资源科学 ›› 2021, Vol. 43 ›› Issue (2): 341-356.doi: 10.18402/resci.2021.02.12
收稿日期:
2020-03-18
修回日期:
2020-09-11
出版日期:
2021-02-25
发布日期:
2021-04-25
作者简介:
陈林,男,广东河源人,博士,广东省“珠江学者”特聘教授,研究方向为环境经济与产业规制。E-mail: 基金资助:
CHEN Lin(), XIAO Qianbing, LAN Shujing
Received:
2020-03-18
Revised:
2020-09-11
Online:
2021-02-25
Published:
2021-04-25
摘要:
环境质量的改善不仅是环境政策单独作用的结果,通常还与政策作用下产业结构的调整密切相关。本文基于中国2006—2016年197个地级及以上城市的PM2.5年均浓度面板数据,以“大气十条”政策作为环境规制的代理虚拟变量,结合双重差分法,研究“大气十条”政策的实施是否有利于降低京津冀晋鲁蒙、长三角、珠三角3个目标区域内城市的雾霾污染水平,并通过构建产业结构升级指标,探索该政策如何通过产业结构升级影响雾霾污染治理。进一步,采用面板门槛回归模型,验证“大气十条”政策实施背景下产业结构合理化与产业结构高度化对雾霾污染治理是否存在门槛效应。回归结果显示:①双重差分回归中,全样本下,“大气十条”的政策效果不显著;分区域样本下,政策对京津冀晋鲁蒙区域产生了显著的雾霾治理效应,而对长三角和珠三角区域没有产生明显的政策效果。②中介效应模型中,京津冀晋鲁蒙区域“大气十条”政策主要通过产业结构高度化的“量”作用于雾霾污染治理,且为部分中介效应;长三角区域产业结构高度化的“量”作用于雾霾污染治理则为完全中介效应;珠三角区域产业结构升级不具有显著的中介效应。③面板门槛回归中,京津冀晋鲁蒙与周边区域样本、长三角与周边区域样本下产业结构合理化与高度化均存在门槛效应。
陈林, 肖倩冰, 蓝淑菁. 基于产业结构门槛效应模型的环境政策治污效益评估——以《大气污染防治行动计划》为例[J]. 资源科学, 2021, 43(2): 341-356.
CHEN Lin, XIAO Qianbing, LAN Shujing. Pollution control effects of environmental policies based on threshold effect model of industrial structure: Taking the Air Pollution Prevention and Control Action Plan as an example[J]. Resources Science, 2021, 43(2): 341-356.
表1
描述性统计"
变量 | 样本量 | 平均值 | 标准差 | 最小值 | 最大值 |
---|---|---|---|---|---|
lnPM2.5 | 2167 | 3.4938 | 0.5062 | 1.5425 | 4.5093 |
lnPM2.5_ | 2167 | 3.6466 | 0.4156 | 1.8961 | 4.6074 |
ris | 2167 | 0.2602 | 0.2060 | 0.0001 | 1.7219 |
ois1 | 2167 | 2.2429 | 0.1400 | 1.8312 | 2.7972 |
ois2 | 2167 | 0.9960 | 0.4770 | 0.1686 | 8.0673 |
lnpopden | 2167 | 2.4696 | 0.4051 | 0.6721 | 3.4229 |
lngdppc | 2167 | 10.1638 | 0.7560 | 8.2013 | 12.9535 |
lngdppc2 | 2167 | 103.8736 | 15.6048 | 67.2622 | 167.7935 |
lninvest | 2167 | 2.5395 | 0.4866 | 1.5194 | 4.9021 |
scale | 2167 | 0.1522 | 0.0789 | 0.0118 | 1.4852 |
tech | 2167 | 0.0142 | 0.0136 | 0.0003 | 0.1656 |
open | 2167 | 0.0196 | 0.0190 | 1.23E-05 | 0.1316 |
temp | 2167 | 14.7718 | 5.3999 | -2.1000 | 26.4000 |
lnra | 2167 | 4.6046 | 0.4524 | 2.8332 | 5.5174 |
humi | 2167 | 65.9720 | 10.3891 | 14.9000 | 95.0000 |
表2
全样本、分区域样本双重差分回归结果"
解释变量 | 全样本 | 京津冀晋鲁蒙与周边区域 | 长三角与周边区域 | 珠三角与周边区域 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | ||||
policy | 0.007 | 0.018 | -0.092*** | -0.072*** | 0.038*** | -0.003 | 0.000 | 0.041 | |||
-0.016 | (0.014) | (0.025) | (0.023) | (0.013) | (0.013) | (0.014) | (0.028) | ||||
经济因素 | Y | Y | Y | Y | Y | Y | Y | Y | |||
气象因素 | Y | Y | Y | Y | Y | Y | Y | Y | |||
ytreat | -0.002 (0.002) | -0.004 (0.003) | 0.009*** (0.002) | -0.009 (0.005) | |||||||
时间固定效应 | Y | Y | Y | Y | Y | Y | Y | Y | |||
城市固定效应 | Y | Y | Y | Y | Y | Y | Y | Y | |||
