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Oral contraceptives (OC) are the most used form of contraception among women in the U.S. and Europe. Like other medications, their packaging must include patient information leaflets. This study quantifies the environmental impact of paper waste generated by these leaflets. We conducted an observational analysis, measuring the weight of leaflets, pills, and packaging components across various OC brands. Significant variations in leaflet weights were observed. On average, leaflets accounted for 55% of the package weight, while pills and blister dispensers represented only 32%. The mean weight of OC leaflets was 12.3 ± 5.5 grams (4.7–21.9 grams), leading to an estimated annual paper waste of 6,118.4 tons, 5,763.5 tons of carbon dioxide equivalent emissions, and the use of approximately 146,841 trees for production. Standardizing leaflet weight to the lightest reported can reduce annual waste by 3780.5 tons of paper. This study highlights the substantial environmental cost of the waste generated from OC leaflets and proposes practical strategies to mitigate waste, including electronic leaflets and standardized packaging. Targeting these materials presents a significant opportunity to enhance sustainability, aligning with global efforts to reduce greenhouse gas emissions from the healthcare sector. [ABSTRACT FROM AUTHOR]

Background: Health care systems contribute significantly to greenhouse gas emissions. One source of these emissions is single-use products. Examination table paper does not confer protection against microbial contamination and thus can be omitted while following infection control standards. The objective is to eliminate the routine use of examination table paper in outpatient oncology clinics at BC Cancer. Methods: A quality improvement approach was used. Examination tables continued to be disinfected using wipes between patients, but table paper was not used. Plan-do-studyact cycles were performed at four regional cancer centres. Results: Pre-intervention, the cancer centres used 19 to 69 rolls of paper monthly. Postintervention, usage declined to 0 to 2 rolls monthly. This was associated with annual cost savings of $3974 and a reduction of 32 501 kg of carbon dioxide emissions. Conclusions: The use of examination table paper can be eliminated in outpatient clinics, resulting in both cost savings and a reduction in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Reducing examination table paper (ETP) use may help curb carbon emissions from health care. Six participants applied Glo Germ (DMA International) to their hands before a common physical examination (abdominal, cardiorespiratory, hip and knee) both with and without ETP. After each exam, UV light was shined on the exam table and photographs were taken. The number of hand touches on ETP-covered areas and uncovered areas were tallied and compared using t tests. Despite covering more surface area, participants touched areas without ETP significantly more than ETP-covered areas (P <.05). Despite its continued use, patients do not have much hand contact with ETP during common clinical examinations. [ABSTRACT FROM AUTHOR]

China's carbon market is an important way to achieve carbon peak by 2030 and carbon neutrality by 2060. Blockchain technology can solve the problems of low trading efficiency, poor liquidity and regional heterogeneity in China's current carbon market. this paper explores the scenario of applying blockchain technology to China's carbon market. Firstly, this paper considers the allocation of carbon quota in the carbon market based on blockchain technology under policy regulation. The study found that the development of carbon trading pilots was uneven, and some pilot carbon trading volumes were low, presenting a situation of "no market price". Secondly, it describes the enhancement effect of blockchain technology on the carbon market from the perspective of technology and multiple factors. Considering only the technical factors, the enhancement process is an "inverted L" shape, while considering the superposition factors, the enhancement effect is approximately an "S" shape. Finally, based on the social learning model of major technological changes, the paper describes the process of dynamic utilization of blockchain technology by carbon trading enterprises and simulates the evolution law. The government should introduce relevant policies to guide the implementation of blockchain technology in the carbon market to strengthen the vitality of the carbon market and meet the expectation of carbon emission reduction. This paper provides theoretical support for the integration of blockchain technology into China's carbon market. [ABSTRACT FROM AUTHOR]

This paper studies the impact of digital economy development on carbon emissions, which is a hot topic in the field of environmental economics. This topic is within the scope of the Frontiers in Environmental Science and is particularly in line with the theme of Environmental Economics and Management section in the journal. Firstly, this paper constructs the Digital Economy Development Index and its variable indicators to measure the development of the digital economy. Secondly, this paper uses urban panel data to reveal the impact of digital economy development on carbon emissions, and further adopts IV method to deal with endogeneity issues. Thirdly, this paper performs the heterogeneity analyses depending on the characteristics of the city. The results can provide a basis for formulating differentiated policies. Fourthly, this paper further explores the mechanism by which the digital economy affects carbon peaking. The results show that the digital economy may influence carbon peaking through upgrading of the industrial structure, technical innovation, and energy consumption. [ABSTRACT FROM AUTHOR]

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China's three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies. [ABSTRACT FROM AUTHOR]

Carbon emissions from the Yangtze River Economic Belt are an important element of China's carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country's overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios. [ABSTRACT FROM AUTHOR]

