中国农业竞争力时空格局演化及其影响因素

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魏素豪¹, 李晶¹, 李泽怡², 宗刚^,²

1.中国人民大学农业与农村发展学院,北京 100872;

2.北京工业大学经济与管理学院,北京 100124

Spatio-temporal evolution and its influencing factors of China's agricultural competitiveness

WEI Suhao¹, LI Jing¹, LI Zeyi², ZONG Gang^,²

1. School of Agricultural Economics and Rural Development, Renmin University of China, Beijing 100872, China;

2. School of Economics and Management, Beijing University of Technology, Beijing 100124, China

通讯作者: 宗刚(1957-), 男, 江苏常熟人, 博士, 教授, 博士生导师, 主要从事区域经济、产业经济等领域的研究。E-mail: zonggang1957@sina.com

收稿日期:2018-12-7修回日期:2019-12-17网络出版日期:2020-06-25

基金资助:

中国人民大学2018年度拔尖创新人才培育资助计划.
国家自然科学基金项目.71673289

Received:2018-12-7Revised:2019-12-17Online:2020-06-25

Fund supported:

Outstanding Innovative Talents Cultivation Funded Programs 2018 of Renmin Univertity of China.
National Natural Science Foundation of China.71673289

作者简介 About authors
魏素豪(1992-),男,河南通许人,博士生,主要从事农业经济理论与政策、农产品国际贸易等领域的研究E-mail:18810925328@163.com。

摘要
基于客观权重赋值法,从自然禀赋、流动投入、产出能力、设施机制4个维度构建指标体系,实证测算了2000—2016年中国29省农业竞争力,利用空间计量模型,剖析了中国农业竞争力时空演化规律及其影响因素,以期为制定针对性、差异化的农业竞争力提升政策提供理论支撑。结果表明:① 中国农业竞争力呈上升趋势,并存在明显的空间自相关性特征,空间溢出强度呈倒“U”型变化趋势,流动投入、产出能力两项子竞争力呈上升趋势,资源禀赋相对稳定,设施机制波动上升;② 东部省份农业竞争力异军突起,中西部省份不断下降,两级分化趋势明显,局域空间集聚格局以高—高和低—低集聚方式为主导,具有较强的稳定性与路径依赖性;③ 农民收入、居民消费水平、经济基础、R&D投入、种植结构、粮食品种选择对本省农业竞争力时空格局演变存在直接效应,对其他省份存在间接溢出效应;④ 应充分发挥农业竞争力及其影响因素的示范效应与扩散效应,推动农业资源优化配置、促进农业生产要素合理流动、谋划农业竞争力均衡发展。
关键词： 农业竞争力;时空演化;影响因素;空间计量模型;中国

Abstract

Based on the objective-weight value-assigning method, by means of establishing one comprehensive index system from four dimensions, namely natural endowment, flow input, output capacity and facilities' mechanism, this paper makes a series of experimental tests on the actual agricultural competitiveness within the 29 provincial-level areas of China from 2000 to 2016. And with application of spatial measurement model, it also makes a profound analysis upon spatio-temporal evolving rules and factors influencing China's agricultural competitiveness in hope for offering certain theoretical supports for formulating a series of targeted and differentiated agricultural competitiveness upgrading policies in the near future. The results of analysis show that: (1) China's agricultural competitiveness, featured by obvious spatial auto-correlation, is on the rise. The spatial spillover intensity presents an inverted "U-shaped" pattern. And the two sub-competitiveness indexes, namely flow input and output capacity, still keep on the upside. The overall resource endowment is relatively stable and the facilities' mechanism is witnessing a fluctuant increasing process. (2) The agricultural competitiveness in the central and western provinces continues to decline as that in the eastern provinces rises rapidly, which presents an apparent polarization. And the local-spatial clustering pattern is dominated by HH clustering mode and LL clustering mode with the characteristics of stronger stability and serious path dependency. (3) Multiple factors, including farmers' income, household-consuming level, economic basis, R&D input, planting structure and grain-variety selection, have direct effects on the spatio-temporal evolution of provincial agricultural competitiveness. At the same time, the above factors have indirect spillover effects on other provinces. (4) We should give full play of the current demonstration effect and diffusion effect brought by the agricultural competitiveness and related influencing factors to push future optimal allocation of agricultural resources, promote subsequent rational flow of the existing agricultural producing factors and plan finally-balanced development of agricultural competitiveness.

Keywords：agricultural competitiveness;space-time evolution;influencing factor;spatial measurement model;China

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本文引用格式
魏素豪, 李晶, 李泽怡, 宗刚. 中国农业竞争力时空格局演化及其影响因素. 地理学报[J], 2020, 75(6): 1287-1300 doi:10.11821/dlxb202006014
WEI Suhao. Spatio-temporal evolution and its influencing factors of China's agricultural competitiveness. Acta Geographica Sinice[J], 2020, 75(6): 1287-1300 doi:10.11821/dlxb202006014

1 引言

2000—2017年中国的城市化率由39.09%上升到了58.52%,未来城市化率还将继续上升,农村人口还会进一步减少,中国正在从传统意义的“乡土中国”向现代意义的“城市中国”转变,“乡土中国”农民自己养活自己的局面将一去不返,“城市中国”对农产品供应与需求的意义也将完全不同于“乡土中国”^[1]。收入增长与营养升级导致国内农产品需求的快速变化,“十九大”报告明确提出“确保国家粮食安全,把中国人的饭碗牢牢端在自己手中”,客观上就要求中国国内农产品供给能力与供给效率的提升^[2]。从国内来看,中国幅员辽阔,各地区农业发展模式、现代化路径差异较大,要深度参与全球农业价值链的重塑,必须更加深入的掌握中国内部农业竞争力的空间分布规律,强化对整体及各区域农业竞争力时空演变规律的认知,剖析其形成的机制以及制约各地区农业竞争力提升的关键因素,为全面提升中国农业竞争力提供理论支撑。

什么是农业竞争力?国内外****针对农业竞争力提出了多种理论解释,农业竞争力这一专业名词也逐步成为评估一个国家或地区农业生产效率的工具。农业竞争力的概念最初源于衡量国家间产品的竞争,例如显示性比较优势指数、显示性竞争优势指数等^[3,4],后来才逐步从国家间产品贸易引申到区域竞争力、不同产业竞争力的衡量上来。目前关于农业竞争力内涵的界定存在3种观点:① 农业竞争力是指一个地区生产出来的农产品中所包含的农业生产者本身的劳动力素质、专业生产技能的掌握程度、生产组织管理的效率等^[5];② 农业竞争力是涵盖了农业生产、农村生活各个领域的复杂体系,主要包括农业生产规模、效益、基础条件、结构状况、现代化水平、成长潜力、特色等,各方面表现出来的综合水平的高低决定了农业竞争力水平的高低^[6];③ 农业竞争力是指一个国家或地区在较长时期内不断适应外部变化、合理运用农业资源、提供农产品与服务过程中形成的发展能力,是各个地区相对而言的一种综合优势^[7]。除了对农业竞争力的内涵作出界定外,****们还对农业产业竞争力、农业贸易竞争力等做了理论解释^[8,9]。关于农业竞争力内涵的界定,争论的核心点在于,是包含农业生产过程中产前、产中和产后部门的综合体系还是仅限于产中部门?是包含农业生产、农村生活的综合体系还是仅限于农业生产?本文认为农业竞争力应该限定在农业生产环节中的投入、产出和外部保障机制领域,不能将农业生产与农村生活混淆,对农业竞争力的定义为:一个国家或地区综合运用既有农业生产要素以获得最大化产出的能力,是在进行农业生产、经营、组织、管理过程中所表现出来的相对比较优势。

