manufacturer's and retailer's low‐carbon effort decisions and discusses the evolutionary stable strategy (ESS) and the evolution process of the manufacturer's and retailer's low‐carbon efforts. The ESS shows that the manufacturer and the retailer will increase their low‐carbon efforts when the initial unit profit is higher. Otherwise, a smaller initial unit profit will reduce their low‐carbon efforts. This paper also analyzes different attributes of fairness concerns and finds that advantageous fairness concerns increase low‐carbon efforts, whereas disadvantageous fairness concerns reduce low‐carbon efforts. In addition, fairness concerns will speed up the manufacturer's and retailer's low‐carbon efforts to ESSs. When the manufacturer and retailer choose the "no effort" strategy, they will stop investing quickly, but when they choose the "effort" strategy, they will not invest quickly. This paper can provide decision support for using the low‐carbon effort investment strategy by supply chain members. [ABSTRACT FROM AUTHOR]

CO2 emissions from industrial processes are the main components of global greenhouse gas emissions. Based on analyzing the industrial activity level, technological progress, raw material substitution, and CO2 recycling ratio from the cement, aluminum, ammonia, steel, lime, and ferroalloy industries, this paper considers three scenarios, namely, national determined contribution (NDC), carbon mitigation scenario (CMS), and deep mitigation scenario (DMS), and systematically explores the CO2 emissions from future industrial processes as well as relevant cumulative emissions, emission sources, mitigation potentials, and mitigation sources in China. The results demonstrate that CO2 emissions of the six main industrial processes can be continuously and significantly reduced in China. In the scenarios, compared with the 2020 level (1448 million metric tons of CO2), the CO2 emissions can be reduced by 71%, 82%, and 94%, respectively, in 2060, mainly owing to the reduction of the activity level, the adjustment of the product structure and the application of carbon capture and storage (CCS) under the DMS scenario. This paper provides several major policy implications to reduce the CO2 emissions through adjusting the industrial structure and current lifestyles, improving the durability of industrial products and the management and technical levels, increasing the substitution ratio of low‐carbon raw materials in industrial production, supporting innovative low‐carbon technologies, the establishment and operation of the carbon market and carbon pricing mechanism, and further promoting the current "leader" system of energy‐intensive industries. [ABSTRACT FROM AUTHOR]

The steel industry is a major source of CO2 emissions, and thus, the mitigation of carbon emissions is the most pressing challenge in this sector. In this paper, international environmental governance in the steel industry is reviewed, and the current state of development of low‐carbon technologies is discussed. Additionally, low‐carbon pathways for the steel industry at the current time are proposed, emphasizing prevention and treatment strategies. Furthermore, the prospects of low‐carbon technologies are explored from the perspective of transitioning the energy structure to a "carbon–electricity–hydrogen" relationship. Overall, steel enterprises should adopt hydrogen‐rich metallurgical technologies that are compatible with current needs and process flows in the short term, based on the carbon substitution with hydrogen (prevention) and the CCU (CO2 capture and utilization) concepts (treatment). Additionally, the capture and utilization of CO2 for steelmaking, which can assist in achieving short‐term emission reduction targets but is not a long‐term solution, is discussed. In conclusion, in the long term, the carbon metallurgical process should be gradually supplanted by a hydrogen–electric synergistic approach, thus transforming the energy structure of existing steelmaking processes and attaining near‐zero carbon emission steelmaking technology. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to evaluate the sustainable efficiency of container terminals (CTs). By the definition of United Nations Conference on Trade and Development (UNCTAD), business sustainable development includes three domains: Economics, Environment, and Society. Accordingly, based on the definition and the practical features of CT operations, this paper assesses sustainable efficiency for CTs from three perspectives: container throughput, CO2 emissions, and occupational safety. In this paper, a modality movement and a fuzzy grey relational analysis (GRA) approach are first proposed to estimate CO2 emissions and occupational safety index for CT operators, respectively. A hybrid slacks‐based measure (SBM)‐DEA model is then developed to assess the sustainable efficiency of CT operators. Finally, the CT operators in the Port of Kaohsiung are empirically investigated to validate the research model. Based on the results, theoretical and practical implications for the management of CT operators and port authorities are discussed. [ABSTRACT FROM AUTHOR]