As organic waste is converted to usable amendments via composting, there are large CO2 emissions associated with the decomposition of organic matter via microorganisms. While the active composting phase produces the largest emissions over a short duration, compost can often be stored during and after the maturation phase for much longer periods of time, increasing cumulative emissions. As such, the objectives of this study were to examine the spatial and temporal variability associated with in situ emissions sampling while identifying the environmental and chemical controls on emissions in windrow composting facilities during and after the maturation phase. A total of 665 flux measurements were taken from four windrows representing different ages and compositions between June and November 2020. Factorial analysis of covariance (ANOVA) was used to determine the variability between sampling locations, while multiple linear regression was used to identify those parameters which had the most influence on CO2 flux. Emissions showed significant variability over time that were attributed to ambient temperatures. During the summer, each windrow reached peak emissions between 5.0 and 32.3 g CO2 m-2 hr-1. As temperatures cooled, the windrows saw a 62%-86% decline in emissions, generally falling below 2 g CO2 m-2 hr-1. Significant differences occurred between the top-most sampling location and all others on the windrow, emitting between 33%-100% more CO2. The environmental controls of surface temperature, moisture content, and internal temperature showed the highest influence on emissions (R2 = 0.62). Chemical properties including organic nitrogen, carbon, pH, magnesium, and nitrate also showed significant influence (R2 = 0.43). This research has shown that environmental factors including temperature and moisture show the strongest influence over emission rates in mature compost. A significant negative effect of organic nitrogen on CO2 flux was found, indicating that increased presence of organic nitrogen would aid in the retention of carbon after the maturation phase, acting to lower total emissions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyses carbon emissions and value-added embodied in trade between two large developed countries, South Korea and the United States, during 2000-2014. Using multi-regional input-output (MRIO) tables, our analysis reveals that carbon emissions and value-added embodied in exports grew by 19% and 101% for South Korea but shrank by 43% and 7% for the United States. As a result, South Korea experienced a 40% increase in net carbon exports and 243% increase in net valueadded exports. At the industry level, the primary drivers of changes in carbon exports were electricity and basic materials. The majority of industries in witnessed improvements in carbon intensities suggesting improved environmental efficiency. While both countries achieved a decoupling of carbon emissions from value-added exports, substantial year-to-year and sectoral variations were observed. Finally, structural decomposition analysis indicates that domestic supply-side factors played a role in decreasing emissions whereas foreign demand-side factors contributed to emissions increases. In line with the main findings, various implications for policy and future research are discussed. [ABSTRACT FROM AUTHOR]

China plans to arrive at its peak in the emissions of carbon dioxide and reach carbon neutrality by 2030 and 2060, respectively. What role does internet technology, as a key twenty-first-century technology, play in China's achievement of its two carbon goals? Based on datasets about Chinese prefecture-level cities collected mainly from statistical reports released by the China Internet Network Information Center (CINIC), China City Statistical Yearbook, and China Energy Statistical Yearbook in 2006–2019, this paper empirically examines how the development of the internet influencing energy efficiency. This paper uses the policy of "network power nation" strategy and "internet plus" (NPNIP) as a policy impact to construct a continuous difference-in-difference (DID) model for an empirical investigation, and use a mediating effect analysis to test the transmission mechanism involved in industrial structure upgrading, technological innovation, and economic agglomeration. Empirical findings show that energy efficiency is improved by the development of internet. But this result has significant regional heterogeneity. Internet development can significantly reduce energy intensity in the eastern region and mature resource-based cities, but not the central and western regions and other resource-based cities. The mediating effect results show that the internet improves energy efficiency in three ways: upgrading industrial structure, stimulating technological innovation, and economic agglomeration. The empirical findings in this paper lead to many policy recommendation, including strengthening the formation of new infrastructure, deepening the organic integration of the internet across industries, and exploring the legal regulations on internet technology in specific application areas. [ABSTRACT FROM AUTHOR]

This paper provides a critical review of the climate university literature. Higher education institutions have increased their climate initiatives among students and society. Climate changes is becoming more and more present in daily activities. Whether it is about carbon dioxide emissions or deforestation, the social and economic consequences of climate change are challenging for each country in the world. Of all the institutions, universities play the most important role in educating people regarding the negative effects of climate change. While some universities use their own resources for this cause, others write plans and attract European projects to fund climate change objectives. The aim of this paper is to review the latest insights on Climate University. For this purpose, the paper analysed a database of scientific papers extracted from the Web of Science. The results underline the decisive role that higher education institutions play in promoting the fight against the causes of climate change. Society must understand the challenges of climate change and further develop solutions to address them. The new insights offered by this paper may be worthwhile for different stakeholders interested in climate change issues. [ABSTRACT FROM AUTHOR]