如何利用统计学的方法去测算、评价农业竞争力?部分****已经从理论上对农业竞争力做了细致分析,但人们总是希望将理论应用到实际领域,用统计与计量分析的方法去测算不同区域的农业竞争水平的高低。指标体系研究方面,由于内涵界定不同,存在多种构建指标体系的维度,例如游士兵等从农业生产要素条件、农产品需求、农业经营主体、相关产业、农业机制4个方面构建了农业竞争力评价指标体系^[7];李晓甜等从农业产出效益、农业基础、农业结构与成长3个方面构建了指标体系^[10];指标分类角度不同,但所涵盖的细分指标差异不大^[11,12,13]。在评价方法方面,主要包括综合评分分析法^[7]、层次分析法^[14]、熵值法^[6]、因子分析法^[15]、主成分分析法^[16]、模糊综合评价法^[17]等,这些农业竞争力评价方法根据权重赋值的不同可以分为客观权重法和主观权重法,客观权重法根据数据本身的特征赋权重,而主观权重法一般依靠专家针对各项指标的主观打分赋权重。主观权重法容易导致评估结果的主观性偏差,客观权重法能够有效避免专家主观性因素对评估结果的影响,客观权重法中比较成熟的是熵值法,其争议最小,应用也最为广泛,因此本文拟运用熵值法评价农业竞争力。

农业竞争力的测算集中在哪些层次?已有文献主要包括3个层次的竞争力评价,① 农业企业竞争力,例如农业上市公司^[18]、农业龙头企业^[19]等竞争力评价;② 特定省份农业竞争力,例如甘肃省^[10]、湖南省^[15]、江苏省^[20]等农业竞争力评价;③ 国家或地区间农业竞争力的比较,例如中国新疆与中亚5国、世界各国间农业竞争力对比分析^{[13, 21]}。

综合来看,现有研究主要集中于农业竞争力内涵、评价方法、指标体系等方面,是本文深入研究的基础。不同时期、不同禀赋状态下农业竞争力的内涵也不尽相同,其指标选取也需因地制宜、因时制宜,突出重点,避免大而全式的指标体系带来评估误差。同时现有研究中鲜有从地理学视角分析中国各省份之间农业竞争力时空分异特征、演化规律及其形成机制。为此,本文基于2000—2016年中国省域尺度面板数据,以地理学的视角刻画中国农业竞争力空间分布格局,分析其演化规律,并基于空间计量模型深入剖析省域尺度农业竞争力非均衡发展的影响因素,揭示中国农业竞争力空间分异的驱动机制,以期为中国农业生产空间规划布局提供参考。

2 研究方法与数据来源

2.1 指标体系和影响因素

2.1.1 农业竞争力评价指标体系根据前文农业竞争力内涵界定,农业竞争力综合反映了某省份的农业自然条件竞争力、流动投入要素竞争力、产出能力竞争力和设施机制竞争力4个系统协调发展的情况,同时考虑到要素配置效率、产出能力、外部发展机制等多维度因素的相互作用,构建农业竞争力评价指标体系如表1所示。相较于已有研究,将衡量农村生活条件的各项指标剥离出来,仅选取农业生产类指标,将产出能力竞争力和设施机制竞争力纳入到指标体系中全面考察。同时需要避免以总量表征各项指标,例如以耕地总量衡量土地资源禀赋,容易忽略不同省份间人口基数的差异,因此按人均、劳均、亩均来折算各项指标更为科学合理。

Tab. 1
表1
表1农业竞争力评价指标体系
Tab. 1Evaluation index system of agricultural competitiveness

目标层	准则层	指标层	计算公式
农业竞争力评价	自然禀赋竞争力	土地资源禀赋(+)	总播种面积/年末常住人口数量
		水资源禀赋(+)	水资源总量/总播种面积
		自然灾害(-)	受灾面积/总播种面积
	流动投入要素竞争力	机械投入强度(+)	机械总动力/总播种面积
		化肥投入强度(o)	化肥投入总量/总播种面积
		农药投入强度(o)	农药投入总量/总播种面积
		农膜投入强度(+)	农膜投入总量/总播种面积
		农业用电投入强度(+)	农业用电量/总播种面积
	产出竞争力	劳动生产率(+)	第一产业产值/第一产业从业人员数量
		土地生产率(+)	粮食总产量/粮食播种面积
			蔬菜产量/蔬菜播种面积
			油料产量/油料播种面积
	设施与机制竞争力	有效灌溉面积占比(+)	有效灌溉面积/总耕地面积
		公路交通密度(+)	公路里程/行政面积
		财政支农力度(+)	财政支农资金/总播种面积

注:+、-、o分别表示正向、负向、适度指标。

新窗口打开|下载CSV

劳均主要农产品产量2012年以后不再统计,不存在种植业从业人员数量的统计口径,乡村从业人员数量和农林牧副渔业从业人员数量2012年以后也不再统计,考虑到数据可行性和连续性,产出指标中劳动生产率的计算采用第一产业劳动生产率替代种植业劳动生产率;财政支持种植业资金统计数据缺失,用财政支持农业资金数量替代,财政支农资金与总播种面积的比值表征财政支农强度。根据农业生产过程中流动投入要素本身的物理、化学特性,其亩均投入强度应该控制在合理的区间范围内,以使额外一单位投入的边际成本等于边际收益的临界值,即存在理想阀值,低于阀值时增加要素投入收益递增,高于阀值时增加要素投入收益递减,各省份机械投入强度、农膜投入强度、农业用电投入强度仍处于快速增加阶段,远未达到理想阀值,界定为正向指标,而各省份化肥投入强度、农药投入强度基本呈现先增后降的趋势,且近年来政府一直鼓励控制化肥与农药的过度使用,可界定为适度指标,理想阀值以平均值替代。

2.1.2 农业竞争力的影响因素农业竞争力为何会呈现出较强的空间异质性特征?需要继续深入挖掘影响农业竞争力及其空间演化的因素,探究农业竞争力时空分异特征及其形成机制。评价指标不能再次被纳入到影响因素的分析中是要素评价的基本准则^[22],从以下5个方面做影响因素的分解分析。

（1）农业经营主体投入能力的分化程度（PIC）。农户本身收入水平的高低决定了生产经营过程中的投入能力,影响到农户技术采纳行为与生产效率^[23],农户收入水平越高,其越容易购买农资、采纳新技术工艺,应对经营风险的能力也更高。以各省份农村居民人均纯收入与全国农村居民人均纯收入之差来表征农业经营主体投入能力的分化程度。

