Energy, economic and environmental impact of government fines in China's carbon trading scheme

A study on the dynamic impact of carbon emission trading on green and high-quality development

As a pivotal element in market mechanisms, carbon trading is increasingly recognized as crucial for achieving China's Carbon Peaking and Carbon Neutrality Goals. This study introduces a comprehensive dynamic model, integrating carbon trading, emissions, economic growth, and green technology innovation, to offer a holistic understanding of the interplay between these domains. Utilizing principles from nonlinear dynamics and chaos theory, the model is adept at simulating various scenarios and assessing the effectiveness of government policies in stabilizing these complex systems. In-depth analysis provided by this research sheds light on the nuanced impact of carbon trading policies on sustainable development. Key findings highlight (1) Carbon trading's essential role as a catalyst in propelling sustainable and high-quality growth. (2) A strong positive relationship is observed between the sophistication of the carbon trading mechanism and its effectiveness in stimulating green technology innovation and fostering high-quality green development. Notably, carbon trading's influence on green technology innovation markedly enhances the efficacy of carbon emission reduction strategies. (3) Government regulations are instrumental in augmenting carbon prices, thus incentivizing increased corporate participation in emission reduction and enhancing the overall impact of carbon emission reduction. Nevertheless, the study identifies a critical threshold in regulatory intensity, beyond which there is a risk of system destabilization (a 3 ≥ 0.032 ). These findings underscore the imperative for developing an integrated national carbon emission trading market, prioritizing sustainable growth strategies and diligently pursuing China's environmental objectives.

Impacts of emission trading scheme on technological progress: A case study in China

Despite its significant role in mitigating climate change, technology was usually exogenously treated in evaluating climate policy, particularly emission trading scheme (ETS); such treatment cannot comprehensively reveal how ETS affects technological progress. To narrow this research gap, we attempt to endogenize ETS-induced technological change in this paper. A dynamic recursive Computable General Equilibrium (CGE) model is employed to quantify ETS-induced progress of clean technology (PCT) and efficiency improvement. The Chinese nationwide ETS is taken as a case study. The CGE model results show that PCT negatively affects anthropogenic emissions, while efficiency improvement decreases GDP loss or abatement cost. Simultaneously considering both technological progress increases emission abatement but slightly decreases GDP in the long term. The most interesting finding is that PCT moderates the relationship between efficiency improvement and emission abatement. Hence, PCT is crucial in emission abatement and economic growth under climate policy.

The impact of international technical cooperation in new energy industry on carbon emissions: evidence from the top 30 countries in the global innovation index

International cooperation has become a global consensus to solve environmental problems. This paper selects the top 30 countries in the global innovation index as the research sample, based on Patent Cooperation Treaties (PCT) data jointly applied in the field of new energy, constructs the international technical cooperation network, and uses the fixed effect panel regression model to verify the influence of international technical cooperation of new energy industry on carbon emission intensity. The results show that the USA, Germany, the UK, France, and the Netherlands are at the center of the network. International technological cooperation in new energy industry has a significant negative impact on carbon emission intensity. The convening of the United Nations Climate Change Conference in Copenhagen has accelerated global industrial upgrading, and the effect of international technical cooperation in new energy on carbon emission reduction has been strengthened. In addition, the level of economic development, international trade, and research and development (R&D) are also important factors affecting carbon emission intensity. Countries with high network centrality should give full play to their network influence to promote global cooperation in the field of new energy and achieve carbon mitigation targets by signing more environmental agreements.

Re-examining the effect of carbon emission trading policy on improving the green innovation of China's enterprises

