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This paper constructs a novel stress measurement system of carbon market from the perspective of trading, emission reduction, and external shocks and simulates the stress indices of national and pilot carbon markets of China with the methods of functional data analysis and criteria importance through intercriteria correlation. It concludes that the overall carbon market stress is in the shape of "W" and still at a high level, with frequent fluctuations and an upward trend. In addition, the stress of Hubei, Beijing, and Shanghai carbon market fluctuates and rises, while the stress of Guangdong carbon market decreases. Moreover, carbon market stress mainly comes from trading and emission reduction. Furthermore, stress fluctuation of Guangdong and Beijing carbon market is more prone to "big waves," indicating that the two markets are sensitive to big events. Finally, the pilot carbon markets are divided into stress-driven and stress-release market and the type of which keeps change in different period. [ABSTRACT FROM AUTHOR]

Since 2020, China has been dedicated to the goal of "carbon peak and carbon neutrality" in the international community, demonstrating its commitment to energy conservation and emissions control. In 2021, in response to the guidance of the State Council, all provinces in China included the creation of a low-carbon economic system as one of their key development goals. This paper aims at investigating the low-carbon economic development efficiency and the redundancy of inefficient areas of Guangdong Province by using the three-stage DEA-Malmquist index model. Panel data of 21 cities from 2011 to 2020 in Guangdong Province were selected. Results revealed that the low-carbon economic development efficiency in the whole province rises, but the growth rate has slowed down in the past three years. Pearl River Delta area is better-developed in low-carbon economy than others. It is recommended to improve the efficiency of scale, make use of environmental advantages and develop energy-saving and emission reduction technologies. [ABSTRACT FROM AUTHOR]

Collaborative innovation can promote scientific productivity and the development of clean technology and thus has a great potential in constraining the ecological footprint. However, current studies on the impact of collaborative innovation on ecological footprint are insufficient, and results remain controversial. To better understand these impacts, this paper took Guangdong-Hong Kong-Macao Greater Bay Area of China as a case, estimated the ecological footprint at the municipal level from 2008 to 2018, measured collaborative innovation both from four dimensions and from a composite approach, then applied threshold regression models to compare the impact of collaborative innovation on the ecological footprint across different economic intervals. The findings showed that: the ecological footprint of the Greater Bay Area displayed an overall upward trend with prominent spatial heterogeneity. The impact of collaborative innovation on the ecological footprint presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted the ecological footprint, which intensified with economic development, while collaboration in technology exerted significant inhibitory effects on ecological footprint, and the influence of inter-city knowledge collaboration was limited. Overall, collaborative innovation inhibited ecological footprint when measured by a composite index. This might inspire policymakers to adopt sustainable strategies depending on the type of collaborative innovation and the economic status of the city to constrain growth of the ecological footprint, thus minimizing the pressures of human activities on the environment and moving towards a more carbon neutral society. [ABSTRACT FROM AUTHOR]

Accurate carbon price prediction is a crucial task for the carbon trading market. Previous studies have ignored the impact of online data and are limited to point predictions, which brings challenges to the accurate forecasting of carbon prices. To address those issues, this paper proposes an interval-valued carbon price forecasting method based on web search data and social media sentiment. First, we collect web search data and social media sentiment to improve prediction performance by synthesizing multiple types of data information. Second, we employ principal component analysis (PCA) to preprocess high-dimensional web search data, and utilize BosonNLP for quantifying social media information, thereby enhancing the predictability of the dataset. Subsequently, a variational mode decomposition (VMD) is applied to the carbon price and online data, followed by utilizing particle swarm optimization support vector regression (PSO-SVR) to predict each sub-modes and summing them up to obtain the ultimate forecasting outcome. Finally, using carbon prices in Guangdong and Hubei provinces as case studies, the experimental results demonstrate that web search data and social media sentiment significantly enhance the predictive accuracy of interval-valued carbon prices. Furthermore, the proposed VMD-PSO-SVR outperforms other comparative models in the accuracy and reliability of interval-valued forecasting. [ABSTRACT FROM AUTHOR]

