Carbon Emission Price Research Articles

Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes.

Asymmetric Impact of Carbon Emission, Oil Price Shocks on Economic Growth in Nigeria (1981 – 2022)

The study examines the asymmetric impact of carbon emissions and oil prices on economic growth in Nigeria. The research is conducted using annual time series data running from 1986 to 2019. The study employs Nonlinear autoregressive distributed lag models (NARDL) as the estimation technique. The variables used in the analysis were Gross Domestic Product (GDP), Carbon Emission (CO2), and Oil Price (OP). The NARDL result shows that carbon emission has a negative and significant impact on economic growth in the long run and the oil price has a positive and significant impact on economic growth in both the short-run and long-run. Based on the empirical outcomes of the result obtained, the following recommendations were offered: The implementation of an economic diversification policy from oil reliance towards dependence on other sustainable energy types (renewable energy resources) should be sustained and strengthened especially. The private sector should also tap into the opportunities that are available in the oil sector, such as the decarbonization programme in order to reduce carbon emissions from fossil fuels.

Two-stage distributionally robust optimization operation of virtual power plant considering the virtual energy storage of electric vehicles

Virtual Power Plant (VPP) is a key to aggregate various distributed energy sources. With the vigorous rise of various distributed energy sources, the direct access of large-scale electric vehicle load will increase the complexity of VPP coordinated operation. Hence, this paper proposes a VPP optimization method for Electric Vehicle Virtual Energy Storage (EV-VES). Firstly, the travel characteristics of electric vehicles are analyzed, and EV-VES model is established to coordinate and manage the charge-discharge behavior of EV. Secondly, the “carbon charge rate” model of energy storage (ES) is introduced to establish the relationship between carbon emission and electricity price, and the VPP operation model considering the “carbon charge rate” of energy storage is established. Finally, the two-stage robust optimization operation model of VPP is constructed, Wasserstein distance is used to describe the confidence set of uncertain probability distribution of wind power generation and load, and the uncertainty of system source and load are described by the confidence set. The Column and Constraint Generation (C&CG) algorithm was used to determine the optimal operational benefit solution. The effectiveness of the proposed VPP optimal operation model was validated through case study.

Differential Game Analysis of Carbon Emission Reduction Effect and Price Strategy Considering Government Intervention and Manufacturer Competition

ABSTRACTGovernments and enterprises are paying more and more attention to carbon emissions. Considering the dynamic of carbon emissions reduction (CER) and the government intervention, this study discusses the optimal CER effort level, price, and government intervention intensity in a two‐echelon supply chain consisting of a government and two manufacturers. The two manufacturers have two competitive behaviors: Cournot and Stackelberg. Two differential game models are constructed for the two different behaviors, and the optimal decisions under the two models are obtained. The comparisons of these optimal solutions are analyzed, and the influence of some parameters on the optimal solution in the two models is investigated under two scenarios. Furthermore, the optimal government intervention intensity is obtained with the goal of maximizing government utility. The results show that the Stackelberg game allows manufacturers to achieve higher profits and CER but is disadvantageous to consumers, and the manufacturer as the leader has a first‐mover advantage. Fierce market competition leads to greater CER and profits, but higher prices reduce consumer surplus. Larger penalties can promote enterprises to reduce carbon emissions when carbon emissions are large. Compared with the Cournot behavior game, the Stackelberg allows manufacturers to obtain higher profits and CER, but the prices are higher that are detrimental for consumers. The fierce market competition is good for manufacturers, the environment, and the society, but it reduces the consumer surplus. The low CER efficiency causes high costs and reduces the manufacturer's motivation to CER, which harm the environment and reduce profits. The government intervention is negatively correlated with the intensity of market competition and the sensitivity of manufacturers to policies.

Effect of electricity policy uncertainty and carbon emission prices on electricity demand in China based on mixed-frequency data models

This paper first constructs the electricity policy uncertainty (EPU) in China with textual methods and analyses the effect of the EPU and carbon emission prices (CEPs) on the total electricity demand. This paper also forecasts the demand for electricity in China with three mixed-frequency data models. The results show that the EPU index efficiently captures the uncertainty of China's electricity policy. The effects of EPU and CEPs on electricity demand are significant, and incorporating them into the forecasting model will improve the accuracy and timeliness. Moreover, compared with the ARMA model and LSTM neural networks, mixed-frequency data models perform better in electricity demand forecasting.

