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China is planning to introduce carbon tax policy to control the carbon emissions of the country better and achieve the "3060 goals", but there is still widespread discussion about how to introduce it and how to combine it with cap and trade. China has already established a national carbon emission trading market; however, there is also disagreement on whether to impose the carbon tax on companies and projects that have been included in scope of cap and trade. This paper adopts the research method of experimental economics to study the effect on social economy and social emission reduction under cap and trade, carbon tax, and carbon tax-carbon trading policies, and analyzes average prices of carbon market under cap and trade and carbon tax-carbon trading policies. The study finds that under the carbon tax-carbon trading policy, carbon emissions cannot be reduced significantly; but the profits of manufacturers will be reduced significantly; meanwhile, this reduction effect is even more severe for high consumption manufacturers; and it will be resulting in a lower average carbon market price under the carbon tax-carbon trading policies than under the cap and trade policy. This paper will provide theoretical suggestions for introducing carbon tax policy into China in the future and make policy recommendations for the better development of China's carbon market. [ABSTRACT FROM AUTHOR]

The shadow price of carbon emissions can measure the marginal output effect under the carbon emission regulation and is also one of the key indicators to construct a low-carbon development path for production units. Currently, international research on shadow price is focused on the industrial and energy sectors. However, in the context of carbon peaking and carbon neutrality targets in China, the use of shadow price to study the cost of reducing emissions from agricultural production, especially forestry fruit industries, is significant. In this paper, we use a parametric approach to construct the quadratic ambient directional distance function. Using the input–output data of peach, we then calculate the environmental technical efficiency and shadow price of carbon emissions from peach production in Guangxi, Jiangsu, Shandong, and Sichuan provinces, and further estimate the values of green output in each province. The results show that (i) the environmental technology efficiency of peach production in Jiangsu province, located in the coastal plain area of eastern China, is the highest among the four provinces, while that in Guangxi province, located in the hilly area of southeast China, is the lowest. (ii) Guangxi province has the smallest carbon shadow price of peach production among the four provinces, while Sichuan province, located in the mountainous area of southwest China, has the largest. (iii) The green output value of the peach production in Jiangsu province is the highest among the four provinces, and that in Guangxi province is the lowest. In order to effectively reduce carbon emissions in peach production without affecting economic benefits, the paper puts forward the following suggestions: for peach-producing areas in the southeast hills of China, it is necessary to increase the application of green environmental technology while reducing the input of production factors in peach production. For peach-producing areas in the northern plains of China, the input of production factors should be reduced. It is not easy for peach-producing areas in the southwest mountains of China to reduce the input of production factors while increasing the application of green technologies. Finally, for peach-producing areas in China's eastern coastal plain, the implementation of environmental regulations for peach production should be gradual. [ABSTRACT FROM AUTHOR]

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations. [ABSTRACT FROM AUTHOR]

The fluctuation of the carbon price and its related components can effectively reflect the overall economy. This paper explores the fluctuation of the carbon price and its influencing factors. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the carbon price series of eight pilot projects at multiple timescales. Second, according to the historical trading records in the eight pilot projects, this paper constructs a national carbon price under a variety of scenarios. Finally, based on the average of the eight pilot market daily trading datasets, the national carbon price is constructed, and a short-term prediction is made. The results show that: (1) the pilot projects in Beijing and Hubei are susceptible to short-term external factors, and Beijing's pilot internal market mechanism has a large impact on the carbon price; (2) in most scenarios, the national price fluctuates, with the highest carbon price approaching 70 CNY/tCO2 and the lowest falling below 10 CNY/tCO2; and (3) China's carbon price is still has ample room to rise in the future. This paper provides a theoretical basis and practical guidance for the prediction of carbon prices in China. [ABSTRACT FROM AUTHOR]