The variation in the urban spatial structure (USS) has profound impacts on carbon emissions. Studying the relationship between the two can provide guidance for carbon neutrality strategies and the construction of low-carbon cities in China. However, there is currently a lack of comparative research on the different regions within a province. In this paper, the spatiotemporal evolution of the USS and carbon emissions, at five-year intervals from 2000 to 2020, is investigated in 21 prefecture-level cities in Guangdong Province, China, and the overall relationship of the USS to carbon emissions and their spatiotemporal variations are analyzed by using a two-way fixed-effects model and a geographically and temporally weighted regression model, respectively. The results show that, first, over the past twenty years, the scale of cities has continued to expand, with increasing continuity and aggregation in the built-up areas, while the complexity and fragmentation of their shapes have gradually decreased. Second, the gap in carbon emissions between the Pearl River Delta and other regions in Guangdong shows a trend of first decreasing and then increasing, with high values concentrated in the Pearl River Delta region and the city of Shantou in the east. Third, compared to socio-economic factors, the USS has a more direct and pronounced impact on carbon emissions. Urban expansion and the increased complexity of land patches promote carbon emissions, whereas improving urban spatial continuity and compactness can reduce carbon emissions. Fourth, the dominant spatial structure indicators of carbon emissions differ among the regions of eastern, western, and northern Guangdong and the Pearl River Delta. This study proposes spatial optimization strategies for the low-carbon development of cities in Guangdong Province, providing a new perspective for integrating urban layout and emission reduction policies. [ABSTRACT FROM AUTHOR]

Accurate price prediction for carbon trading is essential to provide the guidance for investment and production. The current prediction methods mainly depend on the carbon price itself, from which the change pattern of carbon price is studied. However, fusing the multi-source data, e.g. trading message and public sentiment, and taking proper data processing to improve the prediction accuracy need in-depth research. In this paper, a hybrid price prediction method utilizing both the statistical and intelligent models is established on multi-source data, and the data characteristics are fully explored by correlation analysis and multi-frequency analysis. The study on Guangdong market show that: the accuracy of proposed method is superior to the benchmark ones: root mean square error and mean absolute percentage error are reduced by 19.27% and 7.16%, while determination coefficient and trading return are increased by 8.31% and 25.11%. The proposed method is helpful for stakeholders to manage their trading. [ABSTRACT FROM AUTHOR]

As a flexible market mechanism based on the control of total quantity, the carbon emission trading system aims to achieve economic development while reducing carbon emissions and energy consumption. How to improve the effectiveness of carbon emission trading (CET) policy and achieve the established emission reduction targets have attracted widespread attention from scholars. Taking Guangdong Province as an example, this paper constructed a system dynamics (SD) model to investigate the interaction between internal factors of CET system and simulated the effectiveness of CET policy from 2016-2026. The results indicate that (1) Relative errors between historical data and simulated data are controlled within 5%, which indicates that the system model is suitable to simulate real system. (2) The average sensitivity of the quota variation rate, paid ratio and penalty coefficient are 0.42, 0.56 and 0.29, respectively, indicating that these three parameters can be identified as leverage parameters affecting the efficiency of the CET policy. (3) A single type of policy is difficult to achieve emission reduction targets. The policy portfolio scenario will achieve the reduction target in Thirteenth Five-Year plan of Guangdong, that is, the total amount of quota decreases by 0.5% per year, the paid ratio rises 2% in that ratio per year, and the penalty coefficient is triple the carbon price. [ABSTRACT FROM AUTHOR]

This paper studies when and under what conditions the actions undertaken by the power plants involved in China's emission trading scheme (ETS) pilot became cost effective. Based on unique plant-level panel data and the difference-in-differences strategy, we identify that an insignificant initial reduction in cost efficiency occurred at the announcement stage for power plants in the pilot provinces; however, the cost efficiency of the pilot plants increased significantly following formal policy implementation. Additionally, the by-stage treatment effects differed across the pilot provinces due to localized market and non-market variations. Localized conditions of higher marketization, stricter policy enforcement, and lower carbon dependence enhanced this positive effect. The synthetic control results confirmed this variation in the policy effects. The carbon trading pilots resulted in improved efficiency in power plants in Shanghai, Guangdong, and Tianjin during the period 2013--2017, with an associated total cost saving of approximately 29.75 million RMB. To enhance the efficacy of the ETS policy, our findings suggest that the design of the policy should consider localized external factors. [ABSTRACT FROM AUTHOR]

