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The effectiveness of carbon price in emissions trading schemes (ETS) is an important issue in China in the context of its ambitious climate change goals. This paper adopts a staggered difference-in-differences model to estimate whether China's ETS reduced CO2 emissions while maintaining economic growth even under low carbon prices. The results indicate that despite the low carbon prices, the ETS effectively reduced CO2 emissions without undermining the economy. Specifically, an increase of $1 in the carbon price reduced CO2 emissions by 1.69% and increased the per capita GDP by $286. The carbon price primarily achieved emission reduction and promoted economic development through channels, such as technological innovation, foreign direct investment, energy mix, and industrial structure. Carbon leakage to neighboring regions was not evident. Heterogeneity analysis showed that the environmental effects of the carbon price were more pronounced in regions with higher levels of economic development and CO2 emissions. Conversely, the economic effects of the carbon price were more pronounced in regions with lower levels of economic development and CO2 emissions. The carbon price achieved significant economic effects in regions that solely adopted the free allocation mode of emission allowances, while regions that used a combination of free allocation and auctioning experienced substantial emission reduction effects. Despite low carbon prices, China's ETS effectively reduced CO2 emissions without compromising economic growth. This finding provides new empirical evidence for the effectiveness of carbon price as well as decision support for the future promotion of ETS. Technological innovation, FDI, energy mix, and industrial structure are crucial channels through which the carbon price achieves emission reduction and promotes economic development. The absence of carbon leakage to neighboring regions suggests that ETS is an internally effective mechanism for emission control. Heterogeneity analysis showed that regional characteristics and allowance allocation modes can influence the effectiveness of carbon price, thus emphasizing the importance of targeted policy design and appropriate allocation methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • It analyzes the feasibility of achieving net zero emission in the Chinese power sector by 2050 and the required policy measures. • It is essential to foster the concurrent development of renewable energy, energy storage, and demand response technologies. • The carbon pricing mechanism with exponential growth is more suitable. • Referring to the European offshore wind power investment model is beneficial for the development of offshore wind power in China. The low-carbon transition in China's power sector plays a crucial role in the mitigation of climate change worldwide, and increasing renewable energy penetration rate is widely recognized as the key approach to decarbonize this sector. This paper proposes a renewable energy penetration model to examine the feasibility for net zero emission by 2050 in China's power sector taking into account of economic and social development. It also evaluates the impacts of carbon pricing and financial incentives on renewable energy penetration and carbon emissions. The results indicate that along with economic development, China's electricity demand will continue to grow, but at a gradually slowing rate. It is possible for the power sector to achieve net zero emission by 2050 while meeting this growing electricity demand, with a renewable energy penetration rate of 75%. To ensure the achievement of the goal, we recommend setting the carbon price with the current market price as the baseline and increasing it annually by a relative increment index of 10%. Furthermore, given the high initial cost and late start of offshore wind power, we suggest incorporating associated grid connection cost into the transmission and distribution price to ensure the comprehensive and healthy development of renewable energy. [ABSTRACT FROM AUTHOR]

Carbon market is the most effective market tool for carbon emission reduction. China, the largest carbon emitter in the world, established the national carbon market in 2021, covering over 2000 key units in the power sector. Therefore, the forecasting of carbon price has profound implications for environmental and energy policies. In this paper, two traditional econometric model and three kinds of machine learning (ML) algorithms are used to predict carbon returns in the Chinese carbon trading market based on carefully selected predictors. Among all forecasting models, the Random Forest (RF) has the best forecasting performance, followed by some GARCH models using various factors. Compared with the traditional benchmark of ARMA model, the BP neural network (BP) and the GA improved BP method (GA-BP) are less competitive in predicting carbon returns in China because of their large forecasting errors. According to the optimal model, social attention to the carbon market, the international crude oil returns and the overall performance of the stock market are the most important predictors. The findings are robust to the change in the sample set. Overall, the ML approach shows an advantage in forecasting carbon returns, but the selection of predictors is also important. • Machine learning and econometric models are used to forecast carbon returns in China. • The selection of predictors is based on economic analysis. • The Random Forest (RF) outperforms the other ML and econometric methods. • The energy market is extremely important for predicting carbon returns. • Social concerns and stock market performance improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