（2）农产品市场需求扩张能力的分化程度（RCL）。居民消费水平是决定农产品需求结构的关键因素,居民消费水平高的区域会对该地区农业生产效率的提升产生拉力^[24],如果全国农产品市场处于分割的状态,则本省份居民消费水平的提升会拉动本省农业生产效率的提升,如果全国农产品市场整合度较高,则会存在溢出效应。以各省份居民消费水平与全国居民消费水平之差来表征农产品市场需求扩张能力的分化程度。

（3）经济发展水平的分化程度（EDL）。经济基础是各产业发展的先决条件^[25],尤其是农业产业的发展,得益于诸如机械制造、种子培育、农药化肥等行业的发展。以各省份人均GDP与全国人均GDP之差来表征经济发展水平的分化程度。

（4）R&D投入分化程度（RDI）。R&D投入是农业机械研发、种子改良、农药化肥等农业生产技术创新的关键,其投入的增加也会对包括人、财、物在内的多种农业技术研发资源的空间布局产生影响^[26]。以各省份R&D投入与按省份平均的R&D投入之差来表征R&D投入分化程度。

（5）农作物种植结构（GSR）与粮食作物种植结构（PPG）。农作物种植地理集聚现象逐步凸显^[27],不同的种植结构往往表现出不同的收益能力与经营绩效^[28],同样也会影响到农业竞争力空间格局的变化。以粮食播种面积占总播种面积的比重来表征农作物种植结构,以稻谷播种面积占粮食播种面积的比重来表征粮食作物种植结构。

2.2 方法模型

2.2.1 农业竞争力评价方法熵值法的基本思路是依据不同指标变异程度的大小来赋予不同的权重,以此来保证权重能够更加客观、真实、科学的反映各指标的相对重要程度^[29,30,31]。

构建农业竞争力评价指标的特征值矩阵:

（1）

X = x_{(ij)} = \{\begin{matrix} x_{11} & x_{12} & ? & x_{1 n} \\ x_{21} & x_{22} & ? & x_{2 n} \\ ? & ? & ? & ? \\ x_{m 1} & x_{m 2} & ? & x_{mn} \end{matrix}\}

式中:x_ij为第i个省份第j项指标的取值;m为省份个数;n为指标个数,即

i = 1,2, \dots, m; j = 1,2, \dots, n

。不同指标量纲差异巨大,为消除不同量纲对评价结果的影响,对各项指标进行无量纲化处理,本文采取极差法对农业竞争力各项表征指标做无量纲化处理。

正向指标无量纲化处理公式:

（2）

y_{ij} = \frac{[x_{ij} - \min (x_{j})]}{[\max (x_{j}) - \min (x_{j})]}

负向指标无量纲化处理公式:

（3）

y_{ij} = \frac{[\max (x_{j}) - x_{ij}]}{[\max (x_{j}) - \min (x_{j})]}

适度指标无量纲化处理公式:

（4）

y_{ij} = \frac{|x_{ij} - \overset{ˉ}{x_{j}}|}{[\max (x_{j}) - \min (x_{j})]}

式中:y_ij是对x_ij进行无量纲化处理之后的结果;适度指标的适度阀值取平均值

\overset{ˉ}{x_{j}}

;第i个省份第j项指标在该指标中的占比为:

p_{ij} = \frac{y_{ij}}{\sum_{i = 1} y_{ij}}

,由此可以计算第j个指标的信息熵:

e_{j} = - \sum_{i = 1} \frac{p_{ij} \times \ln p_{ij}}{\ln m}

,进而计算出第j项指标的信息冗杂度

d_{j} = 1 - e_{j}

,第j项指标的权重计算公式为:

w_{j} = \frac{d_{j}}{\sum_{i = 1} d_{j}}

,最后运用熵权值加权计算各省份农业竞争力水平综合评价得分,公式为:

f_{i} = \sum_{j = 1} y_{ij} \times w_{j}

。

2.2.2 农业竞争力空间自相关性检验模型经验研究表明,农业生产具有典型的空间关联性特征,相邻地区的农业生产经营活动相互影响,往往呈现出地理上的空间集聚^[32]。为了更全面的分析不同省份间农业竞争力自相关程度及其空间分布状态,本文采取全域空间自相关性和局域空间自相关性方法识别农业竞争力的空间异质性特征。全域空间相关性指标（Global Spatial Autocorrelation）Moran's I模型表达为^[33]:

（5）

Moran's I = \frac{[n \sum_{i = 1} \sum_{j = 1} W_{ij} {(A C_{it} - \overset{C_{t}}{A})}^{2} (A C_{jt} - \overset{C_{t}}{A})]}{\sum_{i = 1} \sum_{j = 1} W_{ij} \sum_{i = 1} {(A C_{it} - \overset{C_{t}}{A})}^{2}}

式中:AC_it为i省份第t年农业竞争力水平;AC_jt为j省份第

t

年农业竞争力水平;n为省份数量;

\overset{C_{t}}{A}

为第t年所有省份农业竞争力的平均值;W_ij为二进制空间权重矩阵。空间权重矩阵设置为经过标准化处理后的一阶地理临接矩阵^[34],

W_{ij} = \frac{w_{ij}}{\sum_{j = 1} w_{ij}}

,其中w_ij的设置标准为地理空间上相邻的省份设置为1,不相邻的省份设置为0,对角线元素为0。

全域空间自相关性检验往往容易忽略局部地区所呈现出来的农业竞争力的非典型分布特征,利用局域空间自相关性（Local Indicators of Spatial Association, LISA）检验弥补这一不足。LISA模型表达为:

（6）

LocalMoran's I = [\frac{(A C_{it} - \overset{C_{t}}{A})}{\sum_{i = 1} {(A C_{it} - \overset{C_{t}}{A})}^{2}}] \sum_{j = 1 `} w_{ij} (A C_{jt} - \overset{C_{t}}{A})

2.2.3 农业竞争力影响因素空间计量模型的设定地理空间样本观察值一般存在相关性,样本数据不能满足独立同分布下的正态分布等假设,传统计量方法会导致样本信息失真,需要纳入地理因素并建立合适的空间计量模型^[34,35]。依据空间计量经济学理论,可以设置3种形式的模型分析中国农业竞争力空间分布格局形成机制,即空间滞后模型（Spatial Lag Model, SLM）、空间误差模型（Spatial Error Model, SEM）和空间杜宾模型（Spatial Durbin Model, SDM）。

SLM模型侧重于揭示农业竞争力在地理空间上是否具有扩散现象,模型设定为:

（7）

A C_{it} = δ \sum_{j = 1} w_{ij} A C_{it} + β_{1} PI C_{it} + β_{2} RC L_{it} + β_{3} ED L_{it} + β_{4} RD I_{it} + β_{5} GS R_{it} + β_{6} PP G_{it} + μ_{i} + ε_{it}

式中:

β_{1}, β_{2}, \dots, β_{6}

为各因素对农业竞争力的影响系数;δ为空间自回归系数;μ_i为个体固定效应;ε_iτ为随机误差项。

SEM模型包含了误差项的交互项,侧重于揭示误差项中被忽略的因变量的决定因素是否具有空间相关性,模型设定为:

（8）

A C_{it} = β_{1} PI C_{it} + β_{2} RC L_{it} + β_{3} ED L_{it} + β_{4} RD I_{it} + β_{5} GS R_{it} + β_{6} PP G_{it} + μ_{i} + γ_{it}

