Rural sustainable development: A case study of the Zaozhuang Innovation Demonstration Zone in China

doi:10.1016/j.regsus.2023.11.004

Abstract

Abstract:

Sustainable development is the central theme of modern global development. With the arrival of the urban era, the vulnerability and instability of rural areas have significantly increased, and rural sustainable development faces serious challenges. To address these issues, the study took the Zaozhuang Innovation Demonstration Zone in China under the National Sustainable Development Agenda as a case, combined with economic, social and land use data during 2016-2020, and applied Granger causality test method to explore the theoretical and practical pathways of “innovation-driven rural sustainable development”. The results showed that rural sustainable development and economic sustainability displayed a trend of synergistic change, with “explosive” growth from 2018 to 2020. The social sustainability steadily increased from 2016 to 2020. Ecological and spatial sustainability continuously declined during the study period. Moreover, the rural innovation capacity of the Zaozhuang Innovation Demonstration Zone displayed rapid growth during 2016-2020. Although the rural innovation capacity of the Zaozhuang Innovation Demonstration Zone has rapidly improved, it has a weak driving effect on rural sustainable development and economic sustainability. There are two primary challenges that must be overcome to ensure the rural sustainable development of the Zaozhuang Innovation Demonstration Zone. The first challenge is the imbalance among the multi-dimensional relationships in the process of rural sustainable development, and the second challenge is the weakening of rural innovation capacity to drive rural sustainable development. To overcome these challenges, this study proposed a systematic pathway for rural sustainable development in the Zaozhuang Innovation Demonstration Zone from multi-dimensions, such as policy actions, technologies, projects, and institutional guarantees, and formed a universal and representative “Zaozhuang model”. This study expands the theoretical foundation of rural sustainable development and provides theoretical and practical support for innovation-driven rural sustainable development.

Key words: Rural sustainable development, Rural innovation capacity, Sustainable Development Goals (SDGs), Economic sustainability, Social sustainability, Ecological sustainability, Zaozhuang Innovation Demonstration Zone

Binsheng LIU, Xiaohui ZHANG, Junfeng TIAN, Ruimin CAO, Xinzhang SUN, Bin XUE. Rural sustainable development: A case study of the Zaozhuang Innovation Demonstration Zone in China[J]. Regional Sustainability, 2023, 4(4): 390-404.

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Target layer	Dimension layer	Index layer	Index description	Property	Weight
Rural innovation capacity (X)	Innovation inputs (X₁)	Proportion of science and technology expenditure to public financial expenditure	Proportion of science and technology expenditure on total public financial expenditure	+	0.1611
		Total power of agricultural machinery per unit of arable land area	Ratio of total power of agricultural machinery to arable land area	+	0.1611
		Number of patent applications per 10,000 people	Ratio of patent applications to total population	+	0.1630
	Innovation support environment (X₂)	Number of municipal-level and above agricultural cooperative demonstration societies	-	+	0.1770
		Number of municipal-level and above rural agriculture and rural tourism demonstration units	-	+	0.1637
		Number of new innovation research and development (R&D) platforms in rural areas	-	+	0.1740

Variable	Augmented Dickey-Fuller (ADF) test value	P-value	Conclusion	First-order difference	Conclusion
Y	-0.4927	0.9705	Unstable	-10.9061	Stable
Y₁	-0.9516	0.7416	Unstable	-10.0355	Stable
Y₂	-2.6372	0.1052	Unstable	-5.6772	Stable
Y₃	-1.0558	0.2507	Unstable	-5.8234	Stable
Y₄	-1.4566	0.5303	Unstable	-3.6239	Stable
X	-2.4651	0.3365	Unstable	-7.2411	Stable
X₁	-2.6096	0.2815	Unstable	-5.7581	Stable
X₂	-0.4325	0.9772	Unstable	-12.4246	Stable

Original hypothesis	Number of lag periods	F-value	P-value	Conclusion
X is not the Granger reason for Y	1	4.1552	0.0719^*	Reject the original hypothesis	X is the reason for Y
Y is not the Granger reason for X	1	1.1183	0.3178	Accept the original hypothesis	Not the reason
X is not the Granger reason for Y₁	1	3.2894	0.0846^*	Reject the original hypothesis	X is the reason for Y₁
Y₁ is not the Granger reason for X	1	0.1540	0.7039	Accept the original hypothesis	Not the reason
X is not the Granger reason for Y₂	1	0.6865	0.4288	Accept the original hypothesis	Not the reason
Y₂ is not the Granger reason for X	1	0.2696	0.6161	Accept the original hypothesis	Not the reason
X is not the Granger reason for Y₃	1	0.0468	0.8334	Accept the original hypothesis	Not the reason
Y₃ is not the Granger reason for X	1	1.0800	0.3258	Accept the original hypothesis	Not the reason
X is not the Granger reason for Y₄	1	1.8317	0.2089	Accept the original hypothesis	Not the reason
Y₄ is not the Granger reason for X	1	1.6429	0.2320	Accept the original hypothesis	Not the reason

Original hypothesis	Number of lag periods	F-value	P-value	Conclusion
X₁ is not the Granger reason for Y	1	3.2721	0.0960^*	Reject the original hypothesis	X₁ is the reason for Y
Y is not the Granger reason for X₁	1	1.0750	0.3269	Accept the original hypothesis	Not the reason
X₁ is not the Granger reason for Y₁	1	0.7321	0.4144	Accept the original hypothesis	Not the reason
Y₁ is not the Granger reason for X₁	1	0.0060	0.9398	Accept the original hypothesis	Not the reason
X₁ is not the Granger reason forY₂	1	0.6865	0.4288	Accept the original hypothesis	Not the reason
Y₂ is not the Granger reason for X₁	1	0.5967	0.4596	Accept the original hypothesis	Not the reason
X₁ is not the Granger reason for Y₃	1	0.2741	0.6132	Accept the original hypothesis	Not the reason
Y₃ is not the Granger reason for X₁	1	1.2852	0.2862	Accept the original hypothesis	Not the reason
X₁ is not the Granger reason for Y₄	1	0.7982	0.3949	Accept the original hypothesis	Not the reason
Y₄ is not the Granger reason for X₁	1	1.1091	0.3197	Accept the original hypothesis	Not the reason

Original hypothesis	Number of lag periods	F-value	P-value	Conclusion
X₂ is not the Granger reason for Y	1	4.0155	0.0761^*	Reject the original hypothesis	X₂ is the reason for Y
Y is not the Granger reason for X₂	1	0.0453	0.8362	Accept the original hypothesis	Not the reason
X₂ is not the Granger reason for Y₁	1	21.0661	0.0013^***	Reject the original hypothesis	X₂ is the reason for Y₁
Y₁ is not the Granger reason for X₂	1	0.9770	0.3488	Accept the original hypothesis	Not the reason
X₂ is not the Granger reason for Y₂	1	0.0246	0.8788	Accept the original hypothesis	Not the reason
Y₂ is not the Granger reason for X₂	1	0.2837	0.6071	Accept the original hypothesis	Not the reason
X₂ is not the Granger reason for Y₃	1	2.0280	0.1882	Accept the original hypothesis	Not the reason
Y₃ is not the Granger reason for X₂	1	0.2474	0.6308	Accept the original hypothesis	Not the reason
X₂ is not the Granger reason for Y₄	1	4.3084	0.0677^*	Reject the original hypothesis	X₂ is the reason for Y₄
Y₄ is not the Granger reason for X₂	1	0.4836	0.5044	Accept the original hypothesis	Not the reason