Impact of population aging and industrial structure on CO2 emissions and emissions trend prediction in China

Along with the adoption of the Paris Agreement in 2015 and China's own action target, the emissions reductions in China, as the largest CO2 emission country in the world, become extremely urgent. In terms of the current status of demographic and industrial structure, the impact factors of CO2 emissions are analyzed by the ridge regression method based on an extended STIRPAT model in this study. The results show that population aging, industrial structure and per-capita wealth have a positive impact on CO2 emissions growth, while energy intensity has a weakly negative effect on CO2 emission. Based on the above studies, eight different scenarios are set to analyze the future energy CO2 emissions. In addition, future CO2 emissions in China are also predicted by the Grey System model. It concludes that CO2 emissions will have an upward trend in the future. As a result, speeding up construction of the sanatoria industry as well as adjusting of the energy and industry structures is proposed as effective ways to control CO2 emissions.

Kaynakça

Bin, S., Dowlatabadi, H., 2005. Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy 33, 197–208.

Cai, F., Du, Y., Wang, M.Y., 2008. Transformation of economic development patterns and inner motivation of emission reduction. Econ. Res. J. 6, 4–36.

Chang, N., 2015. Changing industrial structure to reduce carbon dioxide emissions: a Chinese application. J. Clean. Prod. 103, 40–48.

Chen, W.D., Wu, F.Y., Geng, W.X., Yu, G.Y., 2016. Carbon emissions in China's industrial sectors. Resour. Conservat. Recycl. 117, 264–273.

Chikaraishi, M., Fujiwara, A., Kaneko, S., Poumanyvong, P., Komatsu, S., Kalugin, A., 2015. The moderating effect of urbanization on carbon dioxide emissions: a latent class modeling approach. Technol. Forecast. Soc. Change 90, 302–317.

Deng, J.L., 1982. Control problems of grey systems. Syst. Control Lett. 1 (5).

Deng, J.L., 1983. Grey System theory and forecasting metrology. Future Dev. 3, 20–23.

Deng, J.L., 1987. The Primary Methods of Grey System Theory. Huazhong University of Science & Technology Press, Wuhan.

Dietz, T., Rosa, E.A., 1994. Rethinking the environmental impacts of population, affluence, and technology. Hum. Ecol. Rev. 1, 277–300.

Ehrlich, P., Holdren, J., 1971. Impact of population growth. Science 171, 1212–1217.

Fan, Y., Liu, L.-C., Wu, G., Wei, Y.-M., 2006. Analyzing impact factors of CO2 emissions using the STIRPAT model. Environ. Impact Assess. Rev. 26, 377–395.

Feng, Z.H., Zou, L.L., Wei, Y.M., 2011. The impact of household consumption on energy use and CO2 emissions in China. Energy 36, 656–670.

Guo, C.X., 2012. Impact of industrial structure changes on China's CO2 emissions. China Popul. Resour. Environ. 22 (7), 15–20.

Hassan, K., Salim, R., 2015. Population ageing, income growth and CO2 emission: empirical evidence from high income OECD countries. J. Econ. Stud. 42 (1), 54–67.

IPCC, 2006. IPCC Third Assessment Report: Climate Change 2006. Cambridge University Press, Cambridge.

Jorgenson, A.K., Clark, B., 2010. Assessing the temporal stability of the population/environment relationship in comparative perspective: a cross-national panel study of carbon dioxide emissions, 1960–2005. Popul. Environ. 32 (1), 27–41.

Li, C.P., Qin, J.X., Li, J.J., Hou, Q., 2016. The accident early warning system for iron and steel enterprises based on combination weighting and Grey Prediction Model GM (1, 1). Saf. Sci. 89, 19–27.

Liddle, B., Lung, S., 2010. Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption- related environmental impacts. Popul. Environ. 31, 317–343.

Liddle, B., 2014. Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Popul. Environ. 35 (3), 286–304.

