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While development zones are the main locations of the urban industrial economy and sources of energy consumption, studies on the interactive relationship between mixed land use and CO2 emissions in these zones have not attracted much attention. In this paper, the development zone is selected as the research object, and a honeycomb grid with a side length of 50 meters is used as the unit to measure multiple dimensions of mixed land use. The efficiency and intensity of CO2 emissions are estimated for these units, and the coupling model is used to analyse the interactive relationship between these two factors. The results show that (1) the mixed land use degree of different types of development zones differs; the bonded zone has the highest degree, while the economic and technological development zone and the high-tech development zone have higher degrees than the industrial development zones. (2) The CO2 emissions capacities of economic and technological development zones and high-tech development zones are better than those of industrial development zones and bonded areas. (3) There is a strong interaction between the mixing degree of land use and the intensity of CO2 emissions; the relationship between the two may be diverse and complex in different development zones. (4) The coordinated development level between mixed land use and CO2 emissions in economic and technological development zones and bonded areas is better than that in high-tech development zones, which in turn is better than that in industrial development zones. Based on existing measurements of mixed land use, the index of land use intensity is introduced in this paper and the land use classification in development zones is refined to supplement the land use compatibility matrix. The results of this study have implications for the development zone to enhance mixed land use and low-carbon development. [ABSTRACT FROM AUTHOR]

In the spring of 2022, a new round of epidemic broke out in Shanghai, causing a shock to the Shanghai carbon trading market. Against this background, this paper studied the impact of the new epidemic on the price of Shanghai carbon emission rights and tried to explore the prediction model under the unexpected event. First, because a model based on point value data cannot capture the information hidden in inter-day price fluctuation, based on the interval price of Shanghai carbon emission rights (SHEA) and its influencing factors, an autoregressive conditional interval model with jumping and exogenous variables (ACIXJ) was established to explore the influence of the Russian–Ukrainian conflict and COVID-19 on the interval price of SHEA, respectively. The empirical results show that the conflict between Russia and Ukraine has no obvious influence on the price of SHEA, but COVID-19 led to a decline in the price trend of SHEA over four days before the city was closed, and the volatility changed significantly on the day before the city was closed. The price fluctuation was the strongest within 3 days after the city was closed; In addition, in order to accurately predict the interval data of SHEA against the background of COVID-19, based on the interval data decomposition algorithm (BEMD), a hybrid forecasting model of NDGM-ACIXJ/CNN-LSTM was constructed, in which the discrete gray model of approximate nonhomogeneous exponential series (NDGM) combined with the ACIXJ model is used to predict the high-frequency sub-interval, and the convolution neural network long-term and short-term memory model (CNN-LSTM) is used to predict the low-frequency sub-interval. The empirical results show that the prediction model proposed in this article has higher prediction precision than the reference models (ACIX, ACIXJ, NDGM-ACIXJ, BEMD-ACIX/CNN-LSTM, BEMD-ACIXJ/CNN-LSTM). [ABSTRACT FROM AUTHOR]

In this paper, we first proposed a statistical test for the detrended multiple moving average cross-correlation coefficient DMMC (s). The DMMC (s) mainly was used to analyze the correlation between the dependent variable y and other n independent variables x i . We proved that DMMC (s) approximately obeys the chi-square distribution. We studied the statistical properties of the DMMC (s) between normally distributed random sequences and power-law ARFIMA long memory random sequences. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on DMMC (s). Finally, we further study the application of DMMC (s) to China's stock markets and China carbon emission trading markets to investigate multiple cross-correlation. The empirical results show that there is a strong multiple correlation between China's Shanghai, Shenzhen and Hong Kong stock markets, while the correlation between China's carbon markets is not significant. This paper provides new ideas and theoretical support for exploring the correlation between multiple variables, which has implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

CO2 is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO2 and climate change. The main questions studied in this paper are as follows: What are the drivers of CO2 emissions of expanding megacities? How can they be analyzed from different perspectives? Do the results differ for megacities at different stages of development? Based on the XGBoost model, this paper explored the complex factors affecting CO2 emissions by using data of four Chinese megacities, Beijing, Tianjin, Shanghai, and Chongqing, from 2003 to 2017. The main findings are as follows: The XGBoost model has better applicability and accuracy in predicting carbon emissions of expanding megacities, with root mean square error (RMSE) as low as 0.036. Under the synergistic effect of multiple factors, population, land size, and gross domestic product are still the primary driving forces of CO2 emissions. Population density and population become more important in the single-factor analysis. The key drivers of CO2 emissions in megacities at respective developmental stages are different. This paper provides methods and tools for accurately predicting CO2 emissions and measuring the critical drivers. Furthermore, it could provide decision support for megacities to make targeted carbon-emission-reduction strategies based on their own developmental stages. [ABSTRACT FROM AUTHOR]

China's carbon emission trading market has gradually attracted worldwide attention. In this paper, a structural VAR model and Shenzhen, a typical city in China, are selected to study the dynamic relationships between China's carbon emission rights price, energy prices, macroeconomic level, and weather conditions. Shanghai crude oil futures, the first crude oil futures contract in China, is used to describe changes in oil market as a substitute for Daqing crude oil price. The results show that the price of carbon emission rights is mainly affected by its own historical price; and the price of carbon emission rights is positively correlated with crude oil price and natural gas price, but negatively correlated with coal price; the change of macroeconomic level will still have a relatively large impact on carbon emission rights price in the current stage of economic development in China, but this impact is not significant; The impact of weather conditions on the price of carbon emission rights is not obvious. It is found that the launch of the national unified carbon market has indeed achieved certain results, but the situation that China's carbon market is still in its infancy has not been changed; further efforts are needed. [ABSTRACT FROM AUTHOR]

This paper aimed to predict the trend of carbon emissions during the green transformation process in Shanghai, with a focus on the city's urban system structure. Green development has become an inevitable trend in urban progress, as traditional urban development has led to severe environmental problems caused by the emissions of a large amount of carbon dioxide. This study was motivated by the need for cities to actively pursue green transformation and achieve carbon peaking targets. Through a literature analysis, it was found that urban green transformation is influenced by various factors such as economy, energy, population, technology, and policy. Furthermore, carbon dioxide emissions primarily arise from fossil fuels and are regulated by carbon emission trading (CET) policies. With this knowledge, the urban system was divided, and the flow of carbon was analyzed. Using the general methodology of the IPCC, the carbon production resulting from energy consumption in Shanghai from 2014 to 2019 is calculated to construct an urban system dynamic (SD) model, which is used to predict the carbon emissions expected during the green transformation from 2020 to 2025. The key findings of the study are as follows: (1) The dynamic model of the urban green transformation system proved to be effective in predicting carbon emissions. (2) Based on the current status of green transformation in Shanghai, the city is capable of achieving its expected carbon emission peaking target by 2025. (3) The progress and timing of green transformation and carbon peaking in Shanghai vary across different scenarios, highlighting the importance of collective adjustments to identify the most appropriate path for urban green transformation. These findings provide valuable insights for cities seeking to adopt green development measures, facilitating the acceleration of their green transformation efforts and early attainment of carbon peaking targets. [ABSTRACT FROM AUTHOR]