China's carbon emission trading (CET) policy guides enterprises to carry out green innovation and address the growing environmental challenges through market-based instruments. However, can CET policy effectively promote enterprises' green innovation? It has become a hot issue. Whether it can play the "Porter effect" is also controversial. We have little research on the effectiveness and heterogeneity of CET policy in China. We obtained the following conclusions from the empirical results: (1) CET policy has clearly promoted green innovation in enterprises, the proportion of green innovation of enterprises increased by 13.43%, and the "weak Porter hypothesis" was tenable. And the results of the research have been tested to be robust and reliable. (2) CET policy plays an obvious role in enhancing the enterprises' green innovation with high-stock enterprise, large-scale enterprise, and state-owned enterprises. (3) Carbon quota auction does not motivate enterprises to improve green innovation; the CET policy under the ex-post allowance allocation of government can better inspire enterprises to undertake green innovation events. (4) The pilot policy of carbon emission trading can increase the introduction of scientific research talents, increase the expenditure of scientific research and development, and improve the net profit margin of enterprise assets, thus directly or indirectly promoting the development of green innovation of enterprises. Overall, the research in this article provides theoretical policy and empirical research for implementing carbon emission trading policy in developing countries and provides theoretical support for how to realize the "double dividend" of environmental protection and enterprises' green innovation competitiveness. Meanwhile, it also provides reference for the national CET to be officially run, and it is instructive to establish a flexible market-based instruments.

Effects of Government Regulations on Under-Reporting of Carbon Emission Transfers by Enterprises in Supply Chains

In low-carbon environments, asymmetric carbon information causes the enterprises in a supply chain to face the risk of misstatements about carbon emissions. Such misstatements could affect the decisions about carbon emission transfers in the supply chain. To optimize carbon emission transfers among supply chain enterprises, this study formulates a supplier-led Stackelberg game model incorporating the government’s initial carbon emission allowances and fines. The study also examines the mechanism of the behaviors of enterprises in low-carbon supply chains, the proportions of initial quotas, the impact of government fines on carbon transfers, and the influence of the supply chain and carbon emission transfers on related supply chain decisions and profits. The main findings are as follows. First, the proportion of the government’s initial quota has a positive effect on the carbon emission transfer quantity of the supplier, while government fines and misstatement factors have a negative effect. Second, the carbon emissions of the unit product of the supplier decrease as the under-reporting factor and carbon emission transfer quantity of the supplier increase. The under-reporting factor has a stronger effect on the carbon emissions of the unit product. Third, in a carbon-free market, carbon emission transfers negatively affect the disclosed profits of the supply chain, whereas in a perfect carbon market, the carbon trading price has a certain endogenous regulation mechanism for the suppliers’ operational decisions. Fourth, the supplier’s wholesale price order quantity is negatively correlated with the supplier’s carbon emission transfer quantity, but positively correlated with the initial carbon quota ratio.

Assessing market efficiency and liquidity: Evidence from China's emissions trading scheme pilots

Different from the previous studies that evaluated the degrees of carbon market efficiency as constant, the novelty of this work is to capture degrees of market efficiency that are time-varying over time and its static and dynamic relationship with liquidity in China's five emissions trading system (ETS) pilots (Beijing, Guangdong, Shanghai, Shenzhen and Hubei) from January 2017 to June 2019. For this purpose, this work employs the generalized autoregressive conditional heteroscedasticity in mean (GARCH(1,1)-M) model with state space time-varying parameter (Kalman filter), Modified Amihud illiquidity ratio, fixed effect variable intercept panel regression model and panel impulse response analysis. The empirical results are as follows: (1) Shenzhen pilot is the best performer for the market efficiency, followed by Beijing, Shanghai, Guangdong and Hubei; although the other four except the Shenzhen pilot are inconsistent with weak-form efficiency, the market efficiency of Beijing pilot has improved and the Hubei pilot has the lowest market efficiency; (2) there are the positively long-term equilibrium relationship between market efficiency and market liquidity of pilots; and (3) market liquidity can positively affect the market efficiency of pilots lasting three months. These findings suggest that the market participation ability, linkages between carbon markets, speculation of low market efficiency and carbon derivatives tools can be considered by unified China's national carbon market.

Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm

The accurate prediction of carbon prices poses a tremendous challenge to relevant industry practitioners and governments. This paper proposes a novel hybrid model incorporating modified ensemble empirical mode decomposition (MEEMD) and long short-term memory (LSTM) optimized by the improved whale optimization algorithm (IWOA). This model is based on the nonlinear and non-stationary characteristics of carbon price. The original carbon price is first decomposed into nine intrinsic mode functions (IMFs) and a residual using the MEEMD model. Then, the random forest method is applied to determine the input variables of each IMF and the residual, in the LSTM neural network. The carbon price is then predicted by the LSTM model optimized by the IWOA. The proposed hybrid model is applied to predict the carbon prices of Beijing, Fujian, and Shanghai to assess its effectiveness. The results reveal that the model achieved higher prediction performance than 11 other benchmark models. Our observations indicate that decomposition of carbon price can effectively improve the accuracy of prediction. Moreover, the improved LSTM model is more suitable for time series prediction. The proposed model provides a novel and effective carbon price forecasting tool for governments and enterprises.