To achieve the carbon peaking and neutrality targets in China, carbon price and technological innovation will play increasingly important roles in recent future. It is widely-known that carbon price and technological innovation can contribute significantly to emission reduction, respectively; but it is still unclear whether the cooperation effects of carbon price and technological innovation would be positive or negative. In this paper, we assume that there are 3 types of emission reduction measures in China's industrial enterprises, which are improvement of energy efficiency, adjustment of energy structure, and substitution of pollution inputs and non-pollution inputs; then we introduce carbon price and technological innovation respectively and simultaneously, and establish 12 scenarios based on the Data Envelopment Analysis models combined with material balance principal (DEA-MBP), and estimate the additional emission reductions and additional production costs of China's industrial enterprises when carbon price and technological innovation exist respectively or simultaneously. The counterfactually estimating results show that there would be significant regional and sectorial heterogeneities in carbon emission reduction characteristics for China's industrial enterprises. If low-carbon technologies in some sectors have the ability to reduce carbon emissions at the expense of high additional production cost, carbon pricing policies would encourage enterprises to adopt new mitigation technologies and increase additional emission reduction by more than 20%, especially technologies focusing on the adjustment of energy structure and the substitution of pollution inputs by non-pollution inputs. However, in some sectors which have already been covered by carbon pricing policies, the additional carbon pricing policy may not have a significant effect on emission reduction, and the emission reduction would decrease by 10%. [Display omitted] • Cooperation effects of carbon price and technological innovation are estimated. • DEA-MBP models and multiple emission reduction measures/scenarios are applied. • Introduction of carbon price would encourage the adopting of new mitigation technologies. • Carbon price and technological innovation would have overlapping effects. • Significant regional and sectorial heterogeneities exist in emission reduction features. [ABSTRACT FROM AUTHOR]

This paper estimates the potential influence of China's future nationwide carbon market on clean technology innovation. To overcome data limitation issues, the energy price is adopted as a proxy for the carbon price to estimate its impact on clean innovation and R&D. Then, a counter-factual method is introduced in which the potential carbon price is mapped onto the equivalent percentage change of energy price by sector. According to our results, the carbon market shows both a redirection and a crowding-out effect on technology innovation, with the former overwhelming the latter. If the future nationwide carbon market yields a carbon price of 50 Yuan, our findings estimate that it would result in an increase in the quantity and proportion of clean invention patents in ETS-covered sectors of 2.2% and 3%, respectively, and in an increase in the quantity and proportion of clean utility patents of 3.6% and 1.4% respectively. However, the effect on overall R&D is negative, indicating that carbon costs might crowd out some R&D expenditures, but in a limited manner. Generally, the carbon market in China is expected to help redirect technology innovation onto a cleaner path. [ABSTRACT FROM AUTHOR]

Climate change and carbon emissions are major problems which are attracting worldwide attention. China has had its pilot carbon emission trading markets in seven regions for more than 3 years. What affects carbon emission trading market in China is a big question. More attention is paid to how China promotes the carbon emission trading schemes in the whole country. This paper addresses concerns about the functioning of carbon emission trading schemes in seven pilot regions and takes the weekly data from November 25, 2013, to March 19, 2017. We employ a vector autoregressive model to study how coal price, oil price and stock index have affected the carbon price in China. The results indicate that carbon price is mainly affected by its own historical price; coal price and stock index have negative effects on carbon price, while oil price has a negative effect on carbon price during the first 3 weeks and then has a positive effect on carbon price. More regulatory attention and economic measures are needed to improve market efficiency, and the mechanisms of carbon emission trading schemes should be improved. [ABSTRACT FROM AUTHOR]

This paper explores the optimization scheme of carbon trading in China based on a novel energy-saving and emission-reduction (ESER) system with carbon price constraints. With the aid of nonlinear dynamics theory, the dynamics behavior of the novel system is discussed. Genetic algorithm and back propagation neural network is used to identify the quantitative coefficients according to the statistical data of the second period in European Union (EU). Taking the actual situation in EU for instance, the variables which are sensitive to carbon trading are detailedly researched. Enlightened by the EU’s experience, an optimal road of China ’ s carbon trading is put forward. The results show that carbon emissions could be controlled by carbon trading. The investment to carbon trading hampers economic growth in the near future, and ESER technical progress is negatively correlated with carbon trading in the long run. Demand and supply relationship is closely related to carbon price, both are the important issues in carbon trading system. Excessive government control and extortionate carbon price will deliver the opposite effect and even fatal influence on carbon trading system. [ABSTRACT FROM AUTHOR]

