In this study, a rolling-window estimation test is used to examine the dynamic causalities between China's carbon emission price (CEP) and the new energy industry (NEI). According to the results, the CEP affects the NEI in both positive and negative ways over various subperiods. The positive effect of the CEP on the NEI stems from economic slowdowns during external shocks (e.g., the COVID-19 pandemic), whereas funds released from a lower CEP have a negative effect, enabling enterprises to develop new energy. In contrast, the NEI negatively affects the CEP through two channels. A higher cost triggers a decline in the NEI, driving up the CEP, whereas recovery of the NEI suppresses the CEP by reducing fossil fuel demand and emissions. The findings reveal dynamic causalities, enriching the theoretical framework of carbon trading market–new energy industry interactions and offering valuable insights into China's “dual carbon” goals and energy transition strategies. Thus, the following recommendations are proposed for three stakeholders: governments should improve carbon market efficiency, enterprises should transition to low-carbon operations, and research institutes should develop new technologies.
BashirMFPataUKShahzadL. Linking climate change, energy transition and renewable energy investments to combat energy security risks: evidence from top energy consuming economies. Energy2025; 314: 134175.
2.
StroeveJCNotzDDawsonJ, et al.Disappearing landscapes: the Arctic at +2.7°C global warming. Science2025; 387: 616–621.
3.
SuC-WDingY-MWangK-H. The dynamic connectedness in the carbon-clean energy-climate policy-green finance-innovation system. Econ Change Restruct2025; 58: 69.
4.
SuprayitnoDIskandarSDahurandiK, et al.Public Policy in the Era of Climate Change. Adapting Strategies for Sustainable Futures.
5.
KumlerAKravitzBDraxlC, et al.Potential effects of climate change and solar radiation modification on renewable energy resources. Renew Sustain Energy Rev2025; 207: 114934.
6.
FengXZhaoYYanR. Does carbon emission trading policy has emission reduction effect?—an empirical study based on quasi-natural experiment method. J Environ Manag2024; 351: 119791.
7.
WangXWangKXuB, et al.Digitalisation and technological innovation: panaceas for sustainability?Int J Prod Res2025; 63: 6071–6088.
8.
WangK-HJiangX-YLiX. Digital revolution meets ESG: can AI, blockchain and cloud computing enhance ESG performance?Oeconomia Copernicana2025; 16: 593–641.
9.
ZhangDChenXHLauCKM, et al.The causal relationship between green finance and geopolitical risk: implications for environmental management. J Environ Manag2023; 327: 116949.
10.
ChaudhrySMChenXHAhmedR, et al.Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors. Risk Anal2023; 45: 477–495.
11.
MhadhbiM. The interconnected carbon, fossil fuels, and clean energy markets: exploring Europe and China’s perspectives on climate change. Financ Res Lett2024; 62: 105185.
XuanDMaXShangY. Can China’s policy of carbon emission trading promote carbon emission reduction?J Cleaner Prod2020; 270: 122383.
14.
LinBHuangC. Analysis of emission reduction effects of carbon trading: market mechanism or government intervention?Sustain Prod Consumption2022; 33: 28–37.
15.
ChenFChenZZhangX. Belated stock returns for green innovation under carbon emissions trading market. J Corp Financ2024; 85: 102558.
16.
ZhaoYSunHMaD. Exploring the impact of carbon finance policy on photovoltaic market development – an experience from China. Sol Energy2024; 272: 112494.
17.
HanifWArreola HernandezJMensiW, et al.Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices. Energy Econ2021; 101: 105409.
18.
NiuHCaoS. Spillover and dependence between Chinese carbon and new energy stock markets: a cross-quantile perspective. J Cleaner Prod2024; 479: 144027.
19.
SunXFangWGaoX, et al.Complex causalities between the carbon market and the stock markets for energy intensive industries in China. Int Rev Econ Financ2022; 78: 404–417.
20.
QinMZhuYXieX, et al.The impact of climate risk on technological progress under the fourth industrial era. Technol Forecast Soc Change2024; 202: 123325.
21.
ZhaoTKeHZhangN. Comparing the innovation impacts on firms: pilot vs. National carbon emission trading schemes in China. Appl Energy2025; 377: 124414.
22.
ZhouWChiYTangS, et al.The evolution analysis of low-carbon power transition strategies and carbon emission decoupling based on carbon neutrality target. Energy2024; 311: 133298.
23.
LuanLLiuPMeiY. The impact of pilot carbon market on firms’ performance in China. Energy Econ2025; 142: 108164.
24.
WangBDuanM. Have China’s emissions trading systems reduced carbon emissions? Firm-level evidence from the power sector. Appl Energy2025; 378: 124802.
25.
WangDSunYWangY. Comparing the EU and Chinese carbon trading market operations and their spillover effects. J Environ Manag2024; 351: 119795.
26.
ShenYLiangYYangM. Research on the spillover effect among China’s domestic carbon emission trading markets. Renew Sustain Energy Rev2025; 214: 115506.
27.
YangJXiongMLiX. Energy transition policy and rural revitalization of Chinese ethnic regions. Financ Res Lett2025; 71: 106439.
