“双碳”目标背景下基于熵权TOPSIS法的区域农业高质量发展水平综合评价——基于变化速度特征视角

doi:10.3969/j.issn.1004-1524.2023.04.23

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (4): 962-972.DOI: 10.3969/j.issn.1004-1524.2023.04.23

• 农业经济与发展 • 上一篇

“双碳”目标背景下基于熵权TOPSIS法的区域农业高质量发展水平综合评价——基于变化速度特征视角

曾小春¹(), 李随成¹, 史官清²^,^*(), 邢泽宇³

1.西安理工大学经济与管理学院,陕西西安 710048
2.百色学院马克思主义学院,广西百色 533000
3.浙江工业大学管理学院,浙江杭州 310014

收稿日期:2022-04-23 出版日期:2023-04-25 发布日期:2023-05-05
通讯作者: ^*史官清,E-mail:shiguanqing@126.com
作者简介:曾小春(1988—),男,湖北黄冈人,博士,讲师,研究方向为低碳创新。E-mail:515366383@qq.com
基金资助:
广西一流学科建设项目“百色学院马克思主义理论一流学科建设项目”(桂教科研〔2022〕1号)

Comprehensive evaluation of China’s regional agricultural quality development level based on entropy weight TOPSIS under background of carbon peaking and carbon neutrality goals: from perspective of change speed

ZENG Xiaochun¹(), LI Suicheng¹, SHI Guanqing²^,^*(), XING Zeyu³

1. School of Economics and Management, Xi’an University of Technology, Xi’an 710048
2. School of Marxism, Baise University, Baise 533000, Guangxi, China
3. School of Management, Zhejiang University of Technology, Hangzhou 310014, China

Received:2022-04-23 Online:2023-04-25 Published:2023-05-05

摘要/Abstract

摘要：

基于2015—2020年中国省级面板数据,在“双碳”目标背景下,运用熵权TOPSIS法,从农业低碳经济、农业碳汇量、农业碳排量、农业碳吸量4方面构建指标体系,对区域农业高质量发展状况进行时序评价,并基于变化速度视角构建区域农业高质量发展状况的综合评价模型。实证结果表明:“双碳”目标背景下时序评价值较高的河北、辽宁等地,其整体变化速度却呈现下降趋势,反映了相对“饱和”的高位势下后续发展的动力不足;时序评价值较低的宁夏、甘肃、贵州等地,整体变化速度却呈现上升的趋势,说明虽然这些地方的农业低碳高质量发展现状不尽如人意,但发展的后劲足,展现出“逆马太效应”现象。据此提出因地制宜促进发展、关注后续发力效应,缩小区域农业高质量发展差异的对策建议。

关键词: 农业高质量发展, 变化速度, 综合评价

Abstract:

Based on the provincial panel data of China from 2015 to 2020, a time series evaluation system on the high-quality development of regional agriculture was constructed under the background of carbon peaking and carbon neutrality goals from aspects of agricultural low-carbon economy, agricultural carbon sequestration, agricultural carbon emission and agricultural carbon sequestration. Besides, a comprehensive evaluation model of regional agricultural high-quality development was constructed from the perspective of change speed through information aggregation. The empirical results showed that the overall change speed of Hebei and Liaoning had a downward trend, while their time series evaluation results were relatively high, which reflected the lack of impetus for subsequent development. Ningxia, Gansu, Guizhou showed relatively lower temporal evaluation value, yet their change speed exhibited an upward trend. Although the current situation of high-quality development of agriculture under the background of carbon peaking and carbon neutrality goals in these regions was not satisfactory, but their development potential was sufficient, which showed the phenomenon of “reverse Matthew effect”. Based on these findings, suggestions were put forward, such as promoting development according to local conditions, paying attention to the follow-up effect, and narrowing the gap between regional high-quality development of agriculture.

Key words: high-quality development of agriculture, change speed, comprehensive evaluation

中图分类号:

S-9
F323

曾小春, 李随成, 史官清, 邢泽宇. “双碳”目标背景下基于熵权TOPSIS法的区域农业高质量发展水平综合评价——基于变化速度特征视角[J]. 浙江农业学报, 2023, 35(4): 962-972.

ZENG Xiaochun, LI Suicheng, SHI Guanqing, XING Zeyu. Comprehensive evaluation of China’s regional agricultural quality development level based on entropy weight TOPSIS under background of carbon peaking and carbon neutrality goals: from perspective of change speed[J]. Acta Agriculturae Zhejiangensis, 2023, 35(4): 962-972.