The proposal of "double carbon" goal increases the pressure of power structure transformation. This paper sets up two scenarios according to the timing progress of realizing the "double carbon" goal and explores the transformation planning schemes of China's power structure. The conclusions are as follows: (1) Technological progress and policy support will greatly reduce the levelized cost of electricity (LCOE) of onshore wind power, offshore wind power, photovoltaic power, and photothermal power. The rapid rise in carbon price will lead to the LCOE of coal power in 2060 rising to 2 CNY/kWh. (2) The power consumption of the whole society in the baseline scenario can reach 17,000 TWh in 2060. In the acceleration scenario, this value may triple that in 2020 to 21,550 TWh. (3) The acceleration scenario will pay more newly added power costs and coal power stranded scale than the baseline scenario but can achieve carbon peak and negative emissions earlier. (4) More attention should be paid to the flexible level of power system, improve the allocation proportion and requirements of new energy storage on the power supply side, help the steady exit of coal power, and ensure the safety of low-carbon transformation of power structure. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) plays an important role in achieving the dual-carbon goal, and improving EE is thus indispensable. This paper evaluates the impact of carbon emission trading policy (CETP) on EE based on a difference-in-difference (DID) method, using 16-year data of 30 provinces and cities from 2005 to 2020. Conclusions are as follows: (1) CETP significantly promotes EE, and this conclusion still appears valid after robustness tests. (2) The positive impact of CETP on EE is more effective in regions of high foreign direct investment (FDI) and high government intervention (GOVI). (3) The positive impact of CETP on EE is through impact mechanisms of energy structure adjustment (ESA), green innovation (GI), and industrial structure upgrading (ISU). The findings in this paper may enrich current research in CETP and offer more pragmatic suggestions for policy advancement as well. [ABSTRACT FROM AUTHOR]

With ecological civilization becoming an increasingly high profile topic, terms such as carbon neutrality and carbon markets have entered the research landscape. Carbon prices are the core of carbon markets. After analyzing the eight carbon trading markets in China, this paper finds that carbon prices are poorly balanced, i.e., they vary widely between pilots. Regional carbon prices show a discrete distribution, and regions with better carbon market development do not have a spatial demonstration effect on neighboring regions. Therefore, in the initial stage of China's unified carbon trading market, the mandatory control threshold amount should be lowered to allow more enterprises can participate in emission reduction. Reliance on nonclean energy should be weakened, investment in environmental protection and emission reduction should be strengthened, the proportion of carbon offset in forestry should be increased, and a carbon tax should be gradually introduced. The paper's overall aim is to bridge regional barriers and narrow differences to achieve a smooth transition from pilot regions to national and global carbon markets. [ABSTRACT FROM AUTHOR]

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks. [ABSTRACT FROM AUTHOR]

Carbon emissions from the refining industry are receiving increasing national attention. In view of long-term sustainable development, a carbon pricing mechanism oriented to carbon emission reduction needs to be developed. Currently, the two most common carbon pricing instruments are emission trading system and carbon tax. Therefore, it is important to study the carbon emission problems in the refining industry under emission trading system or carbon tax. Based on the current situation of China's refining industry, this paper constructs an evolutionary game model for backward and advanced refineries to explore which instrument is more effectively applied in the refining industry and identify the effective factors that can promote carbon emission reduction in refineries. According to the numerical results, if the heterogeneity of enterprises is small, the government's implementation of an emission trading system is the most effective measure, while carbon tax can only ensure that the equilibrium strategy solution is (1,1) when the tax rate is high. If the heterogeneity is large, the carbon tax policy will not have any effect, indicating that government implementation of an emission trading system is more effective than the carbon tax. In addition, there is a positive relationship between carbon price, carbon tax, and refineries' agreement to carbon emission reduction. Finally, consumers' preference for low-carbon products, R&D investment level, and R&D spillover effect have nothing to do with carbon emission reduction. Only by reducing refinery heterogeneity and improving the R&D efficiency of backward refineries can all enterprises agree to carbon emission reduction. [ABSTRACT FROM AUTHOR]

