我国省域基础设施投入对社会-生态系统脆弱性的影响

doi:10.13287/j.1001-9332.202205.021

应用生态学报 ›› 2022, Vol. 33 ›› Issue (5): 1395-1404.doi: 10.13287/j.1001-9332.202205.021

我国省域基础设施投入对社会-生态系统脆弱性的影响

熊昌盛¹, 胡宇瑶¹, 周天肖^2*, 谭荣³, 张永蕾¹

¹海南大学公共管理学院, 海口 570228;
²绍兴文理学院商学院, 浙江绍兴 312000;
³浙江大学公共管理学院, 杭州 310058

收稿日期:2021-08-09 接受日期:2022-01-24 出版日期:2022-05-15 发布日期:2022-11-15
通讯作者: * E-mail: zhoutianxiao@zju.edu.cn
作者简介:熊昌盛, 男, 1988年生, 博士。主要从事基础设施外部性研究。E-mail: xiongcs@hainanu.edu.cn
基金资助:
教育部哲学社会科学研究重大课题攻关项目(20JZD013)、国家自然科学基金项目(72004049)、教育部人文社会科学研究一般项目(20XJCZH009)、浙江省科技厅软科学研究计划一般项目(2021C35066)、海南省自然科学基金项目(2019RC025,2019RC122,720QN241)和海南省哲学社会科学规划课题(JD(ZC)19-40)资助。

Impact of provincial infrastructure investment on the vulnerability of social-ecological system in China

XIONG Chang-sheng¹, HU Yu-yao¹, ZHOU Tian-xiao^2*, TAN Rong³, ZHANG Yong-lei¹

¹School of Public Administration, Hainan University, Haikou 570228, China;
²School of Business, Shaoxing University, Shaoxing 312000, Zhejiang, China;
³School of Public Affairs, Zhejiang University, Hangzhou 310058, China

Received:2021-08-09 Accepted:2022-01-24 Online:2022-05-15 Published:2022-11-15

摘要/Abstract

摘要： 基础设施投入对社会经济或生态系统的影响已被广泛讨论,但对社会-生态系统(SES)的作用如何仍不得而知。本研究梳理了基础设施投入对SES脆弱性的影响机理,并在核算省域人均基础设施资本存量及综合评估SES脆弱性的基础上,采用空间自相关与空间计量模型实证分析了我国省域基础设施投入对SES脆弱性的作用。结果表明: 2004—2017年,我国省域人均基础设施投入水平显著提高,且呈北高南低、东西高中间低的空间分布格局;省域SES脆弱性也在不断改善,却表现出由东向西逐步恶化的分布特点;我国省域人均基础设施投入与SES脆弱性具有空间正相关性,存在聚集性分布特征;两者呈倒U型曲线关系,即早期适量基础设施投入会降低SES脆弱性,而过度投入则会导致SES脆弱性逐步增加。本研究揭示了基础设施投入对SES脆弱性的影响机理与动态特点,可为宏观层面统筹协调基础设施建设与SES脆弱性治理提供理论与决策支持。

关键词: 基础设施投入, 社会-生态系统, 脆弱性, 空间自相关性, 空间计量分析

Abstract: The impact of infrastructure investment on social-economic system or ecological system has been widely discussed, yet, the overall impact of infrastructure investment on social-ecological system (SES) is still unknown. This study summarized the impact mechanism of infrastructure investment on social ecosystem vulnerability. We first sorted out the impact mechanism of infrastructure investment on SES vulnerability, and then empirically analyzed the effect of provincial infrastructure investment on SES vulnerability by using spatial autocorrelation and spatial econometric models on the basis of accounting provincial per capita infrastructure capital stock and comprehensive evaluation of SES vulnerability. The results showed that the infrastructure capital stock per capita at provincial level increased significantly during 2004-2017, with a spatial pattern that the north was higher than the south and the east/west was higher than the middle in China. The provincial SES vulnerability was improved, with spatial distribution characteristics of gradually getting worse from east to west in China. There was positive spatial correlation between provincial infrastructure investment and SES vulnerability, with aggregation distribution characteristics. There was inverted U-shaped relationship between infrastructure investment and SES vulnerability in China, that was, the appropriate investment of infrastructure at early could decrease SES vulnerability, while over-investment would increase it. Our results revealed the overall impact mechanism and dynamic characteristics of infrastructure investment on SES vulnerability, and could provide theoretical and policy-making support for the coordination of infrastructure construction and SES vulnerability governance at the macro level for China.

