Research on Optimization Allocation Scheme of Initial Carbon Emission Quota from the Perspective of Welfare Effect
Open accessAbstract
The initial allocation of carbon emission quotas should be of primary concern when establishing China’s unified carbon emission trading market. Essentially, the issue of national carbon quota allocation is an allocation among China’s provinces. The novel bi-level allocation scheme that is based on weighted voting model is put forward, which divides allocation process into two levels, given that there are great regional differences in China. At the first level, k-means clustering is employed to cluster 29 provinces into four categories that are based on emission abatement responsibility, potential, capacity, pressure, and motivation. Subsequently, the national carbon quotas are allocated to the four classes. At the second level, carbon quotas of a class are allocated to each region in this class. The weighted voting models are constructed for the two levels, where each region selects their preferable scheme from three fundamental allocation schemes that are based on their voting rights. The comprehensive index method quantifies each region’s voting rights, which utilizes the information entropy method at the first level and the analytic hierarchy process (AHP) at the second level. The carbon trading market is simulated and welfare effects obtained from carbon trading market under different allocation schemes are measured to verify the rationality of the proposed model. The results indicate: (1) the emission abatement burdens are borne by all provinces in China, but the burden shares are different, which are related to their respective carbon emission characteristics. (2) The differences in carbon intensity among regions in 2030 have narrowed on the basis of the results of 2005, which means that the proposed scheme can balance corresponding differences. (3) When compared with three fundamental allocation schemes, the bi-level allocation scheme can obtain the most welfare effects, while the differences in the welfare effect among regions under this scheme are the smallest, which indicates that the proposed model is feasible for policy-maker.
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Clustering can help identify permit allocation schemes that maximize social welfare, and supervised learning has been used to predict winners and losers from changing electricity tariff schemes.
“Here, clustering can help identify permit allocation schemes that maximize social welfare [855], and supervised learning has been used to predict winners and losers from changing electricity tariff schemes [302].”
From Tackling climate change with machine learning (2023) · cites this paper as [855]
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
Urban energy use and carbon emissions from cities in China and policy implications
Dhakal, S. (2009) · Energy Policy · doi:10.1016/j.enpol.2009.05.020 · Cited by 831 · see citing works
Drivers of carbon emission intensity change in China
Dong, F., Yu, B., Hadachin, T., et al. (2017) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2017.10.035 · Cited by 449 · see citing works
Energy intensity developments in 40 major economies: Structural change or technology improvement?
Voigt, S., Cian, E. D., Schymura, M., & Verdolini, E. (2013) · Energy Economics · doi:10.1016/j.eneco.2013.10.015 · Cited by 412 · see citing works
Shuai, C., Shen, L., Jiao, L., Wu, Y., & Tan, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.11.029 · Cited by 365 · see citing works
The Cost of Slowing Climate Change: a Survey
Nordhaus, W. D. (1991) · The Energy Journal · doi:10.5547/issn0195-6574-ej-vol12-no1-4 · Cited by 356 · see citing works
Cai, W., Lai, K., Liu, C., Wei, F., Ma, M., Jia, S., Jiang, Z., & Li, L. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.02.069 · Cited by 324 · see citing works
Reestimating the capital stock of China
Jie, X., & Duan, W. (2012) · International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII) · doi:10.1109/iciii.2012.6339806 · Cited by 314 · see citing works
Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method
Zhang, Y., Wang, A.-D., & Da, Y. (2014) · Energy Policy · doi:10.1016/j.enpol.2014.08.006 · Cited by 215 · see citing works
Modeling economic performance of interprovincial CO2 emission reduction quota trading in China