China has announced a target of achieving carbon peaking by 2030 and carbon neutrality by 2060. Therefore, it is important to assess the economic impacts and emission reduction effects of China's low-carbon policies. In this paper, a multi-agent dynamic stochastic general equilibrium (DSGE) model is established. We analyze the effects of carbon tax and carbon cap-and-trade policies under both deterministic and stochastic conditions, as well as their ability to cope with stochastic shocks. We found that (1) from a deterministic perspective, these two policies have the same effect. Every 1% cut in CO2 emissions will bring a 0.12% output loss, a 0.5% drop in demand for fossil fuels, and a 0.05% rise in demand for renewable energy; (2) from a stochastic perspective, effects of these two policies are different. This is mainly because economic uncertainty does not change the cost of CO2 emissions under a carbon tax policy, but it does change the price of CO2 quotas and the emission reduction behaviors under a carbon cap-and-trade policy; (3) from an economic volatility perspective, both two policies can act as automatic stabilizers. Compared to a carbon tax, a cap-and-trade policy can better ease economic fluctuations. The results of this study provide implications for policy-making. [ABSTRACT FROM AUTHOR]

Climate change poses an urgent threat, necessitating the implementation of measures to actively reduce carbon emissions. The development of effective carbon emission reduction policies requires accurate estimation of the costs involved. In situations where actual prices of commodities are not available in the market, shadow pricing provides a useful method to calculate relative prices between commodities with and without price information. However, most studies focus on the industry, with few contributions on agricultural sector. This paper estimates the shadow price of carbon emissions in the agricultural sector from a provincial perspective, incorporating the impact of livestock into the calculation of carbon emissions and shadow pricing. Our findings indicate that ignoring livestock may overestimate CSP values. On the whole, the level of carbon shadow price is rising, indicating good green development in China's agricultural sector. The two types of convergence results show that there is sigma convergence and beta convergence in the western and central regions, demonstrating a significant improvement in environmental performance. [ABSTRACT FROM AUTHOR]

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation. [ABSTRACT FROM AUTHOR]

Under the simultaneous demands of combating environmental pollution and decreased carbon emissions, it is critical to investigate the combined effect of agricultural contamination reduction and fiscal policy carbon reduction to support and promote green agriculture and low-carbon transformation. Based on provincial panel data of 2007 to 2020 in China, this paper employs the spatial Dubin model to empirically examine the pollution reduction and carbon reduction effects of fiscal policies supporting agriculture, as well as calculating the synergistic effect of pollution reduction and carbon reduction. Our study's findings reveal that non-point source agricultural pollution and agricultural carbon emissions have a tendency of growing and subsequently reducing, such as increasing from 2007 to 2015 and decreasing from 2016 to 2020. Second, results demonstrate that agricultural carbon emissions and agricultural pollution have a positive geographical dependency in each province, and fiscal policies supporting agriculture have high-high and low-low spatial clustering features. Furthermore, fiscal policies that promote agriculture can lower local agricultural carbon emissions and pollution while also having a considerable beneficial spillover impact on neighboring provinces. According to the study findings, the fiscal policy for supporting agriculture has a negative pollution reduction impact and a positive synergistic effect, resulting in a synergistic effect of agricultural pollution reduction and carbon reduction. The outcomes of this study can serve to promote carbon-reduction measures and provide recommendations for future policy development. [ABSTRACT FROM AUTHOR]

This paper estimates the influences of different industries on the carbon prices and selects the industries with high market power in the carbon market. To realize price discovery in the carbon market and improve the market efficiency, we suggest that industries with higher market power should be included in the market prior to other industries. Using the LASSO regression to analyze the data from the pilot markets of China's ETS and related industrial product outputs, we find that among all related industries, the petrochemical and chemical industries affect the carbon price the most and hence should be included in China's ETS with priority. The power generation industry, though regarded as the major entity of emission control, imposes weaker influences on the carbon price. Industries that may be incorporated in China's ETS later include the transportation equipment manufacturing and the mining and metal industries. [ABSTRACT FROM AUTHOR]