Purpose: Since the withdrawal of IFRIC 3 in 2005, there has been a regulatory freeze in accounting for emission rights that contrasts with the international momentum of climate-related financial disclosures. This paper explores how different narratives and institutional dynamics explain the failure to produce guidance on accounting for emission rights. Design/methodology/approach: This paper mobilises the notion of field-configuring events to examine a sequence of six events between 2003 and 2016, including four public consultations and two dialogues between standard setters. The paper presents a qualitative analysis of documents produced in this space that investigates how different practices and narratives configured the field's positions, agenda, and meaning systems. Findings: Accounting for emission rights was gradually decoupled from climate change and carbon markets, relegated to the research pipeline, and forgotten. The obstacles that the IASB and EFRAG found in presenting themselves as central in the recurring events, the excess of representations, and the increasingly technical and abstract debates eroded the 2003 momentum for regulation, making the different initiatives to revitalise the project vulnerable and open to scrutiny. Lukes (2021) refers to nondecision-making to express that some issues are suffocated before they are expressed. Originality/value: The regulation of accounting for emission rights, an area that has received scant attention in the literature, provides some insights into the different narrative mechanisms that, materialising in specific times and spaces, draw regulatory attention to particular accounting issues, which are problematised and, eventually, forgotten. This study also illustrates that identifying interests is problematic as actors shift from alternative positions over a long period. The case examined also raises some doubts about the previous effectiveness of international standard setters in dealing with matters of connectivity between the environment and finance, as is the case for accounting for emissions rights. [ABSTRACT FROM AUTHOR]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

Implementing watershed carbon compensation under the constraint of fiscal imbalance is a crucial approach for China to achieve its "dual carbon" goals. Focusing on 257 counties within the Pearl River Basin (PRB), this paper first measured and modified the land use carbon emissions, carbon absorption, and the land use carbon budget (LUCB) from 2005 to 2020. Subsequently, their spatiotemporal patterns and the changes before and after the modifications were analyzed. Finally, this paper determined the subject–object, value, and priority order of carbon compensation using the modified LUCB as the baseline value, and a carbon compensation mechanism that combines vertical and horizontal directions was constructed. The following findings were obtained: (1) At the time scale, the proportion of construction land and forestland in the land use structure increased, and carbon emissions, carbon absorption, and the LUCB showed an overall upward trend from 2005 to 2020. (2) At the spatial scale, the areas with high carbon emissions and carbon deficits were mainly located in the lower reaches of the basin, whereas the areas with high carbon absorption and carbon surpluses were widely distributed in the upper and middle reaches. The carbon deficit in urban municipal districts and resource-based counties was relatively serious. (3) In 2020, the total amount of carbon compensation in the PRB was CNY −8088.61 million. The number of counties that needed to be paid and compensated was 75 and 182, respectively. The carbon compensation mechanism constructed in this paper can provide a reference for other countries and regions with financial imbalances to achieve regional carbon neutrality. [ABSTRACT FROM AUTHOR]

Based on panel data for 30 provinces in China from 2000 to 2020, this paper uses entropy method to construct the integrated development indicators of energy Internet, and explores the impact of the development of the energy Internet on reducing carbon emissions in China and its internal transmission mechanism. The research results show that the development of energy Internet can significantly reduce regional carbon dioxide emissions, and increasing the use of renewable energy is an important mechanism for the comprehensive development of energy Internet to curb carbon emissions. Heterogeneity analysis shows that the emission reduction effect brought by the development of energy Internet is more remarkable in the regions with lower carbon emissions and western China. Furthermore, this paper finds that in the setting of the development of energy Internet, the development of renewable energy can promote natural gas consumption and advance energy conversion efficiency. The government's emphasis on regional technology development has an external strengthening effect on carbon emission reduction. The research in this paper expands and diversifies carbon reduction pathways and provides reference for more developing countries to utilize the development of energy internet to achieve carbon reduction. [ABSTRACT FROM AUTHOR]

Energy shortage and excessive carbon emissions have become the main constraints to the sustainable development of China's logistics industry. Improving the efficiency of the logistics industry is crucial to promoting regional ecological environment protection and sustainable development of the logistics industry. In this paper, the efficiency of China's logistics industry in 2013–2019 is evaluated by using the non-oriented EBM model considering the unexpected output, and then the influencing factors of the efficiency of the logistics industry in the central region are analyzed by using the panel Tobit model. The results show that the logistics industry in the central region is at a low efficiency level. Factors, such as the level of economic development, industrial structure and technical level have a significant positive impact on the efficiency of the logistics industry in the central region, while factors such as government support and logistics infrastructure have a significant negative impact on the efficiency of the logistics industry in the central region. Finally, the paper puts forward some targeted recommendations for the sustainable development of the logistics industry in the central region. [ABSTRACT FROM AUTHOR]

