Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market
Abstract
1. Introduction
2. Methodology
2.1. Causality
2.2. Main Equations and Parameter Settings
3. Empirical Simulation Analysis
3.1. Model Validation
3.2. Scenario Setting
3.2.1. Impacts on the Economy, Carbon Emissions, and Energy Consumption
3.2.2. Impact of Different Scenarios
- (1)
- Impact of Carbon Prices
- (2)
- Impact of Quota Auctions
- (3)
- Impact of CCER Prices and Offset Ratios
- (4)
- Impact of Quota Reduction Rates
- (5)
- Impact of Penalty Severities
4. Discussion
4.1. Single-Objective Scheme Selection
4.1.1. Carbon Reduction Benefits
4.1.2. Economic Benefits
4.1.3. Energy Structure Benefits
4.2. Multi-Objective Scheme Selection
4.2.1. Carbon Emissions Reduction Target
4.2.2. Economic Development Target
4.2.3. Energy Structure Target
4.2.4. Multi-Objective Scheme Screening
4.3. Element Regulation of the Carbon Market Under Multi-Objective Constraints
5. Conclusions and Outlook
5.1. Conclusions
- (1)
- The implementation of China’s national carbon market significantly promotes carbon reduction. However, in the short term, it comes at the cost of sacrificing part of economic development. Long-term implementation of the carbon trading mechanism proves more beneficial for carbon reduction. In the short term, China’s national carbon market negatively impacts GDP growth, but its suppression effect on carbon emissions is stronger than the negative impact on economic development.
- (2)
- The effects of carbon reduction strengthen with increases in carbon price, quota auction, CCER price, penalty severity, and the quota reduction rate and weaken with an increase in the CCER offset ratio. The increase in the CCER offset ratio not only alleviates the economic impact of the carbon trading market but also reduces the carbon reduction effect. Therefore, the CCER offset ratio should not be set too leniently.
- (3)
- Gradually slowing down the quota reduction rate is more favorable for the coordinated development of carbon reduction, economic development, and energy structure targets. The E2 scenario is an optimized carbon market scenario that comprehensively considers carbon reduction, economic development, and energy structure targets. It reduces the quota reduction rate compared with the classic scenarios. Slowing down the quota reduction rate helps reduce the economic losses caused by the implementation of the carbon market and achieves coordination with other targets.
- (4)
- Under the constraints of carbon reduction, economic development, and energy structure targets, the reasonable range for carbon prices is between CNY 77.9 and CNY 118.9 per ton. The quota auction ranges from 0% to 23.4%. The reasonable range for the quota reduction rates is between 0.84% and 2.18%, with a penalty severity of 7.
5.2. Limitations and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Abbreviations | Units | Equation Settings or Data Sources |
---|---|---|---|
Change in GDP | Cig | CNY 100 million | Cig = GDP × Ggr – Tcoce + Tdc × GDP |
GDP | GDP | CNY 100 million | GDP = Cig |
Change in quota | Ciq | 10,000 tons | Ciq = Qrr × Tq |
Total quota | Tq | 10,000 tons | Tq = Ciq |
Auctioned quota | Aq | 10,000 tons | Aq = Qa × Tq |
Auction cost | Ac | CNY 100 million | Ac = Ap × Aq/10,000 |
Fines | Fin | CNY 100 million | Fin = Ps × Cp × Ece/10,000 |
Excess carbon emissions | Ece | 10,000 tons | Ece = Ce − Tq − Ctv |