Energy price is a key factor in reducing carbon emissions. This paper assesses the relationship between energy prices and carbon emissions from the industrial sector in China. Using panel data covering 31 industrial sectors for the period 2003 to 2015, we calculate the industrial energy price index and then estimate the effects of sector-level energy prices on carbon emissions based on a panel smooth transition regression model. The results show that the nexus between industrial energy prices and carbon emissions is nonlinear overall, and energy prices have a significantly negative effect on carbon emissions, while this negative effect gradually weakens with an increase in energy prices. Moreover, the negative effect of energy prices on carbon emissions is more significant in energy-intensive sectors when energy prices exceed the threshold value. The findings emphasize the importance of energy prices for energy conservation and emission reduction and imply that energy prices can be used as a regulation tool for government industrial energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

The national unified carbon trading market has been officially launched at the end of 2017. The carbon emission quotas should be primary concern, which can be allocated in the form of free and paid ways. However, few literatures studied the economic and environmental impacts of quotas allocation. Thus, this paper constructs 7 scenarios and employs a dynamic, recursive computable general equilibrium (CGE) model to simulate carbon trading market, to probe the relationship between quota allocation and carbon price, and the economic and environmental impact of carbon trading scheme (ETS). Empirical results indicate (1) carbon price has an upward trend with time, which reflects a corresponding increase in emission reduction pressure. Specifically, carbon price increases from 12.44–90.57 CNY/t in 2017 to 65.20–523.44 CNY/t in 2030. In addition, whether under carbon intensity criterion (CIC) or carbon emission criterion (CEC), there is a positive relationship between carbon price and free allocation ratio due to the change of the relationship between supply and demand of quota. With a given free allocation ratio, the price formed with CIC grows faster than that with CEC. (2) Compared with the benchmark scenario, the GDP of China decreases in all scenarios. However, a high level of free allocation ratio combined with CIC may prevent GDP dropping too fast. (3) As for industrial output, covered industries in ETS undertake the largest output losses with an average decline by 4.03–13.60%. Similar to GDP variation, a high free allocation ratio combined with CIC is helpful for sustainable development of industry. (4) Carbon trading has a remarkable effect on emission reductions both in covered and uncovered industries of ETS. Free allocation will reduce market efficiency, which implies it should be cut down gradually at the later stages. [ABSTRACT FROM AUTHOR]

The advancement of electricity market reform highlights the need for China's photovoltaic (PV) industry to enter the stage of market competition. Under the carbon neutrality, what impacts electricity market reform has on China's PV industry is an important issue that needs to be considered. This paper analyzes the driving mechanism of the marketed on-grid price and constructs a system framework for the internal connection within the PV industry under the background of carbon neutrality. Then, two system dynamics (SD) models are built to perform the numerical simulation and analyze the evolution process. The results indicate that during the market competition stage, (i) the on-grid price will be stable at about 0.07 yuan/kWh by 2060; (ii) China's PV industry will go through three stages in the future: the first stage is the "rush to install" period for carbon peaking before 2030, followed by the "sluggish installation" period from 2030 to 2038. The third stage is the outbreak period of "PV + multi-scenarios."; (iii) under three scenarios by 2060, the proportion of PV power generation will account for 46%, 39%, and 32%, respectively, which are higher than those under the benchmark on-grid price; (iv) it is necessary for the government and enterprises to continue increasing support for research and development (R&D). Finally, we put forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

The international community has generally recognized the key role of developing countries' cities in reducing carbon emissions, an elemental way to mitigate climate change. However, few have empirically analyzed the impact of market-based instruments such as emission trading system on urban carbon emissions in developing economies. This paper examines the effect of China's pilot carbon trading markets, the first emission trading system in developing economies, on cities' carbon intensity. We also explore the mechanism by which the emission trading system achieves its influence. The PSM-DID method is used to analyze the panel data including China's 273 prefecture-level cities from 2010 to 2016. The results illustrate that the emission trading system significantly decreased pilot cities' carbon intensity and this effect endured; as time progressed, the reduction effect was increasing. Through mediating effect analysis, we find that the emission trading system reduced the carbon intensity via increasing the proportion of tertiary industry output value in GDP and decreasing the energy intensity. Overall, the empirical results suggest that the Chinese government should drive the establishment and improvement of a national carbon market, proactively adjust industry structure, and consider the possible influence caused by the potential energy rebound effect. [ABSTRACT FROM AUTHOR]