The development of the online meal-delivery industry is a double-edged sword, with potential to both hamper and drive progress towards achievement of the Sustainable Development Goals (SDGs). Yet most studies examining this industry have been conducted from the narrow perspective of specific academic fields, focusing on single issues or on individual cases. In this paper, by drawing upon the thematic foci of the SDGs as an analytical framework, we conducted a systematic and objective review to assess consequences of the online meal-delivery's development for sustainable development. After selecting and reviewing 128 peer-reviewed articles identified on Web of Science, we systematically coded their contents, and examined key trends along with knowledge gaps. The review shows that direct consequences for the SDGs—for instance, human health, plastic waste, carbon emissions, and labor conditions—have been thoroughly discussed. Conversely, we found a lack of attention to other indirect but important consequences, including impacts on poverty, hunger, education, and gender equality, which are yet to be thoroughly investigated. Particularly, our findings point to a need for more knowledge on how the industry is impacting food access in light of the "digital divide" between IT-literate consumers and those possessing less familiarity with digital technologies, how employment opportunities contribute to poverty alleviation or exacerbation of class gaps, and how the meal-delivery industry can contribute to achieving zero hunger by increasing food access to vulnerable populations. Our findings generate important insights to guide the formulation of more targeted countermeasures to address issues that risk hindering achievement of sustainable development. [ABSTRACT FROM AUTHOR]

In the context of the deep-sea transportation supply chain, this paper addresses the complex decision-making problem of vessel allocation and carbon emission optimization for container shipping routes. A bi-level programming model is established, with the upper level aiming to minimize the total operational cost and the lower level focusing on minimizing carbon emissions. Using an example of an operator with five different types of vessels, a genetic algorithm is employed to determine the optimal vessel allocation scheme. The results indicate that the vessel allocation scheme obtained through multiple iterations of the model effectively reduces both carbon emissions and operational costs. Under the condition that the preset labor cost increases year by year, the use of model optimization can significantly reduce the growth of total operating costs. This paper provides theoretical support and practical guidance for shipping companies aiming to optimize decision-making in order to reduce operational costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Carbon emission goals need China to upgrade its industrial structure to formulate a low-carbon economy. As a new form of industrial structure change, manufacturing and production service industries influence each other and become a collaborative agglomeration. This trend may have some impacts on carbon productivity. However, little research has been conducted whether and how the industrial collaborative agglomeration affects carbon productivity. To fill this research gap, this study employed panel data of 27 cities in the Yangtze River Delta urban agglomerations region from 2006 to 2020. First, by using the location entropy index, this study assessed the degree with regard to the collaborative agglomeration index of manufacturing and production services in the cities of the Yangtze River Delta urban agglomerations. Second, the study used a non-radial non-angular SBM model with the global Malmquist-Luenberger index to decompose the green technological progress index from each city's green total factor productivity. Third, this paper also used a mediating effect model to determine whether green technological progress played a mediating role with regard to the effect among industrial collaboration agglomeration and carbon productivity. The results indicate that (1) Industrial collaborative agglomeration significantly increased carbon productivity and green technological progress, with the industrial collaboration agglomeration index increasing by 1%, carbon productivity increasing by 28.8%, and green technological progress index increasing by 5.3%. (2) Green technological progress shows a significant partial mediating effect in carbon productivity effects of industrial collaborative agglomeration, with the mediating effect accounting for 19.82% of the overall effect. (3) There was a heterogeneity effect between high-end productive service and manufacturing industries collaborative agglomeration along with traditional productive service and manufacturing industries collaborative agglomeration. Former collaborative agglomeration increased 1%, carbon productivity increased by 37.9% and the mediating effect of collaborative agglomeration on carbon productivity through green technological progress accounted for 23.18% of the total effect. This mediating effect was not significant for traditional productive service and manufacturing industries collaborative agglomeration. This paper can provide some suggestions for construction of a low-carbon economy in Yangtze River Delta urban agglomerations to improve the cities' carbon productivity. [ABSTRACT FROM AUTHOR]

Research on how to efficiently utilize solar energy can effectively address the current situation where excessive carbon emissions threaten the natural environment. The solar capture device, as the core component of the solar thermal photovoltaic system, can significantly enhance the absorption properties of the solar thermal photovoltaic system, which is of high research value in the solar energy application area. In this paper, a metamaterial broadband solar capture device based on the top microstructure of semiconductor InAs material is proposed. The model is fabricated from top to bottom with the semiconductor InAs material at the top with Ti material to make hollow cylindrical microstructures, and a combination of SiO2 material film, Ti material film, and Cu material film as the substrate. In addition to incorporating the properties of metamaterials, the model is also inspired by the quantum-limited domain effect of nano-semiconductors by using the incorporation of InAs top microstructures at the top to further improve the model's absorption properties. The model was calculated to have an average absorption in the 280–2500 nm waveband of 96.15% and a weighted average absorption in the 280–4000 nm waveband of 97.71% at AM1.5. Results of calculating the model's reflectivity in the 280–20,000 nm bands show that the reflectivity of the model is higher than 80% in all the bands after the wavelength of 7940 nm, so the model has a certain spectral selectivity. In addition, the thermal radiation efficiency of the model in the 280–2500 nm waveband, when it is used as a thermal emitter, is calculated to reach 94.40% in this paper. Meanwhile, the capture device has good angular insensitivity, which has high potential for practical applications. [ABSTRACT FROM AUTHOR]