（9）

γ_{it} = λ \sum_{j = 1} w_{it} γ_{it} + ε_{it}

式中:γ_it为空间误差自相关项;λ为自相关系数。

SDM模型即涵盖了内生交互项,也涵盖了外生交互项,侧重于揭示某一省份农业竞争力与相邻省份的各项影响因素相关而产生的外生交互效应,模型设定为:

（10）

\begin{array}{l} A C_{it} = δ \sum_{j = 1} w_{ij} A C_{it} + β_{1} PI C_{it} + β_{2} RC L_{it} + β_{3} ED L_{it} + β_{4} RD I_{it} + β_{5} GS R_{it} + β_{6} PP G_{it} + β_{7} \sum_{j = 1} w_{ij} PI C_{it} + \\ β_{8} \sum_{j = 1} w_{ij} RC L_{it} + β_{9} \sum_{j = 1} w_{ij} ED L_{it} + β_{10} \sum_{j = 1} w_{ij} RD I_{it} + β_{11} \sum_{j = 1} w_{ij} GS R_{it} + β_{12} \sum_{j = 1} w_{ij} PP G_{it} + μ_{i} + ε_{it} \end{array}

上述分析的6种因素不仅会对本省份农业竞争力产生影响,也会对相邻省份农业竞争力产生空间溢出效应,实证分析部分将根据多种检验,选择合适的空间计量模型。

2.3 数据来源

本文数据来源于2001—2017年历年《中国统计年鉴》《中国农村统计年鉴》《中国农业年鉴》《中国水利统计年鉴》《中国劳动统计年鉴》各省份统计年鉴等相关统计资料。其中地方财政农林水支出,《中国统计年鉴》仅发布了2007—2016年统计数据,2000—2006年数据根据《中国统计年鉴》中农业、林业、农林水利气象和农业综合开发支持加总计算得到。如存在数据不一致的情况,以《中国统计年鉴》发布数据为准。考虑到数据的连续性与可得性,将香港、台湾、澳门、西藏自治区、海南5省份从样本中略去。

3 中国农业竞争力时空演变特征

3.1 时间演变特征

（1）中国农业竞争力水平整体呈现上升的趋势（图1）。2000—2016年中国农业平均竞争力得分从2.96上升到了3.42,年均增长率达0.91%。将农业竞争力细分为自然资源禀赋、流动投入要素、产出能力、设施机制4项子竞争力,2000—2016年自然资源禀赋竞争力得分基本稳定在0.34左右,按人均、亩均计算的各省份耕地、水等农业自然资源相对丰富程度波动较小;流动要素投入竞争力得分呈现上升趋势,从1.79上升到了2.29,年均增长率达1.66%,这主要得益于各省份亩均化肥、机械、农膜等流动投入要素的较快增长,且在变化趋势上与农业竞争力高度吻合,是主导农业竞争力变化的核心子项竞争力;产出能力竞争力从0.33上升到了0.46,年均增长率达2.10%,这主要得益于亩均与劳均产出的增长;设施机制竞争力,2003年全面实施农村税费改革之前逐年上升,2003年之后农村基础设施投融资机制和农业补贴政策的快速完善,设施机制竞争力逐步回升。

图1

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图12000—2016年中国平均农业竞争力得分的变化

Fig. 1Changes in scores of average agricultural competitiveness in China from 2000 to 2016

（2）农业竞争力具有明显的空间集聚特征,空间溢出强度呈倒“U”型变化趋势。基于Stata14.0计算2000—2016年农业竞争力的全域空间相关性Moran's I指数,结果显示Moran's I指数值均都大于0,且均都通过了5%水平下的显著性检验,表明农业竞争力存在显著的正向空间自相关性,空间溢出效应明显。2000—2005年Moran's I指数波动明显,2005—2012年维持在0.42~0.47之间,存在极强的正向空间自相关性,2012—2016年呈下降趋势,2016年为0.249,依然存在正向自相关（表2）。整体而言,全域Moran's I呈倒“U”型波动下降,但农业竞争力的空间集聚特征依然十分显著。

Tab. 2
表2
表22000—2016年全域空间相关性检验结果
Tab. 2Results of whole-space correlation tests from 2000 to 2016

年份	Moran's I	p值	年份	Moran's I	年份	Moran's I	p值
2000	0.238	0.001	2006	0.462	2012	0.442	0.000
2001	0.248	0.003	2007	0.427	2013	0.254	0.001
2002	0.456	0.000	2008	0.433	2014	0.245	0.003
2003	0.203	0.005	2009	0.423	2015	0.221	0.007
2004	0.277	0.002	2010	0.443	2016	0.249	0.017
2005	0.423	0.000	2011	0.428

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3.2 空间格局演变特征

（1）东部省份农业竞争力异军突起,中西部省份快速下降,两级分化趋势明显。基于ArcGIS地理信息处理系统,将农业竞争力水平分为3类,得分介于0~2为低竞争力,2~5为中等竞争力,5以上为高竞争力,分别绘制2000年、2008年、2016年农业竞争力空间分布图（图2）。农业竞争力空间分异特征明显,呈现3大地理集聚特征,① 东部省份农业竞争力快速上升,辽宁、江苏、浙江、福建、广东东部5省从中等竞争力区域上升到高竞争力区域,北京、上海、天津3市一直处于高等竞争力区域,山东则一直处于中等竞争力区域。② 中西部省份农业竞争力快速下滑,河北、山西、内蒙、江西、河南、湖北、湖南、广西、重庆、四川、云南、陕西12省市从中等竞争力区域下降到低竞争力区域,青海、新疆从高竞争力区域下降到中等竞争力区域,宁夏、贵州两省略有下降,一直处于低竞争力区域,相比之下安徽、甘肃从低竞争力区域上升到了中等竞争力区域。东北地区的吉林、黑龙江农业竞争力也从中等竞争力区域下滑到低竞争力区域。③ 两级分化趋势日益凸显,从各类竞争力水平占比来看,2000—2016年高竞争力省份占比从17.24%上升到了27.59%,低竞争力省份占比从13.79%上升到了55.17%,中等竞争力省份占比则从68.97%下降到了17.24%,省份间农业竞争力的差距逐步拉大,呈现出两级分化的趋势。农产品净输出省份并没有表现出较强的竞争力,相反,农产品净输入的东部省份,却表现出较强的农业竞争力。

（2）局域空间集聚格局具有较强的稳定性与空间依赖特征。为揭示农业竞争力局域空间自相关性特征,将局域Moran's I指数散点图表格化（表3）。农业竞争力局域空间集聚特征明显,以H-H（High-High）、L-L（Low-Low）两种类型集聚方式为主导,H-H集聚的省份主要分布在东部,L-L集聚的省份主要分布在中西部。新疆由H-H型转变为L-H（Low-High）型,江苏由L-H型转变为H-H型,辽宁由L-L型转为H-L（High-Low）型,广西由H-L型转为L-L型,其他省份未发生变动,局域空间集聚格局具有较强的稳定性与空间依赖特征。

图2

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图22000年、2008年和2016年中国农业竞争力空间分布