Research on the price fluctuation transmission of the carbon trading pilot market is of great significance for the establishment of China’s unified carbon market and its development in the future. In this paper, the carbon market transaction prices of Beijing, Shanghai, Tianjin, Shenzhen, and Guangdong were selected from December 29, 2013 to March 26, 2016, as sample data. Based on the view of the complex network theory, we construct a price fluctuation transmission network model of five pilot carbon markets in China, with the purposes of analyzing the topological features of this network, including point intensity, weighted clustering coefficient, betweenness centrality, and community structure, and elucidating the characteristics and transmission mechanism of price fluctuation in China’s five pilot cities. The results of point intensity and weighted clustering coefficient show that the carbon prices in the five markets remained unchanged and transmitted smoothly in general, and price fragmentation is serious; however, at some point, the price fluctuates with mass phenomena. The result of betweenness centrality reflects that a small number of price fluctuations can control the whole market carbon price transmission and price fluctuation evolves in an alternate manner. The study provides direction for the scientific management of the carbon price. Policy makers should take a positive role in promoting market activity, preventing the risks that may arise from mass trade and scientifically forecasting the volatility of trading prices, which will provide experience for the establishment of a unified carbon market in China. [ABSTRACT FROM AUTHOR]

Superficial or cosmetic measures are unlikely to solve the complex challenges of carbon haze governance. Carbon-biased technological progress plays a significant role in low-carbon technology and economic sustainable development. This study introduces carbon resources as a production factor in the transcendental logarithmic function to calculate the biased technological progress index of 30 provinces in China between 2010 and 2020. Subsequently, this study constructs the spatial Durbin model to empirically investigate the spatial spillover effect of carbon-biased technology progress on carbon haze coordinated governance. The findings reveal that China's technological progress is primarily characterized by carbon bias, particularly in terms of labor-using carbon-saving technological progress and capital-using carbon-saving technological progress. Additionally, both levels of carbon haze coordinated governance and carbon-biased technological progress have significant spatial agglomeration characteristics. Moreover, the carbon-biased technological progress can facilitate synergy degree of carbon haze governance through energy-saving effects but can also hinder the carbon haze coordinated governance through rebound effects. Therefore, it is imperative to improve labor productivity, augment green capital investment, and stimulate the induction of technological progress towards carbon saving to achieve sustainable and high-quality economic development. The primary contribution is identifying the uncertainty surrounding the impact of carbon-biased technological progress on coordinated governance of carbon haze, while also providing theoretical explanations for the influence channels of carbon-biased technological progress. [ABSTRACT FROM AUTHOR]

Accurate and stable carbon price forecasts serve as a reference for assessing the stability of the carbon market and play a vital role in enhancing investment and operational decisions. However, realizing this goal is still a significant challenge, and researchers usually ignore multi-step-ahead and interval forecasting due to the non-linear and non-stationary characteristics of carbon price series and its complex fluctuation features. In this study, a novel hybrid model for accurately predicting carbon prices is proposed. The proposed model combines multi-step-ahead and interval carbon price forecasting based on the Hampel identifier (HI), time-varying filtering-based empirical mode decomposition (TVFEMD), and transformer model. First, HI identifies and corrects outliers in carbon price. Second, TVFEMD decomposes carbon price into several intrinsic mode functions (imfs) to reduce the non-linear and non-stationarity of carbon price to obtain more regular features in series. Next, these imfs are reconstructed by sample entropy (SE). Subsequently, the orthogonal array tuning method is used to optimize the transformer model's hyperparameters to obtain the optimal model structure. Finally, after hyperparameter optimization and quantile loss function, the transformer is used to perform multi-step-ahead and interval forecasting on each part of the reconstruction, and the final prediction result is obtained by summing them up. Five pilot carbon trading markets in China were selected as experimental objects to verify the proposed model's prediction performance. Various benchmark models and evaluation indicators were selected for comparison and analysis. Experimental results show that the proposed HI-TVFEMD-transformer hybrid model achieves an average MAE of 0.6546, 1.3992, 1.6287, and 2.2601 for one-step, three-step, five-step, and ten-step-ahead forecasting, respectively, which significantly outperforms other models. Furthermore, interval forecasts almost always have a PICI above 0.95 at a confidence interval of 0.1, thereby indicating the effectiveness of the hybrid model in describing the uncertainty in the forecasts. Therefore, the proposed hybrid model is a reliable carbon price forecasting tool that can provide a dependable reference for policymakers and investors. [ABSTRACT FROM AUTHOR]

