Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

This document is an editorial published in the journal Frontiers in Environmental Science. It discusses the governance of carbon emissions in the context of urban-rural interaction. The editorial highlights the importance of managing carbon emissions and related environmental issues in urban-rural dynamics and proposes strategies for carbon emission reduction and sustainable development. The editorial provides an overview of four papers published in the research topic, which explore topics such as the correlation between environmental governance attention and environmental quality, regional disparities in urban-rural carbon emissions, carbon emissions inequality between urban and rural residents, and carbon emissions from building operations. The papers contribute to the academic community and offer guidance for policy development in carbon emission management. The editors hope that this research will inspire further exploration and collaborative initiatives to mitigate regional carbon emissions and create an environmentally sustainable and socially just future. [Extracted from the article]

With the proposal of "Carbon Peak and Carbon Neutrality" goals, China is facing a more serious carbon emissions reduction situation, and how the booming digital economy effectively helps China's carbon emissions reduction is one of the most urgent things that should be solved. To study the impact of the digital economy on carbon emission intensity, this paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2021, and applies the double-fixed effect model and the threshold effect model to study the impact of the digital economy on carbon emission intensity and the mechanism of its action, as well as to analyze the mechanism of the digital economy's action on carbon emission intensity from the perspective of technological innovation. The results of the study show that: i) The digital economy can reduce the intensity of regional carbon emissions; ii) The carbon emission reduction effect of the digital economy is non-linear, and its carbon emission reduction effect gradually increases with the level of development of the digital economy; iii) In addition to the direct impact of the digital economy on carbon emission intensity, it also has an indirect impact on carbon emissions through technological innovation; iv) There is regional heterogeneity in the carbon emission reduction effect of the digital economy, and the carbon emission reduction effect is more significant in the central and western parts of the country and regions with a high level of human capital development. Based on the conclusions obtained, this paper suggests: i) The rational integration of the digital economy and regional development should be strengthened; ii) Strong provinces in the digital economy should be encouraged to help weaker provinces, to narrow the "digital divide" between provinces; iii) Differentiated development strategies should be formulated in accordance with local conditions, to give full play to the optimal effect of the digital economy in carbon emission reduction. [ABSTRACT FROM AUTHOR]

In the face of global climate change challenges, China's implementation of the carbon emission trading (CET) pilot policy has provided new empirical research opportunities. Based on a dataset covering 281 Chinese cities from 2005 to 2021, this paper employs econometric models to conduct an in-depth analysis of the policy's impact on urban green innovation (UGI). The findings indicate that the CET pilot policy has significantly promoted green innovation activities in affected cities, with positive effects observed both directly in pilot cities and indirectly in non-pilot cities through spatial spillover effects. In addition, the policy has been found to encourage technological investment and enhance public environmental awareness (PEA), further advancing green innovation. The paper also unveils comprehensive policy effects, indicating that the Big Data Comprehensive Test Zone policy and the New Energy Demonstration City policy work synergistically with the CET pilot policy in advancing green innovation. These findings provide valuable experiences and insights for designing environmental policy tools at the national level, promoting green development, and constructing climate change response strategies. [ABSTRACT FROM AUTHOR]

Under the dual-carbon target, hydrogen energy, as a zero-carbon secondary energy source, has great scope for replacing fossil feedstocks in the fields of energy, transportation and industry. However, the current research on the competitiveness of hydrogen energy in various fields is not sufficiently addressed. In this paper, we use the LEAP model to predict the future scale of hydrogen use and the two-factor learning curve to predict the trend of hydrogen price change from 2025 to 2050, using Qinghai Province as the research background. At the same time, considering the carbon emission reduction benefits and raw material costs, the competitiveness of hydrogen energy in energy, transportation, industry and other fields in the future is compared. The results show that: 1) The hydrogen load scale in Qinghai Province will grow fast from 2025 to 2030. From 2030 to 2040, it slows under the steady and basic scenarios but remains high under the accelerated one. By 2040, the consumption scales are 1.057 million, 649,000 and 442,000 tons respectively. 2) The price of hydrogen energy will drop rapidly from the current 28 CNY/kg to about 20 CNY/kg in the next 5 years. By 2040, the price of hydrogen energy will be reduced to about 17 CNY/kg. 3) In terms of hydrogen energy competitiveness, when carbon emissions are not taken into account, hydrogen energy is currently competitive in the transportation field. During 2032-2038, it will be competitive in the field of methanol synthesis. By 2040, hydrogen energy will not be competitive in the fields of ammonia synthesis and power/heating. When considering carbon emissions, the competitiveness of hydrogen energy in the transportation field will become greater. The competitive year in the field of methanol synthesis will be 1-2 years ahead. By 2040, it will not be competitive in the field of synthetic ammonia and power/heating, but the gap will be significantly reduced due to the consideration of carbon emissions. [ABSTRACT FROM AUTHOR]

