Energy supply and environmental capacity are important material foundations for social and economic development and constraints for scientific and sustainable development, and with achieving the strategic goals of carbon peak and carbon neutral at an early date, too. Therefore, the international community generally regards energy-economic- environment (3E) as a necessary research direction. This paper studies the relationship among effective energy utilization, economic growth protection and ecological environment protection by establishing the contribution rate model of energy consumption factors to economic GDP growth. At the same time, this paper puts forward the government needs to use carbon trading market, carbon tax, such as low carbon management mechanism based on the Pareto Optimality of carbon trading market, prompts the excess money of the country or the region back to actively invest in low-carbon production, lets more energy companies to clean energy and low carbon production transformation, and helps to achieve the goal of regional energy industry carbon neutral.
GreenFSternN. China's new normal: structural change, better growth, and peak emissions. Centre Clim Change Econ Policy2015; 2015: 78–82.
2.
TrackerCA. Improvement in warming out look as India and China move ahead, but Paris agreement gap still looms large. Clim Action Tracker Tech Rep2017; 3: 828–838.
3.
MatthewsHS. The importance of carbon footprint estimation boundaries. Environ Sci Technol2008; 42: 5839–5842.
4.
PalaoFPardoA. Assessing price clustering in European carbon markets. Appl Energy2014; 92: 51–56.
5.
ChevallierJ. On the stochastic properties of carbon futures prices. Soc Sci Electron Publishing2020; 58: 127–153.
6.
LiuQLLeiQXuHM, et al.China's energy revolution strategy into 2030. Conserv Recycl2018; 128: 78–89.
7.
WuBS. Development mechanism of energy industry cluster in new normal economy. Energy Sour Part A: Recovery Util Environ Eff2019a; 41: 2853–2860.
8.
WangJFengLPalmer PaulI. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature2020; 586: 66–70.
9.
MojtabaKAlirezaAMehdiN. How does carbon tax affect social welfare and emission reduction in Finland?Energy Rep2020; 6: 6–14.
10.
GarabedianSNarindranjanaharyARicciO, et al.A macroeconomic evaluation of a carbon tax in overseas territories: a CGE model for Reunion Island. Energy Policy2020; 47: 16–20.
11.
Tiangong. How to identify as "carbon neutral"? take a look at these international standards. Nat Gas Ind2021; 41: 103–106.
12.
YeFXiongXYLiLX. Measuring the effectiveness of the Chinese Certified Emission Reduction scheme in mitigating CO2 emissions: A system dynamics approach. J Cleaner Prod2021; 294: –6.
13.
QiSZZhouCBLiK, et al.The impact of a carbon trading pilot policy on the low-carbon international competitiveness of industry in China: an empirical analysis based on a DDD model. J Cleaner Prod2021; 281: 70–76.
14.
ZouCNXiongBXueHQ, et al.The role of new energy in carbon neutral. Pet Explor Dev2021; 48: 480–491.
15.
XiaoXYZhengZX. A new power system with new energy as the main body under the "double carbon" target: contribution, key technologies and challenge contribution. Eng Sci Technol2022; 1: 1–13.
16.
DuML. Research on Fiscal and tax policies supporting the development of new energy industry under the "double carbon" target. Jinan: Shandong University of Finance and Economics, 2022.
17.
SongMLXieQJShenZY. Impact of green credit on high-efficiency utilization of energy in China considering environmental constraints. Energy Policy2021; 153: 112–120.
18.
AbbanJAwoponeA. Techno-economic and environmental analysis of energy scenarios in Ghana. Smart Grid Renewable Energy2021; 12: 160–170.
19.
HolmDMcIntoshJ. Renewable energy-the future for the developing world. Renewable Energy Focus2008; 9: 58–61.
20.
CengizMADunderE. Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers. J Appl Stat2018; 45: –9.
21.
WangZHeWWangB. Performance and reduction potential of energy and CO2 emissions among the APEC's members with considering the return to scale. Energy2017; 138: 552–562.