注:基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2016)1549号的标准地图制作,底图无修改。
Fig. 2Spatial distributions of agricultural competitiveness in China in 2000 (a), 2008 (b) and 2016 (c)

Tab. 3
表3
表32000年与2016年不同局域空间集聚类型涵盖省份的变化
Tab. 3Changing situation in province-level regions with different local-space clustering types in 2000 and 2016

类型	2000年	2016年
H-H	北京、上海、天津、浙江、福建、新疆	北京、上海、天津、浙江、江苏、福建
L-H	河北、江西、江苏、安徽	河北、江西、安徽、新疆
L-L	辽宁、黑龙江、吉林、河南、山东、湖北、湖南、重庆、四川、贵州、云南、山西、甘肃、内蒙、陕西、宁夏	黑龙江、吉林、河南、山东、湖北、湖南、重庆、四川、贵州、云南、广西、山西、甘肃、内蒙、陕西、宁夏
H-L	青海、广东、广西	青海、广东、辽宁

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4 农业竞争力空间格局演化的影响因素及其溢出效应分解

4.1 空间计量模型适用性检验结果分析

依据上述全域Moran's I指数计算结果,省域尺度农业竞争力存在显著的正向空间溢出效应,并表现出H-H、L-L集聚的局域空间相关性特征,考虑到空间交互项的存在,采用SLM、SEM和SDM模型估计不同因素对农业竞争力的影响。但需要根据检验与判定规则,选择合适的空间计量模型,检验结果如表4所示。

Tab. 4
表4
表4模型检验结果
Tab. 4Model resting results

模型	检验	结果	模型	检验	结果
SLM模型	Wald_spatial	27.661^***	SEM模型	Wald_spatial	41.304^***
SLM模型	LR_spatial	24.854^***	SEM模型	LR_spatial	38.060^***
SDM固定与随机效应模型	Hausman检验	11.237^***

注:^*、^**、^***分别表示10%、5%、1%的显著性水平。

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（1）Wald与LR检验结果。判断SDM模型是否能够简化为SLM与SEM模型,可以通过Wald与LR检验,如果同时拒绝原假设,则SDM为最优拟合模型;如果拒绝Wald检验原假设,且R-LMlag值显著,则SDM模型可以简化为SLM模型;如果拒绝LR检验原假设,且R-LMlag值显著,则SDM模型可以简化为SEM模型。根据表4结果,SLM、SEM模型Wald与LR检验结果均都通过了1%水平下的显著性检验,同时拒绝原假设,SDM模型不能简化为SLM、SEM模型。（2）Hausman检验结果。确定选择SDM模型后,需要检验固定效应与随机效应两种模型的适用性。固定效应与随机效应的Hausman检验结果显示如表4所示,检验值为11.24,且通过了1%水平下的显著性检验,拒绝随机效应模型,故采用固定效应的SDM模型进行拟合估计。固定效应模型又分为时期固定、空间固定和时期空间双向固定3种效应模型,为谨慎起见,分别估计无固定效应SDM模型、时期固定效应SDM模型、空间固定效应SDM模型和双向固定效应SDM模型,并对估计结果进行对比分析。

4.2 不同交互效应SDM模型估计结果分析

不同SDM模型的选择,需要根据不同模型估计的LogL值、R²等来综合判断。由不同交互效应SDM模型估计结果（表5）可知,时期固定效应SDM模型估计结果的LogL值最大,其拟合优度R²值也最大,因此时期固定效应的SDM模型要优于无固定、空间固定和双向固定效应SDM模型。

Tab. 5
表5
表5不同交互效应SDM模型估计结果
Tab. 5Results of estimating SDM model under different interaction effects

变量	无固定	时期固定	空间固定	双固定	变量	无固定	时期固定	空间固定	双固定
-cons	1.791^* (1.84)				W×PCI	0.147^** (2.11)	0.314^*** (2.73)	0.123^* (1.86)	0.212^*** (3.03)
PCI	0.077^** (2.40)	0.314^*** (6.21)	0.078^** (2.56)	0.089^*** (2.96)	W×RCL	0.009 (0.17)	0.306 (0.89)	0.009 (0.21)	0.074 (1.48)
RCL	0.121^*** (5.54)	0.235^*** (5.96)	0.124^*** (5.62)	0.118^*** (5.32)	W×EDL	0.014 (1.50)	0.125^*** (6.18)	0.014 (1.36)	-0.004 (-0.29)
EDL	0.005 (0.86)	0.014^*** (4.35)	0.004 (0.60)	0.002 (0.39)	W×RDI	0.186^*** (4.08)	0.372^*** (6.31)	0.154^*** (3.97)	0.232^*** (5.27)
RDI	0.074^*** (3.69)	0.107^*** (4.68)	0.060^*** (3.81)	0.075^*** (4.00)	W×GSR	0.043^*** (2.94)	-0.066^*** (-5.05)	0.042^*** (3.06)	0.048^*** (2.82)
GSR	-0.033^*** (-4.17)	-0.064^*** (-9.02)	-0.029^*** (-3.75)	-0.026^*** (-3.37)	W×PPG	-0.028^* (-1.89)	0.001 (0.14)	-0.022 (-0.95)	0.002 (0.07)
PPG	0.051^*** (3.77)	-0.015^*** (-3.20)	0.055^*** (4.38)	0.057^*** (4.23)	rho	0.211^*** (3.42)	-0.044 (-0.58)	0.168^*** (2.87)	0.070 (1.08)
R²	0.748	0.933	0.645	0.517	Obs	493	493	493	493
LogL	451.898	682.340	357.587	338.463

注:^*、^**、^***分别表示10%、5%、1%的显著性水平。

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（1）农业经营主体投入能力的分化程度直接影响系数与空间滞后系数均显著为正,说明本省与临近省农民收入水平的提升都能促进本省农业竞争力的提升。其原因在于本省农村居民收入水平越高,其增加农业生产资金投入、扩大经营规模的能力越强,本省农业一般会表现出较强的竞争力;相邻省农村居民收入水平的提升能够增加其农资保有量,通过农资的跨区流动尤其是农机的跨区作业,提高本省农业生产效率,带动本省农业竞争力水平的提升。（2）农产品市场需求扩张能力的分化程度直接影响系数显著为正,空间滞后影响系数不显著,说明本省居民消费能力的提升会促进本省农业竞争力的提升。其原因在于本省居民消费水平越高,其营养升级越快,对农产品数量和质量的需求越高,在资源禀赋难以改变的情况下,能够刺激供给效率的提升;目前农产品市场整合度相对较低,相邻省份居民收入水平的提升对本省农业生产效率的影响尚不显著。（3）经济发展水平分化程度直接影响系数与空间滞后影响系数均显著为正。其原因在于农业技术的发展得益于经济的发展,本省份经济发展水平较高,其农业机械制造、生物育种技术、农药化肥生产技术等涉农产业的发展水平也相对较高,为农业技术与生产效率的提升提供保障;农业技术传播存在涟漪效应,邻省经济发展水平高,其先进的农机技术、育种技术等率先向其相邻省份扩散,对本省农业生产效率提升起到推动作用。（4）R&D投入分化程度直接影响系数与空间滞后影响系数均显著为正。其原因在于本省R&D投入的增加能够加快农业工艺、技术的创新与发展,促进生产效率提升;相邻省份R&D投入的增加,同样会促进其农业工艺、技术的创新与发展,再加上农业技术传播存在涟漪效应,带动本省农业技术、工艺的发展,提升生产效率。（5）农作物结构直接影响系数与空间滞后影响系数均显著为负,说明非粮食主产省农业竞争力要显著强于粮食主产省。其原因在于本省粮食播种面积占比越高,务农收入增长能力一般会越弱,经济作物占比越高,务农收入增长能力一般会越强,经济作物普遍需要更高的投入,且具有更高的产品附加值,经济作物占比高的省份往往表现出较强的收入增长能力、投资能力等;相邻省份经济作物占比高,营收能力强,往往会吸引本省份高素质农业劳动力外流,造成人力资本在本省与相邻省份分布的不平衡,同时也会带动其他农业资源的转移与集聚。（6）粮食作物种植结构直接影响系数显著为负,空间滞后影响系数不显著,说明稻谷播种面积占粮食播种面积的比重越高,农业竞争力越弱,即以水稻为粮食主作物的省份其农业竞争力要显著弱于以小麦、玉米为粮食主作物的省份。其原因在于劳动力相对于其他要素成本不断升高的背景下,以小麦、玉米为粮食主作物的省份一般属于旱地、平地,机械化耕种收比例较高,表现出较高的生产效率;而以水稻为粮食主作物的省份一般属于水田、坡地,机械化耕种收比例相对较低,生产效率也相对较低。