In response to global warming, China has formulated the "double carbon" strategic goals of peaking carbon dioxide emissions before 2030 and reaching carbon neutrality before 2060. The problem of rural carbon emissions is often ignored due to underdeveloped industries and services. In this paper, the carbon emission pathways in the rural areas of Guangdong Province are investigated. Since energy consumption is the main source of carbon emissions, the factor analysis was used to analyze the main factors affecting rural household energy consumption and agricultural production energy consumption. Multiple linear regression was conducted to predict the rural energy consumption demand in Guangdong. Furthermore, the current situation and development trend of rural energy supply, demand and consumption structure, and the potential of renewable energy development were considered to predict carbon emissions in the rural areas of Guangdong. Moreover, the carbon emission pathways in the rural areas of Guangdong were discussed under two scenarios: the base scenario and the radical model. [ABSTRACT FROM AUTHOR]

Using the quantile GARCH model estimators to gauge the bidirectional risk magnitude and the Granger causality test in risk distributions to detect the existence of risk spillovers, this paper explores the extreme risk spillovers of China's regional carbon markets to local listed firm's stock returns. From the perspectives of macro region level and micro firm level, the findings are outlined as follows. First, among the top three active carbon trading pilots (Hubei, Guangdong, and Shenzhen), Hubei pilot exhibits significant "low risk and high profit" features. Second, the predominant risk spillover effects to local listed firms are heterogeneous across pilots. Specifically, Hubei pilot is dominated by "up-to-down" effect, and Guangdong pilot is dominated by "down-to-down" effect, while Shenzhen pilot has no predominant effect. The heterogeneous risk spillover performance may be caused by the regional divergence in economic development, industry structure, and cap setting concerning each pilot. Third, the risk transmission performance from carbon allowance price to local listed firm's stock returns depends on the firm's belonging sector. That is, environment-related firms, either environment-friendly firms or pollution-intensive firms, are more susceptible to carbon markets' risks compared with environment-unrelated firms. This paper supplies novel information on the risk transmission from carbon markets to local economic entities, which proves valuable not only for firms to improve risk aversion ability but also for policy-makers to perfect carbon markets' mechanism. [ABSTRACT FROM AUTHOR]

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries. [ABSTRACT FROM AUTHOR]

The power sector plays a significant role in carbon neutrality strategies, and the grid system is a crucial part of the power sector. In particular, with less mitigation potential from technology efficiency, the credit from whole life cycles is critical. This paper describes the investigation of the environmental impacts of various scenarios from the perspective of life cycles. By using the life cycle assessment (LCA) method, various grid systems are examined as a case study, including a traditional, renewable energy, and power storage grid system, as well as a microgrid, in Guangdong. The results highlight the fact that with the systematic improvement of a grid, significant environmental benefits can be achieved. For a grid system, optimization through technology has significant carbon reduction effects even if the power grid structure is not changed. Using renewable energy instead of traditional fuel can reduce the emission of 0.05 kg of CO2-equivalent greenhouse gas per 1 kWh of electricity produced, which is 7.9% of the baseline, and microgrid technology leads to a much greater carbon reduction potential of 23.8% of the baseline. The role of energy storage is undervalued due to the limitations of the data. [ABSTRACT FROM AUTHOR]

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A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity. [ABSTRACT FROM AUTHOR]

Environmental degradation and climate change are two important issues that threaten human survival and sustainable social development. Based on the characteristics of the same source of sulfur dioxide and greenhouse gases, we analyze the collaborative governance mechanism of carbon emission trading on sulfur dioxide. The results show that carbon emission reduction trading may not necessarily deliver sulfur dioxide reduction co-benefits. Then, we conduct an empirical using data of 30 regions in China from 1998 to 2017. The results show that the co-benefits of carbon emission trading in terms of the reduction of sulfur dioxide are regionally heterogeneous. The carbon emission trading policies implemented in Beijing, Chongqing, Hubei, and Guangdong have realized collaborative governance on sulfur dioxide. However, the carbon emission trading policies implemented in Tianjin and Shanghai have not realized collaborative governance on sulfur dioxide. Moreover, Chongqing has the highest average annual sulfur dioxide reduction co-benefits, followed by Guangdong. The gap of sulfur dioxide reduction co-benefits between Beijing and Hubei's synergistic emission reduction effect is small. Moreover, the sulfur dioxide reduction co-benefits of carbon emission trading are mainly achieved by reducing fossil energy consumption. Meanwhile, the carbon emission trading implemented in Beijing also delivers sulfur dioxide reduction co-benefits by improving pollution reduction technology. [ABSTRACT FROM AUTHOR]