图/表 5

参考文献 37

[1]	刘燕华, 李宇航, 王文涛. 中国实现“双碳”目标的挑战、机遇与行动[J]. 中国人口·资源与环境, 2021, 31(9): 1-5.
	LIU Y H, LI Y H, WANG W T. Challenges, opportunities and actions for China to achieve the targets of carbon peak and carbon neutrality[J]. China Population, Resources and Environment, 2021, 31(9): 1-5. (in Chinese with English abstract)
[2]	黄贤金, 张秀英, 卢学鹤, 等. 面向碳中和的中国低碳国土开发利用[J]. 自然资源学报, 2021, 36(12): 2995-3006. DOI
	HUANG X J, ZHANG X Y, LU X H, et al. Land development and utilization for carbon neutralization[J]. Journal of Natural Resources, 2021, 36(12): 2995-3006. (in Chinese with English abstract) DOI URL
[3]	尚光引, 杨欣. 政策认知对农户低碳农业技术采纳决策的影响[J]. 应用生态学报, 2021, 32(4): 1373-1382. DOI
	SHANG G Y, YANG X. Impacts of policy cognition on low-carbon agricultural technology adoption of farmers[J]. Chinese Journal of Applied Ecology, 2021, 32(4): 1373-1382. (in Chinese with English abstract)
[4]	田云, 张银岭. 中国农业碳排放减排成效评估、目标重构与路径优化研究[J]. 干旱区资源与环境, 2019, 33(12): 1-7.
	TIAN Y, ZHANG Y L. Research on effectiveness evaluation, target reconstruction and path optimization of agricultural carbon emission reduction in China[J]. Journal of Arid Land Resources and Environment, 2019, 33(12): 1-7. (in Chinese with English abstract)
[5]	刘玉, 潘瑜春, 唐林楠. 基于Esteban-Marquillas拓展模型的中国农业增长的演进特征[J]. 自然资源学报, 2017, 32(11): 1869-1882. DOI
	LIU Y, PAN Y C, TANG L N. Spatial temporal characteristics of China’s agricultural growth based on Esteban-Marquillas model[J]. Journal of Natural Resources, 2017, 32(11): 1869-1882. (in Chinese with English abstract) DOI
[6]	何培培, 张俊飚, 何可, 等. 农业生产何以存在低碳效率幻觉?: 来自1997—2016年31个省份面板数据的证据[J]. 自然资源学报, 2020, 35(9): 2205-2217. DOI
	HE P P, ZHANG J B, HE K, et al. Why there is a low-carbon efficiency illusion in agricultural production: evidence from Chinese provincial panel data in 1997-2016[J]. Journal of Natural Resources, 2020, 35(9): 2205-2217. (in Chinese with English abstract) DOI URL
[7]	蒋正云, 胡艳. 中部地区新型城镇化与农业现代化耦合协调机制及优化路径[J]. 自然资源学报, 2021, 36(3): 702-721. DOI
	JIANG Z Y, HU Y. Coupling and coordination between new urbanization and agricultural modernization in Central China[J]. Journal of Natural Resources, 2021, 36(3): 702-721. (in Chinese with English abstract) DOI URL
[8]	尹岩, 郗凤明, 邴龙飞, 等. 我国设施农业碳排放核算及碳减排路径[J]. 应用生态学报, 2021, 32(11): 3856-3864. DOI
	YIN Y, XI F M, BING L F, et al. Accounting and reduction path of carbon emission from facility agriculture in China[J]. Chinese Journal of Applied Ecology, 2021, 32(11): 3856-3864. (in Chinese with English abstract) DOI
[9]	田云, 林子娟. 巴黎协定下中国碳排放权省域分配及减排潜力评估研究[J]. 自然资源学报, 2021, 36(4): 921-933. DOI
	TIAN Y, LIN Z J. Provincial distribution of China’s carbon emission rights and assessment of its emission reduction potential under the Paris Agreement[J]. Journal of Natural Resources, 2021, 36(4): 921-933. (in Chinese with English abstract) DOI
[10]	伍国勇, 刘金丹, 杨丽莎. 中国农业碳排放强度动态演进及碳补偿潜力[J]. 中国人口·资源与环境, 2021, 31(10): 69-78.
	WU G Y, LIU J D, YANG L S. Dynamic evolution of China’s agricultural carbon emission intensity and carbon offset potential[J]. China Population, Resources and Environment, 2021, 31(10): 69-78. (in Chinese with English abstract)
[11]	田成诗, 陈雨. 中国省际农业碳排放测算及低碳化水平评价: 基于衍生指标与TOPSIS法的运用[J]. 自然资源学报, 2021, 36(2): 395-410.
	TIAN C S, CHEN Y. China’s provincial agricultural carbon emissions measurement and low carbonization level evaluation: based on the application of derivative indicators and TOPSIS[J]. Journal of Natural Resources, 2021, 36(2): 395-410. (in Chinese with English abstract) DOI URL
[12]	何艳秋, 陈柔, 朱思宇, 等. 策略互动和技术溢出视角下的农业碳减排区域关联[J]. 中国人口·资源与环境, 2021, 31(6): 102-112.
	HE Y Q, CHEN R, ZHU S Y, et al. Regional correlation of agricultural carbon emission reduction from the perspective of strategic interaction and technology spillover[J]. China Population, Resources and Environment, 2021, 31(6): 102-112. (in Chinese with English abstract)
[13]	王帅, 赵荣钦, 杨青林, 等. 碳排放约束下的农业生产效率及其空间格局: 基于河南省65个村庄的调查[J]. 自然资源学报, 2020, 35(9): 2092-2104. DOI
	WANG S, ZHAO R Q, YANG Q L, et al. Agricultural production efficiency and spatial pattern under carbon emission constraint: based on 65 villages of Henan Province[J]. Journal of Natural Resources, 2020, 35(9): 2092-2104. (in Chinese with English abstract) DOI
[14]	张军扩, 侯永志, 刘培林, 等. 高质量发展的目标要求和战略路径[J]. 管理世界, 2019, 35(7): 1-7.
	ZHANG J K, HOU Y Z, LIU P L, et al. The goals and strategy path of high-quality development[J]. Management World, 2019, 35(7): 1-7. (in Chinese)
[15]	黎新伍, 徐书彬. 基于新发展理念的农业高质量发展水平测度及其空间分布特征研究[J]. 江西财经大学学报, 2020(6): 78-94.
	LI X W, XU S B. The measurement of the level of agricultural high-quality development and the study of the spatial distribution characteristics: on the basis of the new development concept[J]. Journal of Jiangxi University of Finance and Economics, 2020(6): 78-94. (in Chinese with English abstract)
[16]	辛岭, 安晓宁. 我国农业高质量发展评价体系构建与测度分析[J]. 经济纵横, 2019(5): 109-118.
	XIN L, AN X N. Construction and empirical analysis of agricultural high-quality development evaluation system in China[J]. Economic Review Journal, 2019(5): 109-118. (in Chinese with English abstract)
[17]	杜丹丽, 曾小春. 速度特征视角的我国高新技术企业创新能力动态综合评价研究[J]. 科研管理, 2017, 38(7): 44-53.
	DU D L, ZENG X C. A dynamic and comprehensive evaluation of the innovation capability of China’s high-tech firms from the perspective of speed characteristics[J]. Science Research Management, 2017, 38(7): 44-53. (in Chinese with English abstract)
[18]	刘微微, 石春生, 赵圣斌. 具有速度特征的动态综合评价模型[J]. 系统工程理论与实践, 2013, 33(3): 705-710. DOI
	LIU W W, SHI C S, ZHAO S B. Dynamic comprehensive evaluation model with the feature of speed[J]. Systems Engineering-Theory & Practice, 2013, 33(3): 705-710. (in Chinese with English abstract)
[19]	杨早立, 綦萌, 黄鲁成, 等. “距离”与“速度”视角下的新兴产业风险评估及其预警: 以航天器制造产业为例[J]. 管理评论, 2021, 33(7): 43-53.
