Research on patent citations and agricultural knowledge spillovers in the Yangtze River Delta

doi:10.3969/j.issn.1004-9479.2023.04.2020922

Abstract

Abstract:

Innovation has increasingly become the primary driving force to improve agricultural productivity, profitability and competitiveness, and knowledge spillovers are of great significance to the growth of agricultural innovation. This article uses patent citation data from three provinces and one city in the Yangtze River Delta from 1985 to 2015, and patent citation data from 1999 to 2019, and employs the patent citation model to analyze the situation of patent citations and agricultural knowledge spillovers in the Yangtze River Delta provinces and cities, and draws the following conclusions: (1) Prior to 2012, agricultural patent knowledge in the Yangtze River Delta region is more inclined to interact with regions outside the region, and the intensity of agricultural knowledge learning is higher than the spillover of agricultural knowledge; The overflow goes hand in hand in both directions. (2) The regional concentration of agricultural patent citations in the three provinces and one city is ranked as Jiangsu, Anhui, Zhejiang and Shanghai in order. Jiangsu is relatively active in the two-way interaction in the region, while Shanghai has shown a relatively independent model of agricultural knowledge innovation, which is relatively weak in driving the overall agricultural innovation in the Yangtze River Delta. (3) Restricted by the level of innovation, recent agricultural patents are more novel and innovative than old patents, and their citation probability is higher than that of the former, but it is still limited by the impact of time gaps. The citation probability shows superiority over time. (4) Knowledge learning in the agricultural field has a threshold effect. In addition to learning novel and innovative knowledge from other provinces and cities in the region, Anhui also learns basic agricultural knowledge with old technology but relatively crucial, which is transformed into universal agricultural knowledge through its own practice. And Anhui's agriculture knowledge is cited fastest by other provinces and cities, but the depreciation rate is also the highest within the region.

Key words: patent citation, agricultural patent, knowledge spillover, citation probability model, Yangtze River Delta Region

摘要：

创新日益成为提高农业生产力、利润率和竞争力的主要驱动力，知识溢出对农业创新增长具有重要意义。以1985—2015年长三角三省一市专利资料，以及1999—2019年期间的专利引证资料为数据源，运用专利引证模型来分析长三角各省市专利引证和农业知识外溢状况，得出以下结论：（1）在2012年之前，长三角区域农业专利知识更倾向于与区域外互动，且农业知识学习强度高于农业知识溢出；2012年之后，区域内部互动程度增加，在农业知识学习和农业知识溢出两个方向上齐头并进。（2）三省一市农业类专利引证的地区集中度依次为江苏、安徽、浙江、上海。江苏在区域内双向互动较为活跃，上海则表现出相对独立的农业知识创新模式，对长三角区域整体农业创新的带动相对较弱。（3）受创新水平的限制，新近农业专利的新颖性和创新性高于老旧专利，其被引证的概率高于前者，仍然受限于时间落差的影响，引证概率随时间推移表现出先增大后减小的趋势。（4）农业领域知识学习具有门槛效应。安徽在区域内部除向其他省市学习新颖性和创新性的知识之外，也同时学习技术老旧但比较关键的基础性农业知识，经自身实践转化为具有普适性的农业知识，被其他省市引用的速度最快，但折旧率在区域内部也是最高的。

关键词: 专利引证, 农业专利, 知识溢出, 引证概率模型, 长三角地区

Saifei NIE, Renxu GU. Research on patent citations and agricultural knowledge spillovers in the Yangtze River Delta[J]. World Regional Studies, 2023, 32(4): 109-118.

聂赛飞, 谷人旭. 长三角地区专利引证与农业知识外溢研究[J]. 世界地理研究, 2023, 32(4): 109-118.

Figures/Tables 6

Fig.1 The distribution of agricultural invention patents in three provinces and one city in the Yangtze River Delta from 1999 to 2015

Tab.1 Statistics on the basic situation of agricultural invention patents cited in stages in the Yangtze River Delta

省市	第一阶段（1999—2007）		第二阶段（2008—2012）		第三阶段（2013—2019）
省市	前向引用	后向引用	前向引用	后向引用	前向引用	后向引用
安徽	26	234	548	1 814	3 993	5 801
江苏	137	1 232	1416	3 733	4 873	5 142
上海	74	690	677	1 527	1 951	1 563
浙江	132	1 247	1 159	2 746	4 046	3 532
三省一市内部引用频次	107	51	1 454	1 120	5 269	5 841
占比	29.00%	1.50%	38.26%	11.41%	47.43%	52.57%

