低碳转型下的中国能源市场风险联动特征研究

盖书文, 刘启航, 李思博

系统科学与数学 ›› 2025, Vol. 45 ›› Issue (2) : 357-375.

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系统科学与数学 ›› 2025, Vol. 45 ›› Issue (2) : 357-375. DOI: 10.12341/jssms240083

低碳转型下的中国能源市场风险联动特征研究

    盖书文1, 刘启航2, 李思博1
作者信息 +

Research on the Risk Linkage Characteristics of China's Energy Market Under the Low-Carbon Transition

    GAI Shuwen1, LIU Qihang2, LI Sibo1
Author information +
文章历史 +

摘要

随着页岩油革命的兴起以及中国能源结构低碳转型进程的加快,清洁能源在中国能源市场中的作用愈发显著.及时发现中国主要能源市场间的风险联动特征及其演变规律,对于中国能源市场的可持续发展具有重要的现实价值. 基于此,文章利用带有结构变点的协整检验方法以及DCC-GARCH-SJC-Copula的研究框架从不同视角对中国主要的六大类细分能源市场进行了详细分析.研究表明,在中国的能源系统中普遍存在着结构突变、动态调整以及非对称性的尾部相依结构等风险特征.OPEC限产、天然气定价改革、可再生能源补贴政策调整等重要宏观事件的发生对中国能源系统的长期联动关系的结构性调整具有显著影响.从测算结果来看, 结构变点前后各能源市场呈现出了差异性的风险特征.其中, 风能太阳能市场间具有最强的风险联动关系, 天然气煤炭市场最弱.油气市场间近年来出现了明显的“脱钩”现象.相对于不可再生能源市场,可再生能源市场表现出了更为显著的风险特征.此外值得注意的是, 受政府管制以及定价机制等因素的影响,在极端事件发生的情境下各能源市场价格间同时“暴跌”的风险要显著大于其同时“暴涨”的风险.据此, 文章提出了系列政策建议,以期为中国能源体系的定价改革提供重要参考.

Abstract

With the rise of shale oil industry and the acceleration of low-carbon transformation process of China's energy structure, the role of clean energy in China's energy market has become increasingly significant. The timely discovery of the risk linkage characteristics and evolution rules of China's major energy markets has important practical value for the sustainable development of China's energy system. Based on this, this paper uses the co-integration test method with structural change points and the research framework of DCC-GARCH-SJC-Copula to conduct a detailed analysis of China's six major segmented energy markets from different perspectives. The research results show that the risk characteristics of structural mutations, dynamic adjustments, and asymmetric tail-dependent structures are common in China's energy system. The occurrence of major macro events such as OPEC production restriction, natural gas pricing reform, and renewable energy subsidy policy adjustments have significantly affected the structural changes in the long-term linkage relationship of China's energy system. According to the calculation results, various energy markets show different risk characteristics before and after the structural change point. Among them, the wind-solar market has the strongest risk linkage correlation, while the natural gas-coal market is the weakest. The crude oil and natural gas markets have “decoupled” significantly in recent years. Compared with the non-renewable energy market, the renewable energy market shows more significant risk characteristics. In addition, it is worth noting that under the influence of government regulation, pricing mechanism and other factors, the risk of simultaneous “slumping” of energy market prices in the context of extreme events is significantly greater than the risk of simultaneous “surging”. Based on this, this paper puts forward a series of policy suggestions with a view to providing important reference for the pricing reform of China's energy system.

关键词

能源市场改革 / 能源结构 / 风险特征 / DCC-GARCH / SJC-Copula

Key words

Energy market reform / energy structure / risk characteristics / DCC-GARCH / SJC-Copula

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导出引用
盖书文 , 刘启航 , 李思博. 低碳转型下的中国能源市场风险联动特征研究. 系统科学与数学, 2025, 45(2): 357-375. https://doi.org/10.12341/jssms240083
GAI Shuwen , LIU Qihang , LI Sibo. Research on the Risk Linkage Characteristics of China's Energy Market Under the Low-Carbon Transition. Journal of Systems Science and Mathematical Sciences, 2025, 45(2): 357-375 https://doi.org/10.12341/jssms240083
中图分类号： 91B84   

