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    Journals >Resources Science >Volume 42 >Issue 7 >Page 1348 > Article
    • Resources Science
    • Vol. 42, Issue 7, 1348 (2020)
    Impact of different environmental regulatory tools on technological innovation of Chinese industrial enterprises above designated size
    Hang XIONG, Zheng JING, and Jintao ZHAN*
    Author Affiliations
  • Nanjing Agricultural University, Nanjing 210095, China
    • show less
    DOI: 10.18402/resci.2020.07.11 Cite this Article
    Hang XIONG, Zheng JING, Jintao ZHAN. Impact of different environmental regulatory tools on technological innovation of Chinese industrial enterprises above designated size[J]. Resources Science, 2020, 42(7): 1348 Copy Citation Text show less
    市场工具非市场工具
    ETS（排放交易计划）：CO2排放交易计划;可再生能源证书交易计划;能源证书排放交易计划;SOX排放交易计划ELV（污染物排放限值）：颗粒物（PM）排放限值;SOX排放限值;NOX排放限值
    Tax(税收)：CO2税;NOX税;SOX税政府对可再生能源的研发支出
    FIT（上网电价补贴）：风力发电电价;风力发电溢价;日光发电电价;日光发电溢价
    Table 1. Environmental regulation tools of environmental policy stringency (EPS) index
    一级指标二级指标二级指标说明
    企业创新行为自主创新规模以上的工业企业的R&D内部投入/百亿元
    技术引进规模以上的工业企业的R&D外部投入/百亿元
    市场激励型环境工具环保税（排污费）排污费与各省GDP的比值/%
    可再生能源发电补贴光伏发电平均上网电价与燃煤平均上网电价的比值/%风力发电平均上网电价与燃煤平均上网电价的比值/%
    环境权交易制度是否实施SO2废气排污权制度（1为是,0为否）碳交易量/t
    命令控制型环境工具法律法规、行政手段累积有效的环保法规数 累积有效的行政规章数 受理行政处罚数
    Table 2. Key variable description
    变量变量解释均值标准差最小值最大值
    rdin规模以上工业企业的R&D内部支出/百亿元3.3694.2070.06518.650
    rdout规模以上工业企业的R&D外部支出/百亿元0.1810.2410.0031.595
    rd规模以上工业企业的R&D总支出/百亿元3.5504.4170.06820.240
    scjl市场激励型工具综合指标0.3780.2540.0001.220
    mlkz命令控制型工具综合指标0.2310.1400.0300.761
    ins实际工业增加值/百亿元97.93081.2205.170359.000
    acindust工业增加值占GDP比重/%0.3640.0820.1180.496
    perGDP人均实际GDP/（万元/人）4.2631.8741.9709.159
    perGDP2人均实际GDP平方21.66020.3303.88083.890
    fdi外商实际直接投资/百亿美元0.9180.7690.0013.326
    Table 3. Descriptive statistics of regression variables
    (1)(2)(3)(4)(5)(6)
    rdinrdoutrdrdinrdoutrd
    mlkz0.288-0.1990.0890.288-0.199***0.089
    (0.417)(0.158)(0.844)(0.170)(0.007)(0.567)
    2014×mlkz-0.031-0.020-0.050-0.031-0.020-0.050
    (0.743)(0.613)(0.665)(0.644)(0.362)(0.562)
    2015×mlkz-0.207-0.040-0.247-0.207*-0.040**-0.247*
    (0.197)(0.500)(0.222)(0.058)(0.042)(0.054)
    2016×mlkz-0.417*-0.126-0.543*-0.417**-0.126***-0.543**
    (0.085)(0.222)(0.080)(0.031)(0.000)(0.017)
    2017×mlkz-0.244-0.055-0.299-0.244-0.055***-0.299*
    (0.346)(0.536)(0.330)(0.115)(0.001)(0.078)
    scjl-0.173-0.095-0.268-0.173-0.095-0.268
    (0.434)(0.630)(0.433)(0.467)(0.383)(0.437)
    2014×scjl-0.602-0.009-0.611-0.602-0.009-0.611
    (0.248)(0.938)(0.292)(0.169)(0.844)(0.196)
    2015×scjl-0.1600.3620.202-0.1600.362***0.202
    (0.730)(0.203)(0.733)(0.668)(0.004)(0.642)