The optimal operation of power systems is one of the most critical issues for operators. Optimizing the operation of power systems while accommodating the increasing penetration of RES, owing to the intermittent and variable nature of these resources, stands as one of the most critical challenges for power systems. Numerous primary challenges, including fluctuations in load and nonlinear constraints, further complicate the issue, given its substantial dimensions. Therefore, this paper introduces an approach for improving the management of optimal generation and the associated carbon emissions costs of traditional power plants, which is achieved through integrating wind farms and incorporating battery energy storage. This enhancement is achieved by integrating wind farms and utilizing battery storage systems while considering the costs associated with traditional units using fossil fuels and the expenses related to carbon emissions. The simulation incorporates seven load customer types and five traditional generation sources integrated with wind farms and battery storage devices. The simulation results demonstrate that the new problem formulation has significantly improved the ability and performance of optimizing the scheduling of power plant generations. [ABSTRACT FROM AUTHOR]

The acceleration of the impact of climate change prompts the cement industry for swift and effective solutions to reduce a large part of the carbon dioxide associated with cement manufacture. The use of Supplementary Cementitious Materials, SCM, such as fly ash and slag have enabled so far, the possibility of partially replacing clinker, the most energy intensive and main responsible component in cement for carbon emissions. However, reserves of both SCM are declining and their price is increasing, and replacing more than 30% clinker with these SCM can compromise early strength. In recent years a global team of scientists from Switzerland, Cuba and India have proven the possibility of dropping clinker content in cement down to 50% or even less through the combined use of calcined kaolinitic clays and limestone, both cheap and abundant materials, to produce a new cement called "LC3". The resulting cement matches the properties of a CEM I (EN‐197) at all ages, and carbon emission reduction is reportedly around 25‐40%, depending on the cement to compare with. This paper presents the accumulated experience in the investigation of the new cementitious system and the introduction at the industrial level. Issues like the choice of the right clay, calcination technology, product formulation, standardization and economic feasibility will be discussed. The information in this paper is intended to encourage industrial partners to invest in the new technology. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) is a comprehensive demand response (DR) technology to aggregate various power supply and consumption resources to help stabilise power grid fluctuations and reduce CO2 emissions. Fast‐charging electric vehicles (EVs) with higher charging power and shorter charging time are the most potential high‐quality DR resources of VPP in the future. In this paper, a new 'spatial‐temporal' bi‐layer optimal control strategy is proposed to solve the daily real‐time DR dispatching problem of large‐scale EVs including fast‐charging ones in a multi‐area VPP oriented to China's dual carbon target. This proposed bi‐layer strategy has two main contributions in both spatial and temporal domains. One is that, compared with the limited geographical area in traditional VPP, the upper layer of the proposed strategy considers the DR problem of EVs in a VPP with multiple geographical areas and establishes a spatial optimisation model of EVs DR considering both energy and carbon transaction costs of the overall VPP. The other is that rather than traditional slow‐charging EVs DR control with 1‐h temporal interval, the lower layer of the proposed strategy adds fast‐charging EVs in the real‐time orderly charging and discharging DR control by a shorter temporal interval of 15 min and designs two different charging and discharging price stimulation scenarios. The simulation results in the last section of this paper show that the proposed 'spatial‐temporal' bi‐layer optimal control strategy can effectively guide the charging and discharging behaviours of EVs in both spatial and temporal domains of a multi‐area VPP, achieving lower energy and carbon transaction cost and smaller load fluctuations. [ABSTRACT FROM AUTHOR]

The article presents the discussion on deployment of distributed energy resources (DERs). Topics include technical issues with protection placement and coordination, voltage and power violation, power quality, and energy losses; and new business model in power systems motivating electricity end-users for playing an active role and participating in electricity transactions and reducing carbon emissions.