Liu, Z.Q., Chen, C., 2010. Research on low carbon economy and industrial structure adjustment. Abroad Soc. Sci. 3, 21–27.

Liu, X.M., Fu, J.F., 2011. Analyzing the target of China's CO2 emission reduction intensity in 2020 based on CGE model. Resour. Sci. 33 (4), 634–639.

Liu, W.D., Zhang, L., Wang, L.M., et al., 2010. A sketch map of low carbon economic development in China. Geogr. Res. 29 (5), 778–787.

Ma, X., Liu, Z.B., 2017. Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in China. J. Comput. Appl. Math. 324, 17–24.

Michael, D., O'Neill, Prskawetz, A., et al., 2008. Population aging and future carbon emissions in the United States. Energy Econ. 30 (2), 642–675. NBSC, 2015. China Statistical Yearbook.

O'Neill, B.C., Chen, B.S., 2002. Demographic determinants of household energy use in the United States. Popul. Dev. Rev. 28, 53–88.

O'Neill, B.C., MacKellar, L., Lutz, W., 2001. Population and Climate Change. Cambridge University Press, Cambridge.

Rafiq, S., Salim, R., Neilsen, I., 2016. Urbanization, openness, emissions and energy intensity: a study of increasingly urbanized emerging economies. Energy Econ. 56, 20–28.

Revelle, R., Suess, H.E., 1957. Carbon dioxide exchange between atmosphere and ocean and the question of an increase of atmospheric CO2 during the past decades. Tellus 9, 18–27.

Schipper, L., 1996. Lifestyles and the environment: the case of energy. Daedalus 125, 113–138.

Tan, F.Y., Zhang, W., 2011. Industrial structure and carbon emissions in China: evidence from province level data. Econ. Issues 9, 32–35.

Tu, Z.G., 2008. The coordination of environment, resources and industrial growth. Econ. Res. J. 2, 93–105.

U. S. Energy Information Administration, 2011. www.eia.gov.

Wang, Z., Zhu, Y.B., 2008. Study on the status of carbon emission in provincial scale of China and counter measures for reducing its emission. Bull. Chin. Acad. Sci. 23 (2), 109–115.

Wang, Z.H., Yin, F.C., Zhang, Y.X., Zhang, X., 2012. An empirical research on the influencing factors of regional CO2 emissions: evidence from Beijing city, China. Appl. Energy 100, 277–284.

Wang, P., Wu, W.S., Zhu, B.Z., Wei, Y.M., 2013. Examining the impact factors of energyrelated CO2 emissions using the STIRPAT model in Guangdong Province, China. Appl. Energy 106, 65–71.

Wang, S., Fang, C., Wang, Y., Huang, Y., Ma, H., 2015. Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis. Ecol. Indic. 49, 121–131.

Wang, Q.R., Liu, L., Wang, S., Wang, J.Z., Liu, M., 2017a. Predicting Beijing's tertiary industry with an improved grey model. Appl. Soft Comput. 57, 482–494.

Wang, Y.N., Kang, Y.Q., Wang, J., Xu, L.N., 2017b. Panel estimation for the impacts of population-related factors on CO2 emissions: a regional analysis in China. Ecol. Indic. 78, 322–330.

Yu, Z.J., Yang, C.H., Zhang, Z., Jiao, J., 2015. Error correction method based on data transformational GM(1,1) and application on tax forecasting. Appl. Soft Comput. 37, 554–560.

Yuan, C.Q., Liu, S.F., Fang, Z.G., 2016. Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model. Energy 100, 384–390.

Zhang, Y.G., 2010. Impact of changing economic development pattern on China's CO2 emission intensity. Econ. Res. J. 4, 120–133.

Zheng, C.D., Liu, S., 2011. Industrial structure and carbon emissions: an empirical analysis based on provincial panel data Chinese. Res. Dev. 2, 26–33.

Zhu, Q., Peng, X.Z., 2012. The impacts of population change on carbon emissions in China during 1978–2008. Environ. Impact Assess. Rev. 36, 1–8.

Kaynak Göster