Spatio-temporal changes of output value from the primary, secondary and tertiary industries for 2020―2050 under the Shared Socioeconomic Pathways

doi:10.12006/j.issn.1673-1719.2019.221

Abstract

Abstract:

Based on the time series of the gross domestic product and the demographic census of China, and statistical yearbooks in 31 provinces (China mainland), the labor participation rate, capital output elasticity and total factor productivity were localized in order to build the Cobb-Douglas model under the Shared Socioeconomic Pathways (SSPs) framework. The changes of national and provincial output value of the primary, secondary and tertiary industries were analyzed for the first half of the 21st century. The results show that: (1) The output value of three industries in China will be different significantly under SSPs. The primary and secondary industries show the same trend, but output value of the latter will be higher than that of the former. And they will increase under SSP1, SSP2 and SSP5, while decline under SSP3 and SSP4. The tertiary industry will continuously grow under all SSPs. (2) The growth rate of output value of three industries will decrease. The differences of industrial growth rates and material and cultural need lead to the gradual decrease of contribution of the primary and the secondary industries to economy, while the increase of contribution of the tertiary industry. (3) Different social and economic development policies have significant impacts on the provincial output value of three industries. Spatially, the output value of the primary, secondary and tertiary industries is high in the east but low in the west in China in 2020-2050. By the mid-21st century, increase in the output value in the eastern region will be the highest for three industries. The increase of the output value of the primary and secondary industries in the northeast region will be the lowest, while that of the tertiary industry in the western region will be the lowest. In 2050, contribution of the primary industry to provincial GDP will be less than 8% and that of the tertiary industry will be higher than 60% generally. Meanwhile, share of the secondary industry will be 25%-30%. The contribution of the output value of the tertiary industry to GDP in the eastern region will be always higher than that in the west, and industrial structure of the eastern region is more reasonable.

Key words: Cobb-Douglas model, Output value from industries, Shared Socioeconomic Pathways (SSPs), China, Province

PAN Jin-Yu, SU Bu-Da, WANG Yan-Jun, JING Cheng, ZHAI Jian-Qing, JIANG Tong. Spatio-temporal changes of output value from the primary, secondary and tertiary industries for 2020―2050 under the Shared Socioeconomic Pathways[J]. Climate Change Research, 2020, 16(6): 725-737.

Figures/Tables 7

Table 1 The initial parameters of economic projection model in 2010 and the assumptions parameters for SSPs in 2050

Fig. 1 Comparison of the recorded values of the primary, secondary and tertiary industries in China for 2011-2018 and the projected results under SSP2

Fig. 2 Output values of the primary, secondary and tertiary industries in China for 2000-2050

Fig. 3 Growth rate of output values of the primary, secondary and tertiary industries in China for 2000-2050

Table 2 Changes of industrial structure in China during the first half of the 21st century under SSPs

Fig. 4 Spatial distribution of output value of primary industry, secondary industry, and tertiary industry in China in 2010 and 2050 under SSPs

Fig. 5 Provincial industrial structures in China in 2020 (a), 2035 (b) and 2050 (c) under SSP2