The rapid growth of the global economy has boosted the shipping industry, but it has also increased energy consumption and exhaust emissions. Based on the system dynamics method, we construct a carbon abatement model of maritime supply chain, in which the interaction among company, economy, energy, environment, and policy is deeply analysed. The effect of various carbon abatement measures is simulated with a case study of Shanghai Port. The results prove that the shore power has tremendous potential for reducing emissions. Interestingly, the emission trading scheme has little effect on emission reduction. Conversely, it will increase CO2 emissions because of the vicious circle of higher CO2 production and decreased CO2 treatment capability. Case 4 is the best alternative for reducing CO2 emissions in maritime supply chain when combined measures are applied, regardless of the emission reduction effect or carbon emission intensity. Thus, it is most effective to mobilise all kinds of resources to reduce CO2 emissions when conditions permit. [ABSTRACT FROM AUTHOR]

City cluster is an effective platform for encouraging regionally coordinated development. Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management, realize sustainable development, and assist China in achieving the carbon peaking and carbon neutrality goals. This paper applies the improved gravity model and social network analysis (SNA) to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the the Yangtze River Delta (YRD) city cluster in China and its influencing factors. The results demonstrate that: 1) the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure. The network association number and density show an upward trend, indicating closer spatial association between cities, but their values remain generally low. Meanwhile, the network hierarchy and network efficiency show a downward trend but remain high. 2) The spatial association network of carbon emissions in the YRD city cluster shows an obvious 'core-edge' distribution pattern. The network is centered around Shanghai, Suzhou and Wuxi, all of which play the role of 'bridges', while cities such as Zhoushan, Ma'anshan, Tongling and other cities characterized by the remote location, single transportation mode or lower economic level are positioned at the edge of the network. 3) Geographic proximity, varying levels of economic development, different industrial structures, degrees of urbanization, levels of technological innovation, energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster. Finally, policy implications are provided from four aspects: government macro-control and market mechanism guidance, structural characteristics of the 'core-edge' network, reconfiguration and optimization of the spatial layout of the YRD city cluster, and the application of advanced technologies. [ABSTRACT FROM AUTHOR]

CO2 emissions from fossil energy have caused global climate problems and threatened human survival. However, there are few studies on the spatiotemporal distribution and driving factors of carbon emissions. This paper takes the Yangtze River Delta (YRD) urban agglomeration as the research object and analyzes the spatiotemporal heterogeneity of carbon dioxide emissions and their driving factors from 2000 to 2017. First, a series of preprocessing, such as resample, interpolation, and image clipping, are conducted on the CO2 emission data and nighttime light remote sensing images. Second, the dynamic time wrapping (DTW) and hierarchical clustering algorithms were involved in manipulating the CO2 emission data. Consequently, the cities' and CO2 emissions' time series were classified into four categories and three stages separately. Finally, the geographical detector model (GDM) and geographical and temporal weighted regression (GTWR) are coupled to evaluate the spatiotemporal heterogeneity and quantify the driving factors. The results show the following: (1) The spatiotemporal distribution of CO2 emissions has spatial consistency from 2000 to 2017. High-emission areas are concentrated in economically developed areas such as Shanghai, Suzhou, and Wuxi. The results are consistent with previous research. (2) Regional aggregation is a revealed new trend. CO2 emissions in the target urban areas are gradually converging into economic center cities and diverse class cities, e.g., Shanghai and Ningbo. (3) In cities of different economic development levels, the driving factors of CO2 emissions are different. The secondary sector and urban infrastructure dominate in the early stages of developed cities. On top of that, the influence of the tertiary industry is more significant in the later development stages. According to the results, in the urban development process, humans should not only pursue the increase in speed but also pay attention to the negative impact of the economic development process on the ecological environment. Besides, since the spatiotemporal characteristics and dominant factors of urban carbon emissions are different in each stage of development, the formulation of carbon reduction policies should be associated with urban features. [ABSTRACT FROM AUTHOR]

Solving the crash risk problem of corporate stock price caused by information asymmetry can mitigate the negative externality of its carbon emission to become green, low-carbon, and high-quality development. Green finance generally profoundly impacts micro-corporate economics and macro-financial systems but remains a giant puzzle of whether they can effectively resolve the crash risk. This paper examined the impact of green financial development on the stock price crash risk using the sample data of non-financial listed companies in Shanghai and Shenzhen A stock market in China from 2009 to 2020. We found that green financial development significantly inhibits the stock price crash risk; this is more obvious in listed companies with a high level of asymmetric information. And companies in high-level regions of green financial development attracted more attention from institutional investors and analysts. As a result, they disclosed more information about their operational status, thus reducing the crash risk of corporate stock price from the torrential public pressure of lousy environmental details. Therefore, this study will help continuously discuss the costs, benefits, and value promotion of green finance for synergy between corporate performance and environmental performance to improve ESG capabilities. [ABSTRACT FROM AUTHOR]

Maximizing the carbon reduction in substations with minimum cost investments can be achieved by taking advantage of the potential of substations in terms of the envelope and renewable energy, which is significant in promoting carbon reduction in substations. Therefore, firstly, the relationship between building cost–energy consumption–carbon emissions is explored, and then the global optimal calculation model of substation envelope–renewable energy is established, with the lowest life-cycle carbon emission of the substation as the optimization goal. Finally, the validity of the model is verified based on a case study of a typical 110 kV outdoor substation. The model calculation results show that, without considering the cost constraint, Harbin has the highest maximum carbon reduction of 180,350 kg, which is 25.15% and 13.74% higher than the maximum carbon reduction in Shanghai and Haikou, respectively. Furthermore, based on the comparison of the cost and benefits of each carbon reduction technology, a prioritization of various carbon reduction technologies is given for each climate zone. The model established in this paper can provide the optimal configuration of substation carbon reduction technologies with different incremental cost constraints, and provide a reference for the low-carbon design of substations. [ABSTRACT FROM AUTHOR]

To achieve a win–win situation for both urbanization and carbon emissions reduction from a spatiotemporal perspective, we need to identify the salient links between urbanization and carbon emissions in different dimensions. Using 2008–2018 panel data on the Yangtze River Delta urban agglomeration, this paper constructs a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model based on four dimensions of urbanization: population, economy, land, and ecology. Additionally, it uses a whole group of variables for reference, constructs a Spatial Durbin model (SDM) to estimate the spatial effect, and empirically investigates the spatial dependence of carbon emissions and the influence of various driving factors. The results show that (1) in the temporal dimension, the historical carbon emissions of the study area continue to increase. However, the extent to which they are doing so is slowing, the number of low carbon emissions areas has significantly decreased, the number of medium carbon emissions areas have significantly increased, the number of high and relatively high carbon emissions areas are relatively stable, and energy intensity continues to decline. (2) In the spatial dimension, Shanghai, Suzhou, and their surrounding cities have always been carbon emissions hotspots, high and relatively high carbon emissions areas are mainly concentrated in these cities. Low carbon emissions areas and cold spots are mainly distributed in Anhui Province. Medium carbon emissions areas show a great spatial and temporal evolution and are distributed in all provinces. (3) In the four dimensions of urbanization, per capita GDP will not only affect regional carbon emissions but also have a spatial spillover effect. For every 1% increase in the economic factors, carbon emissions in neighboring regions will increase by 0.38–0.43%. Population, economic, and technological factors have significant positive effects on carbon emissions, and economic factor is the most important factor. (4) In different dimensions of urbanization, there are obvious heterogeneities in the impacts of different factors on carbon emissions. Among them, the elasticity coefficient of per capita GDP and energy intensity is the smallest among the dimension of land urbanization, and the elasticity coefficient of the total population is the smallest among the dimension of population urbanization. Therefore, when formulating carbon emissions reduction policies, it is necessary to fully consider the spatial spillover effects, determine the optimal population size threshold, advocate for a low-carbon lifestyle, promote clean technology, and realize information exchange and policy interaction across regions from the perspective of holistic governance. [ABSTRACT FROM AUTHOR]