The pilot free trade zone (PFTZ) policy is an important strategic initiative to lead green and high-quality economic development. Based on panel data from 2012 to 2019 of 287 Chinese cities, this study systematically investigates the effect of the PFTZ policy on carbon emissions by building the staggered difference-in-difference (DID) model. The results showed that the PFTZ policy significantly reduced carbon emissions and that this reduction effect increased over time. These findings hold true after robustness tests. Additionally, the heterogeneity analysis showed that the carbon emission reduction effect was more significant in coastal areas. In terms of impact mechanisms, the main driving force of carbon emission reduction from the PFTZ policy is technological progress. Furthermore, the PFTZ policy has a spatial spillover effect, which can drive neighboring cities to reduce their carbon emissions. This study provides useful policy insights for building a green low-carbon trade system and helps achieve carbon peaking and carbon neutrality goals. [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]

As the largest carbon emitter in the world, with its transportation sector contributing the largest shares of its emission, the need for a low-carbon transition economy has become a policy agenda for China because in order to reach carbon neutrality by 2050, lowering the intensity of carbon emissions in the transportation sector will be crucial. In this regard, we used the "bootstrap autoregressive distributed lag model" to explore the impact of clean energy and oil prices on the intensity of carbon emissions in China's transportation sector. The study found that an increase in oil prices decreases the intensity of carbon emissions in the short and long run. Similarly, an increase in the level of renewable energy and economic complexity declines the intensity of carbon emissions in the transportation sector. On the contrary, the research demonstrates that non-renewable energy contributes positively to carbon emission intensity. Therefore, the authorities must promote green technology to neutralize the transportation system's detrimental effects on China's environmental quality. The implications for successfully promoting carbon emission intensity mitigation in the transportation sector are examined in the conclusion. [ABSTRACT FROM AUTHOR]

European energy crisis, triggered by the conflict between Russia and Ukraine, has again drawn attention to the decarbonization of fossil energy sources. However, few studies have objectively considered coal from an integrated life cycle and its position in the energy system. In the present study, we used the integrated life cycle analysis and fixed-effect panel threshold model to reveal that (1) power generation and heating and iron and steel smelting are the highest CO2 emission sectors. In addition, the coal chemical industry and power generation and heating are the two sectors with the highest contribution rate of CO2 emissions. (2) Based on these, underground coal gasification (UCG) and underground coal gasification-integrated gasification combined cycle (UCG-IGCC) technologies were introduced to innovate the coal life cycle (the process cycle of coal production and utilization). The panel threshold model has proved that when the energy intensity falls in the interval 0.363–2.599, UCG-IGCC technology could be the complement in mitigating CO2 emissions. (3) Finally, for the same amount of emission mitigations, the social cost of innovating coal production and utilization processes using UCG-IGCC technology will be lower than phasing out coal-fired power plants using carbon prices. For China, UCG-IGCC and renewable energy should be developed simultaneously. [ABSTRACT FROM AUTHOR]