CLIMATE POLICY UNCERTAINTY AND CARBON PRICE: EVIDENCE FROM FREQUENCY AND QUANTILE PERSPECTIVE

Changes in climate policy are posing enormous challenges to carbon markets. However, the intricate link and time-frequency features between climate policy uncertainty (CPU) and carbon emission price (CEP) have not been extensively explored in the current literature. This paper aims to reveal the dynamic relationship between the two under different time frequencies and quantiles by using a wavelet-based quantile-on-quantile regression (QQR) method. Nine sets of variable pairs with various temporal frequencies are constructed, such as short-term CPU and long-term CEP. The results demonstrate that their coefficients would change from positive to negative or irrelevant, or conversely, under different quantiles and frequencies. China has been a global focus and an interesting case since its relentless efforts towards addressing climate change and constructing a carbon market, which makes one of the contributions of this paper. Additionally, time frequencies combined with different quantiles are employed to explore the connection between CPU and CEP, revealing nonlinear and asymmetric features between variables, and enriching the theoretical influencing mechanisms. Therefore, the government should avoid drastic policy changes, and make full use of policy guidance effect. Besides, top-design and market-oriented reforms for the carbon market are needed.

Impacts of carbon emissions allowance limitations on carbon price with power generation rights trading in China

Carbon pricing is a crucial mechanism for achieving carbon peaking and carbon neutrality through the carbon trading market. Recently, the price of carbon emissions allowances in China's national carbon market has shown a gradual upward trend. The limitations on carbon emissions allowances have also become increasingly stringent. Power generators, which primarily rely on thermal power generation, have a substantial demand for carbon emissions allowances. The rising prices and stricter carbon emissions constraints have increased operational costs. Therefore, exploring the carbon pricing mechanism under more stringent carbon emissions allowance constraints is vital. It encourages thermal power generators to engage in carbon trading and reduce carbon emissions. This paper focuses on an energy trading platform supply chain comprising three power generators, one energy trading platform, and one power retailer. We consider stringent carbon emissions constraints and power generation rights trading. Through the analysis of three scenarios, the study explores optimal pricing decisions for carbon emissions allowances to facilitate carbon emission reduction and promote renewable energy generation and consumption. The findings indicate that, under certain conditions (i.e., the carbon quotas allocation coefficient b1∈[0.73,0.8]), scenario 3, which excludes generation rights trading, yields the highest optimal carbon price. The energy trading platform can adjust various commissions to influence the carbon price, as well as the price and quantity of generation rights trading. Introducing power generation rights trading proves beneficial in reducing carbon emissions (nearly 400 tons less than that in scenario 3 without generation rights trading, when the realized total power demand is around 8600 MWh) and fostering renewable power generation and consumption (the actual renewable power consumption weight is 3% higher than that without generation rights trading). When considering generation rights trading, the high-emission generator and renewable power generator achieve the highest power profits, while for the low-emission generator, the profit in this case is the second best among these scenarios. Moreover, by decreasing the coefficient of carbon quotas allocation and increasing the reduction rate simultaneously, the carbon cost will pass through to the electricity price more sufficiently (e.g., when b1=0.76, the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient.

Time varying characteristics of factors affecting carbon price in emission trading scheme in China: Evidence from SV-TVP-VAR approach

Analysis of the factors influencing the price of carbon emissions trading in China and its time-varying characteristics is essential for the smooth operation of the carbon trading system. We analyse the time-varying effects of public concern, degree of carbon regulation, crude oil price, international carbon price and interest rate level on China’s carbon price through SV-TVP-VAR model. Among them, the quantification of public concern and the degree of carbon emission regulation is based on microblog text and government decisions. The results show that all the factors influencing carbon price are significantly time-varying, with the shocks of each factor on carbon price rising before 2019 and turning significantly thereafter. The short-term shock effect of each factor is more significant compared to the medium- and long-term, and the effect almost disappears at a lag of six months. Thanks to public environmental awareness, low-carbon awareness and the progress of carbon market management mechanisms, public concern has had the most significant impact on carbon price since 2019. With the promulgation of relevant management measures for the carbon market, relevant regulations on carbon emission accounting, financing constraints, and carbon emission quota allocation for emission-controlled enterprises have become increasingly mature, and carbon price signals are more sensitive to market information. The above findings provide substantial empirical evidence for all stakeholders in the market, who need to recognize that the impact of non-structural factors on the price of carbon varies over time. Government intervention also serves as a key aspect of carbon emission control and requires the introduction of relevant constraints and incentives. In particular, emission-controlling firms need to focus on the policy direction of the carbon market, and focus on the impact of Internet public opinion on business production while reducing carbon allowance demand and energy dependence.

