A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China
<p>Relationship diagram of economic growth, carbon trade market, and environment.</p> "> Figure 2
<p>Description of the inscribed core of piecewise linear.</p> "> Figure 3
<p>Steps for inscribed cored grey relational analysis model (IC-GRA) model calculations.</p> "> Figure 4
<p>Seven-pilot carbon trade market’s cumulative trade volume and turnover from May 2014 to January 2018.</p> "> Figure 5
<p>Monthly trade turnover in seven-pilot carbon trade markets.</p> "> Figure 6
<p>Monthly trade volume in seven-pilot carbon trade markets.</p> "> Figure 7
<p>Correlation degree of the influence factors.</p> ">
Abstract
:1. Introduction
1.1. Introduction of Factors Influencing Carbon Emissions Trading and Carbon Pricing
1.2. Introduction of Grey Relational Degree
1.3. Research Motivation and Content
2. Establishment and Properties of the Inscribed Core Grey Relational Analysis Model
2.1. Feature Extraction
2.2. Relation Analysis
2.3. Property Analysis
2.4. Numerical Example
3. Empirical Analysis
3.1. Variables and Data Source
3.2. Correlation Degree of the Factors in the Carbon Market
3.3. Comparative Analysis with Traditional Grey Relational Methods
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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The Grey Correlation Order | ||||
---|---|---|---|---|
IC-GRA model | 0.9459 | 0.8183 | 0.8869 | |
Deng’s correlation degree | 0.5485 | 0.5554 | 0.5482 | |
Grey absolute correlation degree | 0.9405 | 0.9316 | 0.9362 | |
Grey slope correlation degree | 0.9721 | 0.9310 | 0.9715 |
The Difference of the Grey Relational Coefficient | Variance | DD-Value |
---|---|---|
IC-GRA model | 0.004076081 | 0.873214 |
Deng’s correlation degree | 0.000016595 | 0.003555 |
Grey absolute correlation degree | 0.000019508 | 0.004179 |
Grey slope correlation degree | 0.000555723 | 0.119052 |
Carbon Markets | Shenzhen | Shanghai | Beijing | Guangdong | Tianjin | Hubei | Chongqing |
---|---|---|---|---|---|---|---|
Start Time | June 2013 | November 2013 | November 2013 | December 2013 | December 2013 | December 2014 | July 2014 |
Amount of initial controlled enterprises | 635 | 197 | 490 | 211 | 114 | 138 | 242 |
Controlled enterprise’s standard | The average emission amount exceeds 10,000 tons from 2009 to 2011. | The enterprise’s emission amount over 20,000 tons from 2009 | The enterprise’s emission amount over 10,000 tons. | The enterprise’s emission amount over 20,000 tons. | The enterprise’s emission amount over 20,000 tons from 2011 to2014 | The enterprise’s coal conversion over 60,000 tons. | The enterprise’s coal conversion over 10,000 tons from 2013 to 2015. |
