[1]何艳秋,陈柔,朱思宇,等.中国农业碳排放空间网络结构及区域协同减排[J].江苏农业学报,2020,(05):1218-1228.[doi:doi:10.3969/j.issn.1000-4440.2020.05.020]
 HE Yan-qiu,CHEN Rou,ZHU Si-yu,et al.Spatial network structure of agricultural carbon emission in China and regional collaborative emission reduction[J].,2020,(05):1218-1228.[doi:doi:10.3969/j.issn.1000-4440.2020.05.020]
点击复制

中国农业碳排放空间网络结构及区域协同减排()
分享到:

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年05期
页码:
1218-1228
栏目:
耕作栽培·资源环境
出版日期:
2020-10-31

文章信息/Info

Title:
Spatial network structure of agricultural carbon emission in China and regional collaborative emission reduction
作者:
何艳秋陈柔朱思宇王芳
(四川农业大学管理学院,四川成都611130)
Author(s):
HE Yan-qiuCHEN RouZHU Si-yuWANG Fang
(College of Management, Sichuan Agricultural University, Chengdu 611130, China)
关键词:
农业碳排放空间关联网络协同减排
Keywords:
agricultural carbon emissionsspatial correlation networkcollaborative emission reduction
分类号:
X16
DOI:
doi:10.3969/j.issn.1000-4440.2020.05.020
文献标志码:
A
摘要:
研究农业碳排放省际关联网络结构可为建立区域协同减排机制,发挥减排连锁效应奠定基础。突破传统基于地理邻接或地理距离考察区域农业碳排放关联的方法,利用社会网络分析,从空间网络视角考察农业碳排放关联的特点,明确各区域的网络功能,并通过建立非参数回归模型,从空间关联、经济关联、技术关联三纬角度解释农业碳排放关联的深层次原因。发现中国农业碳排放关联网络稳定性高,区域溢出“等级森严”;中部地区为网络核心,西部地区重要性显著提升;八大板块以谄媚者、类经纪人、受益者、贡献者和孤立者角色传递农业碳排放;空间、经济、技术三纬关联是引起农业碳排放关联的主要因素。最终提出通过缩短空间距离、增强经济联系、加强技术溢出扩大省际农业碳排放关联,根据各区域在农业碳排放空间关联网络中的差异化角色实施“引领-跟随”型减排策略,充分发挥中介者的“管道”作用,最终形成省际间的互动协作减排机制。
Abstract:
Study on the inter-provincial correlation and influencing factors of agricultural carbon emission can lay the foundation for establishing the regional collaborative emission reduction mechanism and exerting the chain-effect of emission reduction. Breaking through the traditional methods of examining regional agricultural carbon emission correlation based on geographic adjacency or geographic distance, social network analysis was used to investigate the characteristics of agricultural carbon emission correlation from the perspective of spatial network and clarify the network functions of each region. In addition, the reasons of agricultural carbon emission correlation were explained from the perspective of spatial correlation, economic correlation and technology linkage by nonparametric regression. Agricultural carbon-emission ossociation network had high stability, and regional spillovers were ranked. The central provinces and cities were the core of the network, and the importance of the western provinces and cities had increased significantly. The eight sectors delivered agricultural carbon emissions in the roles of flatterers, brokers, beneficiaries, contributors and solitary. The three-latitude correlation of space, economy and technology was the main factor that caused the carrelation of agricultural carbon emission. It was proposed to expand inter-provincial agricultural carbon emission correlation by shortening space distance, strengthening economic relation and strengthening technology spillover. The "lead-follow" emission reduction strategy was implemental according to the differentiated role of each region in the correlation network. Give full play to the intermediary’s "conduit" role. Finally, an inter-provincial interactive and cooperative emission reduction mechanism will be formed.

参考文献/References:

[1]联合国环境规划署. 《全球升温1.5 ℃特别报告》发布,为气候变暖再敲警钟[EB/OL]. (2018-10-10)
[2019-12-20].http://www.tanpaifang.com/tanguwen/2018/1010/62363.html.
[2]李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口·资源与环境,2011(8):80-86.
[3]COLE M, ELLIOTT A, ROBERT J R.The carbon dioxide emissions of firms:A Spatial analysis[J].Journal of Envionmental Economics and Management, 2013, 65:290-309.
[4]LIU Y, XIAO H W, PRECIOUS Z, et al. Carbon emissions in China: A spatial econometric analysis at the regional level[J].Sustainability,2014,6:6005-6023.
[5]DONG F, LONG R Y, LI Z L, et al. Analysis of carbon emission intensity,urbanization and energy mix:evidence from China[J].Nat Hazards,2016,82:1375-1391.
[6]LI L, HONG X F, TANG D L, et al. GHG emissions, economic growth and urbanization: A spatial approach[J].Sustainability,2016,10:123-126.
[7]GEORGE M, FRANKLIN A. Spatial analysis of emissions in Sweden[J].Energy Economics,2017,10:383-394.
[8]ZHANG Y G. Interregional carbon emission spillover-feedback effects in China[J].Energy Policy, 2017, 100:138-148.
[9]JIAO J L, YANG Y F, BAI Y. The impact of inter-industry R&D technology spillover on carbon emission in China[J].Nat Hazards,2018,91:913-929.
[10]MENG B, XUE J J, FENG K S, et al. China’s inter-regional spillover of carbon emissions and domestic supply chains[J]. Energy Policy, 2013, 61:1305-1321.
[11]孙亚男,刘华军,刘传明,等.中国省际碳排放的空间关联性及其效应研究——基于SNA的经验考察[J].上海经济研究,2016(2):82-92.
[12]杨桂元,吴齐,涂洋.中国省际碳排放的空间关联及其影响因素研究——基于社会网络分析方法[J].商业经济与管理,2016(4):56-68.
[13]张翼.基于空间关联网络结构的中国省域协同碳减排研究[J].统计与信息论坛,2017(2):63-69.
[14]WANG F, GAO M N, LIU J,et al. The spatial network structure of China’s regional carbon emissions and its network effect[J]. Energies, 2018, 11:2706.
[15]李秋萍,李长建,肖小勇,等.中国农业碳排放的空间效应研究[J].干旱区资源与环境,2015(4):30-35.
[16]孙赫,梁红梅,常学礼,等.中国土地利用碳排放及其空间关联[J].经济地理,2015 (3):154-162.
[17]吴贤荣,张俊飚,程琳琳,等.中国省域农业碳减排潜力及其空间关联特征——基于空间权重矩阵的空间Durbin模型[J].中国人口·资源与环境,2015 (6):53-61.
[18]程琳琳,张俊飚,田云,等.中国省域农业碳生产率的空间分异特征及依赖效应[J].资源科学,2016 (2):276 -289.
[19]WU H Y, HUANG H J, TANG J.Net greenhouse gas emissions from agriculture in China: Estimation, spatial correlation and convergence[J]. Sustainability,2019,11:4817.
[20]MA D. Spatial heterogeneity and influencing factors of agricultural energy carbon emission efficiency in China—An empirical research of spatial panel data model[J]. Resour Dev Mark,2018,12:1693-1765.
[21]WU Y, FENG K.Spatial-temporal differentiation features and correlation effects of provincial agricultural carbon emissions in China[J]. Environ Sci Technol,2019,3:180-190.
[22]WANG J, ZHANG Y, TIAN Y,et al. Influencing factors and spatial spillover of agricultural carbon emissions in major grain producing areas in China[J]. J S Chin Agric,2019,7:1632-1639.
[23]PACHAURI K,MEYER A.Climate change 2014:Synthesis report[R]. Geneva:IPCC,2014.
[24]赵欣,龙如银.考虑全要素生产率的中国碳排放影响因素分析[J].资源科学,2010(10):1863-1870.
[25]王智平.中国农田N2O排放量的估算[J].农村生态环境, 1997(2):51-55.
[26]DYER J A, KULSHRESHTHA S N, MCCONKEY B G, et al. An assessment of fossil fuel energy use and CO2 emissions from farm field operations using a regional level crop and land use database for Canada[J]. Energy, 2010, 35(5): 2261-2269.
[27]伍芬琳,李琳,张海林,等.保护性耕作对农田生态系统净碳释放量的影响[J].生态学杂志,2007 (12):2035-2039.
[28]IPCC. Climate change 2007: The physical science basis: Working group Ⅰ contribution to the fourth assessment report of the intergovernmental panel on climate change[M]. New York: Cambridge University Press, 2007.
[29]闵继胜,胡浩.中国农业生产温室气体排放量的测算[J].中国人口·资源与环境,2012 (7):21-27.
[30]刘丽华,蒋静艳,宗良纲.农业残留物燃烧温室气体排放清单研究:以江苏省为例[J].环境科学,2011 (5):1242-1248.
[31]KAYA Y.Impact of carbon dioxide emission on GNP growth:interpretation of proposed scenarios[R].Paris:IPCC Energy and Industry Subgroup,1989.
[32]COMMONER B. Making peace with the planet[M].New York:New Press,1992.
[33]刘卫东,唐志鹏,韩梦瑶.2012年中国31省区市区域间投入产出表[M].北京:中国统计出版社,2018.
[34]STANLEY W. Social network analysis: Methods and applications[M]. Cambridge: Cambridge University Press, 1994.
[35]新华网 . 我国优化布局农业发展新空间[EB/OL]. (2016-12-04)
[2019-12-31].http://www.soozhu.com/wx/article/297884/.
[36]中国改革报我国草原畜牧业发展潜力巨大[EB/OL].(2018-07-25)
[2019-12-31]. http://www.crd.net.cn/2018-07/25/content_24734062.htm.
[37]赵先超,宋丽美,古黄玲. 基于GIS的湖南省农地利用碳排放时空格局研究[J].江苏农业科学,2019,47(9):307-311.
[38]王珧,张永强,田媛,等. 我国粮食主产区农业碳排放影响因素及空间溢出性[J].南方农业学报,2019,50(7):1632-1639.
[39]张治会,李全新. 基于解构模型的2000-2014年甘肃省碳排放核算与分析[J].江苏农业科学,2018,46(5):257-260.

备注/Memo

备注/Memo:
收稿日期:2020-03-22基金项目:国家自然科学基金青年项目(71704127)作者简介:何艳秋(1983-),女,重庆人,博士,副教授,主要研究方向为农业可持续发展、农业资源环境。(E-mail)linxiatingqiu@126.com通讯作者:王芳,(E-mail)11575503@qq.com
更新日期/Last Update: 2020-11-16