Using miscanthus and biochar as sustainable substrates in horticulture: An economic and carbon footprint assessment of their primary and cascading value chains

Conventional substrates like peat, stone wool and coconut coir are responsible for high greenhouse gas emissions in the horticultural sector, necessitating low-emission and cost-efficient alternatives. Herein, using miscanthus and biochar as substrate components as well as in cascading substrate application can be alternative practices in a sustainable bioeconomy. However, the carbon footprint and economic impacts of these practices in relation to crop yields have not yet been investigated. Hence, we combined life cycle carbon footprint assessment and costing to analyze the Global Warming Potential and value chain costs of horticultural substrates in tomato cultivation in North-Rhine Westphalia. We conducted a comparison between conventional substrates (peat, stone wool, and coconut coir) and single use and cascading miscanthus-based substrates with and without 1–2 % biochar addition of the miscanthus mass. Also, a subsequent scenario analysis was carried out to examine alterations in inputs and costs.Our results demonstrate that miscanthus-based substrates are climate-friendly and low-cost alternatives to the conventional practices. Switching to miscanthus-based substrates results in more emission savings than other input scenarios investigated. Additionally, incorporating biochar and adopting cascading methods contribute to lower emissions. Notably, biochar has the most significant impact, as its amount correlates with higher emission reductions. Additionally, costs for cascading miscanthus-based substrates are lower compared to conventional substrates. Overall, there is only a slight variance in costs between conventional and miscanthus-based substrates. However, with the introduction of carbon emission pricing and carbon removal certificates, miscanthus-based and biochar-containing substrates may emerge as more cost-efficient alternatives. Thus, by advancing financial instruments on carbon emissions and removal, introducing cascade use within and beyond the horticulture sector, and supporting cultivation of sustainable biomasses, miscanthus and biochar can effectively contribute to the development of a sustainable bioeconomy.

Configuration and Operation Optimization for Industrial Steam Power System Sustainable Retrofit Considering Renewable Energy Integration

AbstractRenewable energy integration and operational optimization are crucial in energy sustainability and decarbonization, especially for industrial steam power systems (SPS). This study establishes an SPS superstructure that integrates wind, solar, and biomass energy. A mixed‐integer nonlinear programming (MINLP) model is developed to determine an optimal retrofit strategy that minimizes the life cycle cost, which includes carbon emission cost and energy cost. To solve this high‐dimensional complex optimization problem, a multistage strategy fusion differential evolution algorithm with dynamic partitioning of the SVM feasible domain (SVM‐MS‐DE) is developed. The results from the optimal strategy demonstrate a 9.02% reduction in the system's total cost and a 9.64% decrease in carbon emission through the incorporation of wind and solar energy. Additionally, the sensitivity analysis on biomass ratio, carbon emission price, energy demand, and carbon emission limits reveal that the region can contribute significantly to renewable energy initiatives. Several recommended policies are provided to encourage enterprises to move towards sustainable development.

Study on Cost-Effective Performance of Alternative Fuels and Energy Efficiency Measures for Shipping Decarbonization

Within the context of global initiatives to address climate change, the shipping industry is facing increasingly intensified pressure to decarbonize. The industry is engaging in the exploration and implementation of greenhouse gas (GHG) emission reduction measures, including energy efficiency technologies and alternative fuels, with the objective of accelerating the progression towards greenhouse gas mitigation. The application of various GHG emission reduction measures usually requires different levels of investment costs, and economic feasibility is a key factor influencing policy formulation and investment decisions. In this regard, this paper developed a cost-effective model for energy efficiency measures and alternative fuels based on the marginal abatement cost (MAC) methodology. This model can distinguish the differences between energy efficiency measures and alternative fuels in terms of Tank-to-Wake emissions and Well-to-Wake emissions in the GHG emission evaluation system. By taking typical ship types with significant emission contributions as study cases, i.e., bulk carriers (61–63K DWT), container ships (8000 TEU), product tankers (115K DWT), crude oil tankers (315–320K DWT), and Ro-Ro passenger ferries (3500 DWT), the GHG abatement cost-effective performance of major categories of measures such as operational measures, technical measures, renewable energy sources, and alternative fuels were calculated. According to the MAC results, the marginal abatement cost curves were plotted based on the ranking of energy efficiency measures and alternative fuels, respectively. The impacts of bunker fuel prices and carbon market prices on the cost-effectiveness were analyzed. The research results provided the GHG abatement potential of the integrated application of cost-effective energy efficiency measures, the cost-effectiveness ranking of alternative fuels, and the carbon emission price expected to bridge the price gap between alternative fuels and conventional bunker fuel. The presented methodology and conclusions can be used to assist shipping companies in selecting emission reduction measures, and to support maritime authorities in developing market-based measures.