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Cement used in structural concrete accounts for 60% of all cement. Thus, the amount of CO2 emission by cement in structural concrete in a year is about 5% of the amount emitted by mankind. However, the Life Cycle Assessment (LCA) of structural concrete emits CO2 not only at the product stage but also at the use stage after construction. In this paper, carbon neutrality is viewed from an economic point of view. Then a rough indicator is presented to grasp the CO2 emissions of structural concrete. This paper also shows that LCA of structural concrete should consider not only the materials but also the maintenance phase. And low‐carbon technologies currently in use is introduced. Then the need for multi‐cycle structural concrete with a circular economy is presented. Moreover, it is estimated that CO2 emissions due to disasters in the use stage could be enormous. The carbon neutrality of concrete is not a risk but an opportunity for us. [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

A global net zero target by mid‐century and keeping the maximum global temperature rise to below 1.5 °C was agreed by nearly 200 nations at the 26th UN Climate Change Conference of the Parties. The Intergovernmental Panel on Climate Change advises that emission cuts by 45% are needed by 2030 in order to maintain the global temperature rise below the 1.5 °C target. To achieve this target, it is necessary to investigate technological options that are economically viable and globally acceptable for commercial deployment. This review paper looks at the new technologies needed to reduce CO2 emission and to motivate commercial deployment, considering that the commercial deployment of CO2 has been slowed down as a result of high capital and operating expenses which invariably increase the cost of energy. The paper presents a review on early years of process intensification technologies as well as a discussion on potential new process intensification options for CO2 capture, with suggestions on how to modify those technologies for suitable application to solvent‐based carbon capture. The work further discusses current process intensification technologies used for CO2 capture applications. Solvent developments for enhancing CO2 capture also are discussed as they contribute to cutting down the sizes of the units as well as decreasing the cost of regeneration which leads to reduction in capital and operating costs. A process flow diagram using a mop fan for capture of CO2 from buildings is proposed. In addition, a new flow diagram is proposed for using loop reactor technology to capture CO2 from large point sources, and finally a new process diagram using rotating packed bed absorber combined with sono‐assisted regenerator is presented for improved performance of CO2 capture technology. Recommendations on these diagrams are made to help ensure transition toward the net zero target. © 2022 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

Global warming is one of the most challenging issues of the current era. Revolutions in industrial, Information and Communication Technology (ICT) sectors significantly contribute in increasing global warming. Green House Gases (GHG) emissions from industrial, transportation, power and other sectors cause environmental pollution, which results in climate degradation. Environmental experts are well aware of the disastrous consequences of excessive global warming; therefore, several decarbonization strategies are developed and practiced in the recent past. A widely practiced decarbonization strategy is replacing fossil fuels by Renewable Energy Sources (RES) in the power systems. Electricity consumers are also encouraged to shift their consumption load to low carbon emissions' time periods. For accomplishing this task, an estimation of future carbon emissions is required. In this paper, power system's carbon emissions are predicted accurately with the help of a novel and an efficient forecasting model. The proposed model comprises of Spearman Correlation Analysis (SCA) and Improved Shallow Denoising Autoencoder (ISDAE) based feature engineering. Forecasting is performed through an Improved Particle Swarm Optimization (IPSO) based Deep Neural Network (DNN) forecaster. In addition, a comprehensive quantification analysis is also presented in this paper. The impacts of RES integration level on the electricity price, consumption cost and Greenhouse Gases (GHG) emissions are quantified descriptively and graphically. Performance of the proposed forecasting model is evaluated by Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE) and Mean Square Error (MSE). Simulation results prove that the proposed model outperforms Support Vector Machine (SVM) and Multiple Linear Regression (MLR) based carbon emission forecasting models in terms of forecasting accuracy. [ABSTRACT FROM AUTHOR]