4.3 时期固定效应SDM模型空间溢出效应的分解分析

运用偏微分方程将时期固定效应SDM模型的空间溢出效应进行分解,如表6所示,直接效应为本省该解释变量对本省农业竞争力的影响,间接效应为本省该解释变量对临近省份农业竞争力水平的影响,总效应为该解释变量对整体农业竞争力水平的影响。

Tab. 6
表6
表6时期固定效应SDM模型空间溢出效应的分解结果
Tab. 6Results of dissecting space spillover effects by SDM model under period fixed effects

变量	直接效应	间接效应	总效应	变量	直接效应	间接效应	总效应
PCI	0.313^*** (6.17)	0.308^*** (2.58)	0.621^*** (4.49)	RDI	0.108^*** (5.73)	0.370^*** (5.95)	0.478^*** (7.17)
RCL	0.235^*** (3.37)	-0.312 (-0.74)	-0.077^*** (-4.96)	GSR	-0.059^*** (-9.46)	-0.062^*** (-5.50)	-0.121^*** (-14.42)
EDL	0.103^* (1.84)	0.003^*** (5.81)	0.125^*** (5.20)	PPG	-0.012^*** (-3.13)	0.001 (0.22)	-0.011^*** (-3.78)

注:^*、^**、^***分别表示10%、5%、1%的显著性水平。

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农村居民人均纯收入与全国农村居民人均纯收入之差每增加1单位,农业竞争力提升0.621,其中本省提升0.313,对其他省份的间接溢出效应为0.308,溢出效应略小于直接效应,表明提高农民收入水平无论是对本省份,还是对相邻省份农业竞争力提升都具有重要意义。居民消费水平与全国居民消费水平之差每增加1单位,农业竞争力下降0.077,其中本省显著提升0.235,其他省份下降0.312,但不显著,表明提升居民消费水平对促进本省农业竞争提升起到推动作用,但对临近省份溢出效益并不明显。人均GDP与全国人均GDP之差每增加1单位,农业竞争力提升0.125,其中本省农业竞争力提升0.103,对其他省份间接溢出效应为0.003,表明经济发展水平的提升对区域农业竞争力协调发展具有重要作用。R&D投入与全国平均R&D投入之差每增加1单位,农业竞争力提升0.478,其中对本省的直接效应为0.108,对其他省份的间接溢出效应为0.370,表明增加农业生产方面科技研发投入对提升农业竞争力具有重要作用。粮食作物播种面积占总播种面积的比例每增加1个百分点,农业竞争力下降0.121,其中本省下降0.059,其他省份下降0.062,表明粮食主产省农业竞争力普遍较低,为保障全国粮食安全作出了巨大贡献。稻谷播种面积占粮食播种面积的比重每增加1个百分点,农业竞争力下降0.011,本省下降0.012,其他省份提升0.001,但不显著,表明以水稻为粮食主作物的省份相较于以小麦、玉米为粮食主作物的省份提升农业竞争力更为困难。

5 结论与讨论

5.1 结论

（1）时间变化方面,2000—2016年间中国农业竞争力整体呈现上升的趋势,且具有明显的正向空间溢出效应,溢出强度呈现出先上升后下降的倒“U”型变化趋势,其中自然资源禀赋竞争力基本稳定,流动要素投入竞争力、产出能力竞争力稳步上升,设施机制竞争力2003年之前逐年上升,2003年以来经历短暂的下降后逐步回升。

（2）空间格局演化方面,东部省份农业竞争力异军突起,中西部省份快速下降,省份间农业竞争力的差距逐步拉大,两级分化愈发明显;局域空间集聚格局具有较强的稳定性与空间依赖特征,以H-H、L-L两种类型集聚方式为主导,东部分省份分布在H-H集聚区,中西部省份则基本分布在L-L集聚区。

（3）空间计量模型检验与估计结果表明,PCI、RCL、EDL、RDI、GSR、PPG均都通过了1%水平下的显著性检验,是影响农业竞争力时空演化的主要因素,其对农业竞争力的直接效应分别为0.313、0.235、0.103、0.108、-0.059、-0.012,对相邻省份的间溢出效应分别为0.308、-0.312、0.003、0.370、-0.062、0.001,说明某一省份PCI、RCL、EDL、RDI、GSR、PPG这些因素的变化不仅会影响到本省份农业竞争力的变化,而且可以通过空间地理传导机制影响其他省份农业竞争力变化。

5.2 讨论

学术界在农业竞争力概念界定、理论解释、指标体系、评价方法等方面依然存在诸多争论。本文尝试基于客观权重法实证测算中国省域尺度上的农业竞争力,借助地理学方法探究中国农业竞争力时空格局演化特征及其空间溢出效应,并利用空间面板数据的计量模型实证分析时空演化的驱动因子,以期为促进中国农业均衡发展提供理论支撑。但依然存在不足之处:① 本文以临近关系确定空间权重矩阵,未能充分考虑经济距离权重,虽然二者具有一定的替代关系,但空间权重矩阵的深入判别分析值得深入研究;② 考虑到数据的可得性,本文并未将劳动力要素纳入到农业竞争力评价指标体系中,而劳动力是核心投入要素之一,需要进一步探索与完善。

农业竞争力发展不平衡,亟需优化空间范围农业资源的配置、促进农业生产要素的合理流动、推动不同省份农业竞争力的均衡发展,尤其需要关注农业竞争力及其影响因素溢出的扩散效应与示范效应。一方面需要进一步发挥其扩散效应,打破省份间农业生产要素与农产品流通壁垒,提高市场整合度,推动区域市场向全国市场转变,从而降低交易成本。应充分发挥东部省份现代化投入要素的优势,加快农业生产技术、工艺的扩散,带动中西部农业生产效率的提升,中西部地区发挥资源禀赋优势,积极拓展农业发展新空间,实现与相邻省份农业资源的跨区匹配与一二三产的高度融合。另一方面需要进一步发挥示范效应,各省份农业资源禀赋、投入能力、产出能力、基础设施存在明显的区域差异特征,应发挥地方政府的引导作用,形成合理的农业产业空间格局,结合自身的农业资源优势,重点培育一批有竞争力的特殊农业新业态,有效促进农业产业结构的优化升级与协调发展。

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[本文引用: 1]

[26]

Zhang

Rixin

, Chen

Zefeng

, Zeng

Yiwu

. Study on the relationship between agricultural R&D investment and agricultural growth based on VAR model
Journal of Huazhong Agricultural University (Social Sciences Edition), 2014(3):44-49.