Predicting carbon dioxide emissions is crucial for addressing climate change and achieving environmental sustainability. Accurate emission forecasts provide policymakers with a basis for evaluating the effectiveness of policies, facilitating the design and implementation of emission reduction strategies, and helping businesses adjust their operations to adapt to market changes. Various methods, such as statistical models, machine learning, and grey prediction models, have been widely used in carbon dioxide emission prediction. However, existing research often lacks comparative analysis with other forecasting techniques. This paper constructs a new Discrete Fractional Accumulation Grey Gompertz Model (DFAGGM(1,1) based on grey system theory and provides a detailed solution process. The Whale Optimization Algorithm (WOA) is used to find the hyperparameters in the model. By comparing it with five benchmark models, the effectiveness of DFAGGM(1,1) in predicting carbon dioxide emissions data for China and the United States is validated. [ABSTRACT FROM AUTHOR]

Introduction: The development of the digital economy has a profound impact on industrial economics. The article starts from the perspective of industrial organization theory. The thesis aims to analyze the industrial development of the digital economy and its three structural variables: digital manufacturing industry, digital service industry, and the development of industrial digitization on carbon emission intensity. Method: Based on the data of Input-output Tables with extended tables of 30 provinces, this paper analyzes the industrial development of digital economy and its three structural variables: digital manufacturing industry, digital service industry, and the development of industrial digitization on carbon emission intensity. Results and discussion: The empirical results show that: 1) at the national level, the development of China's digital economy industries has a suppressive effect on carbon emission intensity; 2) in terms of spatial effects, the development of digital economy has significant carbon spillover effects, but digital industrialization and industrial digitization present different spatial effect results; 3) from the analysis of regional heterogeneity, in the northeast, central and western regions, the direction of influence of digital industrialization and industrial digitalization on carbon emission intensity is consistent; in the eastern region, the development of digital industrial service sector and industrial digitalization has a suppressive effect on carbon emission intensity, while digital industrial manufacturing sector presents a pro-increasing effect; 4) Non-linear relationship analysis shows that the development of the digital industrial manufacturing sector has a "promoting and then inhibiting" effect on carbon emission intensity. Overall, the impact of digital industry development on carbon intensity exhibits a "promoting increase, then suppressing, then promoting increase" trend. In conclusion, the findings suggest that China's digital economy industry has entered the low-carbon development stage. [ABSTRACT FROM AUTHOR]

The reduction of greenhouse gas emissions is a shared challenge encountered by nations worldwide. As China is on its way toward a green economy, it is worth studying whether producer services agglomeration, a key driver of economic transition, can promote low-carbon urban development. Using panel data of 257 cities across China from 2006 to 2019, this paper examines the influence of producer services agglomeration on urban carbon emissions with spatial econometric models. The findings reveal a positive spatial correlation in regional carbon emissions. The agglomeration of producer services notably decreases the intensity of local carbon emissions, yet it appears to have minimal influence on the emissions from adjacent regions. Enhancing energy efficiency and adjusting the industrial structure are two critical mechanisms by which producer services agglomeration reduces urban carbon emissions. This beneficial effect varies with city type, the abatement effect of producer services agglomeration is more pronounced in non-resource-based cities. When considering city size, the carbon reduction potential of producer services agglomeration is not apparent in smaller cities. As city size increases, the emission reduction effect becomes more apparent. However, in mega-cities, this impact is somewhat diminished. Accordingly, this paper proposes exploring methods of coordinated air pollution management across cities, promoting producer services agglomeration in line with market mechanisms, and driving low-carbon urban development in a manner tailored to local conditions. [ABSTRACT FROM AUTHOR]

The massive CO2 emission has caused frequent occurrence of climate problems, and a typical response to climate change has reached international consensus. Digital finance and green finance, as a subversion of the traditional financial model, have become significant drivers of global carbon emissions reduction efforts. Based on the panel data, this paper profoundly compares the effects of carbon emissions reduction, mechanisms, and heterogeneous results of two forms of finance. Research finds that technology-centered digital finance focuses on suppressing carbon emissions through technological innovation, and the higher the level of regional economic is, the stronger the role of digital finance in suppressing carbon emissions through technological innovation. The concept-centered green finance focuses on carbon emissions reduction through industrial ecologization, and government-led environmental regulation plays a positive regulatory role. Although the paths of affecting carbon emissions are different, there is a natural fit between the two in terms of the essential goal of carbon emissions reduction. Based on the consideration of the differences in geographic location and financial development level, the carbon emission reduction effects of two forms of finance show apparent heterogeneity. Based on the spatial characteristics of digital finance and carbon emissions, this paper further finds that the digital finance's carbon emission reduction effects have apparent spatial spillovers. These findings provide an essential direction to formulate a reasonable carbon emissions reduction plan and accelerate realizing the "double carbon" goal. [ABSTRACT FROM AUTHOR]