Employing a time‐varying difference‐in‐differences model and a data set collected from 2,735 counties in China from 2003 to 2017, this study examines the causal relationship between Chinese government transfer payments to resource‐exhausted cities (TPREC) and carbon emissions for the first time. Our results show that TPREC could significantly reduce carbon emissions of resource‐exhausted cities in China. A variety of robustness tests supports our findings. Our further mechanism analysis shows that TPREC mainly affects carbon emissions through the following three channels: (a) promoting technological progress and industrial upgrades; (b) increasing vegetation carbon sequestration; (c) strengthening government environmental regulations. Finally, this paper analyzes the spatial heterogeneity of the carbon emission reduction effect of TPREC and finds that the most significant reduction effect of TPREC is in the western region and coal‐exhausted cities. This paper provides new empirical evidence for the role of transfer payments in promoting low‐carbon development using a unique set of data. Our findings provide new insights for decision‐makers to facilitate the low‐carbon development of resource‐exhausted cities. Plain Language Summary: By employing a time‐varying difference‐in‐differences model, we examine the causal relationship between central government transfer payments to resource‐exhausted cities and carbon emissions for the first time. We find China's TPREC can significantly reduce carbon emissions by promoting technological progress and industrial upgrades, increasing vegetation carbon sequestration, and strengthening government environmental regulations. Key Points: China's transfer payments to resource‐exhausted cities (TPREC) could significantly reduce carbon emissions, and this conclusion has also been supported by multiple robustness testsThe TPREC could achieve carbon emission reduction by promoting technological progress, industrial upgrading, and economic efficiencyThe most significant carbon emission reduction effect of TPREC is in the western region and coal‐exhausted cities [ABSTRACT FROM AUTHOR]

Now a day, green retail operations drive in many fields to improve the quality of deteriorating items during the delivery of the green items to his customer. In the present paper, retailer receives greening items from the seller and stores it. After received lot of greening items, retailer starts packing of greening items to maintain the quality of greening items and safe from some germs. We are considering that the holding units exit carbon units which are harmful to the environments. Retailer includes carbon emission cost and green packing cost and also invests on greening items to control the greening degree of the product. This paper deals a green economic order quantity (EOQ) inventory model with learning effect for decaying greening items under the inflationary condition and credit financing. An expression for the total cost of the retailer has been minimized with respect to cycle length in the environments of green operations. The objective of this paper is to analyze the impact of greening degree and trade‐credit financing with the learning effect and inflationary condition under carbon emissions on the retailer's total inventory cost. An expression for the total cost of the retailer has been minimized with respect to cycle length in the environments of green operations. Conclusively, sensitive analysis has been presented as a consequence of numerical examples. [ABSTRACT FROM AUTHOR]

Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition‐type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)‐long short‐term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time‐frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN‐LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short‐term forecasting performance is superior to the long‐term and medium‐term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price‐driving mechanism from the point of multiscale time‐frequency characteristics. Particularly, short‐term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions. [ABSTRACT FROM AUTHOR]

This paper empirically examines the effect of biomass energy consumption and economic complexity on environmental sustainability in G7 economies. The current study attempts to report a comprehensive analysis of biomass energy and economic complexity on ecological and carbon footprints and carbon emissions. We employ data from 1990 to 2019 and adopt robust panel econometric techniques that account for the analysis's cross‐sectional dependence. We conduct cointegration analysis, pooled ordinary least squares (OLS), system generalized method of moments (GMM) and conditional quantile model for our empirical analysis. The empirical findings show that both biomass energy consumption and economic complexity are detrimental to the ecological footprint and carbon footprint. Additionally, we find that globalization positively affects the environment, while we find some evidence that bureaucratic quality improves environmental quality. Finally, in line with other research, we find that economic growth has detrimental effects on the environment. Our results suggest that policymakers should be more cautious in promoting biomass as a clean energy source and that the G7 economies should take advantage of their leading position in innovation to invest more in sustainable practices and investment. [ABSTRACT FROM AUTHOR]