Journal of Textile Research ›› 2023, Vol. 44 ›› Issue (10): 149-156.doi: 10.13475/j.fzxb.20220902701

• Apparel Engineering • Previous Articles     Next Articles

Impact of heterogeneous environmental regulations on carbon emissions with China's textile and garment industry

ZHANG Jianlei1, SHEN Pandeng2(), HE Lin1, CHENG Longdi3   

  1. 1. College of Business, Jiaxing University, Jiaxing, Zhejiang 314001, China
    2. College of Fashion Design, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang 314036, China
    3. College of Textiles, Donghua University, Shanghai 201620, China
  • Received:2022-09-12 Revised:2023-07-10 Online:2023-10-15 Published:2023-12-07

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Abstract:

Objective China textile and garment industry (CTGI) sets a green development goal that the amount of carbon emissions per unit of CTGI's value-added should decrease by 18% during the period of the "14th Five-Year Plan". environmental regulations (ER) is an important tool to curb carbon emissions, which include three heterogeneous regulations, i.e. command-based environmental regulation (CER), market-based environmental regulation (MER) and public-based environmental regulation (PER). Studying the relationship between the three different types of ERs and carbon emissions of CTGI is of great practical value for CTGI to achieve the green development goal.

Method The threshold model was used to study the impact of CER, MER and PER on the total carbon emissions and carbon emission intensity of CTGI during 2005—2020. If the threshold test effect was significant, it meant that this type of ER would have a nonlinear relationship with CTGI's total carbon emissions or carbon emission intensity. If not, it is indicated a linear relationship. Then the panel model was used to further investigate whether the impact mechanism was increasing effect or reducing effect. The two models were also used to study the impact mechanism in each region of China.

Results At the national level, the impact of CER on the carbon emission intensity of CTGI presents a single threshold effect. The impact coefficient is positive when CER is less than the threshold and negative when greater than the threshold, respectively. Both of them are significant. This means that the impact mechanism is in an inverted U-shape. Formal government environmental regulation can significantly decrease CTGI's carbon emission intensity after CER exceeds the threshold. The impact of MER on carbon emission intensity shows a double-threshold effect and the impact coefficients are significantly negative and positive, respectively when MER is less and greater than the second threshold. This indicates that the use of MER tools within this range can effectively reduce CTGI's carbon emission intensity. PER shows the increasing effect (namely the green paradox effect) on CTGI's total carbon emissions. At regional level, both of the impacts of CER on the total carbon emissions and carbon emission intensity of eastern textile & garment industry present the single threshold effect. The impact coefficients are all significantly negative and positive, respectively when CER is less and greater than the threshold. This means that after CER exceeds the threshold, its impacts change from the forced emission reduction effect to the green paradox effect. Its impacts on western and northeastern carbon emission intensity are dominated by forced emission reduction effect and the green paradox effect, respectively. MER can effectively reduce eastern carbon emission intensity within certain range. It also shows the forced emission reduction effect on central total carbon emissions, carbon emission intensity and northeastern carbon emission intensity. A significant double-threshold effect is observed between PER and eastern total carbon emissions. The impact coefficients are positive, positive and negative, respectively which means that after PER exceeds the second threshold, it shows the forced emission reduction effect on eastern total carbon emissions. While PER has the green paradox effect on eastern carbon emission intensity.

Conclusion Based on the above research results, the following policy recommendations can be put forward. At national level, China should continue to strengthen the formal government environmental regulation, appropriately develop MER tools and use these tools in a certain range. At regional level, the intensity of CER in the eastern region should be kept in an appropriate range and the use of MER tools should be further strengthened. The intensity of CER in the central, western and northeastern regions can be increased to a higher level and their MER system should be continuously improved. The public supervision on carbon emissions of textile & garment industry is necessary to be enhanced in these regions. Through the comprehensive use of a variety of environmental regulation tools, the carbon emissions of textile & garment industry in China and all the regions can be reduced and the green development goal can be achieved at last.

Key words: textile and garment industry, carbon emission, environmental regulation, green paradox effect, forced emission reduction effect

CLC Number: 

  • F426

Tab. 1

Carbon emissions of textile and garment industry in China and its regions"

年份 碳排放总量/万t 碳排放强度/(t·亿元-1)
中国 东部 中部 西部 东北 中国 东部 中部 西部 东北
2005 2 583 1 970 344 183 86 1 506 1321 2 628 2 926 2 856
2006 2 747 2 125 310 217 95 1 316 1 180 1 823 2 791 2 408
2007 2 795 2 170 323 188 114 1 096 1 003 1 421 1 763 2 212
2008 2 992 2 310 319 267 96 1 007 932 1 066 2 209 1 334
2009 2 829 2 177 295 251 106 868 819 805 1 696 1 187
2010 2 754 2 134 243 273 103 686 667 475 1 406 980
2011 2 717 2 056 304 227 129 583 572 426 953 1 136
2012 2 681 2 046 294 228 113 542 548 355 920 804
2013 2 412 1 827 217 206 162 434 443 219 750 981
2014 2 416 1 871 251 167 128 408 430 220 561 899
2015 2 313 1 817 234 169 93 372 398 190 524 944
2016 2 096 1 691 221 138 46 325 359 168 396 612
2017 1 731 1 429 124 139 38 320 368 107 465 717
2018 1 632 1 268 139 193 32 386 424 144 774 1 120
2019 1 609 1 256 147 186 19 398 437 163 794 659
2020 1 529 1 197 143 171 18 410 454 167 433 644

