Abstract
The main purpose of this article is to link the environment, economy, electricity, and society and put forward a new point of view. The current research mainly explores the relationship between the environment, economy, and society and lacks a discussion on electricity. Using a new research framework, this article examines the relationship between energy intensity, energy consumption structure, population density, urbanization rate, and carbon intensity based on relevant data from 2000 to 2017 in China. In the empirical research, according to the cluster analysis, China’s 30 provinces are divided into three regions according to the electrification rate standard. The cross-sectional dependence test method is used to verify the cross-sectional dependence of the data, and the second-generation panel unit root test method is used. Exploring the relationship between the variables, this article finally uses the convergence analysis method to explore the degree of influence of each variable on the carbon intensity. The empirical results show that there are both short-term effects and long-term relationships in various regions, and the influencing factors of each region are different. It further shows that the carbon intensity of the four panels shows convergence, β absolute convergence, and β conditional convergence, but the main influencing factors in different regions are different. Finally, based on the results of empirical research, policy recommendations for reducing carbon intensity in different regions are put forward.
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References
Omer AM (2007) Focus on low carbon technologies: the positive solution, 12(9):2331-2357
Fatai AF, Adewale AA, Victor BF (2020) An assessment of environmental sustainability corridor: the role of economic expansion and research and development in EU countries.[J]. The Science of the total environment,713
Kasman A (2015) Yavuz Selman Duman. CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Economic Modelling. 4:97–103
Manzoor A, Zeeshan K, Ur RZ, Iqbal KS, Ullah KZ (2021) Can innovation shocks determine CO2 emissions (CO2e) in the OECD economies? A new perspective[J]. Economics of Innovation and New Technology, 30(1):(结论可引用)
Khalid AM, Muhammad U, Iqbal GD, Shahzad SM, Arshian S, Iqbal TM, Bares LL (2021) Does globalization affect the green economy and environment? The relationship between energy consumption, carbon dioxide emissions, and economic growth.[J]. Environmental science and pollution research international. York, Richard. Demographic trends and energy consumption in European Union Nations, 1960–2025. Social science research, 2007, 36(3): 855-872
Arouri MEH, Youssef AB, M'henni H, Rault C (2012) Energy consumption, economic growth and CO2 emissions in the Middle East and North African countries. Energy Policy 45:342–349
Saravanan AP, Pugazhendhi A, Mathimani T (2020) A comprehensive assessment of biofuel policies in the BRICS nations: implementation, blending target and gaps[J]. Fuel, 272
Saravanan AP, Mathimani T, Deviram G, Rajendran K, Pugazhendhi A (2018) Biofuel policy in India: a review of policy barriers in sustainable marketing of biofuel[J]. Journal of Cleaner Production, 193
Azomahou T, Van Phu N (2001) Economic growth and CO2 emissions: a nonparametric approach. Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, Brussels, Belgium, 1-28.
Bhattacharya M, Paramati SR, Ozturk I et al (2016) The effect of renewable energy consumption on economic growth: evidence from top 38 countries. Applied Energy 162:733–741
Breitung J, Pesaran MH (2009) Testing for heteroskedasticity and spatial correlation in a random effects panel data model. Computational Statistics and Data Analysis 53:2897–2922
Breitung J (2001) The local power of some unit root tests for panel data. Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, 161-177.