	YANG Z L, QI M, HUANG L C, et al. A risk assessment and early warning method for emerging industries from the perspective of “distance”and “speed”: case study in spacecraft manufacturing industry[J]. Management Review, 2021, 33(7): 43-53. (in Chinese with English abstract)
[20]	曾小春. 基于区域创新网络的科技型中小企业协同创新演化研究[D]. 哈尔滨: 哈尔滨工程大学, 2016.
	ZENG X C. Research on the evolution of collaborative innovation of science and technology small and medium-sized enterprises based on regional innovation network[D]. Harbin: Harbin Engineering University, 2016. (in Chinese with English abstract)
[21]	易平涛, 郭亚军, 张丹宁. 基于双激励控制线的多阶段信息集结方法[J]. 预测, 2007, 26(3): 39-43.
	YI P T, GUO Y J, ZHANG D N. A multi-phase information aggregation method based on double inspiriting control lines[J]. Forecasting, 2007, 26(3): 39-43. (in Chinese with English abstract)
[22]	陈伟, 杨早立, 周文, 等. 基于突变级数的知识密集型制造业技术创新能力动态综合评价: 变化速度特征的视角[J]. 运筹与管理, 2015, 24(1): 191-201. DOI
	CHEN W, YANG Z L, ZHOU W, et al. Dynamic comprehensive evaluation on technological innovative ability of knowledge-intensive manufactures based on catastrophe progression: perspective of change speed[J]. Operations Research and Management Science, 2015, 24(1): 191-201. (in Chinese with English abstract) DOI
[23]	张玉, 王介勇, 刘彦随. 陕西秦巴山区地域功能转型与高质量发展路径[J]. 自然资源学报, 2021, 36(10): 2464-2477. DOI
	ZHANG Y, WANG J Y, LIU Y S. Regional function transformation and high-quality development path in Qinling-Daba Mountains of Shaanxi Province[J]. Journal of Natural Resources, 2021, 36(10): 2464-2477. (in Chinese with English abstract) DOI
[24]	陈儒, 姜志德. 中国省域低碳农业横向空间生态补偿研究[J]. 中国人口·资源与环境, 2018, 28(4): 87-97.
	CHEN R, JIANG Z D. Transverse space ecological compensation of low-carbon agriculture in China[J]. China Population, Resources and Environment, 2018, 28(4): 87-97. (in Chinese with English abstract)
[25]	杨建辉. 农业化学投入与农业经济增长脱钩关系研究: 基于华东6省1市数据[J]. 自然资源学报, 2017, 32(9): 1517-1527. DOI
	YANG J H. Research on decoupling relationship between agricultural chemical inputs and agricultural economic growth: based on the data of six provinces and one city in East China[J]. Journal of Natural Resources, 2017, 32(9): 1517-1527. (in Chinese with English abstract)
[26]	张发明, 丁峰, 王坪. 中国粮食主产区农业高质量发展水平评价与时空演变[J]. 浙江农业学报, 2021, 33(1): 150-160. DOI
	ZHANG F M, DING F, WANG P. Evaluation of high-quality agricultural development level in major grain producing areas in China and its spatial and temporal evolution[J]. Acta Agriculturae Zhejiangensis, 2021, 33(1): 150-160. (in Chinese with English abstract) DOI
[27]	张建伟, 蒲柯竹, 图登克珠. 中国农业经济高质量发展指标体系构建与测度[J]. 统计与决策, 2021, 37(22): 89-92.
	ZHANG J W, PU K Z, TUBDEN K. Construction and measurement of high quality development index system of China’s agricultural economy[J]. Statistics & Decision, 2021, 37(22): 89-92. (in Chinese with English abstract)
[28]	傅琳琳, 毛小报, 毛晓红, 等. 浙江农业可持续发展水平与区域差异综合评价: 基于高质量发展视角[J]. 浙江农业学报, 2020, 32(10): 1880-1889. DOI
	FU L L, MAO X B, MAO X H, et al. Comprehensive evaluation on sustainable development level of agriculture and regional difference in Zhejiang Province: based on high-quality development perspective[J]. Acta Agriculturae Zhejiangensis, 2020, 32(10): 1880-1889. (in Chinese with English abstract) DOI
[29]	张文妍. 引入低碳经济的生态农业评价指标体系研究[J]. 生态经济, 2021, 37(12): 115-120.
	ZHANG W Y. Establishment and analysis on the evaluation system of ecological agriculture under the mode of low-carbon economy[J]. Ecological Economy, 2021, 37(12): 115-120. (in Chinese with English abstract)
[30]	董艳敏, 严奉宪. 中国农业高质量发展的时空特征与协调度[J]. 浙江农业学报, 2021, 33(1): 170-182. DOI
	DONG Y M, YAN F X. Spatial-temporal characteristics and coordination of agricultural high-quality development in China[J]. Acta Agriculturae Zhejiangensis, 2021, 33(1): 170-182. (in Chinese with English abstract) DOI
[31]	董捷, 曾咪. 基于碳汇收益和木材收益损失的碳汇造林补偿标准研究[J]. 浙江农业学报, 2022, 34(4): 790-800. DOI
	DONG J, ZENG M. Research on compensation standard for carbon sequestration afforestation based on carbon sequestration income and loss of wood income[J]. Acta Agriculturae Zhejiangensis, 2022, 34(4): 790-800. (in Chinese with English abstract) DOI
[32]	杨果, 陈瑶. 中国农业源碳汇估算及其与农业经济发展的耦合分析[J]. 中国人口·资源与环境, 2016, 26(12): 171-176.
	YANG G, CHEN Y. China’s agriculture carbon sink estimation and its coupling analysis with agricultural economy development[J]. China Population, Resources and Environment, 2016, 26(12): 171-176. (in Chinese with English abstract)
[33]	任家丰, 党馨逸, 姚柯渝, 等. 中国食物生产消费系统碳素动态变化及其环境负荷[J]. 中国环境科学, 2020, 40(8): 3693-3702.
	REN J F, DANG X Y, YAO K Y, et al. Carbon dynamics and environmental load of food production and consumption in China[J]. China Environmental Science, 2020, 40(8): 3693-3702. (in Chinese with English abstract)
[34]	冉锦成, 马惠兰, 苏洋. 西北五省农业碳排放测算及碳减排潜力研究[J]. 江西农业大学学报, 2017, 39(3): 623-632.
	RAN J C, MA H L, SU Y. A study on agricultural carbon emission and carbon emission reduction potential in five provinces in northwest China[J]. Acta Agriculturae Universitatis Jiangxiensis, 2017, 39(3): 623-632. (in Chinese with English abstract)
[35]	谢婷, 张慧, 苗洁, 等. 湖北省农田生态系统温室气体排放特征与源/汇分析[J]. 农业资源与环境学报, 2021, 38(5): 839-848.
	XIE T, ZHANG H, MIAO J, et al. Greenhouse gas emission characteristics and source/sink analysis of farmland ecosystem in Hubei Province[J]. Journal of Agricultural Resources and Environment, 2021, 38(5): 839-848. (in Chinese with English abstract)
[36]	SMITH P, BUSTAMANTE M, AHAMMAD H, et al. Agriculture, forestry and other land use (AFOLU)[M]//Climate change 2014: mitigation of climate change: contribution of working group Ⅲ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2014.
[37]	LU X H, JU W M, JIANG H, et al. Effects of nitrogen deposition on water use efficiency of global terrestrial ecosystems simulated using the IBIS model[J]. Ecological Indicators, 2019, 101: 954-962. DOI URL