The integrated energy system considering comprehensive demand response can realize cascade utilization of energy and reduce carbon emissions. However, few studies explore the operation of Integrated energy system considering the coupling markets of electricity and carbon trading. Based on the characteristics and specific needs of the integrated energy system, this paper establishes the mathematical model of each energy supply equipment, and studies the optimal energy supply method of the system. First, demand response is categorized into price and substitution types based on load response characteristics. Second, the price demand response models are established utilizing the price elasticity matrix, and substitution demand response models are developed considering the mutual conversion of electric and heat energy on the user-side. Subsequently, a baseline method is employed to allocate carbon emission quotas to the system without charge with considering the actual carbon emissions from gas turbines and gas boilers. This results in the formulation of an improved carbon trading mechanism tailored for integrated energy system. Finally, a low-carbon optimization operational model for integrated energy system is constructed with the multi-objective functions. The results of numerical case studies are presented to validate the performance of the proposed control method. [ABSTRACT FROM AUTHOR]

In light of the escalating concerns surrounding climate change and air quality degradation, the imperative for energy conservation and emission reduction has garnered widespread attention. Given that factories represent a significant portion of electricity consumption within the power network, a comprehensive analysis of the electricity consumption behavior of energy- intensive enterprises becomes paramount. To meticulously dissect the electricity consumption patterns of energy-intensive enterprises, this paper categorizes them into four distinct production modes: 24-hour all-day production factories, pure daytime production factories, pure nighttime production factories, and environmentally friendly peaking production factories. Employing the dynamic electricity-carbon factor as a guiding force, the analysis encompasses electricity consumption, tariff expenditure, peaking costs, carbon emissions, and comfort levels associated with each production method throughout the year. A delicate equilibrium is sought among multiple objectives, aiming to optimize the user experience while simultaneously mitigating costs and carbon emissions. Furthermore, this paper conducts a comparative analysis of each objective, employing single-objective genetic algorithms and the interior point method. The resultant findings serve as invaluable insights for business users, aiding in informed decision- making processes. [ABSTRACT FROM AUTHOR]

Introduction: Reducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the lowcarbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself? Methods: This work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model. Results: This study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point. Discussion: The findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage. [ABSTRACT FROM AUTHOR]

The energy saving and emissions reduction of the construction industry are crucial for China to achieve the "carbon peaking and carbon neutrality" goals. In order to promote the green development of the life cycle of the construction industry and improve the efficiency of emissions reduction. This paper examines the spatial-temporal distribution of life cycle carbon emissions in China's construction industry (LCCECI) from 2004 to 2018. It uses the SBM-Malmquist total factor productivity (TFP) index to measure technological progress and establishes the spatial econometric model based on the STIRPAT model. The study investigates the driving factors of the LCCECI at the provincial and regional levels, aiming to provide suggestions for low-carbon development in the construction industry. The research results are as follows. © The growth in the SBM-Malmquist TFP index of the construction industry distinctly curbs the LCCECI. © Total population and urbanization level are not the primary driving factors for the LCCECI. The growth of per capita GDP significantly induces the LCCECI, while concurrently exhibiting a notable inhibitory effect on the LCCECI of neighboring regions. © The improvement of the SBM-Malmquist TFP index is conducive to the reduction of the LCCECI in the three major regions. The per capita GDP has the largest positive driving effect of the LCCECI in the eastern region, and the urbanization level the urbanization rate only significantly inhibits the growth of the LCCECI in the central region. [ABSTRACT FROM AUTHOR]

The accountability audit of natural resources (AANR) is a major institutional arrangement for advancing the construction of an ecological civilization in China. Based on the panel data of 271 cities in China from 2005 to 2017, this paper investigates the relationship between the AANR and carbon dioxide (CO2) emissions using a multi-period difference-in-differences (DID) model. The results show that AANR significantly increases the CO2 emission reduction rate by 0.009 units at the 5% significance level. The results still hold after a series of robustness tests. Given all else being equal, this significant effect is 0.001. Further analyses show that AANR improves pilot cities' CO2 emission reduction rates mainly by enhancing their green innovation capability. The mediating effect of cities' green technology innovation capability accounts for 96.00%, while the AANR's direct effect only accounts for 4.00%. The AANR has significantly positive effects of 0.017% and 0.029% for western cities and cities with high fiscal pressure at the 5% and 1% significance levels, respectively. Therefore, strengthening AANR implementation by enhancing the mediating efficiency of cities' green technology innovations and implementing dynamically differentiated AANR policies in Chinese meso-cities will contribute to the achievement of China's carbon peaking and carbon neutrality targets. [ABSTRACT FROM AUTHOR]

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the- loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning. [ABSTRACT FROM AUTHOR]

