自然资源学报 ›› 2022, Vol. 37 ›› Issue (2): 508-520.doi: 10.31497/zrzyxb.20220216
收稿日期:
2020-11-09
修回日期:
2021-05-10
出版日期:
2022-02-28
发布日期:
2022-02-16
通讯作者:
张彩虹(1965- ),女,甘肃兰州人,博士,教授,研究方向为林业经济与生物质能源发展。E-mail: rainbow_zhang2008@163.com作者简介:
刘志华(1988- ),女,山西平定人,博士研究生,研究方向为碳生态补偿、林业经济理论与政策。E-mail: liuzhihua268@126.com
基金资助:
LIU Zhi-hua1(), XU Jun-wei2, ZHANG Cai-hong1(
)
Received:
2020-11-09
Revised:
2021-05-10
Online:
2022-02-28
Published:
2022-02-16
摘要:
基于PVAR模型,以我国30个省(市、自治区)2010—2018年数据为例,从全国和东中西区域层面分析科技创新、产业结构升级与碳排放效率的动态关系。结果表明:(1)从全国层面看,科技创新、产业结构升级与碳排放效率自身具有较强的协调性且相互间能够产生正向的促进作用。(2)从区域内部来看,自东向西,科技创新、产业结构升级与碳排放效率的协调程度逐步递减。东部地区基本实现了三个变量的协调发展,中部地区产业结构升级与碳排放效率尚未形成双向互动关系,碳排放效率对产业结构升级提升的推动力不足;西部地区科技创新水平偏低,产业结构不合理、碳排放效率较低,三者均未形成良性互动。
刘志华, 徐军委, 张彩虹. 科技创新、产业结构升级与碳排放效率——基于省际面板数据的PVAR分析[J]. 自然资源学报, 2022, 37(2): 508-520.
LIU Zhi-hua, XU Jun-wei, ZHANG Cai-hong. Technological innovation, industrial structure upgrading and carbon emissions efficiency: An analysis based on PVAR model of panel data at provincial level[J]. JOURNAL OF NATURAL RESOURCES, 2022, 37(2): 508-520.
表1
我国省域科技创新水平评价指标体系
一级指标 | 二级指标 | 三级指标 | 单位 | 指标权重 |
---|---|---|---|---|
科技创新 | 创新投入 | R&D人员全时当量 | 人 | 0.0651 |
R&D经费投入强度 | % | 0.0403 | ||
科学研究和技术服务业新增固定资产占全社会新增固定资产比例 | % | 0.0817 | ||
R&D项目(课题)数 | 项 | 0.0895 | ||
创新产出 | 国内三种专利申请授权数 | 件 | 0.0930 | |
高新技术企业新产品开发项目数 | 个 | 0.1017 | ||
国外主要检索工具收录我国科技论文数 | 篇 | 0.0672 | ||
技术市场成交额 | 亿元 | 0.1389 | ||
创新环境 | 研究与开发机构数量 | 个 | 0.0278 | |
互联网普及率 | % | 0.0151 | ||
地方财政科技拨款占财政总支出比例 | % | 0.1015 | ||
高技术产业企业数量 | 个 | 0.0914 | ||
规模以上工业企业R&D经费内部支出额中获得金融机构贷款额 | 万元 | 0.0868 |
表3
变量描述性统计分析
变量 | 样本数/个 | 均值 | 标准差 | 最小值 | 中位数 | 最大值 | |
---|---|---|---|---|---|---|---|
全国 | INNO | 270 | 0.1064 | 0.1067 | 0.0075 | 0.0700 | 0.5770 |
IS | 270 | 0.0873 | 0.0890 | 0.0269 | 0.0590 | 0.7666 | |
CEF | 270 | 0.7642 | 0.2194 | 0.5110 | 0.7172 | 1.8509 | |
东部 | INNO | 99 | 0.1879 | 0.1362 | 0.0078 | 0.1507 | 0.5770 |
IS | 99 | 0.1415 | 0.1281 | 0.0385 | 0.1102 | 0.7666 | |
CEF | 99 | 0.9269 | 0.2715 | 0.6414 | 0.8273 | 1.8509 | |
中部 | INNO | 72 | 0.0764 | 0.0316 | 0.0344 | 0.0694 | 0.1779 |
IS | 72 | 0.0560 | 0.0231 | 0.0330 | 0.0516 | 0.2248 | |
CEF | 72 | 0.7094 | 0.0811 | 0.5646 | 0.7184 | 0.8492 | |
西部 | INNO | 99 | 0.0468 | 0.0328 | 0.0075 | 0.0352 | 0.1501 |
IS | 99 | 0.0560 | 0.0168 | 0.0269 | 0.0524 | 0.1228 | |
CEF | 99 | 0.6413 | 0.0975 | 0.5110 | 0.6098 | 0.9700 |
表4
不同准则下模型滞后期选择
Area | Lag | AIC | BIC | HQIC |
---|---|---|---|---|
全国 | 1 | -6.8629* | -5.9589* | -6.5066* |
2 | -6.2768 | -5.2379 | -5.8662 | |
3 | -5.8112 | -4.6214 | -5.3396 | |
东部 | 1 | -6.4896* | -5.6990* | -6.1687* |
2 | -6.2539 | -5.2482 | -5.8454 | |
3 | -6.3093 | -5.0662 | -5.8042 | |
中部 | 1 | -7.8650* | -7.0985* | -7.5537* |