目标指标 Objective indicator	一级指标 First-level indicators	二级指标 Second-level indicators	属性 Attribute
“双碳”目标背景下农业高质量	A1:农业低碳经济	A11:农业总产值Gross agricultural output	正向Positive
发展状况	Low-carbon	A12:投入产出比Input-output ratio	正向Positive
High-quality development of	agricultural economy	A13:土地产出率Land yield	正向Positive
agriculture under the background		A14:农村居民恩格尔系数Engel coefficient of rural residents	负向Negative
of carbon peaking and carbon	A2:农业碳汇量	A21:化肥施用强度Fertilizer application intensity	负向Negative
neutrality goals	Agricultural carbon	A22:农药施用强度Pesticide application intensity	负向Negative
	sinks	A23:柴油施用强度Application intensity of diesel oil	负向Negative
		A24:农业机械化水平Agricultural mechanization level	正向Positive
	A3:农业碳排量	A31:农业碳排放强度Agricultural carbon emission intensity	负向Negative
	Agricultural carbon	A32:农业碳排放密度Agricultural carbon emission density	负向Negative
	emission	A33:农业能源利用效率Energy use efficiency in agriculture	正向Positive
		A34:农业碳生产力Agricultural carbon productivity	正向Positive
	A4:农业碳吸量	A41:森林覆盖率Forest coverage	正向Positive
	Agricultural carbon	A42:有效灌溉系数Effective irrigation coefficient	正向Positive
	uptake	A43:人均耕地占有量Arable land per capital	正向Positive