A Joint Control Model Based on Emission Rights Futures Trading for Regional Air Pollution That Accounts for the Impacts on Employment

To reduce air pollutant control costs and solve the problem of decreased employment caused by air pollution control, we established a double-objective optimization Joint Control Model (JCM) based on emission rights futures trading. The JCM calculates the spot price of emission rights, classifies regions in the trading market for emission rights into buyers and sellers, and calculates the optimal cooperative pollution abatement quantity. Compared with a non-cooperative control mode, the JCM generated benefits of US$2485.19 × 106. We then used a Game Quadratic Programming (GQP) method to distribute the benefits, and applied the JCM to a case study of the abatement of sulfur dioxide in China’s Shanxi, Henan, and Shaanxi provinces. We found that: (i) Compared with a JCM that does not account for employment, employment under the JCM increased by 3.20 × 103 people, and the pollution control cost decreased by US$11.20 × 106 under the JCM that considered employment. The effect of the latter model is better than that of the former. (ii) Employment under the JCM increased by 18.80 × 104 people compared with that under a territorial control mode, reducing the cost by US$99.73 × 106. The JCM is helpful for all participating regions to balance environmental and livelihood issues in the process of air pollution control to achieve sustainable development.

Scenario analysis of ETS revenue allocation mechanism of China: based on a dynamic CGE model

The successful establishment of China's emission trading scheme (ETS) could lead the next generation of global climate carbon markets in industrializing and developing countries. The allocation of ETS revenue from auctioning carbon emission allowance is important for the achievement of China's joint targets of economic growth, mitigation, and welfare improvement. This study develops a dynamic CGE model to evaluate the effects of different ETS revenue allocation mechanisms and identifies the proper mechanism for China's ETS design. Ten scenarios including business as usual (BAU), no ETS revenue allocation incentive (NA) and other eight ETS revenue allocation scenarios are designed. Simulation results indicate that the tradeoff between economic cost and environmental benefit exists under different ETS revenue allocation mechanisms. ETS revenue is suggested to allocate to household sector through reducing indirect tax and, after 2020, a certain proportion of ETS revenue could be allocated to production sector for improving energy-saving technology (i.e., STP mechanism). This study provides references for policymakers in China to design effective and realistic ETS-related policies. A similar study could be conducted to explore the proper ETS and the revenue allocation policies in other countries that have similar national conditions to China, such as other BRICS countries.

A Dynamic Price Game Model in a Low-Carbon, Closed-Loop Supply Chain Considering Return Rates and Fairness Concern Behaviors

In this paper, we developed a dynamic price game model for a low-carbon, closed-loop supply chain system in which (1) the manufacturer had fairness concern and carbon emission reduction (CER) behaviors, and market share and profit maximization were their objectives, and (2) the retailer showed fairness concern behaviors in market competition and provided service input to reduce return rates. The retailer recycled old products from customers, and the manufacturer remanufactured the recycled old products. The effects of different parameter values on the stability and utility of the dynamic price game model were determined through analysis and numerical simulation. Results found that an increasing customer loyalty to the direct marketing channel decreased the stable region of the manufacturer's price adjustment and increase that of the retailer. The stable region of the system shrank with an increase of CER and the retailer's service level, which expanded with return rates. The dynamic system entered into chaos through flip bifurcation with the increase of price adjustment speed. In the chaotic state, the average utilities of the manufacturer and retailer all declined, while that of the retailer declined even more. Changes to parameter values had a great impact on the utilities of the manufacturer and retailer. By selecting appropriate control parameters, the dynamic system can return to a stable state from chaos again. The research of this paper is of great significance to participants' price decision-making and supply chain operation management.