As a typical means of transportation for residents to travel with low carbon in the new era, it is of grea t significance to explore how to achieve sustainable development throughout its life cycle. Based on t he challenges of the development of shared bicycles in China's cities, this paper innovatively proposes a governance framework for the sustainable development of shared bicycles oriented to the circular e conomy and a governance path based on the 3R principle, and constructs a complex system dynamics model that integrates three sub-modules of faulty vehicle reduction, faulty vehicle reuse and end-of-lif e vehicle resource. On this basis, based on the practical case of Shanghai, a multi-scenario simulation of the sustainable development path of shared bicycles from the perspective of circular economy is ca rried out. The study found that: (1) the five strategies of user use behavior governance, recycling parti cipation behavior governance, urban total delivery control, faulty vehicle recycling monitoring and pro ducer responsibility extension system all play a significant role in the sustainable development of the s hared bicycle industry; (2) In terms of a single strategy, the extended producer responsibility system a nd the monitoring of faulty vehicle recycling are the two best governance measures for the governme nt to control faulty and scrapped vehicles, operators to reduce production and recycling disposal cost s, and thus improve the efficiency of resource utilization in the industry, followed by urban total invest ment control; (3) In terms of combination strategies, the comprehensive integration and implementati on of the five strategies is the optimal governance path to promote the sustainable development of sh ared bicycles in China, after full implementation, the total number of scrapped bicycles in Shanghai wil l be reduced by 71.15% from 2021 to 2031, the production and recycling costs of operators will be sav ed by 73.77%, and the carbon emissions will be reduced by 87,800 tons. Based on the above research results, this paper proposes to implement the extended producer responsibility system. [ABSTRACT FROM AUTHOR]

The formulation of long-term step-by-step emission reduction plan is an important step for effective scientific emission reduction. This paper takes Shanghai as the research object, constructs PSO-LSTM model on the basis of STIRPAT model, and further constructs three dynamic policy scenarios combined with China's actual situation and makes short-, medium-, and long-term multivariate predictions for them. The study finds that only the improvement of energy consumption structure has a promotion effect on carbon emission reduction, and urbanization, industrial structure, technology level, population, and economic level all have an increasing effect, and secondly, the carbon emission reduction path of Shanghai basically achieves the core objective of steady decrease under the three scenarios. Secondly, under the three scenarios, Shanghai's carbon emission reduction path basically achieves the core objective of steady decline, but the decline in the GU scenario is more significant. It is recommended that Shanghai further adjusts its industrial structure, optimizes its energy consumption structure, promotes technological innovation and progress, and promotes the development of the circular economy model. [ABSTRACT FROM AUTHOR]

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation. [ABSTRACT FROM AUTHOR]

According to the proposal of Kyoto protocol, carbon dioxide emission rights are traded as a commodity, and carbon emission trading market emerges as the times require. As the world's largest carbon emitter, China has established eight pilot markets for carbon emission trading. Selecting the closing price of eight carbon trading markets from the establishment to June 23, 2020, this paper analyzes the daily, weekly, and monthly return series data, using the first-order autoregressive process to adjust the daily income series to eliminate the weak trading market effect and then comprehensively uses the traditional variance ratio test and multiple variance ratio test to analyze the weak-form market efficiency of the eight carbon trading markets. The empirical results show that most of the carbon trading markets are non-weak-form market efficiency, and only Tianjin, Shanghai, and Hubei markets are weak-form market efficiency under the daily trading data. However, with the increase of carbon holding period, the weak-form market efficiency continues to strengthen. It shows that liquidity, quantity, and information transparency are important factors that affect the market efficiency. [ABSTRACT FROM AUTHOR]

Purpose - Current policies and research on carbon emissions focus on operational emission but overlook the importance of embodied and user-transport emissions in residential buildings. This study built a comprehensive framework to assess the impact of life-cycle carbon emissions on different in-building open public spaces (open roof, open vertical garden, and open ground floor) in affordable housing. Design/methodology/approach -Aparametric model of a typical affordable housing building in Shanghai, China was constructed and 36 variations of open public spaces studied. Embodied, operational, and user-transport carbon emissions were quantified over 50 years. Findings - The results show that the life-cycle carbon emissions decrease with the application of the open public space. In addition, the paper found that the carbon reduction due to user transport is seven times higher than the carbon increment due to construction and over long-term operation. Originality/value - This paper provides quantitative evidence for carbon emissions and in-building open public spaces, and the authors suggest taking multiple aspects into account in addition to the structure of the building is crucial to sustainable building development. [ABSTRACT FROM AUTHOR]

The carbon emission reduction behaviour of enterprises is a crucial element for achieving the "double-carbon" target. Based on the panel data of China's Shanghai and Shenzhen A-share nonfinancial listed companies from 2008 to 2022, this paper explores the impact of corporate carbon emission reduction on financing constraints using the fixed effect model and further dissects the path and heterogeneity of its impact. The results show that carbon emission reduction can significantly alleviate the level of financing constraints. The path test shows that corporate carbon emission reduction alleviates financing constraints through gaining business credit, improving information transparency, and increasing government subsidies. The heterogeneity analysis shows that carbon emission reduction in less economically developed regions, non-heavily polluting industries, and nonstate-owned enterprises has a better effect on alleviating financing constraints than in economically developed regions, heavily polluting industries and state-owned enterprises. Additionally, relevant policy recommendations are put forward, which are conducive to promoting enterprises to actively reduce carbon emissions and facilitate the achievement of the dual carbon goal. [ABSTRACT FROM AUTHOR]

Firstly, through introducing the provincial product trading data into the input–output table, this paper constructs the provincial input–output table and then deduces the provincial MRIO (Multi-Regional Input–Output) model. Secondly, based on the empirical data from 30 provinces in mainland China, the provincial MRIO model was used to analyze the provincial carbon transfer caused by provincial trading and calculate the provincial direct carbon emission and complete carbon emission. Results show that: (1) The provincial MRIO model can separate the provincial self-demand carbon emissions, the net carbon emissions of import and export trading transfer and the net carbon emissions of provincial trading transfer, and can accurately calculate the source and destination of provincial carbon emissions. (2) There is a carbon transfer relationship between any two of China's 30 mainland provinces, and the carbon emissions transferred between provinces are quite different. The five provinces with the largest net provincial trading carbon transfer are Beijing (144.04 million tons), Shanghai (160.96 million tons), Jiangsu (133.85 million tons), Zhejiang (134.12 million tons) and Guangdong (268.32 million tons). (3) Most of the central and western regions dominated by economically underdeveloped provinces in China bear the carbon transfer of economically developed provinces. (4) When considering the dual carbon transfer of import and export trading and provincial product trading, there are only 5 provinces as Beijing (130.97 million tons), Shanghai (59.20 million tons), Jiangxi (3.36 million tons), Chongqing (11.75 million tons) and Qinghai (0.02 million tons) that have the positive difference among the 30 mainland provinces,, realize carbon transfer out, and the remaining 26 provinces are carbon transfer in. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) treatment is one of the major contributors to carbon emissions. The improvement in MSW treatment carbon emission efficiency is crucial for China to achieve its CO2 emission targets. Firstly, this study used the super-efficiency SBM-DEA model to calculate the MSW treatment carbon emission efficiency in 31 provinces in China from 2010 to 2019. The results show that the MSW treatment carbon emission efficiency in all provinces except Shanghai and Jiangsu is less than 1, and the provinces with high efficiency are mainly located in eastern China. Secondly, the spatial auto correlation model and spatial Markov chain are used to test the regional differences and the spatial spillover effect of efficiency. The results show that the national average efficiency shows a fluctuating downward trend, and only the western region achieves a gradual increase. The regional differences in China's MSW processing efficiency of carbon emissions show a fluctuating upward trend, and the regional background affects the spatiotemporal evolution pattern of the efficiency. Finally, the special error model was used to analyze the factors and influence paths that affect the efficiency, and to find that the degree of government intervention as an influencing factor that restricts the improvement of efficiency. Based on the research results, we put forward countermeasures and suggestions to improve the MSW treatment carbon emission efficiency in each province and the country as a whole. [ABSTRACT FROM AUTHOR]