In the context of rapid advancement in automation and increasing global warming, understanding the impact of artificial intelligence (AI) on carbon emissions (CES) is a cutting-edge research topic. However, there is limited focus in existing research on the nonlinear carbon reduction effect (CRE) of AI. This paper first theoretically elaborates the dual impact mechanisms of AI on CES and illuminates the nonlinear carbon reduction mechanisms of AI. Then, this study employs panel data encompassing 30 Chinese provinces between 1997 and 2019 to empirically test the net effect of AI on CES and the nonlinear carbon reduction effect of AI through econometric models. The results are as follows: first, although AI can both reduce and increase CES, AI primarily helps decrease CES. This conclusion holds true even after considering robustness, endogeneity, and spatial heterogeneity. Secondly, relative to the central and western regions, AI has significant achievement in reducing carbon intensity and per capita CES in the eastern region. However, there is still room for improvement in terms of reducing the total CES in the eastern region. Thirdly, improving the AI development level (AIDL) can magnify the marginal CRE of AI and lead to a nonlinear CRE of AI. Lastly, even if the AIDL remains constant, improving the level of marketization, human capital, digital infrastructure, economic development, openness, and government intervention can also amplify the marginal CRE of AI and lead to a nonlinear CRE of AI. To fully harness the potential of AI for green development, concerted efforts should be directed towards enhancing the innovation and application of AI technologies with carbon reduction potential. [ABSTRACT FROM AUTHOR]

To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source- load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

To optimize the energy supply potential and complementary advantages of distributed energy, this paper focuses on the dispatching optimization of cogeneration virtual power plant considering uncertainty. First of all, wind power, photovoltaic, combined heat and power (CHP) units, electric boilers, and controllable loads are integrated into a CHP virtual power plant. Then, carbon capture and electric-to-gas devices are introduced to realize carbon recycling. Furthermore, quantify the risk of real-time dispatching of virtual power plants through uncertainty scenario generation and conditional value at risk (CVaR) theory, and the multi-objective stochastic dispatching optimization model of virtual power plants is built with the aim at minimizing the operation cost, carbon emissions, and operation risk as the objectives, and the CRITIC weighting method is adopted to solve it. Finally, the calculation results show that: 1) the electric boiler can use wind and photovoltaic power to supply heat for the system, reduce the dependence ofthe virtual power plant (VPP) on the CHP unit, and make the electric output of the unit more flexible. 2) The risk quantification method proposed can fully measure the risk situation in real-time dispatching, arrange the wind and photovoltaic power generation plan and backup plan more reasonably, and enable the VPP to get more benefits while avoiding the risks in real-time dispatching. [ABSTRACT FROM AUTHOR]

Integrated energy system is crucial in realizing China's "dual carbon" targets. Considering the carbon capture based electricity to gas and the interaction of multiple markets, this paper proposes a low-carbon operation optimization method of integrated energy system. In terms of market policy, a coupling trading mechanism for carbon trade and green certificates is established. This approach is intended to delve into the profound significance of utilizing green certificates in carbon emission reduction. In terms of equipment models, the coupling model of carbon capture equipment with coal-fired cogeneration unit, as well as power-to-gas equipment with renewable energy, is constructed. In addition, this equipment model is introduced into the operation optimization scheduling of the comprehensive energy systems. A low-carbon economic operational strategy is further proposed to minimize the daily operational costs, by which the integrated energy system is eco-nomically, environmental protection optimized. To verify the effectiveness and feasibility of the proposed model, this paper sets up several comparison scenarios and conducts the simulations using GUROBI solver. The results show that the proposed strategy can effectively improve the uptake rate of renewable energy, reduce the carbon emission, improve the operation economy, and realize the complementary incentive effect between markets. [ABSTRACT FROM AUTHOR]

Based on the data of listed companies in China's iron and steel industry from 2007 to 2020, the article investigates the impact mechanism and the path of action of China's carbon emissions trading pilot on the green total factor productivity of iron and steel enterprises by constructing a multi-period difference-in-difference model difference-in-differences. The study finds that: 1) China's iron and steel enterprises significantly improve their green total factor productivity driven by the carbon trading pilot, and the findings pass the corresponding robustness tests. 2) the mechanism analysis indicates that the carbon trading pilot promotes the green total factor productivity of iron and steel enterprises by forcing the technological progress of enterprises. 3) The heterogeneity analysis shows that the positive effect is more significant for large iron and steel enterprises with high social responsibility rating and high local government competition intensity, but not for small enterprises with low social responsibility rating and low local government competition intensity. 4) the dynamic effect shows that there is a certain lag in the promotion effect of the carbon emission trading pilot on the green total factor productivity of iron and steel enterprises, but its long-term effect is more obvious. This paper puts forward corresponding suggestions for accelerating the construction of a national unified green and low-carbon market system and actively promoting the deepening of the "dual-carbon" goal. [ABSTRACT FROM AUTHOR]