目标指标 Objective indicator	一级指标 First-level indicators	二级指标 Second-level indicators	属性 Attribute
“双碳”目标背景下农业高质量	A1:农业低碳经济	A11:农业总产值Gross agricultural output	正向Positive
发展状况	Low-carbon	A12:投入产出比Input-output ratio	正向Positive
High-quality development of	agricultural economy	A13:土地产出率Land yield	正向Positive
agriculture under the background		A14:农村居民恩格尔系数Engel coefficient of rural residents	负向Negative
of carbon peaking and carbon	A2:农业碳汇量	A21:化肥施用强度Fertilizer application intensity	负向Negative
neutrality goals	Agricultural carbon	A22:农药施用强度Pesticide application intensity	负向Negative
	sinks	A23:柴油施用强度Application intensity of diesel oil	负向Negative
		A24:农业机械化水平Agricultural mechanization level	正向Positive
	A3:农业碳排量	A31:农业碳排放强度Agricultural carbon emission intensity	负向Negative
	Agricultural carbon	A32:农业碳排放密度Agricultural carbon emission density	负向Negative
	emission	A33:农业能源利用效率Energy use efficiency in agriculture	正向Positive
		A34:农业碳生产力Agricultural carbon productivity	正向Positive
	A4:农业碳吸量	A41:森林覆盖率Forest coverage	正向Positive
	Agricultural carbon	A42:有效灌溉系数Effective irrigation coefficient	正向Positive
	uptake	A43:人均耕地占有量Arable land per capital	正向Positive