Allocation Methods | Historical emission method and datum line methods | Historical emission method and datum line methods | Historical emission method | Historical emission method and datum line methods | / | / | Datum line methods |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 0.5082 | 0.5149 | 0.4211 | 0.5124 | 0.5156 | 0.4857 | 0.0803 | 0.0924 | 0.1156 |
Shanghai | 0.6530 | 0.6851 | 0.5042 | 0.6722 | 0.6838 | 0.5909 | 0.0762 | 0.0948 | 0.1115 |
Beijing | 0.6371 | 0.6544 | 0.4739 | 0.6437 | 0.6533 | 0.5697 | 0.1150 | 0.1001 | 0.1093 |
Guangzhou | 0.6333 | 0.6410 | 0.4800 | 0.6408 | 0.6414 | 0.5771 | 0.0804 | 0.0950 | 0.1191 |
Tianjin | 0.7749 | 0.8481 | 0.5063 | 0.8143 | 0.8521 | 0.6793 | 0.1553 | 0.1027 | 0.1150 |
Hubei | 0.7447 | 0.7645 | 0.5355 | 0.7705 | 0.7661 | 0.6599 | 0.0898 | 0.1000 | 0.1128 |
Chongqing | 0.7096 | 0.7595 | 0.4784 | 0.7268 | 0.7657 | 0.6510 | 0.0716 | 0.0871 | 0.1114 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 0.8092 | 0.6076 | 0.9590 | 0.9410 | 0.8637 | 0.8702 | 0.9317 | 0.7304 | 0.7438 |
Shanghai | 0.8411 | 0.6290 | 0.9370 | 0.9377 | 0.9064 | 0.8994 | 0.9320 | 0.7585 | 0.7733 |
Beijing | 0.9013 | 0.6392 | 0.8786 | 0.9086 | 0.9663 | 0.9629 | 0.9317 | 0.8025 | 0.8210 |
Guangzhou | 0.7732 | 0.6036 | 0.9236 | 0.8956 | 0.8202 | 0.8252 | 0.8784 | 0.7054 | 0.7171 |
Tianjin | 0.8093 | 0.5973 | 0.9396 | 0.9266 | 0.8685 | 0.8735 | 0.9256 | 0.7290 | 0.7424 |
Hubei | 0.8827 | 0.6079 | 0.9022 | 0.9259 | 0.9452 | 0.9221 | 0.9408 | 0.7838 | 0.7998 |
Chongqing | 0.7902 | 0.6107 | 0.8990 | 0.8901 | 0.8336 | 0.8330 | 0.8819 | 0.6903 | 0.7061 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 0.5150 | 0.0345 | 0.8977 | 0.5841 | 0.0069 | 0.2407 | 0.6565 | 0.5188 | 0.7823 |
Shanghai | 0.5345 | 0.0763 | 0.7508 | 0.7747 | 0.0394 | 0.5498 | 0.6716 | 0.5149 | 0.7783 |
Beijing | 0.6345 | 0.2504 | 0.6864 | 0.9529 | 0.1339 | 0.6578 | 0.6731 | 0.5127 | 0.7760 |
Guangzhou | 0.5115 | 0.0266 | 0.9956 | 0.5153 | 0.0053 | 0.1844 | 0.6575 | 0.6745 | 0.7844 |
Tianjin | 0.5318 | 0.0706 | 0.7592 | 0.7580 | 0.0342 | 0.5059 | 0.6743 | 0.5152 | 0.7785 |
Hubei | 0.6385 | 0.2563 | 0.6858 | 0.9551 | 0.0623 | 0.6521 | 0.6536 | 0.5127 | 0.7760 |
Chongqing | 0.5359 | 0.0791 | 0.7473 | 0.7820 | 0.0164 | 0.5710 | 0.6546 | 0.5148 | 0.7782 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 0.8838 | 0.8688 | 0.8866 | 0.8831 | 0.8939 | 0.8941 | 0.8924 | 0.8895 | 0.8884 |
Shanghai | 0.9260 | 0.9135 | 0.9298 | 0.9213 | 0.9499 | 0.9486 | 0.9433 | 0.9393 | 0.9382 |
Beijing | 0.9422 | 0.9247 | 0.9467 | 0.9347 | 0.9647 | 0.9631 | 0.9537 | 0.9516 | 0.9508 |
Guangzhou | 0.9094 | 0.8946 | 0.9101 | 0.9017 | 0.9238 | 0.9232 | 0.9200 | 0.9181 | 0.9162 |
Tianjin | 0.9439 | 0.9347 | 0.9471 | 0.9356 | 0.9781 | 0.9748 | 0.9636 | 0.9606 | 0.9588 |