The rapid development of low-carbon energy technologies and energy storage technologies has provided an important and feasible path to decarbonizing the power system. In this context, there is an increasing number of studies on renewable energy, carbon capture, utilization and storage (CCUS) and energy storage expansion planning. However, most of the existing studies attribute the carbon responsibilities to the source side and a small number to the load side. Expansion planning studies that consider the overall carbon emissions of the system to be shared between the source and the load side are still relatively few. Therefore, it is necessary for the source and the load side to share the responsibility for the total system carbon emissions. To fill this research gap, this paper proposes a source-load bilateral carbon incentive mechanism for wind-CCUS-battery power systems based on the carbon emission flow theory. Besides, a bi-layer wind-CCUS-battery expansion stochastic planning framework considering wind and load uncertainties is constructed. The first layer takes the minimum expectation of power generation costs, fixed investment costs of wind turbines and CCUS units and carbon incentive costs as the objective function from a source-side perspective. The second layer takes the minimum battery investment cost and the expectation of electricity purchasing costs and load-side carbon incentive costs as the objective function from a load-side perspective. Finally, the proposed model is tested on the IEEE 24 bus power system for validity and advantage. The results show that the current high investment cost is not favorable to CCUS construction. At this time, the bilateral carbon incentive mechanism is more conducive to promoting system carbon reduction than the unilateral carbon incentive mechanism. In the future, as the cost of CCUS decreases, the source-side carbon incentive mechanism is more conducive to system carbon reduction than the bilateral carbon incentive mechanism. Due to the consideration of the stochastic uncertainty of wind turbines and loads, the research in this paper is closer to the reality, which can provide a reference for the future carbon emission reduction path of the power system, especially for the quantitative analysis of carbon emission reduction of CCUS, which is an important guiding significance for the promotion of the engineering practice of CCUS. [ABSTRACT FROM AUTHOR]

The integrated energy system is an effective way to achieve carbon neutrality. To further exploit the carbon reduction potentials of IESs, an optimal dispatch strategy that considers integrated demand response and stepped carbon trading is proposed. First, an integrated demand response (IDR) pricing approach is proposed based on the characteristics of different load types. Classify multi-energy loads into curtailable and substitutable loads, and incentivize both loads through a price elasticity matrix and low-price energy in the same period. Then, to better incentivize IESs to reduce carbon emissions, a stepped pricing mechanism was introduced in the carbon price. Finally, an optimal dispatch model is developed with an objective function that minimizes the sum of energy purchase cost, carbon trading cost, and operation and maintenance (O&M) cost. Considering the high-dimensional and non-linear characteristics of the model, an improved differential evolution (DE) algorithm is introduced in this paper. In addition, this paper also analyzes the effects of the stepped carbon trading parameters on the optimal dispatching results of the system in terms of carbon trading base price, carbon emission interval length, and carbon price growth rate. Compared to the case of adopting a single IDR model or a single stepped carbon trading, carbon emissions from the IESs decreased by 6.28% and 3.24%, respectively, while total operating costs decreased by 1.24% and 0.92%, The results show that the model proposed in this paper has good environmental and economic benefits, and the reasonable setting of stepped carbon trading parameters can effectively promote the low-carbon development of IESs. [ABSTRACT FROM AUTHOR]

The "load-following" characteristic of the power system makes the electricity consumption behavior on the load side crucial for the low-carbon operation of the distribution network. To address this, this paper proposes an improved dynamic carbon emission factor for the distribution network, taking into account the spatiotemporal characteristics of carbon emission intensity and the integration capacity of photovoltaics (PV). Based on this, a calculation method for the carbon emissions of the distribution network load is provided. Subsequently, for commercial and industrial user scenarios, demand response models are separately constructed for commercial and industrial loads based on different driving mechanisms. Using time-of-use electricity prices as decision variables, optimization scheduling of the distribution network is carried out with the objectives of minimizing scheduling costs and carbon emissions. At the same time, a case study is conducted in an improved IEEE-33 node distribution network. The results indicate that, under the guidance of the improved dynamic carbon emission factor, load transfer can be achieved through fluctuating electricity prices, effectively reducing the scheduling costs of the distribution network, decreasing carbon emissions, and enhancing the PV integration capacity of the distribution network in different user scenarios. Finally, it is hoped that in the future, this optimization method can be widely applied, and further research can explore coordinated strategies among generation, network, load, and storage to advance the development of the power industry. [ABSTRACT FROM AUTHOR]

The carbon factor is the core driving force behind the low-carbon transformation of the power system. The study of Emission Reduction Paths (ERPs) has become a key issue for the New-type Power System to achieve clean and low-carbon goals. In order to measure the relationship between ERP and the economy, this paper considers the retirement requirements of coal-fired power units and emission reduction requirements on a long cycle planning scale, and constructs a multi- scenario planning model that considers electric-thermal coupling, enabling the determination of installed capacity for power and heat sources each year. The effectiveness of the proposed model was verified using an improved IEEE-39 bus power system. A case study was conducted to analyze the economic performance of the system based on the selected ERP set. The results showed that under the rear acceleration's ERP, the system could achieve optimal economic performance based on the selected ERP set. [ABSTRACT FROM AUTHOR]