References 39

[1]	Hansen J, Ruedy R, Sato M, et al. Global surface temperature change[J]. Reviews of Geophysics, 2010, 48 (4):RG4004
[2]	IPCC. Climate change 2013: the physical science basis [M]. Cambridge: Cambridge University Press, 2013
[3]	Trenberth K E, Fasullo J T, Shepherd T G. Attribution of climate extreme events[J]. Nature Climate Change, 2015,5(8):725-730
[4]	IPCC. Climate change 2014: impacts, adaptation, and vulnerability [M]. Cambridge: Cambridge University Press, 2014
[5]	Kuznets S. National income and capital formation, 1919-1935[J]. American Journal of Sociology, 1937,45(2):701
[6]	国家统计局, 国家质检总局, 国家标准委. 国民经济行业分类[Z]. 北京: 中国统计出版社, 2017.
	National Bureau of Statistics, General Administration of Quality Supervision, Inspection and Quarantine, Standards Administration of the People's Republic of China National economy industrial classification [Z]. Beijing: China Statistics Press, 2017 (in Chinese)
[7]	刘伟, 李绍荣. 产业结构与经济增长[J]. 中国工业经济, 2002 (5):14-21.
	Liu W, Li S R. Industrial structure and economic growth[J]. China Industrial Economy, 2002 (5):14-21 (in Chinese)
[8]	刘琦, 任雅慧. 基于三大产业的我国产业结构研究[J]. 商品与质量, 2011 (8):S7-S8.
	Liu Q, Ren Y H. Study on the industrial structure in China based on the three industries[J]. Commodities and Quality, 2011 (8):S7-S8 (in Chinese)
[9]	胡鞍钢. 中国经济发展大趋势[J]. 人民论坛, 2017 (14):16-19.
	Hu A G. The general trend of economic development in China[J]. People's Tribune, 2017 (14):16-19 (in Chinese)
[10]	路甬祥. 从制造大国走向创造强国: 未来20年中国机械工程自主创新之路[J]. 机械研究与应用, 2009 (1):1-3.
	Lu Y X. From manufacturing power to creating power: the road of independent innovation of Chinese mechanical engineering in the next 20 years[J]. Mechanical Research & Application, 2009 (1):1-3 (in Chinese)
[11]	Stock J H. Time series: economic forecasting[J]. International Encyclopedia of the Social & Behavioral Sciences, 2002: 15721-15724
[12]	张景, 石琳, 张换香, 等. 基于回归分析的三次产业产值研究[J]. 内蒙古科技大学学报, 2014,33(3):302-305.
	Zhang J, Shi L, Zhang H X. The research of output value in three industries based on regression analysis[J]. Journal of Inner Mongolia University of Science and Technology, 2014,33(3):302-305 (in Chinese)
[13]	王悦. 基于ARMA模型的上海经济增长预测与趋势分析[J]. 未来与发展, 2012,7:108-113.
	Wang Y. Forecast and trend analysis of Shanghai economic growth based on ARMA model[J]. Future and Development, 2012,7:108-113 (in Chinese)
[14]	Reiss P C, Wolak F A. structural econometric modeling: rationales and examples from industrial organization[J]. Handbook of Econometrics, 2007,6:4277-4415
[15]	Mishra S K. A brief history of production functions [J]. Social Science Electronic Publishing, 2007 (4):6-34
[16]	李春雨, 刘志彪. 区域经济发展与趋势预测研究[J]. 统计研究, 2002,19(11):51-53.
	Li C Y, Liu Z B. The study of regional economic development and projection of development trend[J]. Statistical Research, 2002,19(11):51-53 (in Chinese)
[17]	Felipe J, Adams F G. A theory of production: the estimation of the Cobb-Douglas function: a retrospective view[J]. Eastern Economic Journal, 2005,31(5):427-445
[18]	Dellink R, Chateau J, Lanzi E, et al. Long-term economic growth projections in the Shared Socioeconomic Pathways[J]. Global Environmental Change, 2015,6(4):1-15
[19]	Cuaresma J C. Income projections for climate change research: a framework based on human capital dynamics[J]. Global Environmental Change, 2017,42:226-236
[20]	Leimbach M, Kriegler E, Roming N, et al. Future growth patterns of world regions: a GDP scenario approach[J]. Global Environmental Change, 2017,42:215-225
[21]	戚成蹊. 三种模拟条件下基于生产函数的我国经济增长预测分析[J]. 商业经济研究, 2017 (21):182-185.
	Qi C Q. Analysis of China's economic growth forecast based on production function under three simulation conditions[J]. Journal of Commercial Economics, 2017 (21):182-185 (in Chinese)
[22]	佟贺丰, 杨阳, 王静宜, 等. 中国绿色经济发展展望: 基于系统动力学模型的情景分析[J]. 中国软科学, 2015,6:25-39.
	Tong H F, Yang Y, Wang J Y, et al. Modeling China's green economy 2050: scenario analysis based on the system dynamics model[J]. China Soft Science, 2015,6:25-39 (in Chinese)