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people's mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC. [ABSTRACT FROM AUTHOR]

Ordinary cement mortar is commonly used in building engineering due to its high strength, affordability, and easy access to raw materials. However, it suffers from high shrinkage and poor impermeability, which result in reduced building service life and significant carbon dioxide emissions during production. Polymer additives have been found to enhance the mechanical properties of cement mortar, leading to increased interest in polymer cement mortar by researchers. This study collected and analyzed 420 papers published between 1995 and 2023 in the field of polymer cement mortar. The analysis included publication trends, author cooperation networks, national cooperation networks, published journals, co-citation of references, and keywords. The findings reveal a rapid publication growth from 2018 to 2023, with China making the most significant contribution in this field. Among the scholars, Ru Wang has published the highest number of articles in the field of polymer cement mortar, while Ohama's papers have been cited the most. The journal with the most articles is Construction and Building Materials. Research in polymer cement mortar focuses on mechanical properties, performance, hydration process, microstructure, and other related aspects. The reinforcement effect of polymer-modified cement mortar on reinforced concrete and applying superabsorbent polymer-modified cement mortar and polymer fiber in cement mortar have emerged as recent research frontiers. This study can help scholars quickly identify high-quality references and research frontiers in the field of polymer cement mortar while also providing research directions and ideas. [ABSTRACT FROM AUTHOR]

In order to give full play to the energy supply potential of distributed energy resources, this paper studies the scheduling optimization of CHP-VPP. First, the CHP unit and various distributed energy sources are aggregated into VPP. Carbon recycling and utilizing are realized through carbon capture and power-to-gas devices. At the same time, carbon storage and hydrogen storage devices are added to decouple carbon capture and P2G procedures. Then, the risk of VPP real-time scheduling is quantified through uncertainty scenario generation and CVaR. Finally, with the goals of operating cost, carbon emission, and operation risk, a multi-objective stochastic scheduling optimization model of VPP is constructed, and the subjective and objective ensemble weighting method is used to solve the problem. The example results show that the proposed method can boost the wastage of wind and photovoltaic power, and also lower the carbon emissions of VPPs. [ABSTRACT FROM AUTHOR]

The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent. [ABSTRACT FROM AUTHOR]

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. [ABSTRACT FROM AUTHOR]

As carbon capture and storage (CCS) technologies mature, the concept of a low-carbon or net-zero-carbon economy becomes more and more feasible. While many chemical and energy products do not contain carbon in their compounds, the upstream production process does. An added CCS module allows the removal of carbon emissions from the production process, which expands the value chain. This paper focuses on one of such commodities—low-carbon-intensity ammonia (LCIA). Even though ammonia is carbon-free in its final product, it is commonly made from natural gas, and the production process could generate significant carbon emissions. The idea of LCIA is to reduce the carbon footprint of the ammonia production process (e.g., blue ammonia) or eliminate carbon from the production process (e.g., green ammonia via electrolysis) so that the entire supply chain is decarbonized. The goal of this paper is two-fold. We first explore the US domestic market and the international market for LCIA. We then discuss relevant federal and local policies that could help grow markets for LCIA. The agricultural sector will be the center of the discussion, which consumes an estimated 70–90% of the global ammonia supply as fertilizers. The paper also examines other potential uses of LCIA, such as alternative fuels for decarbonizing agricultural machinery and transportation sectors. Finally, we argue that developing a comprehensive LCIA value chain, supported by dedicated policy measures and broad stakeholder engagement, is critical for materializing the potential of LCIA in contributing to a climate-resilient and sustainable economy. [ABSTRACT FROM AUTHOR]