Tab.2 Characteristics of agricultural invention patents sample statistics in the Yangtze River Delta

变量	平均值	标准差	最小值	最大值
被引证专利核准年（t）	2 009.921	3.940	1 985	2 015
潜在被引证的专利群总数（ $N$ ）	8 876.859	9 730.257	94	34 751
引证专利核准年（ $T$ ）	2 013.671	1.605	1 999	2 019
潜在引证专利群总数（ $M$ ）	90.736	109.733	2	499
引证频次（扣除自我引证）（ $C$ ）	389.826	328.301	0	382
引证频次（ $C `$ ）	369.634	279.479	0	417
引证概率（扣除自我引证）（ $p K, k$ ）	2.51E-06	2.81E-06	0	4.41E-06
引证概率（ $p K, k$ `）	2.64E-06	3.67E-06	0	5.09E-06
引证时间落差 $T - t$	4.559	6.186	1	17

Tab.2 Characteristics of agricultural invention patents sample statistics in the Yangtze River Delta

变量	平均值	标准差	最小值	最大值
被引证专利核准年（t）	2 009.921	3.940	1 985	2 015
潜在被引证的专利群总数（ $N$ ）	8 876.859	9 730.257	94	34 751
引证专利核准年（ $T$ ）	2 013.671	1.605	1 999	2 019
潜在引证专利群总数（ $M$ ）	90.736	109.733	2	499
引证频次（扣除自我引证）（ $C$ ）	389.826	328.301	0	382
引证频次（ $C `$ ）	369.634	279.479	0	417
引证概率（扣除自我引证）（ $p K, k$ ）	2.51E-06	2.81E-06	0	4.41E-06
引证概率（ $p K, k$ `）	2.64E-06	3.67E-06	0	5.09E-06
引证时间落差 $T - t$	4.559	6.186	1	17

Tab.3 Estimated results of patent citation probability model

变化系数	估计系数	渐近标准误	变化系数	估计系数	渐近标准误
$δ 1 - 1$	1	N.A.	$δ 2007$	1.037^**	0.021
$δ 2 - 1$	1.413^***	0.115	$δ 2008$	0.087^**	0.071
$δ 3 - 1$	0.891^**	0.059	$δ 2009$	0.091^**	0.053
$δ 4 - 1$	0.736^*	0.070	$δ 2010$	1.022^**	0.049
$δ 1 - 2$	0.831^***	0.461	$δ 2011$	1.019^**	0.038
$δ 2 - 2$	6.537^**	0.074	$δ 2012$	1.031^***	0.071
$δ 3 - 2$	1.865^**	0.042	$δ 2013$	1.109^***	0.049
$δ 4 - 2$	5.039^**	0.029	$δ 2014$	1.131^***	0.042
$δ 1 - 3$	0.583^***	0.041	$δ 2015$	1.109^**	0.049
$δ 2 - 3$	3.035^**	0.401	$δ 2016$	—	—
$δ 3 - 3$	3.765^**	0.437	$δ 2017$	—	—
$δ 4 - 3$	2.697^**	0.121	$δ 2018$	—	—
$δ 1 - 4$	0.154^**	0.097	$δ 2019$	—	—
$δ 2 - 4$	1.901^**	0.100	$β 1$	0.263^***	0.009
$δ 3 - 4$	0.689^**	0.081	$β 1,1 - 1$	1	N.A.
$δ 4 - 4$	6.309^*	0.131	$β 1,2 - 1$	0.907^**	0.106
被引证专利年期			$β 1,3 - 1$	0.970^**	0.076
$δ 1985 - 1998$	1	N.A.	$β 1,4 - 1$	0.761^**	0.096
$δ 1999 - 2003$	1.057^***	0.139	$β 1,1 - 2$	1.018^**	0.109
$δ 2004 - 2007$	1.136^**	0.048	$β 1,2 - 2$	1.237^**	0.129
$δ 2008 - 2012$	1.238^**	0.071	$β 1,3 - 2$	1.209^**	0.029
$δ 2013 - 2015$	1.184^**	0.173	$β 1,4 - 2$	1.263^*	0.076
引证专利年份			$β 1,1 - 3$	1.218^**	0.039
$δ 1999$	1	N.A.	$β 1,2 - 3$	0.949^***	0.086
$δ 2000$	1.012^***	0.03	$β 1,3 - 3$	1.110^**	0.092
$δ 2001$	0.998^**	0.019	$β 1,4 - 3$	0.879^**	0.073
$δ 2002$	1.019^**	0.024	$β 1,1 - 4$	1.589^**	0.096
$δ 2003$	1.023^**	0.067	$β 1,2 - 4$	1.713^**	0.107
$δ 2004$	1.020^***	0.021	$β 1,3 - 4$	1.529^**	0.039
$δ 2005$	1.021^*	0.053	$β 1,4 - 4$	2.076^**	0.046
$δ 2006$	1.028^**	0.049	$β 2$	5.224E-06^**	4.76E-07
Adjusted-R² 0.761