Understanding carbon emissions through the lens of population mobility is crucial for addressing sustainability challenges and fostering economic development. This study aims to investigate the influence of population mobility on carbon emissions in Australia, providing insights for targeted policy interventions. Utilizing panel datasets spanning from 2007 to 2020, encompassing both international and domestic migration in Australia, empirical analyses were conducted. The focus was on assessing the relationship between population mobility and regional carbon emissions. The study reveals distinct effects of domestic and international population mobility on carbon emissions. Domestic mobility demonstrates a negative correlation, whereas international mobility shows a positive association with carbon emissions. Moreover, significant regional heterogeneity in carbon emissions is observed, influenced by economic development and population size variations between eastern and western regions. These findings underscore the importance of tailored emission reduction strategies based on the nature of population mobility. By elucidating the intricate dynamics between mobility patterns and carbon emissions, this study contributes to a nuanced understanding of CO2 emission trends, informing policy decisions amidst the challenges posed by global climate change. [ABSTRACT FROM AUTHOR]

Under the background of "carbon peak and carbon neutrality", the interconnection of multiple regional integrated energy systems has become an important way to coordinate multiple energy sources and save energy and reduce emissions. This paper proposes a low-carbon economic dispatch model for multi-regional integrated energy systems considering the joint demand response mechanism; first, the concept and model of joint demand response are proposed, and on this basis, the energy conversion model between multiple energy forms in the multi-regional integrated energy system is studied; secondly, green certificate trading and carbon trading mechanisms are introduced, and green certificate trading and carbon trading are combined through the green certificate emission reduction mechanism; finally, a low-carbon economic dispatch model is proposed with the goal of minimizing the sum of system energy purchase cost, green certificate trading cost, carbon trading cost and joint demand response compensation cost. The example results show that the introduction of joint demand response mechanism and green certificatecarbon trading mechanism in the multi-regional integrated energy system reduces the total system cost and carbon emissions by 14.03% and 26.04% respectively. [ABSTRACT FROM AUTHOR]

A correction notice is presented to the article, "Impact of population mobility on regional carbon emissions: empirical evidence from Australia," published in the previous issue of the periodical.

Under the background of the shortage of traditional energy and the increasingly serious environmental problems, this paper proposes a bi-level optimization configuration method for microgrids based on stepped carbon trading and price-based demand response. The stepped carbon trading mechanism is introduced into the planning model to clarify the impact of construction processes on land carbon emissions. Simultaneously, price-based demand response during system operation is introduced, guiding users to change the electricity consumption strategies to promote the consumption of renewable energy. Then, a bi-level optimal configuration framework is constructed. The upper level is optimized to allocate the capacity of the microgrid, with the lowest annual equivalent comprehensive cost as the objective function. The lower level is optimized for microgrid operation, with the minimum sum of the microgrid operation cost and carbon emission cost as the objective function. In view of the advantages of enhanced whale optimization algorithm (E-WOA) in solving multidimensional feature selection problems, E-WOA and CPLEX solver are combined to solve the bi-level optimization model for the first time. Simulation results show that after introducing a stepped carbon trading mechanism, the system carbon emissions decreased by 23.09%. Considering price-based demand response under the premise of introducing a stepped carbon trading mechanism, the total cost decreased by 3.57% and the carbon emissions decreased by 19.83%. [ABSTRACT FROM AUTHOR]

Against the backdrop of global climate change, corporate carbon emissions have increasingly become a focal point, making carbon reduction by companies a pivotal issue. Based on data from Chinese listed manufacturing companies from 2010 to 2020, this paper explores the impact of ESG performance on carbon reduction. The results indicate that ESG performance significantly reduce corporate carbon emissions. Green technology innovation, corporate efficiency, and managerial shortsightedness are vital channels through which ESG promotes corporate carbon reduction. For companies with different environmental regulations, industry competition intensities, and capital intensities, the relationship between ESG performance and carbon reduction varies significantly. Notably, we found that in companies with strict environmental regulations, intense industry competition, and high capital intensity, the carbon-reducing effect of ESG performance is more pronounced. Furthermore, digital transformation positively moderates the relationship between ESG performance and carbon reduction. This study not only provides new empirical evidence for understanding the impact of ESG performance on carbon reduction but also offers valuable insights for businesses and policymakers to promote corporate efforts in carbon reduction and achieve China's "Dual Carbon" goals. [ABSTRACT FROM AUTHOR]

To enhance the precision of predicting enterprise credit risk related to carbon emission reduction, this study focuses on publicly traded companies. It introduces a risk early warning model grounded in MLP deep learning. Primarily, this research employs the FA-TOPSIS fusion model to comprehensively assess the credit risk associated with carbon emission reduction in enterprises. Subsequently, it employs K-means clustering to compute enterprise similarities, which forms the basis for supervised learning in the MLP model to assign credit risk grade labels. Furthermore, the study tackles the challenge of imbalanced enterprise grade distribution using the ADASYN over-sampling algorithm. Ultimately, the effectiveness of the model proposed herein is confirmed through a series of multi-model comparison experiments. The results show that: First, carbon emission reduction indicators exhibit differing degrees of influence on enterprises at various credit risk levels. Notably, the most influential indicator is carbon emission intensity, while the development capacity indicator exerts the least influence. Second, the adoption of the XGBoost algorithm for screening carbon emission reduction indicators significantly enhances the prediction accuracy of the early warning model by 4.27%. Third, compared to other models, the MLP model achieves an impressive prediction accuracy of 99.48%, representing an average improvement of 15.24%. These results underscore the model's feasibility and its potential to provide technical support for financial institutions and government entities in conducting credit ratings for enterprise carbon emission reduction. [ABSTRACT FROM AUTHOR]