Tab. 2

Threshold effect test results"

被解释变量 解释变量 门槛个数 P值 门槛值 门槛区间下限 门槛区间上限
碳排放强度(CEI) 命令控制型环境规制(CER) 单门槛 0.050 0.978 0.840 0.991
碳排放强度(CEI) 市场激励型环境规制(MER) 双门槛 0.027 -3.174 -3.215 -3.135
-0.710 -0.741 -0.709

Tab. 3

Regression results of textile and garment industry in China"

解释变量 碳排放总量(lnCE) 碳排放强度(lnCEI)
结果(1) 结果(2) 结果(3) 结果(4) 结果(5) 结果(6)
命令控制型环境规制(lnCER) -0.068 — — 0.112**(lnCER≤0.978)
-0.221**(lnCER>0.978)		 — —
市场激励型环境规制(lnMER) — 0.065 — — 0.003(lnMER≤-3.174)
-0.323***(-3.174<lnMER≤-0.710)
0.367**(lnMER>-0.710)		 —
公众参与型环境规制(lnPER) — — 0.300* — — 0.252
经济发展水平(lnPGDP) -0.156 -0.159 -0.109 -1.226*** -1.208*** -1.228***
城镇化水平(lnURB) -0.239 -0.162 -0.427 -0.971 -0.864 -0.899
外商直接投资(lnFDI) -0.141* -0.115 -0.124 0.144 0.135 0.121
能源结构(lnES) 0.445*** 0.461*** 0.416*** -0.203 -0.151 -0.169

Tab. 4

Threshold effect test results in every region"

地区 被解释变量 解释变量 门槛个数 P值 门槛值 门槛区间下限 门槛区间上限
东部 碳排放总量(CE) 命令控制型环境规制(CER) 单门槛 0.000 -0.467 -3.817 -2.667
碳排放强度(CEI) 命令控制型环境规制(CER) 单门槛 0.000 -3.467 -3.817 -2.667
碳排放强度(CEI) 市场激励型环境规制(MER) 双门槛 0.017 -3.174 -3.218 -3.135
-0.811 -0.877 -0.804
碳排放总量(CE) 公众参与型环境规制(PER) 双门槛 0.067 -2.314 -2.334 -2.292
-0.878 -0.911 -0.863
东北 碳排放强度(CEI) 命令控制型环境规制(CER) 单门槛 0.040 -1.395 — —
碳排放总量(CE) 市场激励型环境规制(MER) 双门槛 0.000 -1.774 -1.884 -1.557
-1.067 -1.122 -1.059

Tab. 5

Regression results of textile and garment industry in every region"