In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision- making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization in China, urban energy consumption increases rapidly, leading to energy shortages and environmental pollution, of which building operational energy consumption carbon emissions (BECCE) account for a large proportion. It has a vital impact on global warming and urban green and sustainable development. Chengdu city in Sichuan Province is taken as the research area in this paper. First, basic information and power data on four types of single buildings, including large-sized buildings, small- and medium-sized buildings, government agencies, and residential buildings, are collected. Second, the characteristics of the four types of buildings are extracted, and the calculation model of BECCE ("electricity-carbon" model) based on particle swarm optimization algorithm-support vector machine (PSO-SVM) is constructed, and the model is trained and verified using the method of five-fold cross-validation. Then, according to the mean absolute error (MAE), root mean square error (RMSE), and R² evaluation indicators, the constructed "electricity-carbon" model is compared and evaluated. Finally, the generalization ability of the "electricity-carbon" model is verified. The research results show that (1) the "electricity-carbon" model constructed in this paper has a high accuracy rate, and the fitting ability of the PSO-SVM model is significantly better than that of the support vector regression (SVR) model; (2) in the testing stage, the fitting situation of large buildings is the best, and MAE, RMSE, and R² are 858.7, 1108.6, and 0.91, respectively; and (3) the spatial distribution map of regional BECCE can be quickly obtained using the "electricity-carbon" model constructed in this paper. The "electricity-carbon" model constructed in this paper can provide a scientific reference for building emission reduction. [ABSTRACT FROM AUTHOR]

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson's correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks. [ABSTRACT FROM AUTHOR]

With China's rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China's carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are "Low–Low" at the provincial level, and "Low–Low" and "High–High" at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China's carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies. [ABSTRACT FROM AUTHOR]

Accounting for the embodied carbon in construction materials and calculating the carbon footprint of entire construction projects in life-cycle assessments is a rapidly developing area in the construction industry. Carbon emission accounting relies on inventories that claim to represent the values of carbon contained in materials. However, these values vary between different carbon inventories. This scoping review identifies academic research on the carbon inventories used in Australia, as well as the methods used to compare these inventories. The study was conducted in accordance with the JBI methodology for scoping reviews. We identified 182 papers and narrowed these down to 11 that complied with the objectives of this study. Data for a range of construction materials were compared in these papers, as were the methods used to calculate the values. While some carbon inventories were used frequently, no clear preference for the method of calculating carbon values was apparent. The system boundaries also varied between publications, and a range of functional units was used. There was agreement that the variables involved in calculating carbon values for building materials are compounded by the practical issues of extracting and manufacturing materials in different regional or local conditions, cultures, and technological situations. It is therefore understandable that different inventories store different values when so many factors need to be considered. There is thus a clear need for agreement to be reached about standardisation of the processes involved. If the trustworthiness of the data stored in carbon inventories is questionable, so too are the outcomes of subsequent activities. [ABSTRACT FROM AUTHOR]

This paper explores the creation, development, and dissemination of prefabricated concrete rural houses in Jiangsu Province in East China from 1961 to the 1980s, an example of the technological transition provoked by the depletion of forest and timber in China. Through archival research, fieldwork and interviews, the paper examines the two waves of design and dissemination of prefabricated concrete rural houses between 1961 and 1965 and their subsequent 'vernacularisation' in the 1970s and the 1980s. This research provides a twofold insight into the current scholarly debates surrounding built heritage and global climate change. On one hand, it addresses a historical context of concrete overuse in contemporary China, a matter of critical importance in relation to carbon emissions and global climate change. On the other hand, it offers a counter-argument to today's call for reintroducing timber structures in many places, as evidenced by the case of East China, where the widespread use of materials like concrete was primarily a consequence of the ecological crisis following decades of deforestation and timber resource depletion. In addition, the 'vernacularisation' of concrete structures manifested by this case still provides lessons for today's efforts to popularise more eco-friendly construction materials and technologies, especially in rural areas, and the prefabricated concrete houses possess potential heritage values as trackers of ecological changes. [ABSTRACT FROM AUTHOR]

Digital economy is an important direction of the new round of technological revolution and a key driving force for realizing the "double control of carbon emissions". This paper utilizes the panel data of 30 provincial-level administrative regions in China from 2011 to 2021 to measure the development level of the digital economy, total carbon emissions, and carbon emission intensity and explores the impact of the digital economy on the dual control of carbon emissions and the mechanism of its effect by applying the mediating and moderating effect models. The results show that the digital economy can play a significant inhibitory effect on total carbon emissions and carbon emissions intensity, and this conclusion is still robust after a series of tests. From the government level, there exists a transmission path of "digital economy → environmental regulation stringency → dual control of carbon emissions"; from the enterprise and research organization level, there also exists a transmission path of "digital economy → R&D intensity → dual control of carbon emissions". From the perspective of regional imbalance, there are large regional differences in the impact of the digital economy on the dual control of carbon emissions, and there are also large differences in the impact of the various subdivided indicators of the digital economy on the dual control of carbon emissions. In addition, this paper also finds that the positive effect of the digital economy on the dual control of carbon emissions is more obvious in regions with a smaller proportion of SOEs. These findings add new evidence to the study of "the impact of the digital economy on the dual control of carbon emissions" and provide new ideas for accelerating the realization of green and sustainable development. [ABSTRACT FROM AUTHOR]

