中亚五国自华进口贸易技术溢出及碳排放影响研究

doi:10.12118/j.issn.1000-6060.2021.336

Abstract

Abstract:

The five Central Asian countries are the commercial throat of the Land Silk Road, and their strategic position is important. The five Central Asian countries and China depend on each other geographically and promote each other’s economy and trade. The five Central Asian countries are the gateway and transit point of China’s land corridor connecting Europe, while China is the support and shortcut for the five Central Asian countries to move to the Pacific Ocean. Recently, the pace of economic and trade cooperation between China and the five Central Asian countries has accelerated, and remarkable achievements have been made in trade, investment, economic and technological cooperation, and financial cooperation. Between 1992 and 2018, processing the changes in carbon emissions in the five Central Asian countries using Kuznets curve of carbon emissions to simulate the relationship between carbon emissions per capita and gross domestic product (GDP) per capita, then combing with indicators of GDP per capita, the total value added of industry, R&D investment and technology spillover stock of imported intermediate and capital goods from China to construct a panel data with fixed-effect model to explore the impact mechanism of carbon emissions in the five Central Asian countries. (1) The study finds the carbon emissions of the five Central Asian countries, which decreased and then increased; the rate of change of carbon emissions has fluctuated. The carbon emissions in Kazakhstan accounted for the largest proportion among the five Central Asian countries. (2) In relation to the Kuznets curve of carbon emission and GDP per capita, Kazakhstan and Turkmenistan exhibit an inverted N-shaped model. Uzbekistan, Tajikistan, Kyrgyzstan, and the five Central Asian countries as a whole are N-shaped models. (3) The five Central Asian countries’ imports from China have grown, and the intermediate and capital goods have accounted for 3.920% to 10.976% of the total imports from China. In addition, the technology spillover stock of imported intermediate goods from China has exceeded that of imported capital goods in the five Central Asian countries. (4) Considering the factors that affect the carbon emissions of the five Central Asian countries, the regression coefficients of GDP per capita and the proportion of industrial added value in GDP are positive. For every 1% increase in GDP per capita and the proportion of industrial added value in GDP, carbon emissions will increase by 0.337% and 0.343%, respectively. The regression coefficients of R&D investment and the interaction term of LnZJ_it×LnS_it are negative. For every 1% increase in R&D investment, carbon emissions can decrease by about 0.432%. For carbon emission reduction, caused by the increase in the proportion of industrial added value in GDP, the impact of technology spillover stock of intermediate goods is deemed minimal. This study explains the potential law of carbon emissions in five Central Asian countries and provides a scientific reference for Chinese technology export to five Central Asian countries.

Key words: carbon emissions, import trade, technology spillover, impact factors, the five Central Asian countries

HUANG Li,WANG Wulin,GONG Jiao. Technology spillover of import trade from China and the impact of carbon emissions in the five Central Asian countries[J].Arid Land Geography, 2022, 45(3): 986-997.

Figures/Tables 8

Tab. 1

Shape and inflection point calculation formula of per capita CKC with different β values"

情况	β取值	曲线形状类型	拐点个数	拐点计算公式
1	β₁>0, β₂<0, β₃>0	N型	2	$e x p (- β 2 + β 22 - 3 β 1 β 3) (3 β 3)$ , $e x p (- β 2 - β 22 - 3 β 1 β 3) (3 β 3)$
2	β₁<0, β₂>0, β₃<0	倒N型	2	同情况1
3	β₁<0, β₂>0, β₃=0	U型	1	exp[-β₁/(2β₂)]
4	β₁>0, β₂<0, β₃=0	倒U型	1	exp[-β₁/(2β₂)]
5	β₁>0, β₂=β₃=0	单调递增型	0	无
6	β₁<0, β₂=β₃=0	单调递减型	0	无

Tab. 1

Fig. 1

Fig. 2

Tab. 2

Fig. 3

Fig. 4

Tab. 3

Tab. 4

Analysis of factors affecting carbon emissions"