This paper investigates the long-term dynamic cross-correlation evolution between US economic policy uncertainty index (USEPU) and Guangdong carbon emission trading price (GDCP) from the multifractal detrended cross-correlation analysis (MF-DCCA) perspective. With the calculation of correlation statistics and fluctuation function, the beginning procedures of MF-DCCA, we find that the cross-correlation between USEPU and GDCP is significant and presents power law property. Also, with the Hurst exponent, we find that the long-horizon correlations between series are persistent. Moreover, we perform Rényi exponent and spectrum singularity check. The empirical findings reveal that the all the correlations are of multifractality and the correlation of GDCP holds the highest degree. [ABSTRACT FROM AUTHOR]

As a major carbon emitter, the power sector plays a crucial role in realizing the goal of carbon peaking and carbon neutrality. This study constructed a low-carbon power system based on the LEAP model (LEAP-GBA) with 2020 as a statistic base aiming of exploring the low-carbon transformation pathway of the power sector in the Guangdong–Hong Kong, and Macao Greater Bay Area (GBA). Five scenarios are set up to simulate the demand, power generation structure, carbon emissions, and power generation costs in the power sector under different scenarios. The results indicate that total electricity demand will peak after 2050, with 80% of it coming from industry, buildings and residential use. To achieve net-zero emissions from the power sector in the GBA, a future power generation mix dominated by nuclear and renewable energy generation and supplemented by fossil energy generation equipped with CCUS technologies. BECCS technology and nuclear power are the key to realize zero carbon emissions from the power sector in the GBA, so it should be the first to promote BECCS technology testing and commercial application, improve the deployment of nuclear power sites, and push forward the construction of nuclear power and technology improvement in the next 40 years. [ABSTRACT FROM AUTHOR]

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China's future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA. [ABSTRACT FROM AUTHOR]

In today's environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China's carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China's carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China's carbon market. [ABSTRACT FROM AUTHOR]

Emission Trading Scheme (ETS) is one of the most important instruments introduced to meet carbon neutral targets internationally. To reduce carbon emissions, China has established eight regional ETS since 2013 to reduce carbon emissions. However, significant heterogeneities exist across the eight pilots, resulting in distinctively different carbon price movements. Traded as a financial asset, carbon allowance is vulnerable to internal and external shocks. Though carbon prices have been fluctuating fiercely since the inception of the carbon market, little is known about potential carbon price bubbles in any of these pilots. To this end, this paper resorts to the generalized sup Augmented Dickey-Fuller (GSADF) test, which allows the detection of multiple bubbles, as well as the date stamping of bubbles in carbon prices. For the first time, price bubbles are found in the Chinese carbon markets. The empirical results find three bubbles in Guangdong pilot, two bubbles in Tianjin pilot, and one bubble in Hubei pilot. These explosive episodes are closely related to immature market mechanisms and policy implementations. The results provide insightful implications for the upcoming unified national carbon market. • This paper detects the carbon price bubbles in Chinese markets. • Multiple bubbles exist in different pilots. • Not all pilots encounter bubbles. [ABSTRACT FROM AUTHOR]

In this study, we assessed the impacts of the benchmark designs of emissions allowance allocation in China's national carbon emissions trading system with plant-level data and further estimated the marginal clearing price and power supply cost in Guangdong power market under electricity-carbon market coupling with unit commitment and economic dispatch model. We find that the existing allowances benchmark would result in a considerable surplus of allowances at about 222 Mt. But the benchmarking and exemplary levels on the heat rate of power supply would motivate thermal power units to reduce CO2 emissions. Under a tight balance of supply and demand in Guangdong, peaking thermal power plants will become the marginal clearing units and higher clearing prices will add to the revenue of lower cost inframarginal renewable energy power units. However, the combined impact of electricity-carbon market coupling would cause the marginal clearing price fluctuates obviously from 0 to 1159 CNY/MWh. Compared to the baseline scenario with free CO2 allowances allocation, the efficiency of thermal power utilization would decrease by 23%-59% and the net revenue per MWh power supply of coal-fired power units would decrease by 275%-325% under the stress scenario. Our study suggests that setting a more stringent allowances allocation benchmark for carbon price discovery is necessary. As electricity-carbon market coupling changes the role of coal-fired power plants to provide flexibility service and decrease their revenues, it calls for further market designs on proper reimbursement of flexible resources, under which the electricity market can effectively achieve the synergy among accommodating new energy, ensuring resource adequacy, and delivering cost efficiency. In addition, the synergy can be enhanced by formulating a tax program, which can promote renewable energy investment. [ABSTRACT FROM AUTHOR]

Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China's major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions. [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]

This paper examines the risk spillover effect between the carbon market and the stock market in China and the role of corporate social responsibility (CSR) on this effect. Employing Beijing, Hubei, and Guangdong carbon markets, we apply time-domain and frequency-domain spillover approaches and find that during the Chinese stock market crisis in 2015, risk spillovers from the stock market to the carbon market were more pronounced. Additionally, CSR firms are more dominant as information transmitters than those non-CSR (NCSR) firms in the carbon market. However, plausibly, due to the infancy of carbon trading, our results show that the level of connectedness between the carbon market and the stock market in China is relatively low. [ABSTRACT FROM AUTHOR]

Carbon dioxide has a significant impact on global climate change due to its natural greenhouse effect. The objective and credible forecast of carbon emissions is very important for the government to formulate and implement the corresponding emission reduction targets. For controlling the growth of carbon emissions, Chinese government has put forward the low-carbon pilot policy and carbon trading policy. However, the existing grey models cannot measure the impact of policies and their interactions. In order to remedy the defect, a novel grey multivariable model based on dummy variables and their interactions is established. Two kinds of grey multivariable models and back propagation neural network model are chosen as comparison models to highlight that the introduction of dummy variables and their interactions plays an important part in improving the model performance. To verify the effectiveness, these four models are selected to simulate and predict the carbon emissions generated from primary energy consumption in Guangdong Province of China. The empirical results indicate that the mean absolute percentage errors of the novel model are 2.87% and 0.86%, respectively, which is significantly better than these three competing models. Finally, based on the outstanding performance of the novel model, it is chosen to forecast the fluctuating tendency of carbon emissions in the next 5 years. [ABSTRACT FROM AUTHOR]

As a new clean and sustainable energy that can reduce carbon emissions, offshore wind energy has received much more attention in recent years. In response to the complexity and flexibility issues in the decision-making process of offshore wind power investment, this paper proposes an investment decision framework based on a bidimensional binominal lattice that embeds deferral options, aiming to derive the appropriate development timing and investment potential for corporate investors in uncertain environment of market and technological development. And the investment model embedding revenue from carbon emission reduction highlights the advantages of clean energy. Furthermore, we creatively compare the policy effects of three subsidy schemes using the model as a policy analysis tool and use scenario analysis to simulate the impacts of dynamic adjustments in subsidies on investment decisions. For the case of an offshore wind power plant in Guangdong province, China, we find that the local government has to provide a subsidy of 0.11 CNY/kWh to allow the project to be profitable. In the absence of subsidies, however, it is preferable to defer the decision of investment with a wait-and-see attitude. Given the same total government financial input, we find that it will be more beneficial for the government to provide R&D subsidy than electricity tariff subsidy and operation subsidy in terms of long-term sustainable development. Our findings also demonstrate that considerations in the carbon trading market will also contribute to the development of offshore wind power. The conclusions of this article can provide valuable guidelines for corporate investors to adjust the appropriate development timing and for policymakers to optimize subsidy allocation, so as to promote the healthy development of offshore wind power and low-carbon transition. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

The escalation of risks associated with environmental and climatic aspects has accelerated the introduction of voluntary personal carbon trading (PCT), a policy instrument for promotion and facilitation of low-carbon behavior in the domestic sector. This paper examines participants' motivation in the Guangdong pilot project, which aims to develop the first voluntary PCT scheme (Carbon Generalized System of Preferences, CGSP) in China to reduce the domestic sector's emissions and increase the liquidity of the Guangdong pilot emission trading scheme. The CGSP acceptance model is based on the technology acceptance model and explores the drivers of the public's participation willingness (PW) in the CGSP scheme. Further, the structural equation model has been adopted to explore the influence paths of each driver by using survey data (N = 1190) collected from Guangzhou, Guangdong Province, China. The empirical results demonstrated that the strong and direct drivers are associated with the institutional technical environment, perceived usefulness and participation risk. However, other drivers affect PW through the mediation effect of perceived usefulness. Notably, implementation cost had no significant effect on PW. • An innovative voluntary personal carbon trading scheme in China was introduced. • A conceptual model was proposed to study the determinants of public acceptance. • Structural equation model was used to study the influence path of each determinant. • Both the direct and indirect paths of each determinant have been analyzed in depth. [ABSTRACT FROM AUTHOR]