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai's building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity). [ABSTRACT FROM AUTHOR]

In this paper we collect data of corporate bonds and government bonds issued from 2005 to 2019 from CSMAR database. Firstly, we make a stepwise regression analysis and research on the impact of climate risk, selling back and cross-market transaction on corporate bond yield. The impact of climate risk on corporate bond yield is positive because if carbon dioxide emission is more, the earth will be warmer and sea levels will be higher, then companies will face more climate risks and corporate bond yields will increase. The impact from the selling back on corporate bond yield is negative because if the bond can be resold, the risk will be lower and the bond yield will decrease. The impact of cross-market transaction on corporate bond yields is positive because if corporate bonds cannot be traded, the liquidity of corporate bonds will be low, and the risk will increase, then corporate bond yield will increase. Secondly, logarithmic regression analysis of climate risk factors shows that the climate risk is still significant and the model fits well. Selling back, cross-market trading and other factors are significant, and the pattern is more significant. Compared with the original model, it can better explain corporate bond yield. [ABSTRACT FROM AUTHOR]

Carbon emissions in the transportation sector are of great concern, since they are the third leading contributor to China's carbon emissions. This research examines the decoupling relationship between economic outputs and carbon emissions of 11 provinces in 2000–2016 by focusing on Yangtze River Economic Belt (YREB), which is the densest traffic and economic corridor in China. Although many studies have focused on the decoupling state and its driving forces between economic outputs and carbon emissions, few studies further addressed the microscale analysis for decoupling drivers. This paper reveals the characteristic, inequality contribution ratio, and dynamic evolution of the drivers by integrating Dagum's Gini ratio with kernel density estimation in YREB. Results are as follows: (1) The decoupling states presented weak decoupling state at the whole belt in the majority of the latter observed sub-periods. The decoupling states at the provincial level turned more satisfactory during the four observed sub-periods, especially for Shanghai and Zhejiang. (2) The energy intensity (EI) effect is the predominant driver for promoting the decoupling state in the vast majority of provinces, whereas value added per capita effect is the major role for inhibiting the decoupling state. (3) During the four observed sub-periods, the Gini inequality and transvariation intensity of the EI effect between sub-regions are the main sources of the provincial differences in YREB. The driving force of EI effect is increasing, but the provincial differences are expanding in the upstream and downstream regions by analyzing its dynamic evolution. Understanding the temporal and spatial microscale inequality of the decoupling drivers provides governments with differentiated and forward-looking suggestions towards coordinating regional economic growth and carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Effective control of electricity consumption is crucial to achieving China's dual carbon goals, as it can significantly reduce coal consumption and associated carbon emissions. Using the Logarithmic Mean Divisa Index (LMDI) method, this paper constructs a two-stage decomposition model, which can divide total electricity consumption into industry and domestic electricity consumption and further examine the differential driving mechanism of the two types of electricity consumption separately. Taking the Yangtze River Delta as the research object, this paper analyzes the drivers of its temporal differences in total electricity consumption from 2000 to 2019 and fills the research gap in the identification of drivers of spatial differences in electricity consumption. The results showed: (1) Industry electricity consumption is the main driver of the increase in total electricity consumption, with economic growth and domestic electricity intensity being the primary and secondary factors behind this trend. The population scale also contributes to the increase. However, the effects of industrial structure, industry electricity intensity, and urbanization on total electricity consumption are not constant over time. This paper further decomposes the factors influencing changes in total electricity consumption in the provinces of Shanghai, Jiangsu, Zhejiang, and Anhui. (2) The spatial difference of electricity consumption between the other three provinces and Shanghai mainly comes from industry electricity consumption. (3) There are two kinds of conversion relations for the spatial-temporal differences of total electricity consumption. According to the conclusion, the targeted countermeasures and suggestions for total electricity consumption control are obtained. • We use the LMDI model to study the driving factors of total electricity consumption. • Production is the main reason for the increase of total power consumption. • Economic growth is the first factor to promote the total electricity consumption. [ABSTRACT FROM AUTHOR]

Given the increasingly serious ecological and environmental problems in China, research on enterprises' low-carbon sustainable development behavior (LCSDB) has become a heated discussion. This is also because enterprises are a primary source of carbon emissions and environmental pollution. From the perspective of the board of directors' capital (BODC), this study considers empirical evidence from 286 enterprises listed on the Shanghai and Shenzhen stock exchanges in China from 2008 to 2016 to examine the BODC's impact on enterprises' LCSDB and its mechanisms. A group test is conducted using the enterprise's property, nature of rights, and region, among other factors, to investigate the heterogeneity of the impact of board capital on enterprises' LCSDB and its regulatory role. The research indicates (1) an increase in BODC promotes enterprises' LCSDB. (2) An awareness of social responsibility (AOSR) plays an intermediary role in the relationship between BODC and corporate LCSDB. (3) Media attention enhances the BODC's role in promoting enterprises' LCSDB. (4) Government regulatory factors promote the BODC's positive impact on LCSDB. These findings significantly impact the effectiveness of decision-makers within the company, the governance mechanism to address climate change risks, and the possible connection between corporate governance reform and carbon-related policies. [ABSTRACT FROM AUTHOR]

Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse. [ABSTRACT FROM AUTHOR]