变量	模型1	模型2	模型3
常数项 $α 0$	16.278^***	17.161^***	15.991^***
LnPCGDP_it	0.306	0.152	0.337^*
LnS_it	0.258^*	0.258^*	0.343^**
LnRDI_it	-0.389^**	-0.410^**	-0.432^**
LnZJ_it	-0.034	-0.096	0.166
LnZB_it	-0.021	-0.072	-0.122
LnPCGDP_it×LnZJ_it		0.010
LnPCGDP_it×LnZB_it		0.008
LnS_it×LnZJ_it			-0.056^*
LnS_it×LnZB_it			0.027
调整后的R²	0.972	0.972	0.972
F-statistic	497.375	401.528	414.462
Prob(F-statistic)	0.000	0.000	0.000
AIC	0.310	0.337	0.307

Tab. 4

References 40

[1]	Huang R, Chen G, Lü G, et al. The effect of technology spillover on CO₂ emissions embodied in China-Australia trade[J]. Energy Policy, 2020, 144: 111544, doi: 10.1016/j.enpol.2020.111544. doi: 10.1016/j.enpol.2020.111544
[2]	罗良文, 李珊珊. 技术进步、产业结构与中国工业碳排放[J]. 科研管理, 2014, 35(6): 8-13.
	[ Luo Liangwen, Li Shanshan. Technological progress, industrial structure and China’s industrial carbon emissions[J]. Science Research Management, 2014, 35(6): 8-13. ]
[3]	Dogan E, Seker F. The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries[J]. Renewable and Sustainable Energy Reviews, 2016, 60: 1074-1085. doi: 10.1016/j.rser.2016.02.006
[4]	郭庆宾, 柳剑平. 进口贸易、技术溢出与中国碳排放[J]. 中国人口·资源与环境, 2013, 23(3): 105-109.
	[ Guo Qingbin, Liu Jianping. Import trade, technology spillover and China’s carbon emissions[J]. China Population, Resources and Environment, 2013, 23(3): 105-109. ]
[5]	Huang J, Liu Q, Cai X, et al. The effect of technological factors on China’s carbon intensity: New evidence from a panel threshold model[J]. Energy Policy, 2018, 115: 32-42. doi: 10.1016/j.enpol.2017.12.008
[6]	Wyckoff A, Roop J. The embodiment of carbon in imports of manufactured products: Implications for international agreements on greenhouse gas emission[J]. Energy Policy, 1994, 23(3): 187-194.
[7]	黄铭锴. “一带一路”沿线国家新能源进口贸易的技术溢出对碳排放影响[J]. 低碳经济, 2020, 9(1): 10-17. doi: 10.12677/JLCE.2020.91002
	[ Huang Mingkai. The impact of technology spillovers on carbon emissions from new energy import industries along the “One Belt and One Road” Countries[J]. Journal of Low Carbon Economy, 2020, 9(1): 10-17. ] doi: 10.12677/JLCE.2020.91002
[8]	Albornoz F, Cole M A, Elliott R J, et al. In search of environmental spillovers[J]. World Economy, 2009, 32(1): 136-163. doi: 10.1111/j.1467-9701.2009.01160.x
[9]	Lovely M E, Popp D. Trade, technology, and the environment: Does access to technology promote environmental regulation?[J]. Journal of Environmental Economics and Management, 2011, 61(1): 16-35. doi: 10.1016/j.jeem.2010.08.003
[10]	Geng W, Zhang Y. The relationship between skill content of trade and carbon dioxide emissions[J]. International Journal of Ecological Economics and Statistics, 2011, 21(11): 82-91.
[11]	Perkins R, Neumayer E. Do recipient country characteristics affect international spillovers of CO₂-efficiency via trade and foreign direct investment?[J]. Climatic Change, 2012, 112(2): 469-491. doi: 10.1007/s10584-011-0204-8
[12]	王霞, 张丽君, 秦耀辰, 等. 中国制造业碳排放时空演变及驱动因素研究[J]. 干旱区地理, 2020, 43(2): 536-545.
	[ Wang Xia, Zhang Lijun, Qin Yaochen, et al. Spatial-temporal evolution on the manufacturing industry’s carbon emission and its driving factor in China[J]. Arid Land Geography, 2020, 43(2): 536-545. ]