Hubei | 0.9439 | 0.9268 | 0.9484 | 0.9434 | 0.9669 | 0.9665 | 0.9613 | 0.9602 | 0.9579 |
Chongqing | 0.8913 | 0.8733 | 0.8893 | 0.8820 | 0.9141 | 0.9119 | 0.9051 | 0.9020 | 0.9003 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 4 | 2 | 6 | 3 | 1 | 5 | 9 | 8 | 7 |
Shanghai | 4 | 1 | 6 | 3 | 2 | 5 | 9 | 8 | 7 |
Beijing | 4 | 1 | 6 | 3 | 2 | 5 | 7 | 9 | 8 |
Guangzhou | 4 | 2 | 6 | 3 | 1 | 5 | 9 | 8 | 7 |
Tianjin | 4 | 2 | 6 | 3 | 1 | 5 | 7 | 9 | 8 |
Hubei | 4 | 3 | 6 | 1 | 2 | 5 | 9 | 8 | 7 |
Chongqing | 4 | 2 | 6 | 3 | 1 | 5 | 9 | 8 | 7 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 6 | 9 | 1 | 2 | 5 | 4 | 3 | 8 | 7 |
Shanghai | 6 | 9 | 2 | 1 | 4 | 5 | 3 | 8 | 7 |
Beijing | 5 | 9 | 6 | 4 | 1 | 2 | 3 | 8 | 7 |
Guangzhou | 6 | 9 | 1 | 2 | 5 | 4 | 3 | 8 | 7 |
Tianjin | 6 | 9 | 1 | 2 | 5 | 4 | 3 | 8 | 7 |
Hubei | 6 | 9 | 5 | 3 | 1 | 4 | 2 | 8 | 7 |
Chongqing | 6 | 9 | 1 | 2 | 4 | 5 | 3 | 8 | 7 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 7 | 9 | 6 | 8 | 2 | 1 | 3 | 4 | 5 |
Shanghai | 7 | 9 | 6 | 8 | 1 | 2 | 3 | 4 | 5 |
Beijing | 7 | 9 | 6 | 8 | 1 | 2 | 3 | 4 | 5 |
Guangzhou | 7 | 9 | 6 | 8 | 1 | 2 | 3 | 4 | 5 |
Tianjin | 7 | 9 | 6 | 8 | 1 | 2 | 3 | 4 | 5 |
Hubei | 7 | 9 | 6 | 8 | 1 | 2 | 3 | 4 | 5 |
Chongqing | 6 | 9 | 7 | 8 | 1 | 2 | 3 | 4 | 5 |
EUA | CER | WTI | NYMEX | Gas Price | Coal Price | Oil Price | CSI300 | Industry Index | |
---|---|---|---|---|---|---|---|---|---|
Shenzhen | 6 | 8 | 1 | 4 | 9 | 7 | 3 | 5 | 2 |
Shanghai | 6 | 8 | 3 | 2 | 9 | 5 | 4 | 7 | 1 |
Beijing | 6 | 8 | 3 | 1 | 9 | 5 | 4 | 7 | 2 |
Guangzhou | 6 | 8 | 1 | 5 | 9 | 7 | 4 | 3 | 2 |
Tianjin | 5 | 8 | 2 | 3 | 9 | 7 | 4 | 6 | 1 |
Hubei | 6 | 8 | 3 | 1 | 9 | 5 | 4 | 7 | 2 |
Chongqing | 6 | 8 | 3 | 1 | 9 | 5 | 4 | 7 | 2 |
AQI | Shenzhen | Shanghai | Beijing | Guangzhou | Tianjin | Hubei | Chongqing |
---|---|---|---|---|---|---|---|
Price | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Trade volume | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
Turnover | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
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Wang, L.; Yin, K.; Cao, Y.; Li, X. A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China. Int. J. Environ. Res. Public Health 2019, 16, 99. https://doi.org/10.3390/ijerph16010099
Wang L, Yin K, Cao Y, Li X. A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China. International Journal of Environmental Research and Public Health. 2019; 16(1):99. https://doi.org/10.3390/ijerph16010099
Chicago/Turabian StyleWang, Lihong, Kedong Yin, Yun Cao, and Xuemei Li. 2019. "A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China" International Journal of Environmental Research and Public Health 16, no. 1: 99. https://doi.org/10.3390/ijerph16010099