Effect Analysis of China's Carbon Emission Trading Pilot Policy on Carbon Emission Control

Many countries have achieved a fundamental consensus to decrease carbon dioxide emissions in order to address environmental and climate issues. China, as the foremost energy consumer and carbon emitter globally, has significantly contributed to the exacerbation of greenhouse gas issues. The most effective method for lowering carbon emissions is through the trade of carbon emissions. In 2011, China began the process of implementing pilot projects for pricing carbon emissions as a means of addressing environmental and climate-related issues. This study use the literature review approach to examine the current state of China's carbon emission trading pilot policies on carbon emission management. It also analyzes the technical and economic impacts of these pilot policies in comparison to non-pilot carbon emission trading policies. The objective of this study is to propose recommendations for the implementation of the national carbon emission trading market in 2021.

Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model

Accurate forecasting of carbon emission prices is a crucial yet challenging task, given the complexity of the trading market and the variability of influencing factors. To comprehensively address factors related to carbon prices while considering interval forecasts associated with uncertainty, this study introduces a novel multistep ahead point-interval forecasting framework for carbon emission prices. This framework integrates multifactor selection, multivariate decomposition and reconstruction, an intelligent point and interval forecasting network, and an evaluation system. First, influencing factors are selected using a recursive feature elimination algorithm based on the extreme gradient boosting estimator. Second, original carbon prices and related influencing factors are decomposed into a set of multimode components (MMCs) using multivariate variational mode decomposition and sample entropy reconstruction techniques. Third, each row of MMCs is predicted using a long short-term memory model with an attention mechanism, and final point predictions are produced through simple addition. Finally, interval predictions are derived using enhanced kernel density estimation (KDE), which can expand the upper and lower bounds of the prediction interval. The stability and robustness have been tested in European Union Allowance (EUA) price forecasting. The results show that the proposed model is superior to other benchmark models, with MAPEs of 0.83%, 3.54%, and 3.82% in 1-step, 15-step, and 30-step ahead forecasting, respectively. Additionally, the proposed enhanced KDE demonstrates excellent performance; in 1-step ahead forecasting, the F-value is 1.86, 1.82, and 1.75 at the 80%, 90%, and 95% confidence levels, respectively. This proves that the proposed framework can effectively improve the point forecasting performance of EUA prices and quantify the prediction uncertainty.

Blended-amine CO2 capture process without stripper for high-pressure syngas

With the emergence of the hydrogen economy, advanced CO2 capture technologies are essential for cost-effective H2 production until renewable-energy based H2 production technologies gains competitiveness. A novel stripper-free pre-combustion CO2 capture process, in which the stripper was replaced with a low-pressure (LP) flash column, was developed for cost-effective pre-combustion CO2 capture from H2-rich syngas at high pressure. The developed process could regenerate the blended N-methyldiethanolamine/piperazine (MDEA/PZ) solvent at relatively low temperature (<373.15 K). After the sensitivity analysis using key operating parameters, process optimization was performed by the deep learning-based surrogate models using non-dominated sorting genetic algorithm-II (NSGA-Ⅱ). The heat duty of 1.881GJ/tonCO2 was required at the optimal condition, but the required energy could be supplied through the utilization of a waste heat recovery (WHR) system because the regeneration was possible at low temperatures. Compared with advanced stripper-incorporated amine processes, the equivalent work of the developed process with WHR system using a heat source at 370 K was lowered by 50 % and more cost-effective (32.89$/tonCO2 at the discount rate of 10 %) from the economic analysis considering carbon emission price. The performance assessment of the stripper-free CO2 capture process provided design guidelines for effective CO2 capture from H2-rich syngas and emphasized the importance of CO2 capture unit in net-zero scenario.