[23]	陈伟涛, 滕忠铭. 基于分数阶累加灰色模型的福建省第二产业产值预测[J]. 信息系统工程, 2017 (7):149-150.
	Chen W T, Teng Z M. Prediction of output value of secondary industry of the economy in Fujian province based on fractional cumulative grey model[J]. China CIO News, 2017 (7):149-150 (in Chinese)
[24]	李佳校, 武新乾. “十三五”时期洛阳第一产业产值灰色系统预测[J]. 农村经济与科技, 2016,27(1):119-120.
	Li J X, Wu X Q. Grey system forecast of the output values forecast of Luoyang's third industry in the Thirteenth Five-Year Plan period[J]. Rural Economy and Science-Technology, 2016,27(1):119-120 (in Chinese)
[25]	赵艳琨, 武新乾. 洛阳市“十三五”时期第三产业产值预测[J]. 价值工程, 2016,35(18):3-5.
	Zhao Y K, Wu X Q. The output values forecast of Luoyang's third industry in the Thirteenth Five-Year Plan period[J]. Value Engineering, 2016,35(18):3-5 (in Chinese)
[26]	石萍, 唐俊. 灰色GM(1,n)模型在经济预测中的应用: 以包头市经济发展为例[J]. 内蒙古大学学报: 自然科学版, 2015,46(1):43-47.
	Shi P, Tang J. Application of the GM(1,n) Prediction Model in economic forecasting: taking economic development of Baotou as an example[J]. Journal of Inner Mongolia University: Natural Science Edition, 2015,46(1):43-47 (in Chinese)
[27]	姜彤, 赵晶, 曹丽格, 等. 共享社会经济路径下中国及分省经济变化预测[J]. 气候变化研究进展, 2018,14(1):50-58.
	Jiang T, Zhao J, Cao L G, et al. Projection of national and provincial economy under the Shared Socioeconomic Pathways in China[J]. Climate Change Research, 2018,14(1):50-58 (in Chinese)
[28]	Huang J, Qin D, Jiang T, et al. Effect of fertility policy changes on the population structure and economy of China: from the perspective of the Shared Socioeconomic Pathways[J]. Earth's Future, 2019,7(3):250-265
[29]	Cobb C W, Douglas P H. A theory of production[J]. American Economic Review, 1928,18(1):139-165
[30]	Hossain M Z, Alamri K S. Use of Cobb-Douglas production model on some selected manufacturing industries in Oman[J]. Education Business & Society Contemporary Middle Eastern Issues, 2010,3(2):78-85
[31]	Biddle J E. The introduction of the Cobb-Douglas regression and its adoption by agricultural economists[J]. History of Political Economy, 2011,43(1):S235-S257
[32]	张月波. 柯布-道格拉斯生产函数的改进和应用研究[J]. 中国外资, 2014 (3):111-112.
	Zhang Y B. Research on improvement and application of Cobb-Douglas production function[J]. Foreign Investment in China, 2014 (3):111-112 (in Chinese)
[33]	曹梦畅, 袁娟. 基于柯布-道格拉斯生产函数的湖北农业发展分析[J]. 湖北农业科学, 2015,54(3):727-732.
	Cao M C, Yuan J. Research on agricultural development of Hubei province based on Cob Douglas production function[J]. Hubei Agricultural Sciences, 2015,54(3):727-732 (in Chinese)
[34]	宗振利, 廖直东. 中国省际三次产业资本存量再估算: 1978—2011[J]. 贵州财经大学学报, 2014 (3):8-16.
	Zong Z L, Liao Z D. Estimates of fixed capital stock by sector and region: 1978-2011[J]. Journal of Guizhou University of Finance and Economics, 2014 (3):8-16 (in Chinese)
[35]	United Nation. Population Censuses' datasets (1995-present)[J]. Department of Economic and Social Affairs, 2019
[36]	叶裕民. 全国及各省区市全要素生产率的计算和分析[J]. 经济学家, 2002 (3):115-121.
	Ye Y M. Calculation and analysis of total factor productivity across the country and provinces[J]. Economist, 2002 (3):115-121 (in Chinese)
[37]	殷宁宇. 经济增长速度与产业结构关系研究: 对中国不同区域经济增长速度趋势性变化的分析[J]. 中山大学学报: 社会科学版, 2014 (2):174-183.
	Yin N Y. A study on the relationship between economic growth rate and industrial structure: an analysis of the trend change of economic growth rate in different regions of China[J]. Journal of Sun Yat-Sen University: Social Science Edition, 2014 (2):174-183 (in Chinese)
[38]	胡鞍钢. 中国进入后工业化时代[J]. 北京交通大学学报: 社会科学版, 2017,16(1):1-16.
	Hu A G. China entering post-industrial era[J]. Journal of Beijing Jiaotong University: Social Sciences Edition, 2017,16(1):1-16 (in Chinese)
[39]	段欣彤. 我国经济发展现状与对策[J]. 中国国际财经: 中英文, 2017 (17):188-189.
	Duan X T. The current situation and countermeasures of economic development in China[J]. China International Business, 2017 (17):188-189 (in Chinese)