Tab.3 Estimated results of patent citation probability model

变化系数	估计系数	渐近标准误	变化系数	估计系数	渐近标准误
$δ 1 - 1$	1	N.A.	$δ 2007$	1.037^**	0.021
$δ 2 - 1$	1.413^***	0.115	$δ 2008$	0.087^**	0.071
$δ 3 - 1$	0.891^**	0.059	$δ 2009$	0.091^**	0.053
$δ 4 - 1$	0.736^*	0.070	$δ 2010$	1.022^**	0.049
$δ 1 - 2$	0.831^***	0.461	$δ 2011$	1.019^**	0.038
$δ 2 - 2$	6.537^**	0.074	$δ 2012$	1.031^***	0.071
$δ 3 - 2$	1.865^**	0.042	$δ 2013$	1.109^***	0.049
$δ 4 - 2$	5.039^**	0.029	$δ 2014$	1.131^***	0.042
$δ 1 - 3$	0.583^***	0.041	$δ 2015$	1.109^**	0.049
$δ 2 - 3$	3.035^**	0.401	$δ 2016$	—	—
$δ 3 - 3$	3.765^**	0.437	$δ 2017$	—	—
$δ 4 - 3$	2.697^**	0.121	$δ 2018$	—	—
$δ 1 - 4$	0.154^**	0.097	$δ 2019$	—	—
$δ 2 - 4$	1.901^**	0.100	$β 1$	0.263^***	0.009
$δ 3 - 4$	0.689^**	0.081	$β 1,1 - 1$	1	N.A.
$δ 4 - 4$	6.309^*	0.131	$β 1,2 - 1$	0.907^**	0.106
被引证专利年期			$β 1,3 - 1$	0.970^**	0.076
$δ 1985 - 1998$	1	N.A.	$β 1,4 - 1$	0.761^**	0.096
$δ 1999 - 2003$	1.057^***	0.139	$β 1,1 - 2$	1.018^**	0.109
$δ 2004 - 2007$	1.136^**	0.048	$β 1,2 - 2$	1.237^**	0.129
$δ 2008 - 2012$	1.238^**	0.071	$β 1,3 - 2$	1.209^**	0.029
$δ 2013 - 2015$	1.184^**	0.173	$β 1,4 - 2$	1.263^*	0.076
引证专利年份			$β 1,1 - 3$	1.218^**	0.039
$δ 1999$	1	N.A.	$β 1,2 - 3$	0.949^***	0.086
$δ 2000$	1.012^***	0.03	$β 1,3 - 3$	1.110^**	0.092
$δ 2001$	0.998^**	0.019	$β 1,4 - 3$	0.879^**	0.073
$δ 2002$	1.019^**	0.024	$β 1,1 - 4$	1.589^**	0.096
$δ 2003$	1.023^**	0.067	$β 1,2 - 4$	1.713^**	0.107
$δ 2004$	1.020^***	0.021	$β 1,3 - 4$	1.529^**	0.039
$δ 2005$	1.021^*	0.053	$β 1,4 - 4$	2.076^**	0.046
$δ 2006$	1.028^**	0.049	$β 2$	5.224E-06^**	4.76E-07
Adjusted-R² 0.761

Tab.4 Years with the highest citation probability of agricultural invention patents in the Yangtze River Delta

