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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]

The future of the Carbon Market Approach under the United Nations Framework Convention on Climate Change (UNFCCC) is uncertain. However, further development of Carbon Markets is expected. This paper estimates the average carbon price of the carbon emission permits (COP-approved units) acquired by the Spanish government and also a more realistic figure for the average carbon cost of the aforesaid emission permits. This is very useful for evaluating and comparing different carbon prices and markets. In addition, the paper contributes to the existing literature by providing a deeper understanding of the flow of emission permits between the European Union Emission Trading Scheme (EU ETS) and the Emission Trading Scheme (ETS) under the Kyoto Protocol. The main conclusions are that the estimated carbon price and cost are both lower than other reported prices and costs. This may go some way towards explaining why the government preferred to use the Flexibility Mechanisms and acquire an especially large number of COP-approved units (mainly Assigned Amount Units (AAUs)) through bilateral transactions with other Annex B countries, instead of launching actual greenhouse gas (GHG) abatement projects in the country. Moreover, more transparency is needed in regard to bilateral transactions of AAUs between the Spanish government and other Annex B seller countries. [ABSTRACT FROM AUTHOR]

Carbon dioxide emissions pricing is employed as an approach to mitigate climate change. To analyze the impact of carbon prices on the metal mining industry, this paper presents a new block-based carbon dioxide emissions estimation model to assess the carbon dioxide emissions and carbon intensity in varying carbon prices, by employing the engineering-based inputs from life cycle of mineral production. Such engineering-based data of an iron case is provided to test the impact of carbon prices on mining projects. The results indicate that the negative impact of carbon prices on the profit of mining projects is more sensitive in low minerals prices, especially for underground method. On the other hand, carbon price rising facilitates the mitigation of carbon dioxide emissions, by lowering the ratio of excavated waste rocks to mined-out raw ore, which varies linearly with the emissions. The carbon dioxide emissions in the studied case decrease from 39.92 kg carbon dioxide eq per ton to 21.50 kg carbon dioxide eq per ton, and from 51.02 kg carbon dioxide eq per ton to 42.80 kg carbon dioxide eq per ton, for surface and underground method, respectively. The results of carbon intensity show that it keeps increasing due to carbon price rising, until the mining boundaries are optimized based on the increased carbon price. Image 1089 • Block-based carbon dioxide emissions estimation model is introduced for metal mines. • The projects' profit is more sensitive to carbon prices in low minerals prices. • Carbon dioxide emissions vary linearly with the ratio of excavated rocks to raw ore. • Rising carbon price increases carbon intensity before mining boundary is optimized. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the information spillover between carbon price returns and 18 top European electricity companies' stock returns using a connectedness network approach. Our findings show that there is strong information interdependence between carbon price returns and electricity stock returns, as evidenced by a high total connectedness index. In our constructed carbon-electricity market system, the carbon market behaves as an information recipient, receiving varying degrees of information spillover from various power enterprises. Large electricity companies tend to contribute more information to the system than small ones. In addition, our dynamic results show a high, but also volatile, spillover index, which can be a result of changes to the carbon market situation and the financial markets' stability. Finally, an asymmetric effect can be identified when comparing the difference between negative and positive return connectedness networks. Highlights • Information spillover between carbon returns and European electricity companies' stock returns is studied. • There is strong information interdependence between carbon returns and electricity stock returns. • The carbon market behaves as an information recipient, receiving varying degrees of information spillover. • Large electricity companies tend to contribute more information to the system than small ones. [ABSTRACT FROM AUTHOR]

Abstract The objective of traditional fleet optimization models has been to find the economic life of the assets, neglecting their environmental impacts. However, due to the effect of carbon pricing schemes, in addition to the international concerns about global warming and carbon emissions, it is essential for affected fleet owners to incorporate environmental burdens into their asset management systems. The contribution of this paper is twofold. First, a fleet optimization model is proposed, which factors in the environmental impacts of a fleet of assets over a finite horizon, in addition to its total cost of ownership. As an indicator of environmental impacts, the greenhouse gas (GHG) emissions associated with the fleet ownership is considered. GHG emissions are converted into a monetary value, using the expected price of carbon in the Western Climate Initiative (WCI) market. Thus, the second contribution of this study is to develop a model to predict the price of carbon in this market. The optimization model is then applied to a fleet of excavators located in Ontario, Canada, and the price of carbon in the WCI market is forecasted over the planning horizon of the optimization model. [ABSTRACT FROM AUTHOR]

Abstract The European Union Emissions Trading System (EU ETS) has strengthened the information flow and connection between carbon market and energy markets, which makes the carbon-energy system more complicated. This paper investigates information linkages and dynamic spillover effects between the carbon and energy markets. We adopt a systemic time-series approach to study connectedness in both returns and volatility in the carbon-energy system. Moreover, a rolling-windows method is used to show the dynamic features. Empirical results show that Brent oil prices play an important role in affecting carbon price changes and risks. Feedback exists from the carbon market to other energy markets, and electricity prices are shown to be the biggest information receiver in the system. It is also shown that the level of connectedness in the volatility system is substantially higher than that in the returns system. Our results can provide useful implications for policymakers to design market mechanisms and market investors to manage their portfolios. Highlights • Information linkages and spillover between carbon and energy markets are studied. • Both return and volatility connectedness in the carbon-energy system are estimated. • Crude oil, clean energy, and coal play a pivotal role in both returns and volatility systems. • The electricity market is the main net information receiver affected by carbon market. • Spillover effects are much stronger in the volatility system than in the returns system. [ABSTRACT FROM AUTHOR]