参考文献

[1] BP statistical review of world energy-special topic in China. 2021, https://www.bp.com.cn/content/dam/bp/country-sites/zh_cn/china/home/reports/statistical-review-of-world-energy/2021/Statistical-Review-of-World-Energy-2021-China.pdf/.
[2] BP energy outlook. 2023, https://www.bp.com.cn/content/dam/bp/country-sites/zh_cn/china/home/news/2023/pdf/bp-energy-outlook-2023-CN-.pdf.
[3] Sharifzadeh M, Hien R K T, Shah N. China's roadmap to low-carbon electricity and water:Disentangling greenhouse gas (GHG) emissions from electricity-water nexus via renewable wind and solar power generation, and carbon capture and storage. Applied Energy, 2019, 235:31-42.
[4] Destek M A, Sinha A. Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint:Evidence from organisation for economic co-operation and development countries. Journal of Cleaner Production, 2020, 242:118537.
[5] Yao S, Zhang S, Zhang X. Renewable energy, carbon emission and economic growth:A revised environmental Kuznets curve perspective. Journal of Cleaner Production, 2019, 235:1338-1352.
[6] Khuong P M, McKenna R, Fichtner W. Analyzing drivers of renewable energy development in Southeast Asia countries with correlation and decomposition methods. Journal of Cleaner Production, 2019, 213:710-722.
[7] Bórawski P, Bełdycka-Bórawska A, Szymańska E J, et al. Development of renewable energy sources market and biofuels in the European Union. Journal of Cleaner Production, 2019, 228:467-484.
[8] Yang F, Cheng Y, Yao X. Influencing factors of energy technical innovation in China:Evidence from fossil energy and renewable energy. Journal of Cleaner Production, 2019, 232:57-66.
[9] Li K, Liu G, Shrestha A, et al. The role of local private participation in China's transition to domestically developed renewable energy technologies. Journal of Cleaner Production, 2018, 173:217-224.
[10] Zhang H, Lu Z, Hu W, et al. Coordinated optimal operation of hydro-wind-solar integrated systems. Applied Energy, 2019, 242:883-896.
[11] Hu W, Zhang H X, Dong Y, et al. Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks. Applied Energy, 2019, 250:389-403.
[12] Han S, Zhang L, Liu Y, et al. Quantitative evaluation method for the complementarity of wind-solarhydro power and optimization of wind-solar ratio. Applied Energy, 2019, 236:973-984.
[13] Eising M, Hobbie H, Möst D. Future wind and solar power market values in Germany-Evidence of spatial and technological dependencies?. Energy Economics, 2020, 86:104638.
[14] Abrell J, Kosch M, Rausch S. Carbon abatement with renewables:Evaluating wind and solar subsidies in Germany and Spain. Journal of Public Economics, 2019, 169:172-202.
[15] Zhou S, Wang Y, Zhou Y, et al. Roles of wind and solar energy in China's power sector:Implications of intermittency constraints. Applied Energy, 2018, 213:22-30.
[16] Schmidt J, Cancella R, Pereira Jr A O. The role of wind power and solar PV in reducing risks in the Brazilian hydro-thermal power system. Energy, 2016, 115(3):1748-1757.
[17] Csereklyei Z, Qu S, Ancev T. The effect of wind and solar power generation on wholesale electricity prices in Australia. Energy Policy, 2019, 131:358-369.
[18] Pimenta F M, Assireu A T. Simulating reservoir storage for a wind-hydro hydrid system. Renewable Energy, 2015, 76:757-767.
[19] Xu J, Wang F, Lü C, et al. Carbon emission reduction and reliable power supply equilibrium based daily scheduling towards hydro-thermal-wind generation system:A perspective from China. Energy Conversion and Management, 2018, 164:1-14.
[20] Xu B, Zhu F, Zhong P, et al. Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming. Applied Energy, 2019, 253:113535.
[21] Ilak P, Rajšl I, Krajcar S, et al. The impact of a wind variable generation on the hydro generation water shadow price. Applied Energy, 2015, 154:197-208.