[本文引用: 1]

[ 张日新, 陈泽峰, 曾亿武. 基于VAR模型的农业R&D投入与农业增长关系研究
华中农业大学学报(社会科学版), 2014(3):44-49.]

[本文引用: 1]

[27]

Liu

Zhenhuan

, Yang

Peng

, Wu

Wenbin

, et al. Spatio-temporal changes in Chinese crop patterns over the past three decades
Acta Geographica Sinica, 2016,71(5):840-851.

DOI:10.11821/dlxb201605012 URL [本文引用: 1]

The study aims to investigate the spatio-temporal changes in crop patterns in China since 1980. In doing so, the analysis methods of time-series trend and spatial cluster were used to cover the major eleven crops at county scale. The results indicate that (1) There are 16 kinds of crop combinations ranking in the China's top 10 during the past 30 years. Yet since 2002, the simplified cropping structure has been gradually replaced by the multiple cropping structure, which suggests an increase in the diversity index of crop patterns. In 1980, about 82.7% of China's counties have a similar crop pattern which is composed of rice, wheat, corn and their combinations, however, this pattern largely changed after 2002 due to the increase in the planting area of fruit and vegetables. (2) In the same period, rice planting area of 47% of the counties, wheat planting area of 61% of the counties of and corn area of 29.6% of the counties experience a significant decrease, while other crops show an increasing trend. As a result, rice-dominated cereal crops in China are slightly adjusted to the coexistence of rice, wheat and maize crops. In particular, maize area proportion shows a significant change, which forms a so-called "corn decreased belt” spanning from northeast to southwest of China. Urbanization had an important impact on crop patterns as fruit and vegetable planting areas rapidly grow so as to meet the increasing demands in urbanized areas. (3) Crop patterns also show an obvious spatial cluster effect in China's 1300 counties. The proportion of high cluster accounts for 2.86%, 5.64%, 6.11%, 4.53%, 1.62%, 7.77%, 8.24%, 12%, 10%, 1.41% and 9.35% of China's counties for rice, wheat, maize, soybean, fibers, cotton, vegetables, potatoes, fruits, sugars and oils, respectively. These crops are distributed in Northeast China, Xinjiang, Northern Shaanxi Plateau, Yunnan-Guizhou Plateau and the metropolis areas. This finding of this study can support the decision making in agricultural restructuring and adaptation to climate change.

[ 刘珍环, 杨鹏, 吴文斌, 等. 近30年中国农作物种植结构时空变化分析
地理学报, 2016,71(5):840-851.]

DOI:10.11821/dlxb201605012 URL [本文引用: 1]

Dong

Xiaoxia

, Huang

Jikun

, Scott

, et al. Study on the adjustment of geographical location, transportation infrastructure and planting structure
Management World, 2006(9):59-63.

[本文引用: 1]

[ 董晓霞, 黄季焜, Scott Rozelle, 等. 地理区位、交通基础设施与种植业结构调整研究
管理世界, 2006(9):59-63.]

[本文引用: 1]

[29]

Chen

Mingxing

, Lu

Dadao

, Zhang

Hua

. Comprehensive evaluation and the driving factors of China's urbanization
Acta Geographica Sinica, 2009,64(4):387-398.

DOI:10.11821/xb200904001 URL [本文引用: 1]

From the essential meaning of urbanization, this paper establishes a comprehensive evaluation index system, including four aspects changing: population, economy, society and land. Based on the method of entropy, the measure and evolution of China's urbanization are analyzed since 1981. The results show that China's comprehensive urbanization level continues improving. Economic growth and geographical landscape are the main features of rapid evolution of urbanization, followed by the population urbanization, and the medical care level of social urbanization is the least advanced. The evolution of all the four subsystems has unique characteristics. The analysis of multiple regression model shows that the driving factors have been diversified. The market force is the most powerful driving force of China's urbanization, followed by intrinsic force, administration force, exterior force. From different stages of urbanization, the effects of market force, exterior force and the administration force on urbanization are increasing, while intrinsic force is decreasing. China's urbanization is the main endogenous process, hence more policies should be formulated to strengthen the market economy reform and coordinate urban and rural development.

[ 陈明星, 陆大道, 张华. 中国城市化水平的综合测度及其动力因子分析
地理学报, 2009,64(4):387-398.]

DOI:10.11821/xb200904001 URL [本文引用: 1]

Yang

Xingzhu

, Wang

Qun

. Evaluation of rural human settlement quality difference and its driving factors in tourism area of southern Anhui province
Acta Geographica Sinica, 2013,68(6):851-867.

URL [本文引用: 1]

Rapid development of tourism has led to unprecedented changes in the economic, social and environmental conditions of tourism destinations. Under the background of the rapid tourismification, quality of rural human settlements is one of the manifestations of rural transformation development. By choosing the infrastructure, public service facilities, energy consumption structure, living conditions, and environmental sanitation as the evaluation factors, this study built an evaluation index system of the rural human settlement quality. Then, this paper presented an empirical analysis of rural human settlements in tourism area of southern Anhui Province, with the method of factor analysis, entropy method, and canonical correlation. Results show that the overall score of the quality presents "dual-core structure, the rise of central region and the collapse of the periphery" phenomenon. The infrastructure has the pattern of "triple-core structure and more dense than the south". The higher level spatial units are mainly located in Tunxi District, Qingyang County and northern part of the province. The public service facilities have the pattern of "dual-core structure". The mononuclear structure of energy consumption structure and living conditions were concentrated in Tunxi District. However, the core of environmental sanitation is mainly concentrated in Shexian and Ningguo counties. Meanwhile, a variety of influencing factors interact to determine quality of rural human settlements. The paper focused on natural environment, socio-economic development, tourism development, and regional culture. Based on the differences between their effective way and degree to quality of rural human settlements, these influencing factors are classified into positive and negative factors. Climatic conditions, poverty level and spatial distance have direct negative impact on quality of rural human settlements, while the remaining factors have direct positive impact. Natural environment and regional culture play a relatively stable role in rural human settlements evolution. Tourism development is a key driving force to generate the rural human settlement quality difference. Tourism development factors including the level of tourism economy, the endowment of tourism resources, tourism services and facilities, and tourism location, are able to significantly explain quality differences of rural human settlements. At the same time, the two sets of variables between influencing factors and evaluation factors have the typical mutually dependent and interacting relationship. The influencing factors have direct effects on the quality of rural human settlements, and generate the indirect effects on quality of rural human settlements by the evaluation factors.

