Market incentives, carbon quota allocation and carbon emission reduction: Evidence from China's carbon trading pilot policy

Shi, Beibei; Li, Nan; Qiang, Gao; Li, Guangqin

doi:10.1016/j.jenvman.2022.115650

Cited by 195 publications

(56 citation statements)

References 48 publications

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“…For example, the short-term carbon quota supply and demand and other irregular events are important external drivers of short-term shocks in the carbon price. These explanations are consistent with the conclusion of Ji et al 45 and Shi et al 46 that the supply and demand capacity of carbon quotas affected the short-term carbon price significantly. Second, the signal period of the residual term is the longest, and the signal shock frequency is low.…”

Section: Mode Characteristic Analysis Of Carbon Price Signalssupporting

confidence: 92%

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Yun

Huang

et al. 2022

Energy Science & Engineering

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Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition-type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-long short-term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time-frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN-LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short-term forecasting performance is superior to the long-term and medium-term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price-driving mechanism from the point of multiscale time-frequency characteristics. Particularly, short-term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions.

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Section: Mode Characteristic Analysis Of Carbon Price Signalssupporting

confidence: 92%

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Yun

Huang

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…Second, carbon emissions trading supplements the surplus and deficit, and regional cooperation and emission reduction are realized through a market-oriented approach. Domestic and international scholars discussed the importance of establishing a carbon emission trading market [ 27 , 28 , 29 ] as well as potential influencing factors [ 30 ]. The third option is to use the clean development mechanism, which is a cooperative mode in which one country invests in emission reduction projects in order to obtain carbon emissions from other countries in order to achieve emission reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Influencing Intergovernmental Cooperation on Emission Reduction in Chengdu-Chongqing Urban Agglomeration: An Evolutionary Game Theory Perspective

Liu

Meng

Huang

et al. 2022

IJERPH

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In this study, we introduced the realistic problem of a dynamic carbon tax, built several evolutionary game models for cooperative emission reduction by local governments, and determined the factors that influence governments’ willingness to cooperate in emission reduction. The findings revealed that, first, the probability of governments opting for cooperative emission reduction strategies increases at different rates depending on the benefits and costs of cooperation. Second, externalities influence governments’ willingness to cooperate in emission reduction during cooperative carbon emission reduction. Furthermore, the emergence of a free-riding situation reduces the effectiveness of intergovernmental cooperation in reducing carbon emissions. Third, carbon tax policy can influence the likelihood that local governments will choose cooperative emission reduction, and different carbon tax sizes have different effects on their willingness to choose cooperative emission reduction.

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“…Therefore, reducing carbon dioxide emissions has become a common concern of all countries to lessen the impact of climate-related disasters. However, considering the differences in the stages of economic development faced by developed and developing countries, different countries should play different roles in reducing carbon emissions [ 6 ]. Although there are many disputes between developed and developing countries about the contributions of carbon emission reduction [ 7 ], it is important to note that developing countries are currently playing an essential role in achieving carbon emission reduction and tackling climate change [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China

Shi

et al. 2022

IJERPH

Self Cite

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Driven by the information technology revolution, using artificial intelligence to promote intelligent manufacturing while achieving carbon emissions reduction is increasingly the focus of international attention. Given this, based on the fact that China’s industrial manufacturing is more intelligent, this paper uses industrial sector data and robot data from 2000 to 2017 to examine the impact of intelligent manufacturing on industrial carbon dioxide emissions and to discuss its internal mechanism. The research found that intelligent manufacturing significantly inhibits carbon dioxide emissions in the industrial sectors. The emission reduction effect is more obvious in industries with higher carbon emissions and intelligence. The mechanism test shows that intelligent manufacturing mainly achieves industrial emission reduction by reducing fossil energy consumption in the production process and improving energy use efficiency. The research findings of this paper provide favorable evidence for using new technologies, such as artificial intelligence, to achieve carbon emissions reduction, and validate the importance of intelligent manufacturing in tackling climate change in the future. It provides an essential reference for developing countries to use artificial intelligence for their carbon emissions reduction goals.

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Market incentives, carbon quota allocation and carbon emission reduction: Evidence from China's carbon trading pilot policy

Cited by 195 publications

References 48 publications

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Factors Influencing Intergovernmental Cooperation on Emission Reduction in Chengdu-Chongqing Urban Agglomeration: An Evolutionary Game Theory Perspective

Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China

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