28.
LiuXGuoW. Dynamic impact of green finance on renewable energy development: based on scale, structure, and efficiency perspectives. Renew Energy2025; 238: 121854.
29.
WenCXingYWangT, et al.How do green supply chain management and renewable energy consumption influence carbon emissions in China and India? A comparative analysis. Energy Econ2025; 143: 108186.
30.
ZhangZKhanAAWangZ. How does science and technology finance affect the renewable energy development? Evidence from China. Renew Energy2025; 243: 122605.
31.
HeCZhangW. The impact of carbon emission trading on the financing constraints of high-emission enterprises: evidence from China. Financ Res Lett2024; 67: 105927.
32.
JiC-YTanZ-KChenB-J, et al.The impact of environmental policies on renewable energy investment decisions in the power supply chain. Energy Policy2024; 186: 113987.
33.
XuLYangJ. Carbon pricing policies and renewable energy development: analysis based on cross-country panel data. J Environ Manag2024; 366: 121784.
34.
FuchsMStroebelJTersteggeJ., et al.Carbon Price Uncertainty and Decarbonization Investments. Epub ahead of print September 2024. DOI: 10.3386/w32937.
35.
RenY-SHuynhTLDLiuP-Z, et al.Is the carbon emission trading scheme conducive to promoting energy transition? Some empirical evidence from China. Energy Econ2024; 134: 107629.
36.
SunYWeiYWangY. Do green economy stocks matter for the carbon and energy markets? Evidence of connectedness effects and hedging strategies. China Finance Rev Int2024; 14: 666–693.
37.
WuRQinZ. Asymmetric volatility spillovers among new energy, ESG, green bond and carbon markets. Energy2024; 292: 130504.
38.
KumarSManagiSMatsudaA. Stock prices of clean energy firms, oil and carbon markets: a vector autoregressive analysis. Energy Econ2012; 34: 215–226.
39.
DuttaABouriENoorMH. Return and volatility linkages between CO2 emission and clean energy stock prices. Energy2018; 164: 803–810.
40.
van den BerghJSavinI. Impact of carbon pricing on low-carbon innovation and deep decarbonisation: controversies and path forward. Environ Res Econ2021; 80: 705–715.
41.
QiuLChuLZhouR, et al.How do carbon, stock, and renewable energy markets interact: evidence from Europe. J Cleaner Prod2023; 407: 137106.
42.
LilliestamJPattABersalliG. The effect of carbon pricing on technological change for full energy decarbonization: a review of empirical ex-post evidence. WIRES Climate Change2021; 12: e681.
43.
WeiYZhangJBaiL, et al.Connectedness among El Niño-southern oscillation, carbon emission allowance, crude oil and renewable energy stock markets: time- and frequency-domain evidence based on TVP-VAR model. Renewable Energy2023; 202: 289–309.
44.
LiuFUmairMGaoJ. Assessing oil price volatility co-movement with stock market volatility through quantile regression approach. Res Policy2023; 81: 103375.
45.
WuQYanDUmairM. Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Econ Anal Policy2023; 77: 1103–1114.
46.
WenFZhaoLHeS, et al.Asymmetric relationship between carbon emission trading market and stock market: evidences from China. Energy Econ2020; 91: 104850.
47.
LiuJMaoWQiaoX. Dynamic and asymmetric effects between carbon emission trading, financial uncertainties, and Chinese industry stocks: evidence from quantile-on-quantile and causality-in-quantiles analysis. The North Am J Econ Financ2023; 65: 101883.
48.
ChenZSongPWangB. Carbon emissions trading scheme, energy efficiency and rebound effect – evidence from China’s provincial data. Energy Policy2021; 157: 112507.
49.
LiHZhouDHuJ, et al.Dynamic linkages among oil price, green bond, carbon market and low-carbon footprint company stock price: evidence from the TVP-VAR model. Energy Rep2022; 8: 11249–11258.
50.
DzwigolHKwilinskiALyulyovO, et al.The role of environmental regulations, renewable energy, and energy efficiency in finding the path to green economic growth. Energies2023; 16: 3090.
51.
AnkeC-PMöstD. The expansion of RES and the EU ETS – valuable addition or conflicting instruments?Energy Policy2021; 150: 112125.
52.
XiongYDaiL. Does green finance investment impact on sustainable development: role of technological innovation and renewable energy. Renew Energy2023; 214: 342–349.
53.
HongHJiangLZhangC, et al.Do conventional and new energy stock markets herd differently? Evidence from China. Res Int Bus Financ2024; 67: 102120.
54.
DingQHuangJChenJ. Time-Frequency spillovers and the determinants among fossil energy, clean energy and metal markets. The Energy Journal2023; 44: 259–286.
55.
CoaseRH. The problem of social cost. J Law Econ 2013; 56: 837–877.
56.
ŚleszyńskiJ.Analysis of Ronald Coase’s theorem from the perspective of natural resources management. Econ Environ2024; 88: 750.
57.
DolphinGPollittMGNewberyDM. The political economy of carbon pricing: a panel analysis. Oxf Econ Pap2020; 72: 472–500.