Digital inclusive finance (DIF) is a strategic tool that fosters the green transformation of the industrial economy. Based on the data from the 11 provinces and municipalities in the Yangtze River Economic Belt of China between 2012 and 2021, This paper utilizes the Tobit, intermediary effect, and threshold effect models to empirically study the impact of DIF on the industrial land carbon emission intensity (ILCEI). This paper reaches the following conclusions: (1) The ILCEI in the region revealed a downward trend during the study period. There are substantial differences in carbon ILCEI; higher upstream and lower downstream. The average ILCEI in the lower reach provinces is 0.5829 ton/m2 during the research period, while that in the upper reach region is 1.0104 ton/m2. (2) DIF has a significantly inhibitory effect on the ILCEI; this effect has nonlinear characteristics. The impact of DIF on ILCEI exhibits a marginally diminishing trend as the industrial land economic agglomeration degree improves. (3) Regarding the transmission mechanism, the level of industrial R&D investment plays a primary intermediary role in the impact of DIF on ILCEI. (4) Concerning control variables, foreign investment dependence and trade contribute significantly to inhibiting ILCEI. Lastly, this paper proposes a series of measures to promote DIF to fully utilize the emission reduction effect. The research outcomes have substantial implications for the sustainable development of industrial land. [ABSTRACT FROM AUTHOR]

With the growing concern for the environment, sustainable development centred on a low-carbon economy has become a unifying pursuit for the energy industry. Integrated energy systems (IES) that combine multiple energy sources such as electricity, heat and gas are essential to facilitate the consumption of renewable energy and the reduction of carbon emission. In this paper, gas turbine (GT), carbon capture and storage (CCS) and power-to-gas (P2G) device are introduced to construct a new carbon capture coupling device model, GT-CCS-P2G (GCCP), which is applied to the integrated electrical and gas system (IEGS). Multi-agent soft actor critic (MASAC) applies historical trajectory representations, parameter spatial techniques and deep densification frameworks to reinforcement learning for reducing the detrimental effects of time-series data on the decisional procedure. The energy scheduling problem of IEGS is redefined as a Markov game, which is addressed by adopting a low carbon economic control framework based on MASAC with minimum operating cost and minimum carbon emission as the optimization objectives. To validate the rationality and effectiveness of the proposed low-carbon economy scheduling model of IEGS based on MASAC, this paper simulates and analyses in integrated PJM-5 node system and seven nodes natural gas system. [ABSTRACT FROM AUTHOR]

Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China's carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities. [ABSTRACT FROM AUTHOR]

As the proportion of household carbon emissions to global carbon emissions continues to increase, reducing carbon emissions from household consumption has become an important way to achieve the goals of carbon peaking and carbon neutrality. How the trend of miniaturization of household size will affect household carbon emissions is a matter of concern. This paper uses a sample of 9,090 households from the China Family Panel Studies (CFPS) database in 2018 to empirically study the impact of changes in household size on household carbon emissions, from the perspective of household consumption structure and urban-rural areas. The research results indicate that the miniaturization of household size will increase household carbon emissions, the impact of household size on indirect HCEs is greater than on direct HCEs. The impact of household size on indirect HCEs is heterogeneous in consumption structure and the impact of household size on indirect HCEs from housing, transportation is greater than that of other consumption items. The impact of household size on urban household carbon emissions is greater than that in rural areas. The upgrading of household consumption structure and the miniaturization of household size promote the increase of HCEs jointly. Therefore, this paper proposes that under the trend of household miniaturization, energy-saving and emission reduction policies should focus on reducing indirect households carbon emissions, optimizing household structure and household consumption structure, enhancing environmental awareness among family members, establishing and improving the green consumption system, and building environment-friendly households. [ABSTRACT FROM AUTHOR]

To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019-2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule. [ABSTRACT FROM AUTHOR]

Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction. [ABSTRACT FROM AUTHOR]

Transitioning to clean cooking fuels is not only part of achieving SDG7 but also makes a significant contribution to mitigating climate change by reducing carbon emissions. Research projects and pilots across a number of countries in Africa and South Asia have been exploring the suitability and energy performance of different cooking appliances and fuels. The paper presents the first statistical analysis across these multiple datasets to determine the range of energy required to cook dishes using different technologies and fuels. The paper draws out distinctions between African and Asian dishes, notably the impact of energy-intensive dishes prepared mostly in Africa. The paper demonstrates that the standard efficiency-based approaches to comparing the performance of stoves are not appropriate to modern electric cooking devices, so a novel alternative approach based on specific energy consumption and termed energy ratios is developed. Charcoal stoves are shown to use 15 times as much energy as electric pressure cookers (EPCs) to cook African dishes, and a detailed review of how the EPC works explains why this should be. Energy ratios provide a basis for estimating carbon emission reductions associated with transitioning to modern cooking fuels and also for estimating household cooking costs. Fuel and electricity prices from studies show that the cost of cooking with an EPC can be only 20% of the cost of cooking with charcoal, which highlights the potential for modern, energy-efficient electric cooking devices to defy the conventional wisdom of the energy ladder. [ABSTRACT FROM AUTHOR]