CCER transaction volume | Ctv | 10,000 tons | Ctv = Ce × Ceror |
Cost of carbon emissions | Tcoce | CNY 100 million | Tcoce = Ctv × Cerp/10,000 + Ac + Fin |
Energy consumption of the primary industry | Ecotpi | 10,000 tons of standard coal | Ecotpi = Eiotpi × Votpi |
Energy consumption of the secondary industry | Ecotsi | 10,000 tons of standard coal | Ecotsi = Eiotsi × Votsi |
Energy consumption of the tertiary industry | Ecotti | 10,000 tons of standard coal | Ecotti = Eiotti × Votti |
Total residential energy consumption | Trec | 10,000 tons of standard coal | Trec = Pcec × Tp |
Total energy consumption | Tec | 10,000 tons of standard coal | Tec = Trec + Ecotpi + Ecotsi + Ecotti |
Carbon Emission | Ce | 10,000 tons | Ce = Ceefr × Tec |
GDP per capita | Gpc | CNY 100 million/10,000 people | Gpc = GDP/Tp |
Total population | Tp | 10,000 people | Tp = Cip |
Total residential energy consumption | Trec | 10,000 tons of standard coal | Trec = Pcec × Tp |
Change in population | Cip | 10,000 people | Cip = Pgr × Tp |
Value of the primary industry | Votpi | CNY 100 million | Votpi = 0.0594 × GDP + 14,981 (R2 = 0.98) |
Value of the secondary industry | Votsi | CNY 100 million | Votsi = 0.349 × GDP + 45,668 (R2 = 0.99) |
Value of the tertiary industry | Votti | CNY 100 million | Votti = 0.5916 × GDP + 60,649 (R2 = 0.99) |
Technology investment | Ti | CNY 100 million | Ti = 1781.8 × (Time − 2007) + 1328.3 (R2 = 0.97) |
Per capita energy consumption | Pcec | 10,000 tons of standard coal/10,000 people | Pcec = 0.0408 × Gpc + 0.1574 (R2 = 0.99) |
Energy intensity of the primary industry | Eiotpi | 10,000 tons of standard coal/CNY 100 million | IF THEN ELSE (Time < 2017, −0.037 × LN (Time−2006) + 0.222, −0.087 × LN (Time − 2006) + 0.3439) (R2 = 0.94; R2 = 0.98) |
Energy intensity of the secondary industry | Eiotsi | 10,000 tons of standard coal/CNY 100 million | −0.316 × LN(Time − 2006) + 1.6877 (R2 = 0.96) |
Energy intensity of the tertiary industry | Eiotti | 10,000 tons of standard coal/CNY 100 million | −0.071 × LN(Time − 2006) + 0.345 (R2 = 0.95) |
Per capita energy consumption | Pcec | 10,000 tons of standard coal/10,000 people | 0.0408 × Gpc + 0.1574 (R2 = 0.99) |
Population growth rate | Pgr | Dmnl | exogenous variables, sources: China Statistical Yearbook |
GDP growth rate | Ggr | Dmnl | exogenous variables, sources: China Statistical Yearbook |
Technological-driven coefficient | Tdc | Dmnl | exogenous variables, sources: reference [35] |
Carbon price (S0) | Cp | CNY/ton | exogenous variables, sources: reference [36] |
Quota auction (S0) | Qa | Dmul | exogenous variables, sources: reference [37] |
CCER price (S0) | Cerp | CNY/ton | exogenous variables, sources: reference [38] |
CCER offset ratio (S0) | Ceror | Dmul | exogenous variables, sources: reference [39] |
Quota reduction rate (S0) | Qrr | Dmul | exogenous variables, sources: reference [40,41] |
Penalty severity (S0) | Ps | Dmul | exogenous variables, sources: reference [42] |
Year | GDP (CNY 100 Million) | Carbon Emissions (10,000 Tons) | Total Energy Consumption (10,000 Tons of Standard Coal) | ||||||
---|---|---|---|---|---|---|---|---|---|
Fitted Value | Actual Value | Error | Fitted Value | Actual Value | Error | Fitted Value | Actual Value | Error | |
2008 | 319,245 | 319,244.61 | 0.00% | 789,379 | 734,321.02 | −7.50% | 344,650 | 320,611 | −7.50% |
2009 | 348,518 | 348,517.74 | 0.00% | 762,673 | 769,194.1 | 0.85% | 336,787 | 336,126 | −0.20% |