Zubair CM, Manzoor A, Abdul R, Kamran KM (2021) Mitigations pathways towards sustainable development: assessing the influence of fiscal and monetary policies on carbon emissions in BRICS economies[J]. Journal of Cleaner Production,2021(prepublish):
Hoyos D, Rafael E, Sarafidis V (2006) Testing for cross-sectional dependence in panel-data models. Stata Journal 6(4):1–13
Qing D, Iqbal KS, Manzoor A (2021) Towards sustainable production and consumption: assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations[J]. Sustainable Production and Consumption, 27
Dong M, Xu Z, Cunfang L (2019) Simulation of carbon intensity constraints: macroscopic effects, emission reduction effects and structural effects. Management Review 31(05):53–65
Faisal Faisal, Ruqiya Pervaiz, Nesrin Ozatac, Turgut Tursoy (2021) Exploring the relationship between carbon dioxide emissions, urbanisation and financial deepening for Turkey using the symmetric and asymmetric causality approaches[J]. Environment, Development and Sustainability, (prepublish)
Zhang F, Deng X, Phillips F, Fang C, Wang C (2020) Impacts of industrial structure and technical progress on carbon emission intensity: evidence from 281 cities in China[J]. Technological Forecasting & Social Change, 154
Fei L et al (2011) Energy consumption-economic growth relationship and carbon dioxide emissions in China. Energy policy 39(2):568–574
Shuqi F, Jianping Z, Wang Y (2018) Analysis of factors influencing Chinese residents’ direct living energy consumption carbon intensity. Environmental Engineering 36(10):184–188
Gao L (2014) Decomposition analysis of influencing factors of changes in carbon emissions in Sichuan Province. Environment and Life 16:7–9
Grossman GM, Krucger AB (1991) Environmental impacts of the North American Free Trade Agreement[R]. NBER. 1991, working paper 3914
Guo L, Li C, Hongsong P, Shien Z, Zhang Jinhe YH (2021) Study on eco-efficiency evaluation and spatial pattern of provincial tourism in China under the constraint of energy saving and emission reduction [J]. Advances in Geographical Sciences 40(08):1284–1297
Li H, Khattak SI, Ahmad M (2021) Measuring the impact of higher education on environmental pollution: new evidence from thirty provinces in China[J]. Environmental and Ecological Statistics,2021(prepublish)
Qingquan J, Khattak SI, Ahmad M, Ping L (2020) A new approach to environmental sustainability: assessing the impact of monetary policy on CO2 emissions in Asian economies[J]. Sustainable Development, 28(5):(政策)
Tang J, Shuyan C (2014) Regional carbon intensity convergence analysis and countermeasure research [J]. Industrial technology and economy 33(08):99–109
Zirui L (2021) Research on the spatial convergence of China’s industrial carbon productivity and its influencing factors [J/OL]. Enterprise Economics, (09): 88-98 [2021-09-27]. https://doi.org/10.13529/j.cnki.enterprise.ecoo
Levin A, Lin CF, Chu CSJ (2002) Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108(1):1–24
Enwen L, Wang L, Song B, Yaqi F (2019) Parallel clustering analysis of transformer oil chromatographic data based on chaotic sequence [J]. Journal of Electrical Technology 34(24):5104–5114
Lin X, Dan P, Yangyang Z, Xiangyang T (2018) Study on convergence coefficient and correlation of carbon index in industrial high-carbon industry-taking Yunnan and Guangdong as examples [J]. Journal of Kunming university of science and technology (natural science edition) 43(05):129–136
Xianzhao L, Gao C, Yong Z, Dongshui Z, Xie J, Yan S, Wang Z (2018) Spatial heterogeneity of spatial dependence pattern and influencing factors of provincial carbon intensity in China. Geography 38(05):681–690
Yunbo L (2013) Research on the influencing factors of carbon intensity in China’s power industry. Harbin Institute of Technology
Lotfalipour MR, Falahi MA, Ashena M (2010) Economic growth, CO2 emissions, and fossil fuels consumption in Iran. Energy 35(12):5115–5120
Lu H (2000) Analysis of the relationship between China’s environmental problems and economic development – taking air pollution as an example. (5):53-59
Pesaran MH (2007a) A simple panel unit root test in the presence of cross-section dependence[J]. Journal of Applied Econometrics, 22(2)
Ma X, Najid A, Pao-Yu O (2021) Environmental Kuznets curve in France and Germany: role of renewable and nonrenewable energy[J]. Renewable Energy 172
Maddala GS, Wu S (1999) A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and statistics 61(S1):631–652
Arunima M, Jun L, Manfred L (2016) Trends in global greenhouse gas emissions from 1990 to 2010. [J]. Environmental science & technology, 50(9)
Ahmad M, Khattak SI, Khan A, Rahman ZU (2020) Innovation, foreign direct investment (FDI), and the energy–pollution–growth nexus in OECD region: a simultaneous equation modeling approach[J]. Environmental and Ecological Statistics,2020(prepublish):
Strazicich MC, List JA (2003) Are CO2 emission levels converging among industrial countries?[J]. Environmental and Resource Economics, 24(3):
Pesaran MH (2007b) A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics 22(2):265–312
Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=572504, (accessed on 4 August, 2004)
Phillips PCB, Sul D (2003) Dynamic panel estimation and homogeneity testing under cross section dependence[J]. The Econometrics Journal, 6(1):
Van PN (2005) Distribution dynamics of CO2 emissions[J]. Environmental & Resource Economics, 32(4)
Xinyan Q, Fuliang L (2015) Analysis and identification of power quality disturbance based on ITD and K-means clustering [J]. Power System and its acta automatica sinica 27(08):54–59
Roberts JT, Grimes PE (1997) Carbon intensity and economic development 1962-1991: a brief exploration of the environmental Kuznets curve. World Development 25(2):191–198
Khattak SI, Ahmad M, Khan ZU, Khan A (2020) Exploring the impact of innovation, renewable energy consumption, and income on CO2 emissions: new evidence from the BRICS economies[J]. Environmental Science and Pollution Research, 27(12):
Dinda S (2004) Environmental Kuznets curve hypothesis: a survey[J]. Ecological Economics, 49(4)
Stan (2018) The characteristics of the new round of energy revolution and the institutional mechanism construction of energy transformation [J]. Financial think tank, 2018(04):17-25 139-140
Wang S, Wei Z (2017) Analysis of the carbon intensity factors of Beijing-Tianjin-Hebei based on the whole-region-industry decomposition. Soft Science 31(12):96–100
Wang S, Yongyuan H (2019) Spatial spillover effect and driving factors of carbon emission intensity of Chinese cities. Acta Geographica Sinica 74(06):1131–1148
Wang Z, Mehdi BJ, Mara M, Buhari D, Umer S (2021) Does export product quality and renewable energy induce carbon dioxide emissions: evidence from leading complex and renewable energy economies[J]. Renewable Energy 171
Baumol WJ, Wolff EN (1988) Productivity growth, convergence, and welfare: reply[J]. The American Economic Review, 78(5)
Yong X, Bin Q, Ziwen C, Liang H, Sheng S (2020) Detection methods of illegal wireless communication links in power Internet of Things terminals [J]. Journal of Electrical Technology 35(11):2319–2327
Zhang X, Jiang Q, Khattak SI, Ahmad M, Rahman ZU (2021) Achieving sustainability and energy efficiency goals: assessing the impact of hydroelectric and renewable electricity generation on carbon dioxide emission in China[J]. Energy Policy, 155
Xu G (2010) carbon emission convergence: theoretical hypothesis and empirical study of China [J]. Research on quantitative economy, technology and economy, 2010,27(09):31-42
Yingjie Y, Gelui S, Yadong L, Xiuming D, Wang H, Xiuchen J (2016) Transformer state anomaly detection based on sliding window and clustering algorithm [J]. High Voltage Technology 42(12):4020–4025
Xiuyu Y (2016) analysis of regional differences and convergence of agricultural carbon emissions in China [J]. Hubei agricultural sciences, 2016,55(04):1066-1072.
Yang Z, Shuojia K, Yongliang Z (2015) Comparative study on the properties of limited samples of the second generation panel unit root test method [J]. Quantitative Economic and Technical Economic Research 32(12):124–141
Chuanguo Z, Lin Y (2012) Panel estimation for urbanization, energy consumption and CO2 emissions: a regional analysis in China. Energy Policy 49:488–498
Zhu L, Zhen Z (2011) Analysis of influencing factors of carbon emission intensity in Shanghai. Environmental Science Research, 2011, 24 (1): 20 ~ 26
Zhu Z, Yang L, Tian X, Wang Y, Zhang Y (2017) CO2 emissions from the industrialization and urbanization processes in the manufacturing center Tianjin in China. Journal of Cleaner Production 8:67–75
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The data of province-level CO2 emissions are obtained from China Emission Accounts and Datasets, http://www.ceads.net/, and other data are collected from National Statistics Bureau, http://www.statas.gov.cn/.
Funding
This work was supported by the 2018 Key Projects of Philosophy and Social Sciences Research, Ministry of Education, China (grant number 18JZD032) 《Research on constructing energy system policy and mechanism with characteristics of clean, low-carbon emission, safe, and high-efficiency》.
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As the instructor, Jingqi Sun provided guidance on research ideas and methods. Xiaohui Guo conducted main writing work and empirical research. Yuan Wang wrote some literature reviews and subsequent revisions. Jing Shi provided methodological guidance and improvement. Follow-up proofreading and modification were carried out by Yiquan Zhou. Shen Boyang’s main job is to proofread and polish the English
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Sun, J., Guo, X., Wang, Y. et al. Nexus among energy consumption structure, energy intensity, population density, urbanization, and carbon intensity: a heterogeneous panel evidence considering differences in electrification rates. Environ Sci Pollut Res 29, 19224–19243 (2022). https://doi.org/10.1007/s11356-021-17165-3
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DOI: https://doi.org/10.1007/s11356-021-17165-3