As a comprehensive and systematic approach to innovation, green innovation has become an important tool for reducing haze and carbon intensity. Based on balanced panel data at the city level in China, we use the two-way fixed-effects model to explore the impact of urban green innovation on haze pollution and carbon emission intensity. The conclusions are as follows: 1) The influence of green innovation on carbon emissions intensity and haze pollution exhibits a significant inverted U-shaped relationship. 2) We explore the mediating channels through which green innovation affects carbon emissions intensity and haze pollution. Specifically, the inverted U-shaped impacts of green innovation on energy intensity and resource dependence. 3) The inverted U-shaped relationship between green innovation and carbon emissions intensity holds for both resource-based and non-resource-based cities. 4) The U-shaped relationships, whether regarding carbon emissions intensity or haze pollution, are only applicable to non-environmental priority cities. This paper offers valuable insights for the formulation of more effective environmental management measures by governments. [ABSTRACT FROM AUTHOR]

To promote sustainable economic development in the Belt and Road region of China, reducing carbon emissions is essential. The construction industry is a major contributor to carbon emissions in China. Therefore, studying the dynamic evolution of carbon emissions from the construction industry in this region and its driving factors is of great significance for effectively controlling emissions and achieving China’s carbon peak and carbon neutrality targets. This paper first employs the Slope model, Moran’s I index, and standard deviation ellipse to reveal the spatial-temporal characteristics of carbon emissions from the construction industry, and then applies the geographical detector model to identify the main driving factors of carbon emissions. The results indicate that: (1) From 2006 to 2021, the total carbon emissions showed a fluctuating growth trend, and there were significant differences in emissions among different regions. (2) Carbon emissions in most provinces exhibited a moderate growth trend, and there was significant spatial correlation and aggregation of inter-provincial emissions. Regional carbon emissions from 2006 to 2021 showed a spatial distribution pattern from northeast to southwest, with a weakening trend, and the center of gravity mainly distributed in the east of the region. (3) Labor input, urbanization rate, total output value of the construction industry, degree of opening up, and energy intensity are the main factors influencing the spatial heterogeneity of carbon emissions from the construction industry, and the majority of the interaction types between factors were bivariate enhancement. This study aims to provide theoretical support for policymakers to formulate appropriate policies for building energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

Introduction: In the digital era, new digital infrastructures (NDIs) play a pivotal role in fostering economic growth and technological innovation. However, their ecological impact, particularly on carbon dioxide emissions, remains underexplored. Addressing this gap holds significant practical and theoretical value. Methods: Utilizing panel data from 283 Chinese cities spanning 2009 to 2020, this study employs a two-way fixed-effects model to empirically assess the influence of NDIs on urban carbon emissions (UCE). Additionally, a mediation effect model is used to examine the mechanisms of this influence. Results: The findings reveal that: (1) NDIs significantly mitigate UCE levels, a conclusion supported by robustness tests involving instrumental variables and the exogenous policy shocks of smart city pilot programs; (2) NDIs primarily impact UCE through two channels: the digital economy and green technology innovation; and (3) heterogeneity analysis indicates that NDIs predominantly curb carbon emissions in cities with lower administrative levels, while positively contributing to UCE intensity in higher administrative level cities. Notably, NDIs substantially reduce UCE in non-old industrial cities, with a negligible effect in old industrial cities. Discussion: This research expands the understanding of the economicenvironmental implications of NDIs, offering valuable insights for policymakers regarding NDIs' environmental impacts. It also provides strategic guidance for urban low-carbon transitions in the big data era. [ABSTRACT FROM AUTHOR]

In this article, a comprehensive analysis is conducted on the influencing factors of carbon emissions in the transportation sector during the evolution of electrification in Beijing. We also considered the impact of indirect carbon emissions caused by carbon emissions from public and private cars and electricity consumption on overall carbon emissions. Based on the LMDI decomposition theory, nine influencing factors are separated, including development level, energy intensity, vehicle structure, number of private cars, and private transportation energy consumption. The analysis results show that, from 2010 to 2021, the carbon emissions from transportation in Beijing increased from 922.98 x 104t to 1490.6 x 104t. The private road carbon emission accounts for about 77.97%, and has become a decisive factor affecting road carbon emissions. In the public domain the contribution values of the development level and energy intensity are 25.20% and -38.71%, respectively. And they are the two most critical factors affecting carbon emissions on public road carbon emissions. In the private domain, the contribution values of the number of private cars, energy consumption of private transportation, and vehicle structure are 60.17%, 47.86%, and -12.99%, respectively. They are the three key factors affecting the private road carbon emissions. There is a significant difference in the proportion of indirect carbon emissions from electricity in the public and private domain. Indirect carbon emissions from electricity account for about 13.6% of road carbon emissions in the public sector, and about 0.9% in the private sector. The results in this paper provide useful references for decisionmaking in the adjustment of transportation energy structure and the promotion of electrified transportation in Beijing and other cities. [ABSTRACT FROM AUTHOR]