    2016×scjl1.534*0.1461.680*1.534***0.146*1.680***
    (0.078)(0.318)(0.071)(0.004)(0.088)(0.003)
    2017×scjl1.395***0.360*1.755***1.395**0.360***1.755***
    (0.001)(0.065)(0.001)(0.010)(0.006)(0.009)
    ins-0.325***0.024-0.301**-0.325***0.024-0.301***
    (0.002)(0.713)(0.018)(0.000)(0.132)(0.000)
    acindust-0.660**-0.098-0.758**-0.660***-0.098***-0.758***
    (0.012)(0.111)(0.011)(0.001)(0.001)(0.001)
    perGDP-0.871**-0.156-1.027**-0.871***-0.156***-1.027***
    (0.016)(0.226)(0.016)(0.001)(0.004)(0.002)
    perGDP2-1.587***-0.140-1.727***-1.587***-0.140**-1.727***
    (0.007)(0.170)(0.006)(0.001)(0.027)(0.001)
    fdi0.059***0.0050.064***0.059***0.005***0.064***
    (0.000)(0.118)(0.000)(0.000)(0.000)(0.000)
    dummy2014-8.452**-0.989-9.442**-8.452***-0.989***-9.442***
    (0.012)(0.199)(0.012)(0.001)(0.002)(0.001)
    dummy2015-1.725-0.154-1.879-1.725***-0.154-1.879***
    (0.174)(0.308)(0.153)(0.001)(0.291)(0.002)
    dummy20160.0860.0090.0950.086**0.0090.095**
    (0.350)(0.296)(0.308)(0.021)(0.436)(0.030)
    dummy20170.055-0.0300.0260.055-0.030*0.026
    (0.559)(0.410)(0.796)(0.499)(0.052)(0.777)
    常数项5.917**0.4876.405**5.917***0.4876.405***
    (0.043)(0.223)(0.040)(0.000)(0.105)(0.000)
    N150150150150150150
    标准误估计方法WhiteWhiteWhiteD&KD&KD&K
    Table 4. National regression results
    (1)(2)(3)
    rdinrdoutrd
    ctrade0.2220.092**0.315*
    (0.160)(0.030)(0.076)
    mlkz0.468-0.1070.361
    (0.160)(0.228)(0.332)
    ins0.060***0.005***0.066***
    (0.000)(0.000)(0.000)
    acindust-6.183**-0.382-6.565**
    (0.016)(0.572)(0.022)
    perGDP-2.026***-0.187-2.213***
    (0.003)(0.300)(0.004)
    perGDP20.124**0.0130.137**
    (0.013)(0.319)(0.014)
    fdi-0.070-0.064**-0.134
    (0.542)(0.040)(0.299)
    dummy2014-0.184*-0.036-0.220*
    (0.092)(0.212)(0.072)
    dummy2015-0.517***-0.066-0.583***
    (0.002)(0.131)(0.002)
    dummy2016-0.654***-0.080-0.734***
    (0.002)(0.147)(0.002)
    dummy2017-0.317-0.039-0.356
    (0.172)(0.529)(0.171)
    常数项5.908***0.4346.342***
    (0.000)(0.311)(0.001)
    N150150150
    Table 5. Difference in differences (DID) regression results for carbon trading market
    东部地区中部地区西部地区
    rdinrdoutrdrdinrdoutrdrdinrdoutrd
    mlkz-0.315-1.064**-1.3800.330-0.1400.1910.3230.0170.340
    (0.716)(0.013)(0.255)(0.142)(0.118)(0.267)(0.188)(0.545)(0.206)
    2014×mlkz-0.241-0.274***-0.514*-0.336**-0.122***-0.458***0.063**0.016*0.079***
    (0.242)(0.002)(0.071)(0.014)(0.005)(0.005)(0.032)(0.099)(0.004)
    2015×mlkz-0.221-0.292***-0.513-0.275-0.181***-0.455*0.0610.031*0.092
    (0.440)(0.003)(0.131)(0.198)(0.003)(0.077)(0.595)(0.071)(0.390)
    2016×mlkz-0.370-0.489***-0.859-0.582*-0.234**-0.815**-0.0800.002-0.078**