22.
HuangBNHwangMJYangCW. Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach. Ecol Econ2008; 67: 41–54.
23.
LongDN. A critical review on energy efficiency and conservation policies and programsin Vietnam. Renewable Sustainable Energy Rev2015; 52: 623–634.
24.
Al-MansourF. Energy efficiency trends and policy in Slovenia. Energy2011; 36: 1868–1877.
25.
MousaMLuoXW. Utilizing BIM and carbon estimating methods for meaningful data representation. Procedia Eng2016; 145: 1242–1249.
26.
JiaHYAdlandRPrakashV, et al.Energy efficiency with the application of virtual arrival policy. Transp Res D, Transp Environ2017; 54: 50–60.
27.
WangAJ. World resources pattern and outlook. J Earth2010; 31: 621–627.
28.
BilgenSKaygusuzKSariA. Renewable energy for a clean and sustainable future. Energy Sour2004; 26: 1119–1129.
29.
WangQZhaoZShenN, et al.Have Chinese cities achieved the win-win between environmental protection and economic development? From the perspective of environmental efficiency. Ecol Indic2015; 51: 151–158.
30.
LeeCC. Energy consumption and GDP in developing countries: a integrated panel analysis. Energy Econ2005; 27: 415–427.
31.
WuJZuidemaCGugerellK, et al.Mind the gap! barriers and implementation deficiencies of energy policies at the local scale in urban China. Energy Policy2017; 106: 201–211.
32.
ZhangLYuJSovacoolBK, et al.Measuring energy security performance within China: toward an inter-provincial prospective. Energy2017; 125: 825–836.
33.
LoK. A critical review of China's rapidly developing renewable energy and energy efficiency policies. Renewable Sustainable Energy Rev2014; 29: 508–516.
34.
KourtitKSuzukiSNijkampP. Tracing high-sustainability performers among world cities-design and application of a multi-temporal data envelopment analysis. Habitat Int2017; 68: 43–54.
35.
JanssensARebollarJV. Transforming social housing neighbourhoods into sustainable carbon neutral districts. Energy Procedia2017; 132: 549–554.
36.
KangXZhangYZhangH, et al.Research on neural network model for new energy industry economy based on particle swarm optimization. J Intell Fuzzy Syst2020; 38: 7267–7277.
37.
BirkenbergABirnerR. The world's first carbon neutral. J Cleaner Prod2018; 189: 485–501.
38.
GallagherKSZhangF. Assessing the policy gaps for achieving China’s climate targets in the Paris agreement. Nat Commun2019; 10: 1–10.
39.
EstradaFBotzenW. Economic impacts and risks of climate change under failure and success of the Paris agreement. Ann N Y Acad Sci2021; 6: 25–30.
40.
KingLKallisG. Transparency crucial to Paris climate scenarios. Science2022; 2: 2–25.
41.
DongHDaiH. Exploring impact of carbon tax on China’s CO2 reductions and provincial disparities. Renewable Sustainable Energy Rev2017; 77: 596–603.
42.
DingSZhangM. Exploring China's carbon emissions peak for different carbon tax scenarios. Energy Policy2019; 129: 1245–1252.
43.
YiLHeQ. Carbon market construction path research: international experience and enlightenment for China. Clim Change Res2019; 15: 232–245.
44.
NazifiF. Modelling the price spread between EUA and CER carbon prices. Energy Policy2013; 56: 434–445.
45.
Liu Y, Tian J, Zheng W, et al. Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics. Urban Climate 2022; 41: 101031.
46.
Zhang L, Liu D, Cai G, et al. An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading. Int J Electr Power Energy Syst 2023; 144: 108558.
47.
WuBS. Optimized model of energy industry chain considering low-carbon development mechanism. Energy Sour Part A: Recovery Util Environ Eff2019b; 42: 2593–2602.
48.
LinHB. Blueprint of Urban Governance under carbon Neutrality. China Construction News, 2021-04-01(7). 2021.
