Carbon Emission Price Research Articles

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.

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.

The Bidirectional Influence Between Energy and Carbon Emission Prices---- Primarily Studied from Perspective of New Energy and Traditional Energy Sources

The Bidirectional Influence Between Energy and Carbon Emission Prices---- Primarily Studied from Perspective of New Energy and Traditional Energy Sources

Network evolutionary game analysis of green credit: A perspective of carbon emissions trading

AbstractAs an effective measure for the local government to promote reduction of corporate carbon emissions and sustainable development of social economy, the implementation of green finance policies in enterprises and financial institutions has encountered severe strikes from the low returns of low carbon production and low interest rates of green credit business. Under the premise of liberalizing carbon emissions trading, a triple‐level network evolutionary game consisting of a local government, banks, and enterprises is constructed to analyze response behavior and strategy selection mechanism to green credit. The impacts of management tools are investigated in terms of setting carbon market price, limiting the financing upper bound of misbehaving enterprises, limiting the emissions quota of enterprises for fraudulent green credit, and rewarding clean project development on the decisions of individuals in the network. Results show the following: First, increasing the trading price of carbon emissions has a catalytic effect on enhancing the activeness of banks and enterprises to follow green finance policies, but the autonomous regulation ability of which is limited. Second, reducing the funding ceiling for emitting emissions illegally ambiguates enterprises' preferences for production strategies and discourages them from undertaking low‐carbon projects. Third, excessive restrictions on the share of emissions may evoke regulatory inaction by the local government, consequently leading to instability in the evolution network. Fourth, granting reward for clean production is instrumental for the network to promote green finance policies, but exorbitant subsidies increase the fiscal burden of the local government with no significant effect on the node selection of environment‐friendly strategies.

Carbon emission price forecasting in China using a novel secondary decomposition hybrid model of CEEMD-SE-VMD-LSTM

Effective forecasting of carbon prices helps investors to judge carbon market conditions and promotes the environment and economic sustainability. The contribution of this paper is constructing a novel secondary decomposition hybrid carbon price forecasting model, namely CEEMD-SE-VMD-LSTM. It is noteworthy that the sample entropy is introduced to identify the highly complex signals rather than empirically determined in previous studies. Specifically, the complementary ensemble empirical mode decomposition (CEEMD) model is used to decompose the original price signals. The sample entropy (SE) and variational mode decomposition (VMD) are conducted to recognize and secondary decompose the highly complex components, while the long short-term memory (LSTM) model is employed to forecast the carbon price by summing up the predicted intrinsic mode function (IMF) components. The conclusion shows the proposed model has the smallest forecasting errors with the values of RMSE, MAE and MAPE are 0.2640, 0.1984 and 0.0044, respectively, the secondary decomposition models are better than other primary decomposition models and the forecasting performances of LSTM-type models are better than those of other GRU-type models. Further evidence convinces us that short-term forecasting accuracy is superior to long-term forecasting. Those conclusions and model innovation can provide a valuable reference for investors to make trading decisions.

Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices

The growing share of renewables, the retirement of coal generation, and the increasing significance (and price) of carbon emissions continue to reshape electricity market dynamics. Ongoing power sector transformations, along with recurrent energy price shocks, bring new challenges to electricity price prediction and projection. Adept at capturing nonlinear and time-varying dynamics of the power market, machine learning (ML) methods can significantly enhance modeling and projection capabilities. This paper leverages ML modeling to (1) analyze the day-ahead electricity price dynamics and (2) develop future price projections under varying market conditions. First, we offer a methodology for identifying and ranking the factors determining day-ahead power price behavior. Guided by the understanding of power price drivers, we then develop an ensemble ML model for high-resolution (i.e., for each day of the next year) electricity price projection. Hence, the first part of our analysis helps relate fundamental modeling approaches to ML studies, and the second part extends the existing literature, which is limited to high aggregation level (e.g., yearly basis) projections. High-resolution projections provide valuable insights into within-year price dynamics and help answer highly debated questions raised by the growing share of renewables and the current energy market turmoil. We demonstrate our methodology on the German power market, using data on 80 explanatory variables from 2015 to 2021. We identify the most critical power price drivers and develop an ML model to study the within-year impact of high fuel and carbon prices. Our findings indicate that the often-neglected interaction variables may significantly impact price projections and should be included in day-ahead electricity price modeling. We confirm the efficacy of our approach with a forecasting exercise based on day-ahead electricity prices in the first two quarters of 2022.