Abstract
This study estimates the carbon emission intensity of China’s provinces during the period from 2000 to 2015. First, the temporal and spatial pattern evolution of China’s carbon emission intensity was analyzed using spatial statistics. Then, from an innovation-driven perspective, combining the data of innovative technologies and scale factors to construct a spatial panel model to explore the main influencing factors of carbon emission intensity and its spatial spillover effect. The results show that: China’s provincial carbon emission intensity has obvious spatial agglomeration characteristics, and regional differences are improving, and the spatial spillover effect of some influencing factors is obvious; innovation indicators such as the number of patent authorizations, technical market turnover, and foreign direct investment, and GDP have a significant negative impact on carbon intensity, and the effects of general scale variables such as urbanization rate, energy consumption, and population density on carbon intensity are significantly positive.
Similar content being viewed by others
References
Andersson FNG, Karpestam P (2013) CO2, emissions and economic activity: short- and long-run economic determinants of scale, energy intensity and carbon intensity. Energy Policy 61(8):1285–1294. https://doi.org/10.1016/j.enpol.2013.06.004
Anselin L (2001) Rao’s score test in spatial econometrics. J Statist Plann Inference 97(1):113–139. https://doi.org/10.1016/S0378-3758(00)00349-9
Cheng Y, Wang Z, Shouzhi Z et al (2013) Spatial measurement of carbon emission intensity of energy consumption in China and its influencing factors. Acta Geograph Sin 68(10):1418–1431. https://doi.org/10.11821/dlxb201310011
Cheng Y, Wang Z, Ye X, Wei YD (2014) Spatiotemporal dynamics of carbon intensity from energy consumption in China. J Geogr Sci 24(4):631–650. https://doi.org/10.1007/s11442-014-1110-6
Conley TG, Ligon E (2002) Economic distance and cross-country spillovers. J Econ Growth 7(2):157–187. https://doi.org/10.1023/A:101567611
Deng J, Liu X, Wang Z (2014) Analysis and disintegration of regional disparities and evolution characteristics of carbon emissions in China. J Nat Resour (2):189–200. https://doi.org/10.11849/zrzyxb.2014.02.001
Doudou B, Fang Y, Xie M, Tang Y, Lin Z (2015) Temporal and spatial evolution of China’s provincial service industry innovation level and its dynamic mechanism: An empirical study based on spatial econometric model. Econ Geogr 2015 35(10):139–148. https://doi.org/10.15957/j.cnki.jjdl.2015.10.020
Fu Y, Ma S, Song Q (2015) Spatial econometric analysis of regional carbon emission intensity in China. Statist Res 32(6):67–73. https://doi.org/10.3969/j.issn.1002-4565.2015.06.009
Hu Y, Guichun L, Kong X et al (2016) Analysis of spatial and temporal differences in China’s carbon emission intensity. Resour Ind 18(2):67–75. https://doi.org/10.3969/j.issn.1002-4565.2015.06.009
Li L, Hong X (2017) Spatial effects of energy carbon emission and environmental pollution: a spatial Dubin measure model based on energy intensity and technological progress. Ind Technol Econ 36(9):65–72. https://doi.org/10.3969/j.issn.1004-910X.2017.09.009
Liu F, Sun Y (2008) Empirical analysis of the effect of technological innovation and industrial structure adjustment on energy consumption. China Polity, Resour Environ 18(3):108–113. https://doi.org/10.3969/j.issn.1002-2104.03.020
Liu X, Gao C, Zhang Y et al (2016) Spatial regression analysis of spatial dependence and influencing factors of China’s provincial energy consumption carbon emissions. J Arid Land Resour Environ 30(10):1–6. https://doi.org/10.13448/j.cnki.jalre.2016.308
Ma X, Rubing GE, Zhang L (2014) Research on the relationship between air quality and economy development in majorcities of China. Kybernetes 43(8):1224–1236. https://doi.org/10.1108/K-07-2013-0146
Ma D, Chen Z, Wang L (2015) Spatial measurement of China’s provincial carbon emission efficiency. China Polity Resour Environ 25(1):67–77. https://doi.org/10.3969/j.issn.1002-2104.2015.01.010