区域Region	2015	2016	2017	2018	2019	2020	区域Region	2015	2016	2017	2018	2019	2020
北京Beijing	0.039	0.036	0.033	0.025	0.021	0.017	河南Henan	0.781	0.820	0.817	0.797	0.832	0.823
天津Tianjin	0.033	0.034	0.034	0.034	0.036	0.023	湖北Hubei	0.571	0.584	0.586	0.593	0.667	0.664
河北Hebei	0.641	0.662	0.646	0.620	0.645	0.580	湖南Hunan	0.591	0.570	0.570	0.583	0.645	0.562
山西Shanxi	0.149	0.153	0.154	0.146	0.149	0.138	广东Guangdong	0.563	0.559	0.562	0.571	0.645	0.646
内蒙古Inner Mongolia	0.292	0.298	0.291	0.277	0.286	0.294	广西Guangxi	0.421	0.421	0.420	0.431	0.483	0.504
辽宁Liaoning	0.491	0.490	0.481	0.483	0.464	0.410	重庆Chongqing	0.162	0.161	0.161	0.169	0.196	0.192
吉林Jilin	0.299	0.295	0.290	0.290	0.279	0.210	四川Sichuan	0.653	0.637	0.634	0.662	0.727	0.756
黑龙江Heilongjiang	0.477	0.511	0.525	0.521	0.549	0.603	贵州Guizhou	0.166	0.178	0.219	0.275	0.319	0.361
上海Shanghai	0.152	0.026	0.023	0.019	0.015	0.016	云南Yunnan	0.321	0.340	0.345	0.344	0.378	0.412
江苏Jiangsu	0.695	0.699	0.696	0.732	0.771	0.779	陕西Shaanxi	0.274	0.282	0.287	0.283	0.307	0.323
浙江Zhejiang	0.318	0.314	0.299	0.296	0.325	0.325	甘肃Gansu	0.156	0.161	0.163	0.167	0.175	0.154
安徽Anhui	0.449	0.450	0.451	0.451	0.490	0.493	青海Qinghai	0.019	0.021	0.021	0.018	0.018	0.021
福建Fujian	0.363	0.366	0.374	0.380	0.435	0.421	宁夏Ningxia	0.034	0.035	0.034	0.035	0.035	0.038
江西Jiangxi	0.286	0.284	0.286	0.288	0.323	0.323	新疆Xinjiang	0.271	0.280	0.288	0.282	0.305	0.351
山东Shandong	0.855	0.989	0.985	0.989	0.983	0.987

区域Region	2015	2016	2017	2018	2019	2020	区域Region	2015	2016	2017	2018	2019	2020
北京Beijing	0.039	0.036	0.033	0.025	0.021	0.017	河南Henan	0.781	0.820	0.817	0.797	0.832	0.823
天津Tianjin	0.033	0.034	0.034	0.034	0.036	0.023	湖北Hubei	0.571	0.584	0.586	0.593	0.667	0.664
河北Hebei	0.641	0.662	0.646	0.620	0.645	0.580	湖南Hunan	0.591	0.570	0.570	0.583	0.645	0.562
山西Shanxi	0.149	0.153	0.154	0.146	0.149	0.138	广东Guangdong	0.563	0.559	0.562	0.571	0.645	0.646
内蒙古Inner Mongolia	0.292	0.298	0.291	0.277	0.286	0.294	广西Guangxi	0.421	0.421	0.420	0.431	0.483	0.504
辽宁Liaoning	0.491	0.490	0.481	0.483	0.464	0.410	重庆Chongqing	0.162	0.161	0.161	0.169	0.196	0.192
吉林Jilin	0.299	0.295	0.290	0.290	0.279	0.210	四川Sichuan	0.653	0.637	0.634	0.662	0.727	0.756
黑龙江Heilongjiang	0.477	0.511	0.525	0.521	0.549	0.603	贵州Guizhou	0.166	0.178	0.219	0.275	0.319	0.361
上海Shanghai	0.152	0.026	0.023	0.019	0.015	0.016	云南Yunnan	0.321	0.340	0.345	0.344	0.378	0.412
江苏Jiangsu	0.695	0.699	0.696	0.732	0.771	0.779	陕西Shaanxi	0.274	0.282	0.287	0.283	0.307	0.323
浙江Zhejiang	0.318	0.314	0.299	0.296	0.325	0.325	甘肃Gansu	0.156	0.161	0.163	0.167	0.175	0.154
安徽Anhui	0.449	0.450	0.451	0.451	0.490	0.493	青海Qinghai	0.019	0.021	0.021	0.018	0.018	0.021
福建Fujian	0.363	0.366	0.374	0.380	0.435	0.421	宁夏Ningxia	0.034	0.035	0.034	0.035	0.035	0.038
江西Jiangxi	0.286	0.284	0.286	0.288	0.323	0.323	新疆Xinjiang	0.271	0.280	0.288	0.282	0.305	0.351
山东Shandong	0.855	0.989	0.985	0.989	0.983	0.987