As a result of the COVID-19 pandemic, production costs have grown, while human and economic resources have been reduced. COVID-19 epidemic costs can be reduced by implementing green financial policies, including carbon pricing, transferable green certificates, and green credit. In addition, China's tourist industry is a significant source of revenue for the government. Coronavirus has been found in 30 Chinese regions, and a study is being conducted to determine its influence on the tourism business and green financial efficiency. Econometric strategies that are capable of dealing with the most complex issues are employed in this study. According to the GMM system, the breakout of Covid-19 had a negative effect on the tourism business and the efficiency of green financing. Aside from that, the effects of gross capital creation, infrastructural expansion, and renewable energy consumption are all good. The influence of per capita income on the tourism industry is beneficial but detrimental to the efficiency of green finance. Due to the current pandemic condition, this report presents a number of critical recommendations for boosting tourism and green financial efficiency. [ABSTRACT FROM AUTHOR]

Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets. [ABSTRACT FROM AUTHOR]

As a major carbon-emitting country, China has considerable potential for carbon capture and storage (CCS) applications, but economics is critical for CCS deployment. In this study, the levelized cost of electricity (LCOE) at province level was calculated for operational coal-fired power plants after post-combustion carbon capture (CC) retrofitting. The costs of captured CO2 and avoided CO2 were then compared. The economics of CC-retrofitted coal-fired power plants co-firing with biomass (BECC) were also analyzed. CC retrofitting is economical in North and Northwest China (except Qinghai) where coal prices are low. However, BECC retrofit is more suitable for coal-fired power plants in Central China where biomass prices are low. Therefore, the variation in the resource conditions among provinces should be considered when promoting CC and BECC retrofits. Compared with other power sources, coal-fired power generation becomes much more expensive after retrofits. We calculated the carbon price and capacity subsidy intensity that could support the retrofit of coal units. Effective carbon price should be 142.44–406.06 CNY/t for retrofits, and the capacity subsidy intensity ranges from 175.14–721.52 CNY/kW. The current carbon market and capacity market are not sufficiently developed to support retrofitting. The development of the carbon market with effective carbon price signals and proper power market mechanisms is critical to the rollout of CCS in China. [ABSTRACT FROM AUTHOR]

Clarifying the time-varying spillovers among pilot carbon emission permit trading markets in China is an important foundation for building the national carbon emission trading market. We calculate the dynamic spillover of carbon price return among the pilot carbon emission permit trading markets in China with the time-varying connectedness approach. The dataset is constructed from transaction data from seven pilot carbon markets in China during the period of June 23, 2014, to December 31, 2020. The quantitative analysis suggests that (i) Beijing and Chongqing carbon emission trading markets are the main spillover markets of carbon price returns, with strong pricing power, while the Guangdong and Tianjin markets are the main receivers of the price return spillover in other pilot carbon emission trading markets. (ii) The spillover effect among China's carbon markets has a strong policy orientation. The improvement and development of the carbon market driven by macroeconomic regulation and control policies can effectively improve the spillover ability of the carbon market, and the market trading activity, namely the volatility of the carbon price return rate, can amplify the spillover ability of the carbon market in the short term. (iii) There exist three types of price return spillover among China's pilot carbon emission trading markets, including central divergence, one-way chain transmission, and circular spillover. Along with the improvement of market operation efficiency, the central divergent type of spillover shifts to the pattern of circular spillover. It is necessary for the government to improve market efficiency and ensure the coordinated development of China's pilot carbon emission trading market and national carbon emission trading market. [ABSTRACT FROM AUTHOR]