Zhou, P., Zhang, L., Zhou, D., et al. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.04.013 · Cited by 211 · see citing works
Yu, S., Wei, Y., & Wang, K. (2013) · Energy Policy · doi:10.1016/j.enpol.2013.11.025 · Cited by 192 · see citing works
Hao, Y., Peng, H. H., Temulun, T., Liu, L., Mao, J., Lu, Z., & Chen, H. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.10.195 · Cited by 192 · see citing works
Li, W., & Jia, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.086 · Cited by 185 · see citing works
Zhang, Y.-J., & Hao, J.-F. (2017) · Annals of Operations Research · doi:10.1007/s10479-016-2232-2 · Cited by 151 · see citing works
Allocation of carbon permits within a country: a general equilibrium analysis of the United Kingdom
Edwards, T. H., & Hutton, J. P. (2001) · Energy Economics · doi:10.1016/s0140-9883(00)00077-3 · Cited by 136 · see citing works
Song, Y. H., Zhang, M., & Zhou, M. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.02.044 · Cited by 135 · see citing works
Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA
Feng, C., Chu, F., Ding, J., et al. (2014) · Omega · doi:10.1016/j.omega.2014.12.005 · Cited by 130 · see citing works
Pan, X., Teng, F., & Wang, G. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.07.021 · Cited by 121 · see citing works
Qin, Q., Liu, Y., Li, X., et al. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.08.220 · Cited by 121 · see citing works
Understanding volatility dynamics in the EU-ETS market
Sanin, M. E., Violante, F., & Mansanet-Bataller, M. (2015) · Energy Policy · doi:10.1016/j.enpol.2015.02.024 · Cited by 115 · see citing works
Yu, S., Wei, Y., Fan, J., et al. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.11.068 · Cited by 105 · see citing works
Han, R., Tang, B.-J., Fan, J., et al. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.05.001 · Cited by 99 · see citing works
Emissions trading systems and social equity: A CGE assessment for China
Huang, H., Roland-Holst, D. W., Springer, C., et al. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.11.056 · Cited by 89 · see citing works
An, Q., Wen, Y., Xiong, B., et al. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.07.072 · Cited by 85 · see citing works
Zhou, X. Y., James, G., Liebman, A., et al. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2030377 · Cited by 81 · see citing works
Pareto improvement through a reallocation of carbon emission quotas
Pang, R., Deng, Z., & Chiu, Y. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.05.022 · Cited by 80 · see citing works
Short-Run Allocation of Emissions Allowances and Long-Term Goals for Climate Policy
Zetterberg, L., Wråke, M., Sterner, T., Fischer, C., & Burtraw, D. (2012) · Ambio · doi:10.1007/s13280-011-0238-1 · Cited by 79 · see citing works
Low carbon development and local sustainability from a carbon balance perspective
Guo, R., Zhao, Y., Shi, Y. M., Li, F., Hu, J., & Yang, H. (2017) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2017.02.019 · Cited by 79 · see citing works
Xian, Y., Wang, K., Wei, Y., et al. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.11.011 · Cited by 70 · see citing works
A carbon emissions reduction index: Integrating the volume and allocation of regional emissions
Chen, J., Cheng, S., Song, M., et al. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.03.032 · Cited by 69 · see citing works
Wang, S., Chu, C., Chen, G., et al. (2015) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.11.079 · Cited by 68 · see citing works
The effects of carbon reduction on sectoral competitiveness in China: A case of Shanghai
Tian, X., Dai, H., Geng, Y., et al. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.026 · Cited by 67 · see citing works
Resilient Multiscale Coordination Control against Adversarial Nodes
Shang, Y. (2018) · Energies · doi:10.3390/en11071844 · Cited by 66 · see citing works
Pan, X., Teng, F., Ha, Y., et al. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.03.072 · Cited by 64 · see citing works
Decomposing the changes of energy-related carbon emissions in China: evidence from the PDA approach
Zhang, Y., & Da, Y. (2013) · Natural Hazards · doi:10.1007/s11069-013-0752-5 · Cited by 62 · see citing works
An Efficiency Evaluation of the EU’s Allocation of Carbon Emission Allowances
Chiu, Y., Lin, J.-C., Su, W.-N. J., et al. (2014) · Energy Sources Part B Economics Planning and Policy · doi:10.1080/15567249.2010.527900 · Cited by 60 · see citing works