2010 | 380,476 | 412,119.26 | 7.68% | 760,476 | 812,900.59 | 6.45% | 336,106 | 360,648 | 6.80% |
2011 | 449,909 | 487,940.18 | 7.79% | 803,195 | 881,567.84 | 8.89% | 360,406 | 387,043 | 6.88% |
2012 | 532,682 | 538,579.95 | 1.10% | 881,958 | 900,885.63 | 2.10% | 391,630 | 402,138 | 2.61% |
2013 | 587,966 | 592,963.23 | 0.84% | 900,147 | 926,143.05 | 2.81% | 406,391 | 416,913 | 2.52% |
2014 | 647,336 | 643,563.1 | −0.59% | 929,262 | 937,297.59 | 0.86% | 423,087 | 428,334 | 1.22% |
2015 | 702,575 | 688,858.22 | −1.99% | 946,073 | 938,369.98 | −0.82% | 437,275 | 434,113 | −0.73% |
2016 | 752,024 | 746,395.06 | −0.75% | 958,019 | 940,585.46 | −1.85% | 448,258 | 441,492 | −1.53% |
2017 | 814,836 | 832,035.95 | 2.07% | 980,792 | 959,711.84 | −2.20% | 465,614 | 455,827 | −2.15% |
2018 | 908,330 | 919,281.13 | 1.19% | 1,032,960 | 979,055.61 | −5.51% | 496,213 | 471,925 | −5.15% |
2019 | 1,003,570 | 986,515.2 | −1.73% | 1,079,430 | 998,804.41 | −8.07% | 526,243 | 487,488 | −7.95% |
2020 | 1,076,970 | 1,013,567 | −6.26% | 1,104,860 | 1,011,637.22 | −9.22% | 545,399 | 498,314 | −9.45% |
2021 | 1,106,510 | 1,149,236.98 | 3.72% | 1,093,970 | 1,054,989.45 | −3.69% | 545,016 | 525,896 | −3.64% |
2022 | 1,254,620 | 1,204,724 | −4.14% | 1,179,190 | 1,078,169.72 | −9.37% | 594,511 | 541,000 | −9.89% |
Average Error | 2.66% | 4.68% | 4.55% |
Scenario Settings | Policy Settings | ||||||
---|---|---|---|---|---|---|---|
Carbon Prices (CNY/Ton) | Quota Auctions | CCER Prices (CNY/Ton) | CCER Offset Ratios | Quota Reduction Rates | Penalty Severities | ||
Baseline Scenario | BAU | 0 | 0 | 0 | 0 | 0 | 0 |
Classic Scenario | S0 | 85 | 5% | 65 | 10% | 2.5% | 7 |
Carbon prices | A1 | 35 | 5% | 65 | 10% | 2.5% | 7 |
A2 | 60 | 5% | 65 | 10% | 2.5% | 7 | |
A3 | 110 | 5% | 65 | 10% | 2.5% | 7 | |
A4 | 135 | 5% | 65 | 10% | 2.5% | 7 | |
Quota auctions | B1 | 85 | 1% | 65 | 10% | 2.5% | 7 |
B2 | 85 | 10% | 65 | 10% | 2.5% | 7 | |
B3 | 85 | 15% | 65 | 10% | 2.5% | 7 | |
B4 | 85 | 20% | 65 | 10% | 2.5% | 7 | |
CCER prices | C1 | 85 | 5% | 35 | 10% | 2.5% | 7 |
C2 | 85 | 5% | 50 | 10% | 2.5% | 7 | |
C3 | 85 | 5% | 80 | 10% | 2.5% | 7 | |
C4 | 85 | 5% | 95 | 10% | 2.5% | 7 | |
CCER offset ratios | D1 | 85 | 5% | 65 | 5% | 2.5% | 7 |
D2 | 85 | 5% | 65 | 15% | 2.5% | 7 | |
D3 | 85 | 5% | 65 | 20% | 2.5% | 7 | |
D4 | 85 | 5% | 65 | 25% | 2.5% | 7 | |
Quota reduction rates | E1 | 85 | 5% | 65 | 10% | 0.5% | 7 |
E2 | 85 | 5% | 65 | 10% | 1.5% | 7 | |
E3 | 85 | 5% | 65 | 10% | 3.5% | 7 | |
E4 | 85 | 5% | 65 | 10% | 4.5% | 7 | |
Penalty severities | F1 | 85 | 5% | 65 | 10% | 2.5% | 5 |
F2 | 85 | 5% | 65 | 10% | 2.5% | 6 | |
F3 | 85 | 5% | 65 | 10% | 2.5% | 8 | |
F4 | 85 | 5% | 65 | 10% | 2.5% | 9 |
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Ma, Y.; Miao, L.; Feng, L. Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market. Sustainability 2025, 17, 6955. https://doi.org/10.3390/su17156955
Ma Y, Miao L, Feng L. Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market. Sustainability. 2025; 17(15):6955. https://doi.org/10.3390/su17156955
Chicago/Turabian StyleMa, Yue, Ling Miao, and Lianyong Feng. 2025. "Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market" Sustainability 17, no. 15: 6955. https://doi.org/10.3390/su17156955
APA StyleMa, Y., Miao, L., & Feng, L. (2025). Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market. Sustainability, 17(15), 6955. https://doi.org/10.3390/su17156955