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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

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Front. Eng    2024, Vol. 11 Issue (3) : 430-454    https://doi.org/10.1007/s42524-023-0295-8
Energy and Environmental Systems
How to auction carbon emission allowances? A dynamic simulation analysis of spatiotemporal heterogeneity
Xianyu YU1, Luxi XU2, Dequn ZHOU1, Qunwei WANG1(), Xiuzhi SANG2(), Xinhuan HUANG
1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3. School of Economics, Fujian Normal University, Fuzhou 350117, China
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Abstract

There is notable variability in carbon emission reduction efforts across different provinces in China, underscoring the need for effective strategies to implement carbon emission allowance auctions. These auctions, as opposed to free allocations, could be more aligned with the principle of “polluter pays.” Focusing on three diverse regions — Ningxia, Beijing, and Zhejiang — this study employs a system dynamics simulation model to explore markets for carbon emissions and green certificates trading. The aim is to determine the optimal timing and appropriate policy intensities for auction introduction. Key findings include: (1) Optimal auction strategies differ among the provinces, recommending immediate implementation in Beijing, followed by Ningxia and Zhejiang. (2) In Ningxia, there’s a potential for a 6.20% increase in GDP alongside a 21.59% reduction in carbon emissions, suggesting a feasible harmony between environmental and economic objectives. (3) Market-related policy variables, such as total carbon allowances and Renewable Portfolio Standards, significantly influence the optimal auction strategies but have minimal effect on carbon auction prices.

Keywords carbon allowances      carbon allowance auctions      carbon emissions trading      Renewable Portfolio Standard      system dynamics     
Corresponding Author(s): Qunwei WANG,Xiuzhi SANG   
Just Accepted Date: 29 May 2024   Online First Date: 07 June 2024    Issue Date: 26 September 2024
 Cite this article:   
Xianyu YU,Luxi XU,Dequn ZHOU, et al. How to auction carbon emission allowances? A dynamic simulation analysis of spatiotemporal heterogeneity[J]. Front. Eng, 2024, 11(3): 430-454.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0295-8
https://academic.hep.com.cn/fem/EN/Y2024/V11/I3/430
Phase Time Contents
Pilot phase 2005–2007 CEAs were mainly allocated free of charge and the CEAs from auctions were no more than 5%
Optimization phase 2008–2012 The proportion of CEAs from auctions was gradually increased to 10%
Maturity phase 2013–2020 The proportion of CEAs from auctions will exceed 50% of the EU market-wide CEAs, with all CEAs from auctions in the power sector and more than 70% in the manufacturing sector by 2020
Promotion phase 2021 and so far Free allocation will be nearly canceled
Tab.1  Development of EU carbon market (Schiavo, 2012)
Fig.1  Research concept framework.
Fig.2  The research framework.
Fig.3  Causal diagram of regional carbon emissions.
Fig.4  Stock-flow diagram.
Fig.5  Provincial economic level and resource endowment.