Facing the double constraints of the "double carbon" target and high-quality economic development, carbon trading policy is an important tool for realizing the emission reduction commitment; based on the perspective of microenterprises, the specific mechanism and spatial effect of carbon trading policy still need to be evaluated. Taking China's carbon emissions trading pilot as a quasi-natural experiment, this paper empirically investigates the impact of carbon trading policy on the carbon emission intensity of pilot enterprises and its mechanism of action, and its impact on the carbon emission intensity of neighboring enterprises, based on the multi-temporal double-difference model, moderating effect model, and spatial Durbin model with the A-sharelisted enterprises in the period of 2009-2019 as the samples. It is found that: 1) Carbon trading policy will reduce the carbon emission intensity of enterprises to different degrees, and there are significant differences under different ownership types, degrees of marketization and the level of digitization. 2) Under the influence of environmental uncertainty, ESG disclosure will weaken the effectiveness of carbon emission reduction in the pre-pilot stage of the policy; with the gradual improvement of the carbon trading policy and ESG disclosure mechanism, ESG ratings will positively regulate the inhibitory effect of the carbon trading policy on the carbon emission intensity of enterprises through multiple paths. 3) Carbon trading policy effectively reduces multiple negative spillovers through the demonstration effect and competition effect of neighboring enterprises, driving the carbon emission reduction behavior of non-pilot enterprise. The research in this paper enriches the research paradigm of carbon emission intensity influencing factors, provides reference suggestions for the government to improve its policies, and better contributes to the realization of the "dual-carbon" vision in China as soon as possible. [ABSTRACT FROM AUTHOR]

Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries' technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector's potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector. [ABSTRACT FROM AUTHOR]

The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China's national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries. [ABSTRACT FROM AUTHOR]

The synergistic relationship between water, energy, and food faces increasing challenges. Green finance as a policy tool promotes high-quality and efficient development of water, energy, and food subsystems. However, whether it can improve the synergistic relationship of "water–energy–food" (WEF) still needs to be studied. Using the panel data of 38 prefecture-level cities in the Yangtze River Delta (YRD) from 2013 to 2021, and network DEA and panel regression models, we study the impact of green finance on the level of synergy of "water–energy–food". The results of the study show that green finance can promote the synergy level of "water–energy–food" in the Yangtze River Delta city cluster, which was first inhibited and then promoted from 2013 to 2021. The impacts of green finance on the efficiency of the three subsystems are also characterized by a "U" shape. However, linear impacts show differences, with green finance improving the efficiency of the water subsystem but not the energy and food subsystems. The implementation of China's national strategies empowers green finance. The digital strategy, the "dual carbon" strategy, and the new urbanization strategy have increased the effectiveness of green finance in contributing to the level of water–energy–food synergy. Regional heterogeneity analysis shows that the promotion effect of green finance mainly exists in non-resource-based cities, non-main grain-producing areas, and non-cities along the South-to-North water diversion route and large cities. Finally, this paper puts forward relevant policy recommendations. [ABSTRACT FROM AUTHOR]

Since 1990, global forest area has been reducing; tropical forests have suffered from different anthropogenic and natural factors that account for forest loss. Tropical deforestation is the second driver of anthropogenic emissions; increasing demand and investments in tropical forests drive these emissions. These forests attract significant foreign direct investments, but the effects of these investments on carbon emissions from land use, land-use change, and forestry are not well enshrined in the literature for the countries under study. This paper seeks to analyse the impact of foreign direct investments on carbon emissions from land use, land-use change, and forestry amongst 30 tropical forest countries from 1996 to 2019. The sampled countries were disaggregated by tropical blocs: Amazon, Congo basin, Australasia, and Southeast Asia, and by income levels; low-income, lower-middle, upper-middle, and high-income. The findings reveal a U-shape structure of the impact of FDI on carbon emissions from LULUCF within Congo basin and Amazon countries; at higher levels of FDI, emissions from LULUCF will increase while Australasia and Southeast Asian countries show an inverted U-shape impact, thus at higher levels of FDI, there will be a negative and significant impact on carbon emissions from LULUCF. The income levels reveal an inverted U-shape for low-income and high-income countries and a U-shape for upper-middle-income countries; the impact for lower-middle-income countries is not significant. Overall, for the whole sample, the impact depicts a U-shape. This paper proposes high-level development of environmental conditions for FDI for different sectors that align with country and regional green growth plans. Enhancing national and regional governance systems to enforce decisions and fight corruption effectively can significantly promote green FDI for green growth. [ABSTRACT FROM AUTHOR]