地区	上海	江苏	浙江	安徽
上海	3.813	4.174	3.933	5.000
上海	（0.394）	（0.048）	（0.301）	（0.073）
江苏	3.739	3.071	3.145	3.026
江苏	（0.015）	（0.038）	（0.328）	（0.394）
浙江	3.134	3.975	3.439	4.310
浙江	（0.061）	（0.074）	（0.306）	（0.106）
安徽	2.375	2.234	2.494	1.813
安徽	（0.073）	（0.105）	（0.033）	（0.053）

Tab.5 Analysis of the characteristics of Anhui agricultural patents cited in Shanghai patents

专利号	被引证次数	专利核准年	专利申请人	相对一般性	相对原创性
CN1147223C	19	2000	上海丰康科技开发有限公司	1.029	—
CN1324946C	8	2004	上海增靓生物科技有限公司	1.725	2.165
CN1291645C	9	2005	复旦大学	—	2.148
CN1281112C	5	2003	上海浦东天厨菇业有限公司	1.039	1.732
CN1250073C	5	2003	上海交通大学	0.986	1.521
CN1194609C	3	2003	复旦大学	1.056	—
CN1126444C	3	2001	复旦大学	1.563	1.715
CN1138465C	2	2000	上海众伟生化有限公司	1.365	1.542