Type Factor Unit Value/Equation Data sources
Constants CECoef t/TCE 1.229 MEE
TGC price yuan/a 202.9 NCETN
Carbon price yuan/t 62.1 NCETN
PSREF / 8.485 NDRC
CorrCoef / 1.0374 NDRC
Flows GDP growth BCNY 7.880 + 0.004 × Profits – 0.0004 × EnvCost NBS
RPS variation / TGC policy (Time) Endogenous
Stocks GDP BCNY d(GDP)/dt = GDP growth NBS
RPS / d(RPS)/dt = RPS variation MEE
Variables Profits BCNY 0.1 × GDP – 0.1 × AbsCost Endogenous
EE TCE/BKWH (22–15e–0.925+0.000136GDP)/(1 + e–0.925+0.000136GDP) NBS
RECsm GWH 480.32 × log(GDP) – 3703.7 NBS
TECsm GWH TCsm – RECsm NBS
EnvCost BCNY 0.0258 × CE – 78.215 MEE
CE 10 kt TECsm × CECoef × EE NBS
REAbs GWH TCsm × RPS MEE
TVol billion (REAbs – RECsm)/10000 Endogenous
UnQty billion (REAbs – RECsm)/10000 – TVol Endogenous
AbsCost BCNY TVol × TGC price – UnQty × TPEN price Endogenous
TPEN price yuan/a 4 × TGC price Endogenous
Free CEA 10 kt CE × Free ratio Endogenous
CVol 10 kt Total CEA – Free CEA Endogenous
CPEN 10 kt CE – Free CEA – CVol Endogenous
CECost BCNY CVol × Carbon price + CPEN × CPEN price Endogenous
CPEN price yuan/t 4 × Carbon price Endogenous
Tab.2  Settings of the main variables and parameters
Year GDP (billion yuan) Renewable energy electricity consumption (billion kW·h)
Fitted value Actual value Error Fitted value Actual value Error
Ningxia 2015 293.4517 291.177 0.78% 11.6363 11.8 –1.39%
2016 312.6148 316.859 –1.34% 16.9842 16.9 0.50%
2017 332.5961 344.356 –3.42% 20.9675 20.6 1.78%
2018 353.3388 370.518 –4.64% 23.3821 23.747 –1.54%
2019 374.8553 374.848 0.00% 24.0348 23.1 4.05%
Beijing 2015 2342.0592 2301.459 1.76% 7.2097 7.2 0.13%
2016 2570.7622 2566.913 0.15% 9.4502 9.1 3.85%
2017 2820.8635 2801.494 0.69% 11.4932 11.1 3.54%
2018 3094.3155 3031.998 2.06% 13.2401 13.342 –0.76%
2019 3393.4213 3537.128 –4.06% 14.5686 14.1 3.32%
Zhejiang 2015 4394.273 4288.649 2.46% 8.7993 8.4 4.75%
2016 4804.552 4725.136 1.68% 12.8563 13.8 –6.84%
2017 5250.605 5176.826 1.43% 17.7255 17.6 0.71%
2018 5735.449 5619.715 2.06% 23.5598 23.956 –1.65%
2019 6262.885 6235.174 0.44% 30.5473 31.9 –4.24%
Tab.3  Validity test results
Scenarios Phase II Phase III Phase IV
BAU
1 2022 2024 2026
2 2022 2024 2028
3 2022 2024 2030
4 2022 2026 2028
5 2022 2026 2030
6 2022 2028 2030
7 2022 2030
8 2024 2026 2028
9 2024 2026 2030
10 2024 2028 2030
11 2024 2030
12 2026 2028 2030
13 2026 2030
14 2028 2030
15 2030
Tab.4  Scenario settings of implementation time
Variables Scenarios Policy scenarios
Carbon price Reduction rate of total CEA RPS
Ningxia Beijing Zhejiang
Baseline scenario BAU 62.1 0 20% 15% 7.5%
Carbon price A1 49.68 0 20% 15% 7.5%
A2 55.89 0 20% 15% 7.5%
A3 68.31 0 20% 15% 7.5%
A4 74.52 0 20% 15% 7.5%
Reduction rate of total CEAs B1 62.1 1% 20% 15% 7.5%
B2 62.1 2% 20% 15% 7.5%
B3 62.1 3% 20% 15% 7.5%
RPS C1 62.1 0 25% 20% 12.5%
C2 62.1 0 30% 25% 17.5%
Tab.5  Policy simulation scenarios
Fig.6  The trend of CO2 emissions under different scenarios.
Scenario CO2 emissions GDP di+ di C i Rank
1 0 1 1 1 0.5 9
2 0.128658 0.872326 0.880646 0.881763 0.500317 8
3 0.26597 0.735746 0.780148 0.782344 0.500703 6
4 0.348192 0.653806 0.73804 0.740743 0.500914 4
5 0.488277 0.513928 0.705781 0.708898 0.501102 1
6 0.723717 0.278053 0.773007 0.775293 0.500738 5
8 0.481033 0.52117 0.706119 0.709233 0.501100 2
9 0.619815 0.382269 0.72535 0.728217 0.500986 3
10 0.856512 0.144578 0.867373 0.868629 0.500362 7
11 1 0 1 1 0.5 9
Tab.6  TOPSIS evaluation results
Fig.7  CO2 emissions, GDP, and carbon trading of BAU and S5.