区域Region	2016—2017	2017—2018	2018—2019	2019—2020	区域Region	2016—2017	2017—2018	2018—2019	2019—2020
北京Beijing	-0.003 0	-0.005 5	-0.006 0	-0.004 0	河南Henan	0.018 0	-0.011 5	0.007 5	0.013 0
天津Tianjin	0.000 5	0	0.001 0	-0.005 5	湖北Hubei	0.007 5	0.004 5	0.040 5	0.035 5
河北Hebei	0.002 5	-0.021 0	-0.000 5	-0.020 0	湖南Hunan	-0.010 5	0.006 5	0.037 5	-0.010 5
山西Shanxi	0.002 5	-0.003 5	-0.002 5	-0.004 0	广东Guangdong	-0.000 5	0.006 0	0.041 5	0.037 5
内蒙古Inner Mongolia	-0.000 5	-0.010 5	-0.002 5	0.008 5	广西Guangxi	-0.000 5	0.005 0	0.031 5	0.036 5
辽宁Liaoning	-0.005 0	-0.003 5	-0.008 5	-0.036 5	重庆Chongqing	-0.000 5	0.004 0	0.017 5	0.011 5
吉林Jilin	-0.004 5	-0.002 5	-0.005 5	-0.040 0	四川Sichuan	-0.009 5	0.012 5	0.046 5	0.047 0
黑龙江Heilongjiang	0.024 0	0.005 0	0.012 0	0.041 0	贵州Guizhou	0.026 5	0.048 5	0.050 0	0.043 0
上海Shanghai	-0.064 5	-0.003 5	-0.00 4	-0.001 5	云南Yunnan	0.012 0	0.002 0	0.016 5	0.034 0
江苏Jiangsu	0.000 5	0.016 5	0.037 5	0.023 5	陕西Shaanxi	0.006 5	0.000 5	0.010 0	0.020 0
浙江Zhejiang	-0.009 5	-0.009 0	0.013 0	0.014 5	甘肃Gansu	0.003 5	0.003 0	0.006 0	-0.006 5
安徽Anhui	0.001 0	0.000 5	0.019 5	0.021 0	青海Qinghai	0.001 0	-0.001 5	-0.001 5	0.001 5
福建Fujian	0.005 5	0.007 0	0.030 5	0.020 5	宁夏Ningxia	0	0	0.000 5	0.001 5
江西Jiangxi	0	0.002 0	0.018 5	0.017 5	新疆Xinjiang	0.008 5	0.001 0	0.008 5	0.034 5
山东Shandong	0.065 0	0	-0.001 0	-0.001 0

“双碳”目标背景下基于熵权TOPSIS法的区域农业高质量发展水平综合评价——基于变化速度特征视角

Comprehensive evaluation of China’s regional agricultural quality development level based on entropy weight TOPSIS under background of carbon peaking and carbon neutrality goals: from perspective of change speed

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 37

相关文章 15

编辑推荐

Metrics

本文评价

区域Region	2016—2017	2017—2018	2018—2019	2019—2020	区域Region	2016—2017	2017—2018	2018—2019	2019—2020
北京Beijing	1.000 0	0.986 0	1.011 2	1.000 0	河南Henan	0.882 2	0.952 3	1.154 2	0.876 6
天津Tianjin	0.997 2	1.000 0	1.005 6	0.957 9	湖北Hubei	0.969 1	1.014 0	1.187 9	0.784 1
河北Hebei	0.896 2	0.972 0	1.143 0	0.747 7	湖南Hunan	1.058 9	1.036 5	1.137 4	0.594 0
山西Shanxi	0.991 6	0.974 8	1.030 9	0.960 7	广东Guangdong	1.019 6	1.016 8	1.182 3	0.795 3
内蒙古Inner Mongolia	0.963 5	0.980 4	1.064 5	0.997 2	广西Guangxi	0.997 2	1.033 7	1.115 0	0.913 1
辽宁Liaoning	0.977 6	1.030 9	0.941 1	0.901 8	重庆Chongqing	1.002 8	1.022 4	1.053 3	0.913 1
吉林Jilin	0.997 2	1.014 0	0.969 1	0.837 4	四川Sichuan	1.036 5	1.086 9	1.103 8	0.899 0
黑龙江Heilongjiang	0.943 9	0.949 5	1.089 8	1.072 9	贵州Guizhou	1.081 3	1.042 1	0.966 3	0.994 4
上海Shanghai	1.344 6	0.997 2	1.000 0	1.014 0	云南Yunnan	0.960 7	0.983 2	1.098 2	1.000 0
江苏Jiangsu	0.980 4	1.109 4	1.008 4	0.913 1	陕西Shaanxi	0.991 6	0.974 8	1.078 5	0.977 6
浙江Zhejiang	0.969 1	1.033 7	1.089 8	0.918 7	甘肃Gansu	0.991 6	1.005 6	1.011 2	0.918 7
安徽Anhui	1.000 0	0.997 2	1.109 4	0.899 0	青海Qinghai	0.994 4	0.991 6	1.008 4	1.008 4
福建Fujian	1.014 0	0.994 4	1.137 4	0.806 5	宁夏Ningxia	0.994 4	1.005 6	0.997 2	1.008 4
江西Jiangxi	1.011 2	1.000 0	1.092 6	0.901 8	新疆Xinjiang	0.997 2	0.960 7	1.081 3	1.064 5
山东Shandong	0.613 5	1.022 4	0.972 0	1.028 0