2 | -6.9255 | -5.9061 | -6.5119 | |
3 | -3.4839 | -2.1872 | -2.9586 | |
西部 | 1 | -5.7601* | -4.9695* | -5.4392* |
2 | -5.0004 | -3.9946 | -4.5919 | |
3 | -3.9099 | -2.6667 | -3.4047 |
表5
PVAR模型GMM估计结果
变量 | 区域 | lnINNO | lnIS | lnCEF |
---|---|---|---|---|
L. lnINNO | 全国 | 0.6286*** (-8.8339) | 0.1183*** (-1.5559) | 0.4247*** (-7.1227) |
东部 | 0.7277*** (-8.9029) | 0.1268*** (-1.5348) | 0.6025*** (-4.3786) | |
中部 | 0.2468*** (-1.2720) | 0.5072*** (-2.5542) | 0.6588*** (-4.1325) | |
西部 | 0.0852 (-0.2592) | 0.9121 (-2.7161) | 0.8082*** (-3.4448) | |
L. lnIS | 全国 | 0.2424*** (-4.2494) | 1.0055*** (-16.0422) | 0.4283*** (-9.2186) |
东部 | 0.1480*** (-2.7887) | 1.7763*** (-13.014) | 0.5848*** (-6.6336) | |
中部 | 0.0611*** (-1.0257) | 0.6001 (-10.254) | 0.5972*** (-7.6057) | |
西部 | 0.9385 (-2.7760) | 0.7716 (-5.1722) | 0.8155 (-3.3404) | |
L. lnCEF | 全国 | 0.0615* (-1.7881) | 0.0148 (-0.4023) | 0.9865*** (-25.0555) |
东部 | 0.0348 (-0.8969) | -0.0794* (-1.7701) | 1.0963*** (-11.8905) | |
中部 | 0.0319 (-0.5287) | -0.0782 (-1.2926) | 0.9386*** (-11.8641) | |
西部 | 0.2001 (-1.4414) | 0.1793 (-1.2998) | 1.0325*** (-8.9207) |
表6
PVAR模型方差分解结果
响应变量 | 冲击变量 | ||||
---|---|---|---|---|---|
预测期 | lnINNO | lnIS | lnCEF | ||
全国 | lnINNO | 10 | 0.490 | 0.160 | 0.350 |
lnINNO | 20 | 0.569 | 0.166 | 0.265 | |
lnINNO | 30 | 0.575 | 0.164 | 0.261 | |
lnIS | 10 | 0.070 | 0.554 | 0.376 | |
lnIS | 20 | 0.094 | 0.469 | 0.437 | |
lnIS | 30 | 0.097 | 0.459 | 0.444 | |
lnCEF | 10 | 0.018 | 0.084 | 0.898 | |
lnCEF | 20 | 0.020 | 0.079 | 0.901 | |
lnCEF | 30 | 0.020 | 0.080 | 0.900 | |
东部 | lnINNO | 10 | 0.538 | 0.003 | 0.458 |
lnINNO | 20 | 0.582 | 0.021 | 0.396 | |
lnINNO | 30 | 0.573 | 0.031 | 0.395 | |
lnIS | 10 | 0.024 | 0.654 | 0.322 | |
lnIS | 20 | 0.020 | 0.723 | 0.257 | |
lnIS | 30 | 0.022 | 0.719 | 0.259 | |
lnCEF | 10 | 0.038 | 0.051 | 0.911 | |
lnCEF | 20 | 0.045 | 0.060 | 0.896 | |
lnCEF | 30 | 0.048 | 0.065 | 0.887 | |
中部 | lnINNO | 10 | 0.750 | 0.139 | 0.111 |
lnINNO | 20 | 0.732 | 0.174 | 0.094 | |
lnINNO | 30 | 0.727 | 0.176 | 0.097 | |
lnIS | 10 | 0.134 | 0.654 | 0.211 | |
lnIS | 20 | 0.190 | 0.587 | 0.223 | |
lnIS | 30 | 0.198 | 0.578 | 0.224 | |
lnCEF | 10 | 0.043 | 0.035 | 0.922 | |
lnCEF | 20 | 0.067 | 0.052 | 0.881 | |
lnCEF | 30 | 0.070 | 0.054 | 0.877 | |
西部 | lnINNO | 10 | 0.661 | 0.045 | 0.294 |
lnINNO | 20 | 0.671 | 0.013 | 0.317 | |
lnINNO | 30 | 0.667 | 0.009 | 0.325 | |
lnIS | 10 | 0.025 | 0.715 | 0.260 | |
lnIS | 20 | 0.008 | 0.691 | 0.301 | |
lnIS | 30 | 0.006 | 0.679 | 0.315 | |
lnCEF | 10 | 0.017 | 0.377 | 0.605 | |
lnCEF | 20 | 0.010 | 0.485 | 0.504 | |
lnCEF | 30 | 0.009 | 0.524 | 0.466 |
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