Allocation of emission permits for China’s power plants: A systemic Pareto optimal method
Ji, X., Li, G., & Wang, Z.-H. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.033 · Cited by 55 · see citing works
A comparison of carbon allocation schemes: On the equity-efficiency tradeoff
Pan, X., Teng, F., & Wang, G. (2014) · Energy · doi:10.1016/j.energy.2014.05.109 · Cited by 49 · see citing works
Hong, T., Koo, C., & Lee, S. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.10.035 · Cited by 47 · see citing works
Causal relationships of energy consumption, price, and CO2 emissions in the U.S. building sector
Lee, S., & Chong, W. (2016) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2016.01.003 · Cited by 42 · see citing works
A comparability analysis of global burden sharing GHG reduction scenarios
Ciscar, J.-C., Saveyn, B., Soria, A., Szabó, L., Regemorter, D. V., & Ierland, T. V. (2013) · Energy Policy · doi:10.1016/j.enpol.2012.10.044 · Cited by 40 · see citing works
Yang, M., An, Q., Ding, T., et al. (2017) · Annals of Operations Research · doi:10.1007/s10479-017-2682-1 · Cited by 39 · see citing works
Impact of self-healing capability on network robustness
Shang, Y. (2015) · Physical Review E · doi:10.1103/physreve.91.042804 · Cited by 36 · see citing works
Unveiling robustness and heterogeneity through percolation triggered by random-link breakdown
Shang, Y. (2014) · Physical Review E · doi:10.1103/physreve.90.032820 · Cited by 32 · see citing works
Chybowski, L., & Gawdzińska, K. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-31307-8_71 · Cited by 26 · see citing works
Li, H., Lo, K., Wang, M., et al. (2016) · Energies · doi:10.3390/en9070549 · Cited by 24 · see citing works
Wiśnicki, B., Chybowski, L., & Czarnecki, M. (2017) · Management Systems in Production Engineering · doi:10.1515/mspe-2017-0001 · Cited by 24 · see citing works
Multi-criteria Decision making in Components Importance Analysis applied to a Complex Marine System
Chybowski, L., Twardochleb, M., & Wiśnicki, B. (2016) · Naše more · doi:10.17818/nm/2016/4.3 · Cited by 23 · see citing works
Education and Energy Intensity: Simple Economic Modelling and Preliminary Empirical Results
Sequeira, T. N., & Santos, M. S. (2018) · Sustainability · doi:10.3390/su10082625 · Cited by 22 · see citing works
Piao, M., Piao, Y., & Lee, J. Y. (2019) · Symmetry · doi:10.3390/sym11040498 · Cited by 21 · see citing works
On the Present State-of-the-Art of a Component Importance Analysis for Complex Technical Systems
Chybowski, L., & Gawdzińska, K. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-31307-8_70 · Cited by 19 · see citing works
Initial allocation of carbon emission permits in power systems
Wang, T., Wang, X., Gong, Y. L., et al. (2016) · Journal of Modern Power Systems and Clean Energy · doi:10.1007/s40565-016-0194-7 · Cited by 18 · see citing works
Bi, C., Jia, M., & Zeng, J. (2019) · Sustainability · doi:10.3390/su11030629 · Cited by 15 · see citing works
The Shadow Price of Industrial SO_2 Emission:A New Analytic Framework
Zhengge, T. (2010) · China Economic Quarterly · Cited by 8 · see citing works
A carbon-intensity based carbon allowance allocation scheme among enterprises in Shenzhen
Ying, Y., Kong, -, Z., et al. (2016) · 生态经济：英文版 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Chen, S.Y. Shadow price of industrial carbon dioxide: Parametric and non-Parametric method. World Econ. 2010, 33, 93-111.
- Shang, Y.L. Resilient multiscale coordination control against adversarial nodes. Energies 2018, 11, 1844.
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Wu, Q., & Zhang, H.
- Affiliations
- North China Electric Power University (CN)
- Corresponding author
- Zhang, Hongjie
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2019-06-03
- Volume
- 12
- Issue
- 11
- Pages
- 2118
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2947921724
- Cited by
- 10
- References
- 55
- Citations by year
- 2026: 2 · 2025: 1 · 2023: 2 · 2022: 2 · 2021: 3
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the National Social Science Fund (17BGL252)
- the Humanities and Social Sciences Planning Foundation of the Ministry of Education of China (16YJA790052)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable energy and sustainable power systems
- Keywords
- K-means clustering, entropy weight method, analytic hierarchy process, welfare effects, carbon intensity, China carbon market
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