自然资源学报 ›› 2021, Vol. 36 ›› Issue (1): 223-239.doi: 10.31497/zrzyxb.20210115
收稿日期:
2020-05-18
修回日期:
2020-07-17
出版日期:
2021-01-28
发布日期:
2021-03-28
通讯作者:
涂建军
E-mail:yanxiao85China@163.com;654532972@qq.com
作者简介:
阎晓(1985- ),女,山西大同人,博士,副教授,研究方向为产业生态、生态经济与区域可持续发展。E-mail:基金资助:
Received:
2020-05-18
Revised:
2020-07-17
Online:
2021-01-28
Published:
2021-03-28
Contact:
TU Jian-jun
E-mail:yanxiao85China@163.com;654532972@qq.com
摘要:
生态效率是区域发展质量和人地协调程度的综合体现。采用TOPSIS法测度2003—2017年黄河流域37个资源型城市的生态效率,借助泰尔指数、全局空间自相关和热点分析揭示其时空演变规律,利用面板Tobit回归模型探寻关键驱动因素。结果表明:(1)黄河流域资源型城市生态效率总体上以2007年为分水岭,先平稳、后上升;不同城市生态效率的增速和增幅差异较大,下游城市明显高于中、上游城市,再生型城市明显高于成长、成熟和衰退型城市。 (2)城市间生态效率差异大小先略微下降、后持续上升;生态效率空间格局由随机分布向集聚分布演变,低值集聚区从山西、河南交界处向山西中、北部移动,高值集聚区分布具有空间粘性,一直位于下游山东境内。(3)产业转型、科技创新、基础设施完善和区位条件对生态效率改善具有显著正向驱动作用,外向型经济、资源依赖和环境规制抑制生态效率提高,城镇化、工业化和外资利用对生态效率演变的影响不显著;不同类型资源型城市生态效率改善的主要驱动因素存在异质性。
阎晓, 涂建军. 黄河流域资源型城市生态效率时空演变及驱动因素[J]. 自然资源学报, 2021, 36(1): 223-239.
YAN Xiao, TU Jian-jun. The spatio-temporal evolution and driving factors of eco-efficiency of resource-based cities in the Yellow River Basin[J]. JOURNAL OF NATURAL RESOURCES, 2021, 36(1): 223-239.
表2
资源型城市生态效率影响因子"
指标名称 | 符号 | 具体指标 | 指标说明 |
---|---|---|---|
城镇化水平 | urban | 城镇人口占比 | 城镇常住人口/常住人口总量 |
工业化水平 | indus | 工业增加值占比 | 工业增加值/GDP |
产业转型程度 | third | 第三产业增加值占比 | 第三产业增加值/GDP |
外资吸引度 | fdi | 外商及港澳台商投资企业产值占比 | 外商及港澳台商投资企业产值/工业总产值 |
经济外向度 | trade | 对外贸易系数 | 进出口总额/GDP |
科技投入强度 | tech | 科技支出强度 | 科学技术支出/财政支出 |
科技产出水平 | innov | 人均专利授权量 | 专利授权总量/常住人口总量 |
资源依赖度 | rely | 资源型产业从业人员占比 | 采掘业、电力煤气及水生产供应业从业人员数/年末单位从业人员数 |
基础设施保障度 | traff | 人均城市道路面积 | — |
环境规制强度 | envir | 污染治理综合指数 | 工业SO2去除率、工业烟尘去除率、一般工业固体废物综合利用率和生活垃圾无害化处理率4项指标熵权法均值 |
区位条件 | locat | 在黄河流域中的位置 | 上游附值为1,中游附值为2,下游附值为3 |
表3
黄河流域资源型城市生态效率影响因素回归结果"
变量 | (1) 流域整体 | (2) 成长型城市 | (3) 成熟型城市 | (4) 衰退型城市 | (5) 再生型城市 |
---|---|---|---|---|---|
ln(urban) | 0.088 | 0.070 | 0.311*** | 0.596*** | 0.778*** |
(1.291) | (0.667) | (3.126) | (7.359) | (3.104) | |
ln(indus) | -0.070 | -0.067 | 0.154** | 0.128 | -0.443** |
(-1.167) | (-0.590) | (2.194) | (1.557) | (-2.286) | |
ln(third) | 0.112** | -0.242*** | 0.287*** | 0.130** | 0.135 |
(2.182) | (-2.943) | (4.819) | (2.134) | (0.384) | |
ln(fdi) | -0.008 | -0.027** | 0.001 | -0.016** | -0.299*** |
(-0.919) | (-2.040) | (0.045) | (-2.251) | (-4.253) | |
ln(trade) | -0.032*** | -0.020 | 0.014 | -0.011 | -0.073 |
(-2.981) | (-1.134) | (0.924) | (-1.245) | (-0.985) | |
ln(tech) | 0.064*** | -0.025 | 0.037*** | -0.006 | 0.169*** |
(5.861) | (-1.107) | (2.828) | (-0.527) | (5.727) | |
ln(innov) | 0.036*** | 0.003 | 0.021 | -0.003 | 0.045 |
(2.947) | (0.147) | (1.267) | (-0.231) | (1.113) | |
ln(rely) | -0.109*** | -0.039 | -0.136*** | -0.069*** | -0.327*** |
(-4.855) | (-1.124) | (-4.180) | (-2.799) | (-2.954) | |
ln(regul) | -0.145*** | 0.084 | -0.169*** | -0.073** | -0.054 |
(-4.243) | (1.396) | (-4.472) | (-2.328) | (-0.269) | |
ln(traff) | 0.100*** | 0.182*** | 0.081*** | 0.011 | 0.119** |
(4.397) | (3.973) | (3.254) | (0.350) | (2.233) | |
ln(locat) | 0.207*** | -0.138 | 0.411** | 0.375** | 0.593*** |
(3.003) | (-1.256) | (2.354) | (2.437) | (4.019) | |
cons | -0.939** | -0.895 | -3.276*** | -4.124*** | -2.143 |
(-2.121) | (-1.320) | (-5.320) | (-7.524) | (-1.155) | |
N | 555 | 105 | 300 | 90 | 60 |
Wald chi2 | 366.70 | 64.00 | 268.86 | 295.94 | 340.12 |
Prob>chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Log likelihood | 274.949 | 82.512 | 213.438 | 131.872 | 32.400 |
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