Cointegration relationship between carbon price and its factors: evidence from structural breaks analysis

Zhao, Xuelian; Zou, Yu; Yin, Jiuli; Fan, Xinghua

doi:10.1016/j.egypro.2017.12.190

Cited by 23 publications

(10 citation statements)

References 15 publications

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“…This shows that compared with the European carbon market, China’s carbon market is at an early stage of development, and the efficiency of the carbon market is relatively low. The relationship between the carbon price and its influencing factors is a contingency and dynamic change [ 19 ]. Therefore, when the domestic carbon emission trading market develops to a new stage, a comprehensive approach should be adopted to analyze the new form of interaction between the carbon price drivers of the carbon market.…”

Section: Literature Reviewmentioning

confidence: 99%

Effect Mechanism Research of Carbon Price Drivers in China—A Case Study of Shenzhen

Chen

Zhang

2022

IJERPH

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Revealing the effect mechanism of carbon price drivers is the basis to establish the pricing mechanism of carbon emission exchange, which also promotes the development of the carbon emission exchange market and can reduce the investment risk. Based on the previous research, the cointegration test, Granger causality test, and ridge regression estimate are used to analyze the effect mechanism between the domestic carbon price and its drivers. Johansen’s cointegration analysis reveals that there is a long-term equilibrium relationship between the domestic carbon price and energy price, industrial development level, climate change, and financial prosperity. Ridge regression estimates reveal that the international spot price of thermal coal in the ARA port and the spot price of Brent crude oil in Britain are negatively correlated with the domestic carbon price, while CER futures price is positively correlated with the domestic carbon price. There is a linkage between the international carbon price and the domestic carbon price. Since 2017, the domestic carbon price has been lower than the equilibrium value, and the value of carbon emission rights has been underestimated. With the continuous improvement of the domestic carbon market, the carbon price will rise and fluctuate around the equilibrium price in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Effect Mechanism Research of Carbon Price Drivers in China—A Case Study of Shenzhen

Chen

Zhang

2022

IJERPH

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“…Zhao et al [174] studied the effect of coal price, economy, and temperature on carbon price in six Chinees' ETS pilots. They concluded that coal price has the greatest influence on the carbon price, suggesting the existence of different weights for the carbon price driving forces.…”

Section: Previous Workmentioning

confidence: 99%

Sustainable asset and project management

Abdi¹

2021

Preprint

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There is a growing demand for companies to report and demonstrate their environmental credentials and corporate responsibility, which presents an opportunity for them to differentiate and gain a competitive advantage in the marketplace. They are now recognizing the limited capacity of the environment to endure the current level of development and economic growth, depletion of natural resources, increasing problem of waste, worrying carbon dioxide emissions, and other environmental impacts. In fact, for asset and project managers, financial criteria are no longer the sole considerations to achieve success and shareholder value. Therefore, environment is being considered as a future source of risk or opportunity. The present research proposes methodology and mathematical models for a sustainable asset and project management, with the focus on the environmental aspect of sustainable development and more specifically the issue of greenhouse gas (GHG) emissions. The following models have been presented in this dissertation. First, a mathematical fleet optimization model is developed, which incorporates the environmental impacts of a fleet of assets over a finite horizon, in addition to its total cost of ownership. As a unique feature of the model, it allows the assets to be kept in storage over any time period, in which such assets do not deteriorate as in-use assets do. The mathematical model optimizes the number of new, in-use, in-storage, and salvaged assets in each time period, so that the total economic costs and environmental impacts are minimized. The application of this work is illustrated in a fleet of excavators. Second, a hybrid Bayesian network (BN) is proposed for fleet availability analysis, focusing on the uncertainty of assets failure and repair rates. We model the common causes to individual rates, as well as the common causes that affect both failure and repair rates at the same time. The proposed model explicitly quantifies uncertainty in repair and failure rates of a fleet of assets and provides an appropriate method for modeling complex dependencies and factors affecting reliability, maintainability, or both, by considering influencing factors, either technical (such as working temperature, environment, quality, stress, etc.) or organizational (such as staff quality, management policies, etc.). We will then extend the model to consider extremely rare and/or previously unobserved risks (e.g. heavy storms, droughts, floods, etc.) that can significantly weaken reliability or maintainability levels. Third, a deterministic model for equipment repair-replacement (R/R) decision with both economic and environmental considerations is formulated. We converted the model into an algorithm and an automatic R/R Calculator. A probabilistic version of this model is then developed to factor in the quality of preventive maintenance, repair perfection, and risk events. We also model the causal relationship between equipment reliability and its GHG emissions during the operation phase. A plastic shredder case study was used to present the models’ results. Fourth, we aim to capture the uncertainty of carbon price in the Western Climate Initiative (WCI) market, by determining the causality between carbon price and its driving forces. A probabilistic model is developed using BNs to infer the possible ranges of each driving force that could have an escalation/depreciation effect on price as well as the magnitude of this effect. The model is developed and run based on a database of historical and projection on the selected driving factors in all the jurisdictions of the WCI market, providing the most probable price(s) over the next ten years. Finally, we developed two models to estimate and control project GHG emissions. The first model is developed based the earned value management (EVM) technique, a common practice in project cost and schedule performance measurement. The proposed model provides project managers with metrics to measure project GHG performance at any point in time over the life of a project and forecast the final emissions. In addition, we proposed a probabilistic model to quantify the uncertainty of project GHG emissions using Monte Carlo Simulation and BN techniques. The model provides a quantitative risk analysis mechanism to estimate the total emissions of the project as well as prediction of final emissions during the implementation process. The proposed models are applied to a work package of a real construction project.

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“…Hintermann [9] suggested that coal prices have a negative impact on carbon allowance prices in the EU emissions trading system (EU ETS) market. Zhao et al [10] demonstrated that carbon prices are significantly impacted by coal prices, although carbon prices vary obviously in different carbon markets in China. Second, oil prices are generally substantiated to have played dominant roles in price change trends among energy markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors

Han

Ding

Zhao

et al. 2019

Energy

130

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In this study, the hybrid of combination-mixed data sampling regression model and back propagation neural network (combination-MIDAS-BP) is proposed to perform real-time forecasting of weekly carbon prices in China's Shenzhen carbon market. In addition to daily energy, economy and weather conditions, environmental factor is introduced into predictive indicators. The empirical results show that the carbon price is more sensitive to coal, temperature and AQI (air quality index) than to other factors. It is also shown that the forecast accuracy of the proposed model is approximately 30% and 40% higher than that of combination-MIDAS models and benchmark models, respectively. Given these forecast results, China's government and enterprises can effectively manage nonlinear, nonstationary, and irregular carbon prices, providing a better investing and managing tool from behavioural economics.

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Cointegration relationship between carbon price and its factors: evidence from structural breaks analysis

Cited by 23 publications

References 15 publications

Effect Mechanism Research of Carbon Price Drivers in China—A Case Study of Shenzhen

Effect Mechanism Research of Carbon Price Drivers in China—A Case Study of Shenzhen

Sustainable asset and project management

Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors

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