[Display omitted] • Regions where feed-in tariff higher than valley price should follow dual objective optimization. • The economy of the household PV-BES-EV system mainly depends on the purchase cost of EVs. • Decreasing electricity price will not undermine the effectiveness of the household PV-BES-EV system. • The energy bills and carbon emissions decrease significantly with the optimal scheduling schemes. The household PV-BES-EV system (photovoltaic - battery energy storage - electric vehicle system) connected to the grid can guarantee the electricity demand of the household and facilitate the flexible trade with the grid to achieve the goals of cutting energy bills and reducing carbon emissions. In related issues, past studies often assume that renewable energy is both economical and environmentally friendly, and thus regarded it as the primary power source. While in fact, in some regions where the feed-in tariff is higher than the sales price at a certain time, households may be driven by arbitrage opportunities to frequently purchase electricity from the grid at the certain time to gain benefits, resulting in far greater carbon emissions than expected within the household measurement boundary. To avoid the environmental burden caused by pure economic incentives and to provide practical and sustainable solutions consistent with various energy price policies, the optimal scheduling schemes of the household PV-BES-EV system that considers both economic and environmental incentives for different scenarios solved by the genetic algorithm is proposed in this paper. Household data in Shanghai, China and Hamburg, Germany were selected for modelling, representing the dual-objective optimization scenario with arbitrage opportunities and the single-objective optimization scenario with sales price higher than feed-in tariff, respectively. Results show that for urban and rural residents in Shanghai, the energy bill and carbon emission would decline about 105%, 48% and 88%, 62%, respectively. As for Hamburg, the accordingly results would be 69% and 64%. In addition, based on the cost-effectiveness of the system in long-term, this paper puts forward policy recommendations that align with local conditions to further improve the economy of the system. [ABSTRACT FROM AUTHOR]

Pairs trading is one of the mainstream statistical arbitrage strategies in current financial market practice that can seize possible arbitrage opportunities caused by mispricing between similar assets. Based on the theory of statistical arbitrage and the similar attributes of carbon emission allowances traded in different carbon markets, this paper studies the applicability of the pairs trading strategy among different pilot markets in China. The novelty and possible contribution of this paper are as follows. Firstly, this paper extends the statistical arbitrage-based pairs trading framework to the analysis of carbon market transactions in China, which broadens the understanding of the correlation between China's carbon markets. Secondly, this study explores the arbitrage opportunities among China's regional carbon markets that have not been revealed by previous studies. Thirdly, based on the pairs trading analytical model adopted in this study, this paper further investigates the operation characteristics of China's carbon markets and the interaction between markets, as the sensitivity of strategy performance to the key parameters is also discussed. The results show that arbitrage based on pairs trading is profitable. In the baseline case, the annualized arbitrage returns between the Shanghai emission allowance and Beijing emission allowance is about 9.11%, the highest among all pairs. However, there is almost no pairing relationship between the Tianjin carbon market and other markets. By discussing the time distribution of arbitrage signals generated by the pairs trading strategy, it is found that most signals are sent out during the inactive trading period in a compliance cycle. The conclusions provide valuable insights for formulating relevant carbon market regulatory policies and investors' participation in arbitrage activities in China's carbon trading markets. • The application of the pairs trading strategy is extended to the Chinese carbon markets. • Based on relative value arbitrage theory, arbitrage opportunities between markets are examined. • Pairs trading strategy can also yield sustainable and distinctive returns in the Chinese carbon markets. • Arbitrage trading between the Shanghai and Beijing markets may yield promising annualized returns with about 9.11%. [ABSTRACT FROM AUTHOR]

As human consumption expands, four environmental footprints (EFs) exceed the planetary boundaries (PBs) at the global scale. Managing absolute environmental sustainability (AES) based on PBs and EFs at the subnational level is crucial for policy insights. However, a consumption‐based AES management framework still needs to be developed. A framework, including five nexus environmental pressures embodied in the supply chain, was developed and tested in our study across China's 30 provinces to address this knowledge gap. The framework involved three steps: (a) assessing AES for five environmental pressures, (b) measuring environmental surplus and overshoot and composition of EFs, and (c) identifying priority areas for AES management. The results showed that only some provinces are sustainable for three impact‐oriented indicators, especially those with larger populations. Moreover, the embodied environmental pressure mainly flows from the Northwest to Southeast China. For two resource‐oriented indicators, over 74% of provinces are absolutely sustainable. From a nexus perspective, Shandong and Shanghai are identified as priorities for AES management due to their low IESI values of 0.32, 0.33, and 0.40, respectively, which means the worst performance. To improve their IESI, Shanghai needs to control the consumption of blue water‐intensive products, while Shandong and Henan should consume fewer CO2 emissions and N‐ and P‐loss‐intensive products. This framework can clarify subnational responsibilities of environmental overshoots, guide sustainable development, and be widely used at the subnational level in countries worldwide. Plain Language Summary: Earth's ecosystem has a limited ability to provide natural resources and accept pollutants. Absolute environmental sustainability (AES) assumes that irreversible impacts will occur once human needs surpass this capacity, emphasizing the need for policymaking based on the current situation. As a crucial policy‐related entity, subnational regions urge AES management. Here, we focus not only on assessing AES of five environmental pressures (including carbon dioxide (CO2) emission, freshwater use, Nitrogen (N) loss, Phosphorus (P) loss, and land use) embodied in supply chains but also on identifying the priorities of AES management among 30 Chinese provinces. We found that the CO2, N, and P driven by final demands have transgressed the absolute limits in all provinces, especially with larger populations. They mainly consume products from Northwest China. More than 74% of the provinces are within the freshwater and land use boundaries. Shanghai, Shandong, and Henan are identified as priorities of AESA management because of their lousy performance from a nexus perspective. They should be responsible for consumption. Applying this framework globally at the subnational scale is crucial for consumption‐based AES management and global environmental preservation. Key Points: A consumption‐based integrated framework for subnational environmental sustainability management within planetary boundaries was developedAll Chinese provinces overconsumed Nitrogen, Phosphorus, and Carbon dioxide‐intensive productsHenan, Shandong, Shanghai, and Jiangsu were identified as priorities for absolute environmental sustainability management [ABSTRACT FROM AUTHOR]

To achieve the goals of carbon peaking and carbon neutrality, China is actively promoting carbon reduction in many areas. Agriculture is one of the main sources of greenhouse gas emissions, and promoting the development of low-carbon agriculture is a critical way to achieve carbon reduction targets. Taking Chongming District in Shanghai as an example, this study summarizes the experience of low-carbon agricultural development in Chongming and analyzes the problems and challenges faced during its development. Finally, based on the system dynamics method, the causal relationship of carbon emission in Chongming's agricultural development is constructed, and feasible loop optimization suggestions are put forward. [ABSTRACT FROM AUTHOR]

PV windows are considered to be a promising building-integrated PV technology due to their excellent electrical, daylighting and thermal performance. However, the shading of the PV cells increases the heating load in winter and the high temperature of PV cells exacerbates the cooling load in summer. Moreover, a significant portion of the heat absorbed by the PV modules is usually wasted. To address these issues, this paper proposed a multi-functional PV/T window. This system could recover the heat on PV cells for producing warm air in winter and hot water in other seasons. As a result, the proposed system can realize the combined PV/T utilization of solar energy and reduce the seasonal thermal needs of the building. First, the mathematical model of the proposed window was developed and validated. Next, the developed model was integrated into building energy software to evaluate the overall energy performance of the proposed window in four different cities of Yangtze River region: Shanghai, Nanjing, Wuhan, and Chongqing in China. These cities experience the subtropical humid climate (Cfa in the Köppen-Geiger classification) and belong to the zones with medium global horizontal irradiation. The sensitivity analysis revealed that the optimal slat angles are between 0o and 40o, while the optimal orientation angles range from 30o to 60o. Selecting low-iron glass as the inner glass resulted in the lowest overall energy consumption. By adopting the MFPV/T window, the annual operation cost of the studied case could be reduced by 34% (6.4 $/m2), 36% (7.0 $/m2), 28% (5.7 $/m2), and 16% (2.9 $/m2) in these four cites, respectively. Additionally, the annual CO 2 emissions could be curtailed by 53.5, 58.5, 47, and 22.3 kg/m2, respectively. • A novel PV/T window was proposed for addressing the common issues of PV windows. • The mathematical model of proposed window was developed and validated. • The parameter sensitivity was analyzed. • The overall energy performance was predicted in Yangtze River region. [ABSTRACT FROM AUTHOR]