Key words: infrastructure investment, social-ecological system, vulnerability, spatial autocorrelation, spatial econometric analysis

熊昌盛, 胡宇瑶, 周天肖, 谭荣, 张永蕾. 我国省域基础设施投入对社会-生态系统脆弱性的影响[J]. 应用生态学报, 2022, 33(5): 1395-1404.

XIONG Chang-sheng, HU Yu-yao, ZHOU Tian-xiao, TAN Rong, ZHANG Yong-lei. Impact of provincial infrastructure investment on the vulnerability of social-ecological system in China[J]. Chinese Journal of Applied Ecology, 2022, 33(5): 1395-1404.

参考文献

[1] Kumatani K, Takahashi E, Kato T. Current and future infrastructure networks aimed at resolving issues related to the changing and diversifying activities in society. Fujitsu Scientific & Technical Journal, 2013, 49: 271-279
[2] 金凤君. 基础设施与人类生存环境之关系研究. 地理科学进展, 2001, 20(3): 276-285
[3] 刘秉镰, 武鹏, 刘玉海. 交通基础设施与中国全要素生产率增长——基于省域数据的空间面板计量分析. 中国工业经济, 2010, 28(3): 54-64
[4] 宋爽, 王帅, 傅伯杰, 等. 社会-生态系统适应性治理研究进展与展望. 地理学报, 2019, 74(11): 2401-2410
[5] Seto KC, Guneralp B, Hutyra LR. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Aca-demy of Sciences of the United States of America, 2012, 109: 16083-16088
[6] Chen Z, Xue J, Rose AZ, et al. The impact of high-speed rail investment on economic and environmental change in China: A dynamic CGE analysis. Transportation Research Part A: Policy and Practice, 2016, 92: 232-245
[7] Ostrom E. A diagnostic approach for going beyond panaceas. Proceedings of the National Academy of Sciences of the United States of America, 2007, 39: 15181-15187
[8] Cumming GS, Barnes G, Perz S, et al. An exploratory framework for the empirical measurement of resilience. Ecosystems, 2005, 8: 975-987
[9] Turner BL, Kasperson RE, Matson PA, et al. A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100: 8074-8079
[10] Ostrom E. A general framework for analyzing sustainability of social-ecological systems. Science, 2009, 325: 419-422
[11] Polsky C, Neff R, Yarnal B. Building comparable global change vulnerability assessments: The vulnerability scoping diagram. Global Environmental Change, 2007, 17: 472-485
[12] Frazier TG, Thompson CM, Dezzani RJ. A framework for the development of the SERV model: A spatially explicit resilience-vulnerability model. Applied Geography, 2014, 51: 158-172
[13] Thompson CM, Frazier TG. Deterministic and probabilistic flood modeling for contemporary and future coastal and inland precipitation inundation. Applied Geography, 2014, 50: 1-14
[14] 陈佳, 杨新军, 尹莎, 等. 基于VSD框架的半干旱地区社会-生态系统脆弱性演化与模拟. 地理学报, 2016, 71(7): 1172-1188
[15] Yu DJ, Qubbaj MR, Muneepeerakul R, et al. Effect of infrastructure design on commons dilemmas in social-ecological system dynamics. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112: 13207-13212
[16] Anderies JM, Smith-heisters S, Eakin H. Modeling interdependent water uses at the regional scale to engage stakeholders and enhance resilience in Central Arizona. Regional Environmental Change, 2020, 20: 1-16