区域Region	D	区域Region	D	区域Region	D	区域Region	D
北京Beijing	-0.018 5	上海Shanghai	-0.095 7	湖北Hubei	0.087 8	陕西Shaanxi	0.037 3
天津Tianjin	-0.003 8	江苏Jiangsu	0.078 1	湖南Hunan	0.032 0	甘肃Gansu	0.006 6
河北Hebei	-0.033 7	浙江Zhejiang	0.009 0	广东Guangdong	0.084 5	青海Qinghai	-0.000 5
山西Shanxi	-0.007 4	安徽Anhui	0.042 0	广西Guangxi	0.073 1	宁夏Ningxia	0.002 0
内蒙古Inner Mongolia	-0.005 0	福建Fujian	0.063 8	重庆Chongqing	0.032 5	新疆Xinjiang	0.055 4
辽宁Liaoning	-0.049 4	江西Jiangxi	0.038 0	四川Sichuan	0.097 3
吉林Jilin	-0.045 8	山东Shandong	0.037 9	贵州Guizhou	0.170 3
黑龙江Heilongjiang	0.084 5	河南Henan	0.025 0	云南Yunnan	0.065 6

[1]	刘艺平, 张一琪, 苏少文, 刘红利, 贺丹, 孔德政. 不同品种荷花耐盐碱性评价及鉴定指标筛选[J]. 浙江农业学报, 2023, 35(1): 103-111.
[2]	杨海龙, 王晖, 雷锦超, 蔡金洋. 浙江省早籼稻种质资源的表型多样性分析与评价[J]. 浙江农业学报, 2022, 34(8): 1571-1581.
[3]	杨蕾, 洪林, 刘兆俊, 杨海健, 王武. 六个金柑品种果实品质与营养综合评价[J]. 浙江农业学报, 2022, 34(3): 534-547.
[4]	莫言玲, 张文静, 罗亚兰, 曾静, 陈静静, 刘义华. 宽柄芥种质资源农艺性状与营养品质性状鉴定与评价[J]. 浙江农业学报, 2022, 34(2): 317-328.
[5]	贺鹏, 张涛, 宋海云, 郑树芳, 覃振师, 谭秋锦, 黄锡云, 汤秀华, 许鹏, 陈海生, 王文林. 适于开口加工澳洲坚果HAES695最佳采收期的确定[J]. 浙江农业学报, 2021, 33(8): 1489-1496.
[6]	张发明, 丁峰, 王坪. 中国粮食主产区农业高质量发展水平评价与时空演变[J]. 浙江农业学报, 2021, 33(1): 150-160.
[7]	董艳敏, 严奉宪. 中国农业高质量发展的时空特征与协调度[J]. 浙江农业学报, 2021, 33(1): 170-182.
[8]	牛素贞, 安红卫, 宋勤飞, 陈正武. 贵州野生茶树立地土壤养分状况分析及综合评价[J]. 浙江农业学报, 2020, 32(6): 1039-1048.
[9]	张淑文, 梁森苗, 朱婷婷, 任海英, 郑锡良, 戚行江. 不同杨梅品种的耐低温能力比较[J]. 浙江农业学报, 2020, 32(10): 1772-1779.
[10]	陈越, 张敦宇, 丁明亮, 王玲仙, 肖素勤, 柯学, 程在全. 多个省份水稻资源的表型多样性与优异资源的筛选[J]. 浙江农业学报, 2019, 31(11): 1779-1789.
[11]	毛小报, 傅琳琳, 毛晓红, 阿迪拉·艾海提. 浙江省农业可持续发展水平评价[J]. 浙江农业学报, 2019, 31(11): 1926-1934.
[12]	李春兰, 杨永花, 杨振坤, 王金秋, 廖伟彪. 五个观赏海棠品种抗旱性比较[J]. 浙江农业学报, 2017, 29(5): 782-790.
[13]	陈英, 王东, 王顺然. 黄土高原丘陵沟壑区高标准农田建设时序研究——以天水市麦积区为例[J]. 浙江农业学报, 2017, 29(4): 660-667.
[14]	叶霜, 熊博, 邱霞, 孙国超, 黄胜佳, 付佳玲, 汪志辉. 果实品质综合评价体系的建立及其在黄果柑果实上的应用[J]. 浙江农业学报, 2017, 29(12): 2038-2050.
[15]	付滨, 窦学诚. 基于AHP-模糊综合评价法的玉米品种安全评价及阻碍因素诊断——以河西制种基地为例[J]. 浙江农业学报, 2017, 29(10): 1611-1619.