Cities have become significant sources of greenhouse gas emissions. Effective land management may be the solution to carbon neutrality targets for megacities with limited land resources. This paper takes Shanghai as a case study to investigate the regional land use dynamics and its impact on carbon emissions following the implementation of land conservation and intensive use policy. During 2010–2020, the land use pattern in Shanghai changed from the previous urban land expansion to a combination of industrial land reduction and woodland expansion. Meanwhile, the area proportion of land-use mixture grids increased from 90.50% to 92.28% with the spatial pattern of mixed types also changing. Furthermore, the notable land-use mixture does not necessarily lead to carbon emission reduction, but it can reduce carbon emission hotspots in industrial agglomerations by promoting the mixed use of industrial land and other land use types. However, megacities cannot achieve carbon balance through land use management alone. Due to the increasing carbon emission density of hybrid industrial land, the joint implementation of a land conservation and intensive use strategy with industrial and energy structure adjustments may be an effective way forward. [Display omitted] • Land use change in Shanghai over the past decade resulted in positive carbon sinks. • Impact of land-use mixture on carbon emission was evaluated for the first time. • The reduction and intensive use of industrial land is the new land-use pattern. • Hybrid industrial land helps reduce carbon hotspots in industrial agglomerations. [ABSTRACT FROM AUTHOR]

Most literature focusing on the issue of ship pollutant emission and diffusion is based on static meteorological conditions. The relevant literature seldom analyzes the emission and diffusion of ship pollutant gas according to real-time meteorological data when wind direction, wind speed and other meteorological conditions have changed. This paper presents a fast translational diffusion algorithm, and empirically demonstrates it through ship and meteorological data from the Huangpu River field in Shanghai. First, CO 2 emissions were estimated using the actual speed of the ship engine changed under the influence of variable wind, and simulation analysis was performed to the scenario of hourly diffusions. The research findings are as follows: In comparison with the traditional algorithms, the fast algorithm can significantly improve calculation efficiency, save calculation time and achieve the long-distance and long-time parallel calculation of multiple ships. As proven by the empirical research using the ships on Huangpu River in Shanghai, the maximum difference of CO 2 emissions is 34.5%, 36.85% and 19.97% respectively after upwind, downwind and real-time meteorological conditions are taken into account. The research finding indicates that wind direction changes have a greater impact on the carbon emission of ships. • Air pollutant emissions and dispersion from ships under variable wind conditions were calculated. • A fast translational diffusion algorithm was used to efficiently simulate multi-vessel, long-distance diffusion in real time. • Ship and meteorological data from the Huangpu River field in Shanghai were validated. • Empirical analysis in variable wind direction scenario shows that the difference in CO2 emissions can be up to 36.85%. [ABSTRACT FROM AUTHOR]

• City-centric global multi-region input-output models for Beijing and Shanghai are developed. • Factor contribution and critical path analysis are adopted. • Sector aggregation effect varies across sectoral datasets. Sector aggregation under the input-output framework may lead to deviation in results and the lack of detailed sectoral information, which hinder the targeted implementation of accurate sectoral adjustment policies. This paper explores the effects of sector aggregation on the embodied carbon emission of the residential consumptions of Beijing and Shanghai based on the city-centric global multi-region input-output (CCG-MRIO) model. Integrating with structural path analysis and structural decomposition analysis, the impacts of sector aggregation on the identifying of critical emission transmission paths and driving forces are also revealed. Three sector aggregation datasets are discussed to track the sector aggregation effect in 2012 and 2017. The findings show that the carbon emission results at 8 sector level better align with the results at the 22 sector level than those at 32 sector level. Moreover, sector aggregation will lead to the absence of some critical carbon transmission paths, some factor contributions on carbon emission also changed their directions at different sector levels. On the basis of clarifying the sector aggregation effect on embodied carbon emission, the research results provide a reference for similar studies to select the appropriate level of sector aggregation, to realize the goal of simplifying the calculation or gaining more detailed sectoral emission information. [ABSTRACT FROM AUTHOR]

Nationwide unification of carbon emission right market in China has long been proposed yet not achieved. Launching of the nationwide trading center at Shanghai was a recent great step towards this target but the regional pilots covering respective zones are still in operation. With all trading data in those markets from their inceptions, this paper investigates the development status and trend of each submarket, based on which, it is revealed that sudden replacement of regional markets by the unified nationwide trading center is infeasible at the present stage, furthermore, feasibility of market merging is analyzed and optimized route for essential unification is designed. This paper contributes to deconstruct the value of carbon emission right and interprets its properties of option value, the equivalence of which value from the perspective of financial investment and enterprise operations is explored and formally established. Applying the geometric Brownian motion and optimal stopping time to model the carbon asset operation, option value of carbon emission right is estimated and marginal return-ability of its consumption is calculated for each region. Based on the analysis regionally, market merging is simulated and optimized route for nationwide market unification is proposed with policy suggestions. [ABSTRACT FROM AUTHOR]

With the new round of technological revolution and industrial change, industrial robots have an important role to play in the fight against climate change and in achieving the goal of "carbon peaking and carbon neutrality." Based on the panel data of the application level of industrial robots in Shanghai and Shenzhen A-share listed companies from 2011 to 2019, this study examines the impact of industrial robots on carbon emission performance and discusses specific ways industrial robots can affect carbon emission performance. The results show that industrial robots can significantly improve carbon emission performance. Mechanism analysis shows that industrial robots can improve carbon emission performance through productivity and competition effects. Heterogeneity analysis shows that the application effect of industrial robots varies based on enterprise nature, regional location, and carbon emission intensity. The study can make potential contributions. First, this study systematically analyzes the impact of artificial intelligence technology on carbon emissions from the perspective of carbon emission performance, which can supplement the research on carbon emission performance. Second, this study calculates application levels of artificial intelligence technology at the enterprise level and uses panel and linear intermediary effect models to analyze the transmission mechanism between the application of artificial intelligence technology and carbon emission performance. Third, the heterogeneity analysis results can provide empirical support for formulating differentiated artificial intelligence carbon reduction strategies and be used as a reference to further promote the green development of artificial intelligence technology. [ABSTRACT FROM AUTHOR]