[ 杨兴柱, 王群. 皖南旅游区乡村人居环境质量评价及影响分析
地理学报, 2013,68(6):851-867.]

URL [本文引用: 1]

Wang

Yuming

. The analysis of entropy changes on the evolutional tendency of geographical environment
Acta Geographica Sinica, 2011,66(11):1508-1517.

DOI:10.11821/xb201111007 URL [本文引用: 1]

The evolution of geographical environmental system obeys the second law of thermodynamics: the order degree changes of the system are anti-related to its total entropy changes. The order degree increases when the total entropy changes are negative, and it is useful for human beings' survival and development, and vice versa. The analysis shows that the spontaneous evolution of natural geographical environment is developing towards ordering. The impacts of large-scale human activities on city system and human geographical environment are totally positive, and push its ordering development; their impacts on natural geographical development are both positive and negative. The positive impact is mainly the incremental production of low-entropy material and energy, which has been reflected in the increase of agricultural products and the application of new energy technology, such as solar energy, and ecological environment construction. The negative impacts are consumption of low-entropy material and energy, discharge of "three wastes" and elimination heat to the environment during the process of consumption and industrial production. These impacts lead to decrease of stock of low-entropy material and expansion of entropy increase during the geographical development, and have already caused common anxiety. Ordering of background evolution of natural geographical environment and technological possibility indicate that the evolution of geographical environment can continue ordering by introducing more low-entropy material and energy and decreasing the discharge of entropy.

[ 王玉明. 地理环境演化趋势的熵变化分析
地理学报, 2011,66(11):1508-1517.]

DOI:10.11821/xb201111007 URL [本文引用: 1]

Anselin

, Bongiovanni

, Lowenberg-DeBoer

. A spatial econometric approach to the economics of site-specific nitrogen management in corn production
American Journal of Agricultural Economics, 2004,86(3):675-687.

DOI:10.1111/ajae.v86.3 URL [本文引用: 1]

[33]

Wang

Shaojian

, Su

Yongxian

, Zhao

Yabo

. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions
Acta Geographica Sinica, 2018,73(3):414-428.

DOI:10.11821/dlxb201803003 URL [本文引用: 1]

Carbon emissions are increasing due to human activities related with the energy consumptions for economic development. Thus, attention has been paid to the reduction of the growth of carbon emissions and formulation of policies for addressing climate change. Although most studies have explored the driving forces behind carbon emissions in China, literature lacks studies at the city-level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we applied the spatial autocorrelation, spatial Markov-chain transitional matrices, dynamic spatial panel model and Sys-GMM to empirically estimate the key determinants of carbon emissions at the city-level and discuss its spatial spillover effects in consideration of spatiotemporal lag effects and different geographical and economic weighting matrices. Results indicated that the regional inequalities of city-level carbon emissions decreased over time and presented an obvious spatial spillover effect and high-emission "club" agglomeration. In addition, the evolution of the emission pattern has the characteristic of obvious path dependence. Panel data analysis results indicated that there was a significant U-shaped curve that can reflect the relationship between carbon emissions and GDP per capita. In addition, carbon emissions per capita are increasing with economic growth for most cities. High-proportion of secondary industry and extensive growth of investment exerted significantly positive effects on China's city-level carbon emissions. Conversely, rapid population agglomeration, the improvement of technology level, the increase of trade openness and road density play an inhibiting role in carbon emissions. Therefore, in order to reduce carbon emissions, the Chinese government should inhibit the effects of promotion factors and enhance the effects of mitigation factors. Combining with the analysis of results, we argued that optimizing the industrial structure, streamlining the extensive investment, increasing the level of technology and improving the road accessibility are the effective ways to increase energy savings and reduce carbon emissions in China.

[ 王少剑, 苏泳娴, 赵亚博. 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素
地理学报, 2018,73(3) : 414-428.]

DOI:10.11821/dlxb201803003 URL [本文引用: 1]

Cheng

Yeqing

, Wang

Zheye

, Zhang

Shouzhi

, et al. Spatiotemporal dynamics of carbon intensity from energy consumption in China
Acta Geographica Sinica, 2013,68(10):1418-1431.

DOI:10.11821/dlxb201310011 URL [本文引用: 2]

The economic and social development has been facing with serious challenge brought by global climate change due to carbon emissions. As a responsible developing country, China pledged to reduce its carbon emission intensity by 40%-45% below 2005 levels by 2020. The realization of this target depends on not only the substantive transition of society, economy and industrial structure in national scale, but also the specific action and share of energy saving and emissions reduction in provincial scale. Based on the method provided by the IPCC, this paper examines the spatio-temporal dynamic patterns and domain factors of China's carbon emission intensity from energy consumption in 1997-2010 using spatial autocorrelation analysis and spatial panel econometric model. The aim is to provide scientific basis for making different policies on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt in 1997-2010, with an annual rate of 7.15%, which was much slower than that of annual growth rate of GDP (11.72%); therefore, China's carbon emission intensity tended to decline. Secondly, the changing curve of Moran's I indicated that China's carbon emission intensity from energy consumption has a continued strengthening tendency of spatial agglomeration at provincial scale. The provinces with higher and lower values appeared to be path-dependent or space-locked to some extent. Third, according to the analysis of spatial panel econometric model, it can be found that energy intensity, energy structure, industrial structure and urbanization rate were the domain factors that have impact on the spatio-temporal patterns of China's carbon emission intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, we should improve the utilizing efficiency of energy, and optimize energy and industrial structure, and choose the low-carbon urbanization way and implement regional cooperation strategy of energy conservation and emissions reduction.

[ 程叶青, 王哲野, 张守志, 等. 中国能源消费碳排放强度及其影响因素的空间计量
地理学报, 2013,68(10):1418-1431.]

DOI:10.11821/dlxb201310011 URL [本文引用: 2]

Hong

Guozhi

, Hu

Huaying

, Li

Xun

. Analysis of regional growth convergence with spatial econometrics in China
Acta Geographica Sinica, 2010,65(12):1548-1558.

DOI:10.11821/xb201012010 URL [本文引用: 1]

This paper proposes to combine the standard analysis method of economic convergence with spatial econometrics to explore regional convergence based on a total of 240 cities in China. To investigate the kind of spatial autocorrelation and agglomeration, the Moran's I statistic is used, finding that the existence of strongly positive global autocorrelation of GDP per capita and what's more, the local spatial structure is rather stable. The findings suggest that the non-spatial models applied to analyse β-convergence suffer from the risk of misspecification and a spatial model is competent. The results based on the spatial models indicate the existence of absolute convergence between cities. Taking into account effects results in a significant faster rate of convergence. The sensitivity test of the absolute convergence with respect to assumption of a common steady state and robustness over space suggest that the finding of absolute convergence is not stable. The mechanism of diminishing return and technology spillover is both important for absolute convergence. Finally, a set of regional policies are discussed.

[ 洪国志, 胡华颖, 李郇. 中国区域经济发展收敛的空间计量分析
地理学报, 2010,65(12):1548-1558.]

DOI:10.11821/xb201012010 URL [本文引用: 1]