58.
ChenZZhangXChenF. Do carbon emission trading schemes stimulate green innovation in enterprises? Evidence from China. Technol Forecast Soc Change2021; 168: 120744.
59.
BoltonPKacperczykM. Do investors care about carbon risk?J Financ Econ2021; 142: 517–549.
60.
SiemrothCHornufL. Why do retail investors pick green investments? A lab-in-the-field experiment with crowdfunders. J Econ Behav Organ2023; 209: 74–90.
61.
AhmadMPengTAwanA, et al.Policy framework considering resource curse, renewable energy transition, and institutional issues: fostering sustainable development and sustainable natural resource consumption practices. Resources Policy2023; 86: 104173.
62.
GaoJHuaGRandhawaA, et al.Heterogeneous environmental regulations and carbon emission efficiency in China: a perspective of resource endowment. Energy & Environ2024: 0958305X241270274.
63.
Martí-BallesterC-P. Do European renewable energy mutual funds foster the transition to a low-carbon economy?Renew Energy2019; 143: 1299–1309.
64.
D’ArcangeloFMPisuMRajA, et al.Estimating the CO2 emission and revenue effects of carbon pricing: New evidence from a large cross-country dataset. Paris: OECD. Epub ahead of print 21 November2022. DOI: 10.1787/39aa16d4-en.
65.
SimsCAStockJHWatsonMW. Inference in linear time series models with some unit roots. Econometrica1990; 58: 113–144.
TodaHYPhillipsPCB. Vector autoregression and causality: a theoretical overview and simulation study. Econom Rev1994; 13: 259–285.
68.
BalcilarMOzdemirZAArslanturkY. Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics2010; 32: 1398–1410.
69.
BalcilarMOzdemirZA. The export-output growth nexus in Japan: a bootstrap rolling window approach. Empir Econ2013; 44: 639–660.
70.
AndrewsDWK. Tests for parameter instability and structural change with unknown change point. Econometrica1993; 61: 821–856.
71.
AndrewsDWKPlobergerW. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica1994; 62: 1383–1414.
HansonBE. Tests for parameter instability in regressions with I(1) processes. J Bus Econ Stat2002; 20: 45–59.
74.
PesaranMHTimmermannA. Small sample properties of forecasts from autoregressive models under structural breaks. J Econom2005; 129: 183–217.
75.
ZhangHBouteAAcworthW.China’s pilot emissions trading systems and electricity markets (Hubei and Shenzhen) Influence of market structures and market regulations on the carbon market Case study report. 1862–4804, Germany, 2021.
76.
ChenJZhangJ. Periodic study of carbon market fluctuation in China based on H-P filter and ARCHModels: a case study of Shenzhen. Pol J Environ Stud2024; 33: 5027–5035.
77.
HuangWWangQLiH, et al.Review of recent progress of emission trading policy in China. J Cleaner Prod2022; 349: 131480.
78.
ShiHWeiAXuX, et al.A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: a case of Shenzhen’s carbon market in China. J Environ Manag2024; 352: 120131.
79.
PratamaAYAlessandroGNabilaNK, et al.China’s policy developments in the implementation of the Paris agreement: strategies and challenges in addressing the 2020–2024 carbon emission problem. PROIROFONIC2025; 1: 197–205.
80.
LiuT-YMaJ-T. Exchange rate and inflation between China and the United States: a bootstrap rolling-window approach. Econ Syst2024; 48: 101152.
81.
BalcilarMGuptaRMillerSM. Regime switching model of US crude oil and stock market prices: 1859 to 2013. Energy Econ2015; 49: 317–327.
82.
ZhangHYanJYuQ, et al.1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown. Appl Energy2021; 283: 116341.
83.
LiZXingYShaoX, et al.Transitioning the energy landscape: AI’s role in shifting from fossil fuels to renewable energy. Energy Econ2025; 149: 108729.
84.
WangX-QWangJWangK-H, et al.How does trading market promote green technology innovation?Econ Innov New Technol2024; 101420 DOI: 10.1080/10438599.2025.2551760
85.
WangK-HLiS-MLobonţO-R, et al.Is green innovation the “Golden Ticket” in achieving energy security and sustainable development?Econ Anal Policy2025; 87: 297–314.
86.
QinBWangHLiaoY, et al.Challenges and opportunities for long-distance renewable energy transmission in China. Sustain Energy Technol Assess2024; 69: 103925.
87.
WangXWangKSafiA, et al.How is artificial intelligence technology transforming energy security? New evidence from global supply chains. Oeconomia Copernicana2025; 16: 15–35.
88.
GuoJChenZMengJ, et al.Picturing China’s photovoltaic energy future: insights from CMIP6 climate projections. Renew Sustain Energy Rev2024; 189: 114026.
89.
MaWWangK-HLiX. Revolutionizing green finance: the synergistic spillover effects of AI, cloud computing, and block chain. Technol Soc2025; 83: 103030.
90.
XuGYangMLiS, et al.Evaluating the effect of renewable energy investment on renewable energy development in China with panel threshold model. Energy Policy2024; 187: 114029.