Generally, the carbon emissions of road transportation have substantial negative effects on biological ecosystem and sustainable development. Thus, to achieve the strategy of peaking carbon emissions before 2030, research on measuring vehicle emissions in large metropolitan road networks is indispensable. With that in mind, a carbon emission macroscopic fundamental diagram model (CE-MFD) is presented to assess network-wide road emissions, and an indicator, carbon emission intensity (CEI), is quantified to capture the influence of traffic dynamics on emissions. Further, the results of CE-MFD show a positive correlation between road congestion and emissions. Based on the CEI indicator, the corresponding network-wide traffic controls are proposed for effectively reducing the emissions. In addition, the effects of electric vehicle (EV) penetration are explored. This study suggests that by 2030, even if the traffic becomes more congested, the CE-MFD of the urban expressway network will reach a peak with an EV penetration of 73%. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a carbon emission inventory in Shanghai for the period from 2000 to 2008 covering six sectors of stationary combustion, transportation, industrial processes, waste disposal and treatment, agricultural activities and forestry sink and three greenhouse gases (CO2, CH4, and N2O). The aim is to reflect carbon emissions in Shanghai, and guides policymakers to take effective actions to mitigate carbon emissions. Several results are obtained: (1) the total carbon emissions in Shanghai increased by 48%, from 136TgCO2e in 2000 to 200TgCO2e in 2008; (2) only the sector of agriculture activities saw reduced emissions; (3) the comparisons among Shanghai, China and world average level confirm that during 2000–2008 Shanghai''s carbon emissions per capita are higher than both the world and the China average level, and its carbon emission per GDP is also higher than the world average level, but both of them are lower than the China average level; (4) in 2008, Shanghai''s carbon emission per GDP is around 10tons CO2 per $10,000 and higher than that of Taiwan, Hong Kong, the G7 and the BRIC (Brazil, Russia, India and China) except China and India. In addition, this paper also illustrates the problems about China statistical system in terms of emission inventory establishment, including the classification system, data quality and temporal resolution. [Copyright &y& Elsevier]

Highlights • We estimate the energy rebound effect caused by technological progress in Shanghai. • We use the state space model with time-varying parameter based on the IPAT identity. • Shanghai has a high partial rebound effect on average at the overall-economy level. • Mitigating the energy rebound effect can significantly reduce carbon emissions. • Energy efficiency improvement should not be the only way to conserve energy. Abstract The energy rebound effect is regarded as an obstacle of achieving the expected target of energy-saving policies, especially under a rapid urbanization background in developing counties, such as China. This has become a substantial drag of sustainable development in some cities. Shanghai is the economic center of China, and it is also a typical energy import-dependent mega-city. Investigating the evolution of Shanghai's energy-saving performance and the energy rebound effect is significant for the implementation of energy-saving policies in other similar cities of China and other developing countries. Using the state space model with time-varying parameters and based on the IPAT identity and the Solow residual approach, this paper is the first study to present a specific estimation on Shanghai's energy rebound effect caused by technological progress. The results show that, during the period of 1991–2016, the average energy rebound effect of overall economy and secondary industry in Shanghai was 93.96% and 73.10%, respectively, indicating a high partial rebound effect. Most of expected energy saving caused by improved energy efficiency is offset by extra energy consumption caused by technological progress. Regarding tertiary industry, the average rebound effect was 146.61%, indicating a backfire effect. However, the average energy rebound amount of tertiary industry is less than that of secondary industry. In particular, there is an increasingly negative impact of the rebound effect of tertiary industry on energy conservation in recent years, with the sector's rapid expansion and corresponding increase in energy demand. Furthermore, we estimate the carbon rebound amount (i.e., carbon emissions caused by the energy rebound effect) and find that, on average, the energy rebound effect caused 13.1% and 0.41% increases in carbon emissions in Shanghai and China, respectively. Therefore, mitigating the energy rebound effect can significantly reduce carbon emissions. Due to the substantial impact of the rebound effect, technological progress and energy efficiency improvement should not be the only way to achieve energy-saving target, especially in energy import-dependent mega-cities like Shanghai. Some supporting policies should be implemented to ensure that the expected outcome of energy-saving effort can be realized as far as possible. [ABSTRACT FROM AUTHOR]

• A CCTS (chance-constrained two-stage) stochastic planning model is formulated to realize the planning of building IES. • Various of uncertainties and their correlation of IES are considered through HMMSG. • The benefits of ESS and V2G bidirectional interaction on low-carbon building IES are verified. • The SAA method is combined with HMMSG method to solve chance-constrained optimization problem. The low-carbon building has been proposed to mitigate the climate change caused by environmental problems and realize carbon neutrality in urban areas. In addition, the integrated energy system (IES) has been developed to reduce renewable energy curtailment in the power distribution system and improve energy efficiency due to the independent operation of traditional energy systems. In this paper, we propose a stochastic planning method for low-carbon building IES, in which the Vehicle to Grid (V2G) is also considered to further increase the flexibility of low-carbon buildings. The proposed planning method optimizes the investment and operation costs, and CO2 emission of the building IES, to achieve the maximum benefit of the low-carbon building and help realize carbon neutrality. By considering the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, a two-stage stochastic programming model is formulated with chance constraints, in which the heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) methods are applied to deal with the uncertainties. In the case study, a real IES office building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as the test system to verify the effectiveness of the proposed planning method, and the functions of the ESS and EV in IES are analyzed in detail in different operation scenarios. The case study results show that the proposed planning scheme can reduce the total cost and carbon emission significantly. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

This study explores the impact of green mergers and acquisitions (M&A) on corporate green innovation and its impact mechanism under the goal of achieving 'carbon emission peak and carbon neutrality'. Through the analysis of M&A data of China's Shanghai and Shenzhen A-share industrial listed companies, the results show that green M&A can significantly improve the corporate green innovation capabilities. External stakeholder's support has played an intermediary role, as enterprises can obtain more government subsidies and commercial credit through green M&A, which reduces financing constraints and enhances the corporates' green innovation capabilities. [ABSTRACT FROM AUTHOR]

In recent years, as China's peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan's carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan's XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction. [ABSTRACT FROM AUTHOR]

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future. [ABSTRACT FROM AUTHOR]

With the rapid development of global industrialization and modernization, carbon emissions have brought about serious climate warming and environmental pollution problems. Chinese enterprises, as the major players in carbon emissions, are important in terms of promoting the green transformation of the economy. It is particularly important to investigate the relationship and mechanism of action between carbon emission reduction and corporate sustainable development in Chinese enterprises. This study aims to determine whether reducing the intensity of carbon emissions can make businesses more sustainable and to analyze the moderating influences of government environmental subsidies, media monitoring, and executives' green opinions on the link between the two variables. The study sample consists of Shanghai and Shenzhen A-shares data from 2015 to 2020, and a fixed-effects model is employed for analysis. Data were obtained from the China Stock Market & Accounting Research database, the Financial News Database of Listed Companies, and enterprise financial statement notes, etc. Stata17.0 was used to clean and analyze the data. The results indicate that businesses can greatly improve their long-term viability by lowering their carbon emissions. Additionally, government environmental subsidies, media monitoring, and executives' green perceptions all enhance the correlation between corporate sustainability and reduce carbon emission intensity. This study not only enriches the relationship between environmental governance and sustainable development from a theoretical perspective, but also further expands the stakeholder theory. It also finds the mechanism of the role of the government and media on corporate carbon emissions for sustainable development in practice, which provides effective guidance to accelerate the promotion of carbon emission reduction and, thus, the sustainable development of Chinese enterprises. [ABSTRACT FROM AUTHOR]

