纺织学报 ›› 2023, Vol. 44 ›› Issue (10): 149-156.doi: 10.13475/j.fzxb.20220902701

• 服装工程 • 上一篇    下一篇

异质性环境规制对中国纺织服装业碳排放的影响

张建磊1, 申攀登2(), 何琳1, 程隆棣3   

  1. 1.嘉兴学院 商学院, 浙江 嘉兴 314001
    2.嘉兴职业技术学院 时尚设计学院, 浙江 嘉兴 314036
    3.东华大学 纺织学院, 上海 201620
  • 收稿日期:2022-09-12 修回日期:2023-07-10 出版日期:2023-10-15 发布日期:2023-12-07
  • 通讯作者: 申攀登(1992—),女,讲师,硕士。主要研究方向为纺织服装经济与服装工程。E-mail:pt5008@163.com。
  • 作者简介:张建磊(1991—),男,讲师,博士。主要研究方向为纺织产业经济。
  • 基金资助:
    国家重点研发计划项目(2017YFB0309100)

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 Published:2023-10-15 Online:2023-12-07

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摘要：

为精准发挥不同类型环境规制对中国纺织服装业的碳减排效用,利用计量模型实证研究命令控制型、市场激励型和公众参与型环境规制对2005—2020年中国纺织服装业碳排放总量和强度的影响,并分析其地区异质性。结果表明:整体上,命令控制型对中国纺织服装业碳排放强度的影响呈倒U型,市场激励型对碳排放强度在一定范围内呈倒逼减排效应,公众参与型对碳排放总量呈绿色悖论效应;从地区看,命令控制型对东部碳排放的影响呈U型,对西部和东北碳排放强度的影响分别呈倒逼减排效应和绿色悖论效应;市场激励型在一定范围内能降低东部碳排放强度,对中部与东北碳排放的影响呈倒逼减排效应;公众参与型对东部碳排放总量与强度的影响分别呈倒逼减排效应和绿色悖论效应。

关键词: 纺织服装业, 碳排放, 环境规制, 绿色悖论效应, 倒逼减排效应

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

中图分类号: 

  • F426

表1

中国及各地区纺织服装业碳排放"

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

表2

门槛效应检验结果"

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

表3

中国纺织服装业碳排放的回归结果"

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

表4

各地区门槛效应检验结果"

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

表5

各地区纺织服装业碳排放回归结果"

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
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