The Impact of Carbon Emission Pricing Policy on the Transformation of Low Carbon Economy and Its Application in the Energy Industry

The Impact of Carbon Emission Pricing Policy on the Transformation of Low Carbon Economy and Its Application in the Energy Industry

Low-carbon joint dispatch of integrated energy systems based on differentiated dynamic pricing mechanisms

World is facing the severe crisis of energy supply and great pressure of carbon emissions reduction. The development of an integrated energy system (IES) is a way to meet the goal of carbon peaking and carbon neutrality. To ensure the coordination of economic benefits of source and load sides in the low-carbon dispatch, an internal multi-stakeholder’s joint optimal dispatch model of an IES considering differentiated dynamic pricing mechanisms is proposed. A joint optimization coordination center (JOCC) is established responsible for differentiated dynamic pricing and joint optimal dispatch of an IES. The JOCC sets the selling prices of electricity and heat from different sources by introducing the concept of additional carbon emission price of ladder-type for non-clean units. The JOCC is then responsible for the information interaction of the integrated energy operator (IEO) on the source side and the load aggregator (LA) on the load side. In addition, the JOCC sets the joint optimization objective function to determine the optimal dispatch strategy which realizes the coordination of economic benefits of source and load sides. CPLEX was used to solve the mixed integer linear programming (MILP) model. Case studies verified that the proposed joint optimal dispatch model of an IES considering differentiated dynamic pricing mechanisms can endow users with the right to choose energy from different sources and improve their enthusiasm to participate in integrated demand response (IDR), that realizes the coordination of economic benefits of multi-stakeholders on the basis of ensuring the low-carbon characteristics of the IES. Compared with the time-of-use pricing strategy, carbon emissions are reduced by 2.41%, wind curtailment is reduced by 49.31%, and the total cost of LA is reduced by 26.53%.

The Impact of Carbon Emission and Carbon Price on International Trade Volume

In recent years, environmental issues have become an international focus, under the influence of global climate change, greenhouse effect is becoming more severe with each passing day. Therefore, it is urgent to control greenhouse gas emissions and improve the quality of the ecological environment by reducing carbon emissions. However, in the context of the current low-carbon economy, the environmental costs of enterprises are bound to rise, which will inevitably have a more serious impact on enterprises and the national economy. This article will mainly focus on how low carbon economy policies affect a country's international trade over a given period of time based on a concrete example. Regarding the methodology, chose the object to be India's carbon related data which are carbon intensity, carbon emission and carbon price in the period 1990-2020. And mainly based on the regression model using excel. The results of the study show that the Carbon Intensity and Carbon emissions are positively correlated with International Trade while the Carbon Price is negatively correlated. The volume of Indias International Trade will grow steadily as it moves towards Low-Carbon Economy policy. In summary, moving towards low carbon economy can increase the sustainability of international trade and reduce trade risks, while also creating more trade opportunities for countries, especially in the areas of environmental protection.

Research on Carbon Emission Reduction and Pricing Decision of Dual-Channel Green Supply Chain Considering Altruistic Preference under Carbon Tax

Research on Carbon Emission Reduction and Pricing Decision of Dual-Channel Green Supply Chain Considering Altruistic Preference under Carbon Tax

Carbon emission and asset prices: new evidence from machine learning

Carbon emission and asset prices: new evidence from machine learning

탄소배출권 거래가 전남지역 산업 생산활동에 미치는 영향

When comparing South Korea's emission trading market on a global scale, such as in Europe, the United States, and China, South Korea possesses the hardware infrastructure required for the operation of an emission trading system. However, it appears to be lacking in the software aspect, including the implementation of various carbon reduction policies to complement the carbon emission trading system. The study empirically analyzes the impact of the carbon emission trading system on industries in Jeonnam. First, the carbon emission trading system does not seem to significantly constrain short-term production activities of businesses. Second, when examining the impact of carbon emission prices (KAU, EUA) on major industries in the Jeonnam region, carbon-related variables act as information intermediaries. It is evident that the chemical and steel industries in Jeonnam play the role of information recipients. Third, concerning the connectivity network with KAU as the central variable, it is apparent that the chemical and steel industries in Jeonnam receive more influence from KAU rather than influencing it. In the case of the connectivity network with EUA as the central variable, EUA plays the role of information recipients for the chemical and steel industries. This can be attributed to the participation of these industries in the European carbon emission trading market while exporting chemical and steel products to Europe, affecting EUA demand and prices. In the future, the impact of carbon emission reduction policies is likely to increase for domestic industries and companies in the Jeonnam region in the short term. To achieve regional and national carbon neutrality, policy measures tailored to the industrial structure characteristics of the Jeonnam region must be developed.