[13]	Yang Y, Cai W, Wang C. Industrial CO₂ intensity, indigenous innovation and R&D spillovers in China’s provinces[J]. Applied Energy, 2014, 131: 117-127. doi: 10.1016/j.apenergy.2014.06.033
[14]	魏龙, 潘安. 出口贸易和FDI加剧了资源型城市的环境污染吗?--基于中国285个地级城市面板数据的经验研究[J]. 自然资源学报, 2016, 31(1): 17-27.
	[ Wei Long, Pan An. Do export and FDI aggravate environmental pollution in resources-based cities? An empirical analysis based on panel data of 285 prefecture cities in China[J]. Journal of Natural Resources, 2016, 31(1): 17-27. ]
[15]	张兵兵, 徐康宁, 陈庭强. 技术进步对二氧化碳排放强度的影响研究[J]. 资源科学, 2014, 36(3): 567-576.
	[ Zhang Bingbing, Xu Kangning, Chen Tingqiang. The influence of technical progress on carbon dioxide emission intensity[J]. Resources Science, 2014, 36(3): 567-576. ]
[16]	查奇芬, 成鑫. 技术进步对中国区域碳排放的影响研究[J]. 资源与产业, 2017, 19(6): 71-77.
	[ Zha Qifen, Cheng Xin. Influence of technical advances on China’s regional carbon emission[J]. Resources & Industries. 2017, 19(6): 71-77. ]
[17]	Yang X, Wang S, Zhang W, et al. Impacts of energy consumption, energy structure, and treatment technology on SO₂ emission: A muti-scale LMDI decomposition analysis in China[J]. Applied Energy, 2016, 184: 714-726. doi: 10.1016/j.apenergy.2016.11.013
[18]	Grossman G M, Krueger A B. Environmental impacts of a North American free trade agreement[R]. Cambridge:National Bureau of Economic Research Working Paper, 1991.
[19]	林伯强, 蒋竺均. 中国二氧化碳的环境库兹涅茨曲线预测及影响因素分析[J]. 管理世界, 2009(4): 27-36.
	[ Lin Boqiang, Jiang Zhujun. Prediction of environmental Kuznets curve of carbon dioxide and analysis of influencing factors in China[J]. Management World, 2009(4): 27-36. ]
[20]	Wagner M. The carbon Kuznets curve: A cloudy picture emitted by bad econometrics?[J]. Resource and Energy Economics, 2008, 30(3): 388-408. doi: 10.1016/j.reseneeco.2007.11.001
[21]	Galeotti M, Lanza A, Pauli F. Reassessing the environmental Kuznets curve for CO₂ emissions: A robustness exercise[J]. Ecological Economics, 2006, 57(1): 152-163. doi: 10.1016/j.ecolecon.2005.03.031
[22]	张庆宇, 张雨龙, 潘斌斌. 改革开放40年中国经济增长与碳排放影响因素分析[J]. 干旱区资源与环境, 2019, 33(10): 9-13.
	[ Zhang Qingyu, Zhang Yulong, Pan Binbin. Analysis of factors affecting China’s economic growth and carbon emissions during the 40 years of reform and opening[J]. Journal of Arid Land Resources and Environment, 2019, 33(10): 9-13. ]
[23]	杨万平, 班斓. 陕西省二氧化碳库兹涅茨曲线的形状、拐点与影响因素[J]. 统计与信息论坛, 2012, 27(3): 72-76.
	[ Yang Wanping, Ban Lan. Shape, inflection point and factors of carbon Kuznets curve of Shaanxi Province[J]. Journal of Statistics and Information, 2012, 27(3): 72-76. ]
[24]	袁富华. 低碳经济约束下的中国潜在经济增长[J]. 经济研究, 2010, 45(8): 79-89, 154.
	[ Yuan Fuhua. The potential economic growth of China with restraint of low carbon economy[J]. Economic Research Journal, 2010, 45(8): 79-89, 54. ]
[25]	谢守红, 王利霞, 邵珠龙. 国内外碳排放研究综述[J]. 干旱区地理, 2014, 37(4): 720-730.
	[ Xie Shouhong, Wang Lixia, Shao Zhulong. Review on carbon emissions researches at home and abroad[J]. Arid Land Geography, 2014, 37(4): 720-730. ]
[26]	Shao S, Yang L, Gan C, et al. Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO₂ emission changes: A case study for Shanghai (China)[J]. Renewable and Sustainable Energy Reviews, 2016, 55: 516-536. doi: 10.1016/j.rser.2015.10.081