    (0.588)(0.000)(0.236)(0.077)(0.012)(0.035)(0.113)(0.924)(0.047)
    2017×mlkz-0.437-0.327**-0.764-0.287-0.204**-0.492-0.0740.022-0.052
    (0.556)(0.014)(0.298)(0.413)(0.039)(0.209)(0.131)(0.322)(0.186)
    scjl0.1500.1810.3300.971**0.2401.210***-0.165-0.044*-0.210
    (0.357)(0.411)(0.128)(0.021)(0.193)(0.002)(0.181)(0.076)(0.156)
    2014×scjl0.0420.494**0.536-0.3730.081-0.292-0.377-0.036-0.413
    (0.868)(0.027)(0.201)(0.286)(0.380)(0.327)(0.190)(0.191)(0.169)
    2015×scjl0.498**1.298***1.796***-0.0500.229*0.179-1.003***-0.133***-1.135***
    (0.044)(0.003)(0.001)(0.929)(0.057)(0.733)(0.001)(0.002)(0.000)
    2016×scjl2.1970.843*3.041*-2.231***0.236**-1.995***-1.185***-0.211***-1.396***
    (0.107)(0.075)(0.097)(0.005)(0.023)(0.006)(0.000)(0.003)(0.000)
    2017×scjl1.590**0.783***2.373***1.598*0.2561.855**0.608***0.0170.625***
    (0.040)(0.006)(0.005)(0.067)(0.197)(0.026)(0.000)(0.543)(0.000)
    ins-0.2760.484***0.208-0.1570.027-0.130*-0.0300.011-0.019
    (0.215)(0.001)(0.418)(0.147)(0.683)(0.097)(0.438)(0.400)(0.668)
    acindust-2.089**-0.263**-2.352**-0.387*0.113*-0.275*0.279-0.0060.273
    (0.029)(0.013)(0.023)(0.055)(0.066)(0.068)(0.130)(0.807)(0.133)
    perGDP-2.582***-0.482**-3.064***-0.504*0.069-0.435*0.944***0.093*1.037***
    (0.006)(0.013)(0.006)(0.080)(0.275)(0.065)(0.007)(0.054)(0.005)
    perGDP2-2.657***-0.541**-3.199***0.3120.0300.342*1.371***0.156**1.528***
    (0.002)(0.014)(0.003)(0.100)(0.554)(0.056)(0.003)(0.028)(0.001)
    fdi0.064***0.006**0.070***0.036***0.0030.040***0.016*0.0010.018*
    (0.002)(0.013)(0.002)(0.001)(0.120)(0.001)(0.079)(0.334)(0.071)
    dummy2014-14.097-4.463***-18.560-9.885**-0.401-10.287***-4.790***-0.021-4.810***
    (0.268)(0.007)(0.146)(0.026)(0.697)(0.008)(0.008)(0.932)(0.003)
    dummy2015-3.107***0.048-3.059**-0.182-0.861-1.0420.9440.079**1.024
    (0.006)(0.910)(0.029)(0.846)(0.151)(0.346)(0.137)(0.035)(0.122)
    dummy20160.160**-0.0070.153*0.2020.1040.307*0.000-0.003-0.002
    (0.029)(0.778)(0.092)(0.193)(0.202)(0.093)(0.995)(0.609)(0.977)
    dummy20170.2660.0300.2971.644**0.210**1.855***-1.410**-0.128***-1.538**
    (0.308)(0.228)(0.255)(0.012)(0.037)(0.007)(0.032)(0.004)(0.023)
    常数项14.142**1.13115.274**-0.6421.4650.823-1.155-0.211*-1.366
    (0.017)(0.289)(0.024)(0.753)(0.150)(0.710)(0.281)(0.061)(0.230)
    N555555404040555555
    Table 6. Regional regression results
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    Hang XIONG, Zheng JING, Jintao ZHAN. Impact of different environmental regulatory tools on technological innovation of Chinese industrial enterprises above designated size[J]. Resources Science, 2020, 42(7): 1348
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