References 28

1	叶建亮. 知识溢出与企业集群. 经济科学,2001(3):23-30.
	YE J. Knowledge spillover and enterprise clusters. Economic Science, 2001(3): 23-30.
2	彭向,蒋传海. 产业集聚、知识溢出与地区创新——基于中国工业行业的实证检验. 经济学(季刊), 2011, 10(3): 913-934.
	PENG X, JIANG C. Industrial agglomeration, knowledge spillover and regional innovation: An empirical test based on China's industrial industries. Economics (Quarterly), 2011,10(3): 913-934.
3	魏江,徐蕾. 知识网络双重嵌入、知识整合与集群企业创新能力. 管理科学学报,2014,17(2): 34-47.
	WEI J, XU L. Double embedding of knowledge network, knowledge integration and innovation capability of cluster enterprises. Journal of Management Science, 2014, 17(2): 34-47.
4	刘刚. 上海农业创新网络演化研究.上海: 华东师范大学,2017.
	LIU G. Research on the evolution of Shanghai agricultural innovation network. Shanghai: East China Normal University, 2017.
5	刘承良,牛彩澄.东北三省城际技术转移网络的空间演化及影响因素.地理学报,2019,74(10):2092-2107.
	LIU C, NIU C.The spatial evolution and influencing factors of the inter-city technology transfer network in the three provinces of Northeast China.Acta Geographica Sinica,2019,74(10):2092-2107.
6	李在军,胡美娟,尹上岗,等.中国区域知识资本时空差异及影响因素分析.地理科学,2019,39(1):80-88.
	LI Z, HU M, YIN S, et al. Spatial-temporal differences and influencing factors of regional knowledge capital in China. Scientia Geographica Sinica, 2019,39(1):80-88.
7	张玲,王珺,李雪灵. 知识社会化、网络位置与集群企业竞争优势——基于知识流动双向性的实证研究. 科技管理研究,2020, 40(16): 140-148.
	ZHANG L, WANG J, LI X. Knowledge socialization, network location and competitive advantage of clustered enterprises--An empirical study based on the bi-directionality of knowledge flow. Science and Technology Management Research, 2020, 40(16): 140-148 .
8	JAFFE A, TRAJTENBERG M, HENDERSON R. Geographic location of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 1993, 108(3): 577-598.
9	JAFFE A, TRAJTENBERG M. Flows of knowledge from universities and federal laboratories: Modeling the flow of patent citations over time and across institutional and geographic boundaries. Proceedings of the National Academy of Sciences of the United States of America, 1996, 93(23): 12671-12677.
10	JAFFE A, TRAJTENBERG M, FOGARTY M. Knowledge spillovers and patent citations: Evidence from a survey of inventors. The American Economic Review, 2000, 90(2): 215-218.
11	王腾飞,谷人旭,马仁锋. 知识溢出研究的"空间性"转向及人文与经济地理学议题. 经济地理,2020, 40(6): 47-59.
	WANG T, GU R, MA R. The "spatial" turn of knowledge spillover research and the issues of human and economic geography. Economic Geography, 2020, 40(6): 47-59.
12	孙瑜康,李国平,席强敏. 技术机会、行业异质性与大城市创新集聚——以北京市制造业为例. 地理科学. 2019, 39(2): 252-258.
	SUN Y, LI G, XI Q. Technology opportunities, industry heterogeneity and innovation agglomeration in big cities: Taking Beijing's manufacturing industry as an example. Geographical Sciences, 2019, 39(2): 252-258.
13	GLAESER E L, KALLAL H D, SCHEINKMAN J A, et al. Growth incities. Journal of Political Economy, 1992, 100(6): 1126-1152.
14	杨丽君. 供给侧改革视阈下中国制造业的知识溢出效应. 科研管理,2019, 40(10): 161-168.
	YANG L. The knowledge spillover effect of China's manufacturing industry from the perspective of supply-side reform. Scientific Research Management, 2019, 40(10): 161-168.
15	盛垒.西方空间知识溢出研究进展探析与展望.外国经济与管理.2010,32(10):2-9.
	SHENG L.Analysis and prospect of the research progress of western spatial knowledge spillovers.Foreign Economics and Management,2010,32(10): 2-9
16	李长英,赵忠涛. 技术多样化对企业创新数量和创新质量的影响研究. 经济学动态, 2020(6): 15-29.
	LI C, ZHAO Z. Research on the impact of technological diversification on the quantity and quality of enterprise innovation. Economic Developments, 2020(6): 15-29.
17	贺灿飞,陈韬. 外部需求冲击、相关多样化与出口韧性. 中国工业经济, 2019(7): 61-80.
	HE C, CHEN T. External demand shocks, related diversification and export resilience. China Industrial Economics, 2019(7): 61-80.
18	DE N, GANZAROLI A, ORSI L.The impact of intra- and inter-regional knowledge collaboration and technological variety on the knowledge productivity of European regions.Technological Forecasting and Social Change,2017,117: 108-118.
19	司月芳,梁新怡,曾刚,等. 中国跨境知识溢出的地理格局及影响因素. 经济地理, 2020, 40(8): 1-8.
	SI Y, LIANG X, ZENG G, et al. The geographical pattern and influencing factors of cross-border knowledge spillovers in China. Economic Geography, 2020, 40(8): 1-8.
20	ISAKSSON O, SIMETH M, SEIFERT R W. Knowledge spillovers in the supply chain: Evidence from the high tech sectors. Research Policy, 2016, 45(3): 699-706.
21	CHUANG Y, HSU P. FDI, trade, and efficiency spillover : Evidence from China's manufacturing sector. Applied Economics, 2004, 36(10): 1103-1115.
22	张应青,范如国,罗明. 知识分布、衰减程度与产业集群创新模式的内在机制研究. 中国管理科学, 2018, 26(12): 186-196.
	ZHANG Y, FAN R, LUO M. Research on the internal mechanism of knowledge distribution, attenuation and industrial cluster innovation mode. China Management Science, 2018, 26(12): 186-196.
23	MANSFIELD E. How rapidly does new industrial technology leak out? Journal of Industrial Economics, 1985, 34(2): 217-223.
24	辛晓睿,刘刚,曾刚.上海农业创新网络及其空间结构研究.世界地理研究,2017,26(6):110-118.
	XIN X, LIU G, ZENG G. Research on Shanghai agricultural innovation network and its spatial structure. World Regional Studies, 2017, 26(6): 110-118.
25	宋涛,蔡建明,刘军萍,等.世界城市都市农业发展的经验借鉴.世界地理研究,2013,22(2):88-96.
	SONG T, CAI J, LIU J, et al. Experience of urban agriculture development in the world. World Regional Studies,2013,22(2):88-96.
26	LI E, YAO F, XI J, et al. Evolution characteristics of government-industry-university-research cooperative innovation network for China's agriculture and influencing factors: Illustrated according to agricultural patent case. Chinese Geographical Science,2018,28(1):137-152.
27	马恩朴,蔡建明,林静,等.国外城市农业的角色演变、潜在效益及其对中国的启示.世界地理研究,2021,30(1):136-147.
	MA E, CAI J, LIN J, et al. The role evolution, potential benefits of urban agriculture in foreign countries and its enlightenment to China. World Regional Studies,2021,30(1):136-147.
28	郭笑然,周李,虞虎,等.日本乡村振兴政策演变及其效果分析.世界地理研究,2020,29(5):905-916.
	GUO X, ZHOU L, YU H, et al. An analysis of the evolution and effect of rural revitalization policy in Japan. World Regional Studies,2020,29(5):905-916.