This paper aims to provide an improvement in the modeling of supply chain designs by incorporating correlated uncertainty among multiple parameters, resulting in a more resilient design. A new methodology to generate forecasts for historically correlated time series, regardless of their underlying probability distributions, is presented and applied to generate scenarios for energy and carbon prices, which historically proved to be correlated. These scenarios are then used in a stochastic computation to obtain a three-echelon supply chain design in Europe maximizing the economic performance. The emissions were monetarized through the incorporation of the European Union cap-and-trade emissions trading system into the model. The social impact of the supply chain network is measured in terms of the direct, indirect and induced jobs it creates, which are proportional to the economic performance. By combining the developed methodology with data mining algorithms, a reduction in the number of required scenarios by more than 90% was achieved. The numerical case study moreover shows that the stochastic design ensures an average reduction of emissions by more than 3 ktons compared to the use of a deterministic approach. In comparison, the computation of a stochastic supply chain design without parameter correlation takes 5 times longer. [Display omitted] • Method to generate correlated time series regardless of probability distribution. • Exploit correlation to reduce number of scenarios leading to robust network design. • Characterization of 2 correlated uncertain parameters with only around 100 scenarios. • Stochastic simulation reduces 3 ktons of CO2-eq. without losing economic performance. • Considering correlated uncertainty leads to reduction in emissions and runtime. [ABSTRACT FROM AUTHOR]

The industrial revolution has brought about great development in the economy, but it has also increased the dependence on fossil energy. The emissions of CO 2 and other greenhouse gases have contradicted economic development and the ecological environment. The establishment of the EU Emission Trading System (EU ETS) has improved the global carbon emission price mechanism, but as a new commodity, its price trend will affect buyers' risk evaluation. Therefore, it is influential to master the driving factors behind carbon emission prices and make effective predictions. First, the paper points out that the driving factors are divided into macroeconomic risk factors and energy factors. Second, the Bayesian Network is used to select variables and make prediction of carbon prices. The results show that its accuracy exceeds other machine learning algorithms. Third, a structural equation model is used to study the impact of the selected markets on the carbon market. Finally, from the perspective of global carbon emission reduction, the relationship between driving factors and the carbon futures market is explained. The empirical results show that Cotation Assistée en Continu 40, natural gas and Brent crude oil will directly affect the yield of European Union Allowances and Certified Emission Reduction futures, and the Standard Poor 500 and Global Clean Energy Index will indirectly affect the yield of European Union Allowances and Certified Emission Reduction futures. The energy market will affect the carbon market through the intermediary effect of the stock market, in which the clean energy index is the most relevant factor. From the perspective of how to improve the carbon trading system, this paper proposes suggestions for the sustainable development of the world to promote the virtuous cycle of the global carbon emission market and the high-quality development of the global economy. • Multimarket Carbon Factor Selection Model. • Forecasting Carbon Market Price by Bayesian Network. • Markets Impacts Research and Path Analysis with Structural Equation Model. • Clean Energy Investment and Global Energy Structure Analysis. [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]

This paper aims to identify the effect that a carbon price will have on the economic viability of a high grade copper-cobalt mine in the Democratic Republic of the Congo, through examination of Scope 1 and 2 emissions produced directly through mining and processing activities. Four scenarios were analysed ranging from no carbon pricing scaling up to $100/t assuming hydroelectric power was unavailable and replaced with coal fired power. Limited academic literature exists that discusses the effect carbon can have on the economics of a mining operation, with literature predominantly focusing on quantities of carbon produced from mining and processing activities. Results show that a $30/t carbon price increases the mining cost by 2%, while the associated cost in processing from coal and diesel consumption producing carbon, results in an increase of 0.2%. Raising the carbon price to $100/t results in the mining cost increasing by 5%, and processing costs increasing by 0.3%. Quantifying the effect of hydroelectric power over coal fired power concludes that processing costs increase by 12.7% resulting in a cut-off grade change removing 2% Cu of the ore from being economic. The revenue factor 1.00 shell examined in all cases varied less than 1% in volume, however undiscounted cash flow varied as the quantity and cost of carbon in each scenario increased. When carbon does not influence the economic viability of the operation, 4.2 Mt of CO 2 is produced with mining activities producing 3.6 Mt and processing activities producing 0.6 Mt, respectively. The use of coal for power generation instead of hydroelectric results in 25.3 Mt of carbon being produced over the life of mine from both mining and milling activities, an additional 21.1 Mt when compared to the base case scenario that utilises hydroelectric power. The influence of a carbon price on a high grade copper-cobalt deposit with relative high processing costs due to hydrometallurgy processing requiring gangue acid consumption, has a minor effect on optimal pit limits and cut-off grade. [ABSTRACT FROM AUTHOR]

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]

The "Carbon Neutral Growth from 2020″ strategy (CNG2020 strategy) has predicted the total emission reduction amount but has not arranged the special emission reduction proportion for an individual airline. In this paper, we build a DEA model to calculate the emission reduction allocation proportion and explore the impacts of a different carbon price. We select Number of Employees, Fleet Size, and Aviation Kerosene as then inputs, and set Total Revenue as the output. Then we apply BP neural network to predict the data from 2021 to 2023 based on the actual data of 27 global airlines from 2008 to 2018. We discuss the allocation proportions when the price is 8 dollars/ton, when the price is 14 dollars/ton and when the price is 20 dollars/ton. We have some findings: 1. Skyteam will have the largest average reduction allocation proportion in 2021 and 2023, while Oneworld will have the largest allocation proportion in 2022.2. The largest average ERAP in 2021 and 2023 is from Delta Air Lines, and the largest average ERAP in 2022 is from Air France-KLM. [ABSTRACT FROM AUTHOR]