[17] Albizua A, Zaga-Mendez A. Changes in institutional and social-ecological system robustness due to the adoption of large-scale irrigation technology in Navarre (Spain). Environmental Policy and Governance, 2020, 30: 167-181
[18] 王群, 杨万明, 朱跃, 等. 贫困区旅游地社会-生态系统恢复力时空分异研究——以安徽境内大别山区12个贫困县(市)为例. 地理科学, 2021, 41(6): 1030-1038
[19] Siqueira-Gay J, Sonter LJ, Sánchez LE. Exploring potential impacts of mining on forest loss and fragmentation within a biodiverse region of Brazil's northeastern Amazon. Resources Policy, 2020, 67: 101662
[20] 秦志龙, 陈晓光. 高铁开通改善了沿线城市的空气质量吗？——基于断点回归设计的分析. 环境经济研究, 2020, 5(2): 76-94
[21] 中华人民共和国国家统计局. 中国统计年鉴 [EB/OL]. (2022-01-13) [2022-01-13]. http://www.stats.gov.cn/tjsj/ndsj/
[22] 中华人民共和国生态环境部. 中国环境状况公报 [EB/OL]. (2022-01-13) [2022-01-13]. https://www.mee.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/
[23] 中华人民共和国自然资源部. 中国国土资源统计年鉴 [EB/OL]. (2022-01-13) [2022-01-13]. https://data.cnki.net/yearbook/Single/N2020030130
[24] 中华人民共和国国家统计局农村社会经济调查司. 中国农村统计年鉴 [EB/OL]. (2022-01-13) [2022-01-13]. https://data.cnki.net/yearbook/Single/N2021-120010
[25] 胡李鹏, 樊纲, 徐建国. 中国基础设施存量的再测算. 经济研究, 2016, 51(8): 172-186
[26] 金戈. 中国基础设施资本存量估算. 经济研究, 2012, 47(4): 4-14
[27] Adger WN. Vulnerability. Global Environmental Change, 2006, 16: 268-281
[28] Smit B, Wandel J. Adaptation, adaptive capacity and vulnerability. Global Environmental Change, 2006, 16: 282-292
[29] 叶亚平, 刘鲁君. 中国省域生态环境质量评价指标体系研究. 环境科学研究, 2000, 13(3): 33-36
[30] Murray V, Ebi KL. IPCC special report on managing the risks of extreme events and disasters to advance climate change adaptation (SREX). Journal of Epidemiology & Community Health, 2012, 66: 759-760
[31] 白仲林. 面板数据模型的设定、统计检验和新进展. 统计与信息论坛, 2010, 25(10): 3-12
[32] 李玉文, 徐中民, 王勇, 等. 环境库兹涅茨曲线研究进展. 中国人口·资源与环境, 2005, 15 (5): 11-18
[33] 方创琳, 任宇飞. 京津冀城市群地区城镇化与生态环境近远程耦合能值代谢效率及环境压力分析. 中国科学: 地球科学, 2017, 47(7): 833-846
[34] 杜江, 夏誉芸. 我国制造业空间集聚和产业转移的趋势与特征. 财经理论研究, 2020(5): 55-65
[35] 王育宝, 陆扬. 财政分权、税收负担与区域生态环境质量. 北京理工大学学报: 社会科学版, 2020, 22(3): 1-13
[36] 龙小宁, 朱艳丽, 蔡伟贤, 等. 基于空间计量模型的中国县级政府间税收竞争的实证分析. 经济研究, 2014, 49(8): 41-53
[37] Bai CE, Ma H, Pan W. Spatial spillover and regional economic growth in China. China Economic Review, 2012, 23: 982-990
[38] 孙东琪, 张京祥, 朱传耿, 等. 中国生态环境质量变化态势及其空间分异分析. 地理学报, 2012, 67(12): 1599-1610
[39] 蔡晓慧, 茹玉骢. 地方政府基础设施投资会抑制企业技术创新吗?——基于中国制造业企业数据的经验研究. 管理世界, 2016(11): 32-52
[40] 李金凯, 程立燕, 张同斌. 外商直接投资是否具有“污染光环”效应? 中国人口·资源与环境, 2017, 27(10): 74-83
[41] 刘守英. 土地制度变革与经济结构转型——对中国40年发展经验的一个经济解释. 中国土地科学, 2018, 32(1): 1-10
[42] 唐未兵, 傅元海, 王展祥. 技术创新、技术引进与经济增长方式转变. 经济研究, 2014, 49(7): 31-43
[43] Pedro C, Mercedes GA, Joaquín M. Transport infrastructures, spillover effects and regional growth: Evidence of the Spanish case. Transport Reviews, 2005, 25: 25-50

我国省域基础设施投入对社会-生态系统脆弱性的影响

Impact of provincial infrastructure investment on the vulnerability of social-ecological system in China

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