[27]	王少剑, 田莎莎, 蔡清楠, 等. 产业转移背景下广东省工业碳排放的驱动因素及碳转移分析[J]. 地理研究, 2021, 40(9): 2606-2622. doi: 10.11821/dlyj020200916
	[ Wang Shaojian, Tian Shasha, Cai Qingnan, et al. Driving factors and carbon transfer of industrial carbon emissions in Guangdong Province under the background of industrial transfer[J]. Geographical Research, 2021, 40(9): 2606-2622. ] doi: 10.11821/dlyj020200916
[28]	高赢, 冯宗宪. “一带一路”沿线国家低碳发展效率测评及影响因素探究[J]. 科技进步与对策, 2018, 35(21): 39-47.
	[ Gao Ying, Feng Zongxian. Study on efficiency measurement and impact factors of low-carbon development for countries along the “One Belt and Road”[J]. Science & Technology Progress and Policy, 2018, 35(21): 39-47. ]
[29]	杜德斌, 马亚华. “一带一路”: 中华民族复兴的地缘大战略[J]. 地理研究, 2015, 34(6): 1005-1014. doi: 10.11821/dlyj201506001
	[ Du Debin, Ma Yahua. One Belt and One Road: The grand geo-strategy of China’s rise[J]. Geographical Research, 2015, 34(6): 1005-1014. ] doi: 10.11821/dlyj201506001
[30]	焦建玲, 卫欢, 白羽. 进口贸易技术溢出对区域碳排放强度的影响[J]. 地理与地理信息科学, 2017, 33(3): 72-77.
	[ Jiao Jianling, Wei Huan, Bai Yu. Effects of technology spillovers of import trade on regional carbon emission intensity[J]. Geography and Geo-Information Science, 2017, 33(3): 72-77. ]
[31]	Galeotti M, Lanza A. Desperately seeking environmental Kuznets[J]. Environmental Modelling & Software, 2005, 20(11): 1379-1388.
[32]	Fujii H, Managi S. Economic development and multiple air pollutant emissions from the industrial sector[J]. Environmental Science and Pollution Research, 2016, 23(3): 2802-2812. doi: 10.1007/s11356-015-5523-2
[33]	李惠娟, 龙如银. 资源型城市环境库兹涅茨曲线研究--基于面板数据的实证分析[J]. 自然资源学报, 2013, 28(1): 19-27.
	[ Li Huijuan, Long Ruyin. Environmental Kuznets curve of resource-based cities in China: An empirical research based on panel data[J]. Journal of Natural Resources, 2013, 28(1): 19-27. ]
[34]	Lichtenberg F R, La Potterie B V. International R&D spillovers: A comment[J]. European Economic Review, 1998, 42(8): 1483-1491. doi: 10.1016/S0014-2921(97)00089-5
[35]	Akira G, Kazuyuki S. R&D capital, rate of return on R&D investment and spillover of R&D in Japanese manufacturing industries[J]. Review of Economics & Statistics. 1989, 71(4): 555-564.
[36]	Coe D T, Helpman E. International R&D spillovers[J]. European Economic Review, 1995, 39(5): 859-887. doi: 10.1016/0014-2921(94)00100-E
[37]	Grossman G M, Krueger A B. Economic growth and the environment[J]. Macroeconomics and the Environment, 2002, 110(2): 353-377.
[38]	刘华军, 闫庆悦. 贸易开放、FDI与中国CO₂排放[J]. 数量经济技术经济研究, 2011, 28(3): 21-35.
	[ Liu Huajun, Yan Qingyue. Trade openness, FDI and China’s carbon dioxide emissions[J]. The Journal of Quantitative & Technical Economics, 2011, 28(3): 21-35.
[39]	王莉雯, 卫亚星. 沈阳市经济发展演变与碳排放效应研究[J]. 自然资源学报, 2014, 29(1): 27-38.
	[ Wang Liwen, Wei Yaxing. The research on economic development and carbon emission effect of Shenyang[J]. Journal of Natural Resources, 2014, 29(1): 27-38. ]
[40]	林伯强, 徐斌. 研发投入、碳强度与区域二氧化碳排放[J]. 厦门大学学报(哲学社会科学版). 2020(4): 70-84.
	[ Lin Boqiang, Xu Bin. R&D Investment, carbon intensity and regional carbon dioxide emissions[J]. Journal of Xiamen University (Arts & Social Sciences Edition), 2020(4): 70-84. ]