Ma Y, Lu Y, Yutao S (2016) Technological advancement, structural adjustment, and carbon emission intensity: an empirical study based on the spatial panel data model at the provincial level in China. Res Dev Manag 05:23–33. https://doi.org/10.3969/j.issn.1004-8308.2016.05.003
Mosier A, Kroeze C, Nevison C, Oenema O, Seitzinger S, Cleemput O (1999) An overview of the revised 1996 IPCC guidelines for national greenhouse gas inventory methodology for nitrous oxide from agriculture. Environ Sci Pol 2(3):325–333. https://doi.org/10.1016/S1462-9011(99)00022-2
Peizhen J, Zhang Y, Peng X (2014) Double-edged effect of technological progress in reducing emissions of carbon dioxide-evidence-based Chinese industry 35 industries. Sci Res, 2014 32(05):706–716. https://doi.org/10.16192/j.cnki.1003-2053.2014.05.006
Qin D (2014) Climate change science and human sustainable development. Prog Geogr 33(07):874–883. https://doi.org/10.11820/dlkxjz.2014.07.002
Qin J, Tang H (2015) Research on the mechanism of technological innovation promoting the development of low-carbon economy. Ecol Econ 09:39–42. https://doi.org/10.3969/j.issn.1671-4407.2015.09.009
Guest R (2011) Global demographic change, carbon emissions, the optimal carbon price and carbon abatement. Glob Econ J 10(2). https://doi.org/10.2202/1524-5861.1466
Sadorsky P (2014) The effect of urbanization on CO2, emissions in emerging economies. Energy Econ 41(1):147–153. https://doi.org/10.1016/j.eneco.2013.11.007
Wang X, Zhang Y, Qin Y et al (2016) Spatial-temporal differentiation and regulation of China’s carbon emission influencing factors. Econ Geogr 36(8):158–165. https://doi.org/10.15957/j.cnki.jjdl.2016.08.023
Xie S, Wang L, Shao Z (2013) Analysis of industry differences and causes of China’s carbon emission intensity. Environ Sci Res 2013(11):1001–6929. https://doi.org/10.13198/j.issn.1001-6929.2013.11.015
Xin S, Kemeng Z (2014) An empirical analysis of influencing factors of carbon emission in China. Statist Res 31(02):61–67. https://doi.org/10.19343/j.cnki.11-1302/c.2014.02.009
Xin Sun, Yongchang, Shen, Ran Tao (2016). The measurement of low carbon technology progress in china and its effect on carbon emission intensity. Jianghuai Forum, (06):64–71. https://doi.org/10.19343/j.cnki.11-1302/c.2014.02.009
Xuezhi L, Xu C, Zhang M (2013) China’s energy consumption intensity, structure and technology innovation and carbon emissions: empirical analysis based on panel data model. Fri Acc 2013(09):53–57. https://doi.org/10.3969/j.issn.1004-5937.2013.09.013
Yang G, Wu Q (2016) Empirical test of the spatial spillover effects of provincial carbon emissions in China. Statist Decision 21:87–90. https://doi.org/10.13546/j.cnki.tjyjc.2016.21.023
Yanmei Y, Wang Z, Wu L, Liu C (2016) An analysis of the effect of China’s carbon emission intensity factors on regional disparities. J Environ Sci, 2016 36(09):3436–3444. https://doi.org/10.13671/j.hjkxxb.2016.0102
Zhang C, Zhang Z (2015) Spatial effects of energy endowments and technological progress on China’s carbon emission intensity. China Polity Resour Environ 25(9):37–43. https://doi.org/10.3969/j.issn.1002-2104.2015.09.006
Zhou X, Zhang J, Li J (2013) Industrial structural transformation and carbon dioxide emissions in China. Energy Policy 57(3):43–51. https://doi.org/10.1016/j.enpol.2012.07.017
Funding
The authors thank the support of the National Social Science Fund of China; the project number is 16BJL076 (Research on the impact of carbon trading on China’s regional economic development from the perspective of spatial and temporal differentiation).
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Muhammad Shahbaz
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liang, S., Zhao, J., He, S. et al. Spatial econometric analysis of carbon emission intensity in Chinese provinces from the perspective of innovation-driven. Environ Sci Pollut Res 26, 13878–13895 (2019). https://doi.org/10.1007/s11356-019-04131-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-019-04131-3