[22] Ming B, Liu P, Guo S, et al. Hydropower reservoir reoperation to adapt to large-scale photovoltaic power generation. Energy, 2019, 179:268-279.
[23] Tarroja B, Forrest K, Chiang F, et al. Implications of hydropower variability from climate change for a future, highly-renewable electric grid in California. Applied Energy, 2019, 237:353-366.
[24] Liu Z, Zhang Z, Zhuo R, et al. Optimal operation of independent regional power grid with multiple wind-solar-hydro-battery power. Applied Energy, 2019, 235:1541-1550.
[25] Marques A C, Fuinhas J A, Pereira D A. Have fossil fuels been substituted by renewables?An empirical assessment for 10 European countries. Energy Policy, 2018, 116:257-265.
[26] Zhang J, Zheng Y N. The flexibility pathways for integrating renewable energy into China's coal dominated power system:The case of Beijing-Tianjin-Hebei Region. Journal of Cleaner Production, 2020, 245:118925.
[27] 王茹.人与自然和谐共生的现代化:历史成就、矛盾挑战与实现路径.管理世界, 2023, 39(3):19-30.(Wang R. The modernization of harmonious coexistence between humanity and nature:Historical achievements, contradictory challenges and realization paths. Journal of Management World, 2023, 39(3):19-30.)
[28] 张希良,张达,余润心.中国特色全国碳市场设计理论与实践.管理世界, 2021, 37(8):80-95.(Zhang X L, Zhang D, Yu R X. Theory and practice of China's national carbon emissions trading system. Journal of Management World, 2021, 37(8):80-95.)
[29] Leibowicz B D. Growth and competition in renewable energy industries:Insights from an integrated assessment model with strategic firms. Energy Economics, 2015, 52:13-25.
[30] 靳玮,王弟海,张林.碳中和背景下的中国经济低碳转型:特征事实与机制分析.经济研究, 2022, 57(12):87-103.(Jin W, Wang D H, Zhang L. China's low-carbon economic transitions towards carbon neutrality:Characteristics and mechanisms. Economic Research Journal, 2022, 57(12):87-103.)
[31] Bachmeier L J, Griffn J M. Testing for market integration crude oil, coal, and natural gas. The Energy Journal, 2006, 27(2):55-71.
[32] Batten J A, Ciner C, Lucey B M. The dynamic linkages between crude oil and natural gas markets. Energy Economics, 2017, 62:155-170.
[33] Aloui R, Aïssa M S B, Hammoudeh S, et al. Dependence and extreme dependence of crude oil and natural gas prices with applications to risk management. Energy Economics, 2014, 42:332-342.
[34] Zhu F, Zhu Y, Jin X, et al. Do spillover effects between crude oil and natural gas markets disappear?Evidence from option markets. Finance Research Letters, 2018, 24:25-33.
[35] Ji Q, Geng J B, Fan Y. Separated influence of crude oil prices on regional natural gas import prices. Energy Policy, 2014, 70:96-105.
[36] Geng J B, Ji Q, Fan Y. The impact of the North American shale gas revolution on regional natural gas markets:Evidence from the regime-switching model. Energy Policy, 2016, 96:167-178.
[37] Brigida M. The switching relationship between natural gas and crude oil prices. Energy Economics, 2014, 43:48-55.
[38] Onour I A. Natural gas markets:How sensitive are they to crude oil price changes?. OPEC Energy Review, 2009, 33(2):111-124.
[39] 柴建,毛敏.不同省份和区域间天然气需求驱动路径及差异性分析——基于贝叶斯结构方程模型.系统科学与数学, 2020, 40(12):2431-2444.(Chai J, Mao M. Natural gas demand driven paths and difference between different provinces and regions. Journal of Systems Science and Mathematical Sciences, 2020, 40(12):2431-2444.)
[40] 柴建,赵逸杰,梁婷,等.中国非居民用户用气量的行业异质性动态分析及预测——基于一种新的综合集成分析预测模型框架.系统科学与数学, 2022, 42(2):318-336.(Chai J, Zhao Y J, Liang T, et al. Dynamic analysis and forecast of industry heterogeneity of natural gas consumption by Chinese non-resident users-Based on a new integrated analysis and forecasting model framework. Journal of Systems Science and Mathematical Sciences, 2022, 42(2):318-336.)
[41] Lin B, Li J. The spillover effects across natural gas and oil markets:Based on the VEC-MGARCH framework. Applied Energy, 2015, 155:229-241.