The environmental consequences of economic policy uncertainty (EPU) and oil price changes have attracted much attention in recent years, but few studies have estimated the asymmetric impact. This research investigates the symmetric and asymmetric effects of EPU and oil prices on carbon emissions in the USA and China. Based on monthly data from 1995:1 to 2019:12, we address this issue by applying the ARDL and non-linear ARDL cointegration methodology. The symmetric results show that the change in EPU has no evident impact on carbon emissions in the short and long run for the USA and China, and oil prices have a lag effect in this regard. The non-linear ARDL estimation documents significant asymmetric effects of EPU and oil prices. Specifically, the negative change in EPU facilitates emissions in the USA in the long and short run, and a unit increase in EPU increases emissions by 2.24% and 4.95%, respectively, whereas its positive change does not produce any significant effect. Carbon emissions in China are significantly and positively affected by the positive change in EPU only over the long run, and an increase in EPU increases emissions by 0.04%. Furthermore, the positive shock of oil prices significantly and positively influences emissions in the long run, and a unit increase in this factor increases emissions by 15.73% and 0.44% in the USA and China, respectively. Moreover, the short-term effect of the negative shock of oil prices exhibits a lag effect. The empirical results provide a practical reference for the USA and China to craft robust policies on emission cutting. [ABSTRACT FROM AUTHOR]

Climate change and increased greenhouse gas emissions boost the global average temperature to less than 2°C, which is the estimated breakeven point. The globe is moving into blue pollution economies as the environmental sustainability objective becomes more distorted. The study looked at three United Nations Sustainable Development Goals, namely (i) affordable and clean energy; (ii) industry, innovation, and infrastructure; and (iii) climate change, to see how far the Chinese economy has progressed toward green and clean development strategy. In the context of China, the "pollution damage function" was intended to refer to carbon damages related to carbon pricing, technological variables, sustained economic growth, incoming foreign investment, and green energy. The data was collected between 1975 and 2019 and analyzed using various statistical approaches. The results of the autoregressive distributed lag model suggest that carbon taxes on industrial emissions reduce carbon damages in the short and long run. Furthermore, a rise in inbound foreign investment and renewable energy demand reduces carbon damages in the short term, proving the "pollution halo" and "green energy" hypotheses; nonetheless, the results are insufficient to explain the stated results in the long run. In the long run, technology transfers and continued economic growth are beneficial in reducing carbon damages and confirming the potential of cleaner solutions in pollution mitigation. The causal inferences show the one-way relationship running from carbon pricing and technology transfer to carbon damages, and green energy to high-technology exports in a country. The impulse response estimates suggested that carbon tax, inbound foreign investment, and technology transfers likely decrease carbon damages for the next 10 years. On the other hand, continued economic growth and inadequate green energy sources are likely to increase carbon pollution in a country. The variance decomposition analysis suggested that carbon pricing and information and communication technology exports would likely significantly influence carbon damages over time. To keep the earth's temperature within the set threshold, the true motivation to shift from a blue to a green economy required strict environmental legislation, the use of green energy sources, and the export of cleaner technologies. Source: Authors' self-extract [ABSTRACT FROM AUTHOR]

Environmental sustainability has become one of the most common phrases in discussions about climate change. This study examines the impact of clean energy investment and financial development on environmental sustainability and China's economic growth, using manufacturing value-added and urbanization as moderator variables from 1970 to 2016. We used advanced econometric methodologies for empirical estimations, used structural break unit root tests, fully modified least square, dynamic least square, and robust least square multiple regressions for long-run estimates. Overall, the results determine that clean energy investment is negatively associated with CO2 emissions and ecological footprint while positively associated with China's economic growth. Financial development, manufacturing value-added, and urbanization are positively associated with CO2 emissions, ecological footprint, and China's economic growth. Moreover, clean energy investment improves environmental sustainability at the expense of economic growth. Financial development, manufacturing value-added, and urbanization encourage economic growth at the expense of environmental sustainability. We argued that the local governments play a critical role in lifting the outstanding barriers to cleaner energy investment, addressing disincentives, including pricing carbon dioxide emissions, reforming inefficient nonrenewable fossil fuel subsidies, and addressing regulatory and market rigidities that can undesirably affect the attractiveness of clean energy investment. Policymakers are suggested to encourage green finance strategy for the financial sector to broader sustainable development objectives. At the heart of green manufacturing, industrialization policies are needed to integrate diverse intentions, like inclusive growth, environmental protection, and productivity through a wider range of economic, social, and environmental policy frameworks suitable for decoupling growth from social and environmental unsustainability. [ABSTRACT FROM AUTHOR]