Constant | 8.350*** | 10.397*** | 10.543*** | 14.813*** | 63.198*** | -3.715 | 5.746*** | 9.022*** | |||
(1.245) | (2.715) | (2.659) | (4.712) | (1.463) | (3.604) | (1.342) | (2.476) | ||||
Observations | 2167 | 2167 | 990 | 990 | 726 | 726 | 561 | 561 | |||
R2 | 0.262 | 0.264 | 0.366 | 0.367 | 0.580 | 0.591 | 0.741 | 0.746 |
表3
中介效应回归结果"
样本区间 | 变量 | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|---|
京津冀晋鲁蒙与周 边区域 | policy | -0.092*** | -0.088*** | |||
(0.025) | (0.025) | |||||
ris | 0.020 | -0.148 | ||||
(0.021) | (0.089) | |||||
ois1 | 0.009 | -0.718*** | ||||
(0.009) | (0.175) | |||||
ois2 | 0.129** | 0.044** | ||||
(0.060) | (0.021) | |||||
趋势项 | N | N | ||||
长三角与周边区域 | policy | -0.004 | -0.003 | |||
(0.013) | (0.013) | |||||
ris | 0.029 | 0.052 | ||||
(0.019) | (0.047) | |||||
ois1 | 0.018** | -0.273*** | ||||
(0.008) | (0.087) | |||||
ois2 | -0.038 | -0.026** | ||||
(0.056) | (0.013) | |||||
趋势项 | Y | Y | ||||
珠三角与周边区域 | policy | 0.000 | -0.002 | |||
(0.014) | (0.015) | |||||
ris | 0.014 | 0.056 | ||||
(0.025) | (0.055) | |||||
ois1 | -0.001 | -0.109 | ||||
(0.012) | (0.127) | |||||
ois2 | -0.063 | -0.017 | ||||
(0.042) | (0.019) | |||||
趋势项 | N | N |
表4
面板门槛模型回归结果"
解释变量 | 京津冀晋鲁蒙与周边区域 | 解释变量 | 长三角与周边区域 | |||||
---|---|---|---|---|---|---|---|---|
(1) | (2) | (4) | (1) | (2) | (4) | |||
policy_n | -0.099*** | -0.088*** | -0.100*** | policy_c | 0.043*** | 0.058*** | 0.046*** | |
(0.015) | (0.015) | (0.014) | (0.018) | (0.018) | (0.018) | |||
经济因素 | Y | Y | Y | 经济因素 | Y | Y | Y | |
气象因素 | Y | Y | Y | 气象因素 | Y | Y | Y | |
时间趋势 | N | N | N | 时间趋势 | Y | Y | Y | |
时间固定效应 | Y | Y | Y | 时间固定效应 | Y | Y | Y | |
城市固定效应 | Y | Y | Y | 城市固定效应 | Y | Y | Y | |
ris·I (ris≤ 0.445) | -0.053 | ris·I (ris≤ 0.409) | 0.127** | |||||
(0.062) | ||||||||
ris·I (ris>0.445) | 0.059 | ris·I (0.409<ris≤ 0.669) | 0.013 | |||||
(0.046) | (0.052) | |||||||
ris·I (0.669<ris≤ 0.762) | 0.124** | |||||||
(0.056) | ||||||||
ris·I (ris>0.762) | 0.015 | |||||||
(0.057) | ||||||||
ois1·I (ois1≤ 2.059) | -0.290** | ois1·I (ois1≤ 2.405) | -0.423*** | |||||
(0.122) | (0.113) | |||||||
ois1·I (2.059<ois1≤ 2.535) | -0.323*** | ois1·I (ois1>2.405) | -0.448*** | |||||
(0.117) | (0.112) | |||||||
ois1·I (ois1>2.535) | -0.360*** | |||||||
(0.117) | ||||||||
ois2·I (ois2≤ 0.277) | -0.019 | ois2·I (ois2≤ 0.889) | -0.035 | |||||
(0.256) | (0.030) | |||||||
ois2·I (0.277<ois2≤ 0.352) | 0.884*** | ois2·I (0.889<ois2≤ 0.913) | 0.066** | |||||
(0.141) | (0.032) | |||||||
ois2·I (0.352<ois2≤ 2.652) | 0.075*** | ois2·I (0.913<ois2≤ 0.981) | -0.051* | |||||
(0.023) | (0.026) | |||||||
ois2·I (ois2>2.652) | 0.023* | ois2·I (ois2>0.981) | -0.023 | |||||
(0.013) | (0.019) |
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