• The influencing factors of carbon prices in China's carbon market are analysed. • The influencing factors and historical data are combined to predict the carbon price. • The EEMD-BP-ELM model and the CHINAGEM-E model are constructed and combined. Carbon price is one of the core indicators of the carbon market. Making accurate predictions of carbon prices based on insight into the volatility characteristics of the carbon market will not only provide a scientific basis for investors and regulators to make decisions and greatly promote emission reduction, but also effectively promote the healthy development of the carbon financial market. Based on the historical carbon price data of the national carbon market, this paper constructs the EEMD-BP-ELM model to predict the high and low frequency components of China's future carbon price. On this basis, according to China's goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, the CHINAGEM-E model is used to predict the trend component of the carbon price under the baseline and the 2060 carbon neutral scenarios. Finally, the trend component is combined with the high and low frequency components to obtain the price range from 2022 to 2060. We find that under the baseline scenario, China's carbon price range in 2060 is [343, 785] CNY/tCO2, while under the 2060 carbon neutral scenario, this price range is [1543, 3531] CNY/tCO2. [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]

Abstract: This paper establishes a carbon capture and storage (CCS) investment evaluation model based on real options theory considering uncertainties from the existing thermal power generating cost, carbon price, thermal power with CCS generating cost, and investment in CCS technology deployment. The model aims to evaluate the value of the cost saving effect and amount of CO2 emission reduction through investing in newly-built thermal power with CCS technology to replace existing thermal power in a given period from the perspective of power generation enterprises. The model is solved by the Least Squares Monte Carlo (LSM) method. Since the model could be used as a policy analysis tool, China is taken as a case study to evaluate the effects of regulations on CCS investment through scenario analysis. The findings show that the current investment risk of CCS is high, climate policy having the greatest impact on CCS development. Thus, there is an important trade off for policy makers between reducing greenhouse gas emissions and protecting the interests of power generation enterprises. The research presented would be useful for CCS technology evaluation and related policy-making. [Copyright &y& Elsevier]

The switch of coal to gas in power generation enterprises is of great significance for tackling air pollution and mitigating climate change. Under China's emissions trading system, power generation enterprises have incentives to switch to low-carbon clean power, and the cost-benefit of switching is the basis of their decision-making. To this end, this paper studied the impact of changes in carbon and fuel prices on switching prices. The results show that: (1) For per yuan increase in the amount of natural gas, coal, and carbon, the switching price increased 4.768, decreased 5.845 and decreased 0.9994 yuan, respectively. (2) The carbon price is relatively low, and the impact of it on switching price is small. (3) The fluctuation in the carbon price, coal price and the natural gas price has a hysteresis effect on switching price. [Display omitted] • This study assesses the coal-to-gas switch possibility in Guangdong Province. • A new mode of operation of coal-to-gas switch is published. • For per yuan increase of carbon price, the switching price decreased 0.99 yuan. • The impact of carbon price fluctuations on switching price has a lag effect. [ABSTRACT FROM AUTHOR]

To realize China's low-carbon development, coal needs to be produced and consumed in a clean and efficient way. In this paper, a multi-regional coal supply model is developed to gain insights into China's coal supply system up to 2050. Regional disparity, coal classification, and the development of clean coal technologies are specified in the model. Based on MESSAGEix, this model takes full consideration of coal mining, preparation, transformation, and transportation processes. Moreover, the effects of non-fossil energy development and carbon price are discussed. With the above framework, the future of China's coal supply system is optimized. Results indicate that: 1) China's national raw coal production will peak in 2030 under the business- as -usual scenario, while it has already peaked under the GREEN and carbon price scenarios, 2) The amount of coal used in final consumption and transformed into coke decreases, while coal transformed into liquids and SNG increases from 11 Mt in 2015 to 221 Mt in 2050 under the business- as -usual scenario, 3) both non-fossil fuel development and carbon price strategies have a positive effect on coal supply regulation and coal-related GHG emission reduction, 4) carbon price could facilitate the adoption of CCS technology and can effectively reduce coal-related GHG emissions. • A multi-regional coal supply model with four types of raw coal is developed. • Raw coal production in China will peak in 2030 at 3791 Mt under the BAU scenario. • Coal production will be more concentrated in JSMN region and XJ region. • Raw coal output in the GREEN scenario is 9% lower than the BAU scenario in 2050. • Carbon price scenarios have a significant effect on GHG emissions reduction. [ABSTRACT FROM AUTHOR]