[42] Caporin M, Fontini F. The long-run oil-natural gas price relationship and the shale gas revolution. Energy Economics, 2017, 64:511-519.
[43] Zhang J, Leng R X, Chen M Y, et al. The future role of nuclear power in the coal dominated power system:The case of Shandong. Journal of Cleaner Production, 2020, 256:120744.
[44] Figueiredo R, Nunes P, Meireles M, et al. Replacing coal-fired power plants by photovoltaics in the Portuguese electricity system. Journal of Cleaner Production, 2019, 222:129-142.
[45] Chen X M, Liang Q M, Liu L C, et al. Critical structural adjustment for controlling China's coal demand. Journal of Cleaner Production, 2019, 235:317-327.
[46] 潘冬阳,陈川祺, Grubb M.金融政策与经济低碳转型——基于增长视角的研究.金融研究, 2021,(12):1-19.(Pan D Y, Chen C Q, Grubb M. Financial policy and low-carbon transition of the economy:A growth perspective. Journal of Financial Research, 2021,(12):1-19.)
[47] Wang X P, Zhang Z M, Guo Z H, et al. Energy structure transformation in the context of carbon neutralization:Evolutionary game analysis based on inclusive development of coal and clean energy. Journal of Cleaner Production, 2023, 398:136626.
[48] Jiao Y Q, Su M, Ji C W, et al. Cleaner production instruments assisting sustainable transition at urban scale:A case study of Dongguan, a typical manufacturing city in China. Journal of Cleaner Production, 2019, 210:1449-1461.
[49] Wang Q, Wang S S. Is energy transition promoting the decoupling economic growth from emission growth?Evidence from the 186 countries. Journal of Cleaner Production, 2020, 260:120768.
[50] Bao C, Xu M T. Cause and effect of renewable energy consumption on urbanization and economic growth in China's provinces and regions. Journal of Cleaner Production, 2019, 231:483-493.
[51] 张云丰,秦滢,龚本刚.碳税政策下三级供应链碳减排与定价决策及社会福利研究.系统科学与数学, 2023, 43(5):1177-1206.(Zhang Y F, Qin Y, Gong B G. Research on carbon emission reduction, pricing and social welfare of the three-echelon supply chain under the carbon tax policy. Journal of Systems Science and Mathematical Sciences, 2023, 43(5):1177-1206.)
[52] 段玉婉,蔡龙飞,陈一文.全球化背景下中国碳市场的减排和福利效应.经济研究, 2023, 58(7):121-138.(Duan Y W, Cai L F, Chen Y W. The emission and welfare effects of China's carbonmarket in the context of globalization. Economic Research Journal, 2023, 58(7):121-138.)
[53] 宋卓,王红蕾.碳排放配额约束下可再生能源电价补贴机制优化研究.系统科学与数学, 2021, 41(4):1007-1023.(Song Z, Wang H L. Study on the optimization of price subsidy mechanism for renewable energy under carbon emission quotas. Journal of Systems Science and Mathematical Sciences, 2021, 41(4):1007-1023.)
[54] Wang, J M, Wang L X, Wan H. Study on the impact of reducing fossil energy use on China's existing economic structure under carbon neutrality goals. Journal of Cleaner Production, 2024, 449:141819.
[55] Hu Y J, Zhang R, Wang H L, et al. Synergizing policies for carbon reduction, energy transition and pollution control:Evidence from Chinese power generation industry. Journal of Cleaner Production, 2024, 436:140460.
[56] Wen S B, Liu H M. Research on energy conservation and carbon emission reduction effects and mechanism:Quasi-experimental evidence from China. Energy Policy, 2022, 169:113180.
[57] Gregory A W, Hansen B E. Tests for cointegration in models with regime and trend shifts. Oxford Bulletin of Economics and Statistics, 1996, 58(3):555-560.
[58] Engle R. Dynamic conditional correlation:A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business&Economic Statistics, 2002, 20(3):339-350.
[59] Patton A J. Modelling asymmetric exchange rate dependence. International Economic Review, 2006, 47(2):527-556.
[60] Renewables global status report. 2016, https://www.ren21.net/wp-content/uploads/2019/05/REN21_GSR2016_FullReport_en_11.pdf.
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