地区 解释变量 碳排放总量(lnCE) 碳排放强度(lnCEI)
命令控制型环境规制(lnCER) -0.330***(lnCER≤-3.467) -0.188*(lnCER≤-3.467)
0.134***(lnCER>-3.467) 0.402***(lnCER>-3.467)
0.047(lnMER≤-3.174)
东部 市场激励型环境规制(lnMER) 0.854*** -0.455***(-3.174<lnMER≤-0.811)
0.619***(lnMER>-0.811)
0.338(lnPER≤-2.314)
公众参与型环境规制(lnPER) 0.007(-2.314≤lnPER<-0.878) 0.854***
-1.014***(lnPER>-0.878)
命令控制型环境规制(lnCER) -0.134 -0.052
中部 市场激励型环境规制(lnMER) -0.511** -0.818***
公众参与型环境规制(lnPER) -0.359 -0.229
命令控制型环境规制(lnCER) -0.106 -0.194*
西部 市场激励型环境规制(lnMER) -0.264 -0.002
公众参与型环境规制(lnPER) -0.035 -0.095
命令控制型环境规制(lnCER) -0.079 0.450*(lnCER≤-1.395)
-0.186(lnCER>-1.395)
东北 0.316(lnMER≤-1.774)
市场激励型环境规制(lnMER) -0.337(-1.774≤lnMER<-1.067) -1.111***
0.464(lnMER>-1.067)
公众参与型环境规制(lnPER) 0.909 -0.583
[1] 关大博. 中国碳核算数据库[DB/OL]. [2021-11-21]. https://www.ceads.net.cn/data/province.
GUAN Dabo. China carbon emission accounts & data-sets[DB/OL]. [2021-11-21]. https://www.ceads.net.cn/data/province.
[2] SINN H. Public policies against global warming: a supply side approach[J]. International Tax and Public Finance, 2008, 15(4): 360-394.
doi: 10.1007/s10797-008-9082-z
[3] WANG H, WEI W. Coordinating technological progress and environmental regulation in CO2 mitigation: the optimal levels for OECD countries & emerging econo-mies[J]. Energy Economics, 2020, 87(3): 1-11.
[4] PORTER M E, LINDE C V. Toward a new conception ofthe environment-competitiveness relationship[J]. Journal of Economic Perspectives, 1995, 9(4): 97-118.
doi: 10.1257/jep.9.4.97
[5] PAN X F, AI B W, LI C Y, et al. Dynamic relationshipamong environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China[J]. Technological Forecasting and Social Change, 2019, 144(7): 428-435.
doi: 10.1016/j.techfore.2017.12.012
[6] WANG H P, ZHANG R J. Effects of environmental regulation on CO2 emissions: an empirical analysis of 282 cities in China[J]. Sustainable Production and Consumption, 2022, 29: 259-272.
doi: 10.1016/j.spc.2021.10.016
[7] YANG Y H, YANG X, TANG D L. Environmentalregulations, Chinese-style fiscal decentralization, and carbon emissions: from the perspective of moderating effect[J]. Stochastic Environmental Research and Risk Assessment, 2021, 35(10): 1985-1998.
doi: 10.1007/s00477-021-01999-x
[8] NEVES S A, MARQUES A C, PATRICIO M. Determinants of CO2 emissions in European Union countries: does environmental regulation reduce environmental pollution?[J]. Economic Analysis and Policy, 2020(68): 114-125.
[9] 张华, 冯烽. 非正式环境规制能否降低碳排放?:来自环境信息公开的准自然实验[J]. 经济与管理研究, 2020, 41(8): 62-80.
ZHANG Hua, FENG Feng. Does informal environmental regulation reduce carbon emissions?: evidence from a quasi-natural experiment of environmental information disclosure[J]. Research on Economics and Management, 2020, 41(8): 62-80.
[10] CHENG Z H, LI L S, LIU J. The emissions reduction effect and technical progress effect of environmental regulation policy tools[J]. Journal of Cleaner Production, 2017, 149(2): 191-205.
doi: 10.1016/j.jclepro.2017.02.105
[11] WU R X, LIN B Q. Environmental regulation and itsinfluence on energy-environmental performance: evidence on the Porter Hypothesis from China's iron and steel industry[J]. Resources, Conservation & Recycling, 2022, 176: 1-13.
[12] 卢安, 马月华. 我国纺织服装行业碳排放量与产业GDP的脱钩关系研究[J]. 毛纺科技, 2016, 44(4): 65-70.
LU An, MA Yuehua. Decoupling analysis on the relationship between carbon emission and IGDP of textile & apparel industry[J]. Wool Textile Journal, 2016, 44(4): 65-70.
[13] 巩小曼, 柳疆梅, 衣芳萱, 等. 新疆纺织服装行业碳排放与经济增长的关系研究[J]. 丝绸, 2021, 58(2): 79-84.
GONG Xiaoman, LIU Jiangmei, YI Fangxuan, et al. The study on the relationship between carbon emission and economic growth of the textile and apparel industry in Xinjiang[J]. Journal of Silk, 2021, 58(2): 79-84.
[14] 李菁, 李小平, 郝良峰. 技术创新约束下双重环境规制对碳排放强度的影响[J]. 中国人口·资源与环境, 2021, 31(9): 34-44.
LI Jing, LI Xiaoping, HAO Liangfeng. Impact of dual environmental regulations on carbon emission intensity under the constraint of technological innovation[J]. China Population, Resources and Environment, 2021, 31(9): 34-44.
[15] HANSEN B E. Threshold effects in non-dynamic panels: estimation, testing and inference[J]. Journal of Econometrics, 1999, 93(2): 345-368.
doi: 10.1016/S0304-4076(99)00025-1
[16] PARGAL S, WHEELER D. Informal regulation of industrial pollution in developing countries: evidence from Indonesia[J]. Journal of Political Economy, 1996, 104(6): 1314-1327.
doi: 10.1086/262061
[17] 付凌晖, 刘爱华. 2021中国统计年鉴[M]. 北京: 中国统计出版社, 2021: 418-458.
FU Linghui, LIU Aihua. 2021 China statistical year-book[M]. Beijing: China Statistics Press, 2021: 418-458.
[18] 卢山. 2021中国工业统计年鉴[M]. 北京: 中国统计出版社, 2021: 302-309.
LU Shan. 2021 China industry statistical yearbook[M]. Beijing: China Statistics Press, 2021: 302-309.
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