Against the backdrop of "carbon peaking and carbon neutrality" goals in China, the park-level integrated energy system is a significant approach to meeting the energy consumption demand of users and improving the utilization rate of new energy. To take into account both the interests of integrated energy services (IES) providers and the current power market rules in China, targeting at low-carbon energy consumption, a balancing service mode for integrated energy services providers is created herein based on the idea of balancing multiple interests of regional integrated energy services providers. It aims to break down the market barriers and address the tricky problem that the integrated energy services industry fails to establish a sophisticated industrial system under the original mode. Combining the integrated energy services with the auxiliary market of power balance, the paper analyzes the characteristics of energy flow within the system, and puts forward the optimization scheme of regional integrated energy balancing services under the low-carbon background. The objective is solved by Mayfly algorithm and compared for analysis, and the results show that the optimal daily operating cost is 2632.59 yuan and the daily carbon emission is 3869.90 kg under the typical industrial scenario. The examples provided here show that the optimization plan for integrated energy balancing services can reduce carbon emissions from the park-level integrated energy system and boost the revenue for integrated energy service providers. It provides theoretical support for the green transformation of energy enterprises and promotes the healthy development of the global integrated energy industry. [ABSTRACT FROM AUTHOR]

Accurate CO2 tracking in electric substation construction is vital for climate efforts, using monocular SLAM for monitoring despite challenges like sunlight and complex terrain. Additionally, these methods typically yield only aggregate carbon emission data, thereby lacking the granularity necessary for precise monitoring throughout the construction process. These limitations compromise mapping accuracy and impede the integration of digital twins and IoT technologies. Addressing these issues, this paper proposed a methodology combining red, green, and blue (RGB) cameras and multi-camera collaboration with digital design systems, enhancing SLAM capabilities. The advanced technique integrated methods including overlap estimation, depth reasoning, noise reduction, and surface reconstruction to create accurate 3D models, enhancing scene reconstruction and real-time CO2 tracking during construction and operation. Leveraging continuous on-site camera monitoring as a substitute for manual inspections, it significantly contributes to the compilation of a comprehensive carbon emission database within a digital twin framework. Experimental results confirmed the proposed method's superiority over previous works in real-time CO2 estimation, enhancing decision-making, resource management, and sustainable energy development. Overall, besides its application in substation construction for CO2 monitoring, this methodology can also be applied to carbon tracking in various other construction projects. [ABSTRACT FROM AUTHOR]

China's rapid economic development has consumed a large amount of energy, causing serious environmental pollution problems and contributing to global warming. This paper calculates the carbon emissions of the fishery sector and uses Random Forest (RF) for the first time to analyze the influencing factors of future carbon emissions. The results of the study show that increasing carbon sinks dominate the reduction of carbon emissions in the fisheries sector. Carbon sinks will continue to dominate emission reductions in the fisheries sector if positive mitigation measures are taken. Continuing the current pattern of fisheries development, the fishery population has a significant impact on future carbon emissions. Per capita incomes under a crude economic model will inhibit carbon emission reductions. The research results can provide guidance for the development of fishery low carbon economy and the formulation of emission reduction policies. [ABSTRACT FROM AUTHOR]

China is actively heeding the call and striving for a low-carbon and environmentally friendly development route as part of the general trend toward a global low-carbon economy. The rapid economic development of our nation has brought to light the issue of carbon emissions resulting from the consumption habits of residents. This paper delves into the topic by conducting a thorough analysis of the carbon emission of residents' consumption using the input-output method. Based on the survey findings, the disparity in carbon emission between urban and rural communities in Shandong Province is evident not only in terms of total emissions but also in different categories and regions. Urban residents generate a total carbon emission of approximately 70.2921 million tons, which is three times higher than that of rural residents at 23.7846 million tons. The carbon emission of both urban and rural residents is primarily attributed to their embodied carbon emission. In examining the composition of the implied carbon emission of residents' consumption in Shandong Province, urban residents' carbon emission is concentrated in three areas, namely, food, culture, education, entertainment, transportation, and communication, accounting for 21.45%, 20.88%, and 15.10%, respectively. Conversely, rural residents' carbon emission is concentrated in four areas, including clothing, food, culture, education, entertainment, transportation, and communication, accounting for 29.01%, 17.45%, 15.43%, and 13.36%, respectively. Finally, according to the characteristics of the consumption carbon emission of urban and rural residents in Shandong Province, we give corresponding emission reduction strategies. [ABSTRACT FROM AUTHOR]