Fig.8  CO2 emissions in Beijing.
Scenario CO2 emissions GDP di+ di C i Rank
1 0 1 1 1 0.5 3
2 0.368863 0.63114 0.731021 0.731025 0.500001 2
3 0.767393 0.232631 0.801849 0.801878 0.500009 1
4 1 0 1 1 0.5 3
Tab.7  TOPSIS evaluation results
Fig.9  CO2 emissions, GDP, and carbon trading of BAU and S3.
Fig.10  CO2 emissions in Zhejiang.
Scenario CO2 emissions GDP di+ di C i Rank
1 1 0 1 1 0.5 14
2 0.999786 0.04814 0.95186 1.000945 0.512568 13
3 0.999592 0.092151 0.907849 1.00383 0.525104 11
4 0.999379 0.140472 0.859528 1.009203 0.540047 10
5 0.999195 0.182441 0.81756 1.015715 0.554044 8
6 0.998837 0.264775 0.735226 1.033335 0.58428 4
7 0.444536 0.583171 0.694468 0.733281 0.513592 12
8 0.999079 0.209097 0.790903 1.020726 0.56343 7
9 0.998897 0.251031 0.74897 1.029957 0.578976 5
10 0.998549 0.331478 0.668524 1.05213 0.611471 2
11 0.444221 0.648209 0.657759 0.785816 0.544354 9
12 0.998292 0.391342 0.608661 1.072257 0.6379 1
13 0.443941 0.706447 0.628788 0.834357 0.570249 6
14 0.443692 0.75856 0.606442 0.878792 0.591686 3
15 0 1 1 1 0.5 14
Tab.8  TOPSIS evaluation results
Fig.11  CO2 emissions, GDP, and carbon trading of BAU and S12.
Fig.12  CO2 emissions in Ningxia.
Fig.13  CO2 emissions in Beijing.
Fig.14  CO2 emissions in Zhejiang.
Fig.15  CO2 emissions and GDP of S5 and B1–B3 in Ningxia.
Fig.16  TGC trading volume and renewable energy power consumption of S5 and B1–B3 in Ningxia.
Fig.17  CO2 emissions and GDP of S3 and B1–B3 in Beijing.
Fig.18  CO2 emissions and GDP of S12 and B1–B3 in Zhejiang.
Fig.19  CO2 emissions and GDP of S5 and C1–C2 in Ningxia.
Fig.20  TGC trading volume and renewable energy power consumption of S5 and C1–C2 in Ningxia.
Fig.21  CO2 emissions and GDP of S3 and C1–C2 in Beijing.
Fig.22  TGC trading volume and renewable energy power consumption of S3 and C1–C2 in Beijing.
Fig.23  CO2 emissions and GDP of S12 and C1–C2 in Zhejiang.
Fig.24  TGC trading volume and renewable energy power consumption of S12 and C1–C2 in Zhejiang.
Nomenclature
Abbreviations Variables
CET Carbon Emissions Trading
TGC Tradable Green Certificate
RPS Renewable Portfolio Standard
EU European Union
ETS Emissions Trading System
MAC Marginal abatement cost
CEA Carbon Emission Allowance
MEE Ministry of Ecology and Environment of China
NDRC National Development and Reform Commission
NBS National Bureau of Statistics
TCE Tons of coal equivalent
10K-ton Ten thousand tons
NCETN National Carbon Emission Trading Network
GWh Gigawatt hour
B Billion
Parameters
CECoef CO2 emission coefficient
CorrCoef Correction coefficient
Variables
CE CO2 emissions
CVol Carbon trading volume
PSREF Power supply reference value
REAbs Renewable energy to be absorbed
RECsm Renewable energy consumption
TECsm Traditional energy consumption
TCsm Total electricity consumption
EnvCost Environmental cost
AbsCost Absorption cost
CECost Carbon emission cost
EE Energy efficiency
CPEN Carbon penalty
CPEN price Carbon penalty price
TPEN price TGC penalty price
UnQty Unabsorbed quantity
Tvol TGC volume
  
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