The electricity sector is a complex system, especially in the Yangtze River Delta (YRD) of China. Thus, the carbon dioxide (CO2) emission of YRD's electricity sector during 2000–2020 was first calculated and then evaluated from two systematical dimensions of cross-region and the whole process (production, trade, transmission, and consumption) by an extended logarithmic mean Divisia index (LMDI). (1) During 2000–2020, the CO2 emission of YRD's electricity sector increased from 228.12 Mt to 807.55 Mt, with an average annual growth rate of 6.52%. Compared to other regions, the YRD's electricity mix effect had the strongest mitigation impact on CO2 growth. Therefore, it is important for YRD to build a low-carbon electricity system itself, including the de-carbonization of electricity production and the carbon reduction of the electricity-use process. (2) Nationally, electricity trade had an overall mitigating impact on emission growth during 2000–2020. This result means that cross-regional cooperation or trade in the electricity sector is beneficial to emission reduction. So, it is important to improve the national power grids to promote trade. (3) Jiangsu had the largest CO2 emissions, while Anhui had the fastest average annual growth rate (9.71%). Moreover, the economic activity effect was the most significant driver in all provinces, especially in Jiangsu and Anhui. Thus, Jiangsu and Anhui should strive to improve the quality of economic growth while vigorously cutting carbon emissions. (4) Electricity transmission loss had an overall driving impact on emission growth in each YRD province, especially in Zhejiang and Anhui. Meanwhile, electricity structure, electricity trade, and electricity intensity were the inhibiting factors. Particularly, the inhibiting effect of Shanghai's electricity structure was notably weak (−2.17 Mt). So, Shanghai should try hard to increase the proportion of renewable energy, while Zhejiang and Anhui should upgrade their electricity transmission equipment. [ABSTRACT FROM AUTHOR]

Hydrogen energy contributes to China's carbon peaking and carbon neutralization by serving as an important energy carrier. However, the calculation of the cost of hydrogen production by the power grid ignores the current cost of carbon emissions. To measure the cost of hydrogen-production projects in various provinces more comprehensively and accurately, this study incorporates the carbon-emission cost into the traditional levelized cost of hydrogen model. An analysis of the energy structure of the power supply is conducted in each province of China to calculate carbon-emission costs, which are then subjected to a sensitivity test. Based on the results, the carbon-emission costs for hydrogen in each province are between 0.198 and 1.307 CNY/kg, and the levelized cost of hydrogen based on carbon-emission costs varies from 24.813 to 48.020 CNY/kg; in addition, carbon-emission costs range from 0.61% to 3.4% of the total costs. The results also show that the levelized cost of hydrogen considering carbon-emission costs in the Shanghai municipality specifically is most sensitive to the carbon-emission price, changing by 0.131 CNY/kg for every 10% fluctuation in the carbon-emission price. [ABSTRACT FROM AUTHOR]

In order to deal with the increasing environmental challenges, the high carbon-emitting transport system keeps transitioning to achieve carbon peaking and carbon neutrality goals. This paper addresses the calculation of carbon emission estimation and identifying carbon reduction driving factors for the hinterland-based container intermodal network. Firstly, an analysis of the carbon emissions composition of the hinterland-based container intermodal networks is conducted. Furthermore, a carbon emission estimation model for the hinterland-based container intermodal network is developed. Subsequently, taking the Shanghai port and the Yangtze River Delta (YRD) hinterland as a case, we estimate the carbon emission of the hinterland-based container intermodal network under the Well-to-Wheel (WTW) and the Tank-to-Wheel (TTW) modes in the past ten years. Finally, the critical driving factors are identified by sensitivity analysis, and the variation of carbon emissions under different scenarios with carbon reduction policies are analyzed. The results indicate that the transportation structure and the fuel choices affect network emissions reductions significantly. In the current policy-based scenario, the WTW mode achieves the carbon peaking between 2030 and 2035, while the TTW mode peaks earlier. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at the regional to city scale. Very few studies have focused on combustion efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for interpreting and deriving spatially resolved ERs from asynchronous satellite measurements, including (1) variations in meteorological conditions given the mismatch in satellite overpass times, (2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), (3) interferences from the biosphere and biomass burning, and (4) the mismatch in the daytime variations of CO and CO 2 emissions. In this study, we extended an established emission estimate approach to arrive at spatially resolved ERs based on retrieved column-averaged CO 2 (XCO 2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ER CO values from the sounding level to the overpass and city level. We found that the differences in overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially resolved ER CO. Specifically, a time difference of >3 h typically led to dramatic changes in wind directions and urban plume shapes, thereby making the calculation of accurate sounding-specific ER CO difficult. After removing such cases from consideration and applying a simple plume shift method when necessary to account for changes in wind direction and speed, we discovered significant contrasts in combustion efficiencies between (1) two megacities versus two industry-oriented cities and (2) different regions within a city, based on six nearly coincident overpasses per city. Results suggest that the ER CO impacted by heavy industry in Los Angeles is slightly lower than the overall city-wide value (<10 ppb-CO / ppm-CO 2). In contrast, the ER CO related to heavy industry in Shanghai is much higher than Shanghai's city mean and more aligned with the city means of two selected industry-oriented cities in China (approaching 20 ppb-CO / ppm-CO 2). Although investigations based on a larger number of satellite overpasses are needed, our unique approach (i.e., without using sector-specific information from emission inventories) provides new insights into assessing combustion efficiency within a city from future satellite missions, such as those that will map column CO 2 and CO concentrations simultaneously with high spatiotemporal resolutions. [ABSTRACT FROM AUTHOR]

To date, along with the rapid development of urban rail transit (URT) in China, the evaluation of operational efficiency and energy efficiency has become one of the most important topics. However, the extant literature regarding the efficiency of URT at the line level and considering carbon emissions is limited. To fill the gap, an evaluation model based on slacks-based measure (SBM) data envelopment analysis (DEA) is proposed to measure the efficiencies, which is applied to 61 URT lines in China's four megacities. The findings are summarized as follows: (1) The average operational efficiency and energy efficiency of URT lines are low, and both have great room for improvement. (2) There are significant disparities in the efficiency of URT lines in the case cities. For instance, the average operational efficiency of URT lines in Guangzhou is higher than that of other cities, while the average energy efficiency of URT lines in Shanghai is higher than that of other cities. (3) The URT lines operated by state-owned enterprises have higher average operational efficiency, while the lines operated by joint ventures have higher average energy efficiency. Finally, some suggestions are provided to improve the efficiencies. [ABSTRACT FROM AUTHOR]

Nowadays, it is noticed that more than a third of the carbon dioxide (CO2) emitted in the atmosphere comes from the construction sector. This CO2 concentration has a significant effect on climate change. In the new cities, tall buildings multiply as mushrooms. Some specialists believe that they can be one of the solutions to reduce the carbon content in the atmosphere. The main aim of this study is to simulate, analyze and compare the embodied carbon and operational carbon of the two tallest buildings located in the United States and China, by using Design Builder and Pleiades software: the One World Trade Center in USA and the Shanghai Tower in China. Even if the embodied carbon of these super tall buildings is very high, the operational carbon remains the most important source of carbon emissions on their whole life cycle. Future carbon emissions of these two buildings were estimated in three periods (2030, 2050 and 2080) following the A2 scenario from the Intergovernmental Panel on Climate Change (IPCC). The results show that the operational carbon will increase by 10.6% at One World Trade Center (1WTC, USA) and 7.8% at Shanghai Tower (ST, China) in 2050. In addition, this study analyzed the impacts of the electricity mix and photovoltaic panels on their carbon dioxide emissions. Replacing energy production based on coal by renewable energy sources in the electricity mix of these countries could induce a reduction of 47.5% and 65.6% of total operational carbon emitted by the 1WTC and ST, respectively, by 2050. Finally, 46% of the building construction cost of these skyscrapers is related to their structure and superstructure. [ABSTRACT FROM AUTHOR]