国家	β₁	β₂	β₃	R²	模式	人均GDP拐点值/USD
哈萨克斯坦	-9.636	1.154	-0.005	0.547	倒N型	1.345×10³,2.064×10⁴
乌兹别克斯坦	27.056	-3.926	0.187	0.651	N型	4.805×10²,2.437×10³
土库曼斯坦	-0.132	0.090	-0.006	0.901	倒N型	2.215,1.176×10⁴
塔吉克斯坦	26.948	-4.257	0.223	0.090	N型	3.415×10²,1.013×10³
吉尔吉斯斯坦	83.854	-12.931	0.664	0.304	N型	5.681×10²,7.602×10²
中亚五国	26.373	-3.452	0.150	0.261	N型	1.470×10³,3.039×10³

变量	LLC检验	IPS检验	ADF-Fisher检验	PP-Fisher检验	是否为平稳序列
LnCP_it	-1.507（0.066）	-0.836（0.202）	14.187（0.161）	9.617（0.475）	否
D(LnCP_it)	-6.513（0.000）	-6.753（0.000）	58.599（0.000）	64.398（0.000）	是
LnPCGDP_it	0.661（0.746）	2.608（0.995）	2.201（0.995）	1.314（0.999）	否
D(LnPCGDP_it)	-4.162（0.000）	-3.724（0.000）	31.420（0.000）	30.839（0.001）	是
LnS_it	-2.714（0.003）	-4.557（0.000）	41.728（0.000）	40.713（0.000）	是
LnRDI_it	-1.894（0.029）	-0.128（0.449）	8.928（0.539）	4.276（0.934）	否
D(LnRDI_it)	-5.365（0.000）	-4.242（0.000）	37.403（0.000）	33.823（0.000）	是
LnZJ_it	-1.058（0.145）	0.868（0.807）	6.321（0.788）	8.568（0.574）	否
D(LnZJ_it)	-6.548（0.000）	-7.393（0.000）	67.763（0.000）	99.910（0.000）	是
LnZB_it	-2.678（0.004）	-0.494（0.312）	11.486（0.321）	6.996（0.726）	否
D(LnZB_it)	-15.213（0.000）	-13.221（0.000）	116.794（0.000）	124.175（0.000）	是

Technology spillover of import trade from China and the impact of carbon emissions in the five Central Asian countries

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[12]	ZHU Ni, ZHANG Yan-fang. Mechanism of carbon emissions intensity response to industrial evolvement and energy consumption structure change in Shaanxi Province [J]. , 2015, 38(4): 843-850.
[13]	LI Jian-bao，QU Jian-sheng，ZHANG Zhi-qiang. Characteristics and influencing factors of household carbon emissions in northwest China [J]. , 2014, 37(4): 759-766.
[14]	XIE Shou-hong，WANG Li-xia，SHAO Zhu-long. Review on carbon emissions researches at home and abroad [J]. , 2014, 37(4): 720-730.
[15]	. Variations and influence factors of carbon emission of primary energy consumption in Kazakhstan [J]. , 2013, 36(4): 757-763.