The relationship between technological innovation and green transformation efficiency in China: An empirical analysis using spatial panel data

2021 ◽  
Vol 64 ◽  
pp. 101498
Author(s):  
Xueqi Zhai ◽  
Yunfei An
Keyword(s):  
Panel Data ◽  
Empirical Analysis ◽  
Technological Innovation ◽  
Transformation Efficiency ◽  
Spatial Panel ◽  
Spatial Panel Data ◽  
The Relationship

Spatial analysis of dairy yields response to intensive farming in New Zealand

2019 ◽  
Vol 11 (1) ◽  
pp. 79-99
Author(s):  
Wei Yang ◽  
Basil Sharp
Keyword(s):  
New Zealand ◽  
Panel Data ◽  
Nutrient Pollution ◽  
Spatial Effects ◽  
Content Type ◽  
Spatial Panel ◽  
Spatial Spillovers ◽  
Intensive Farming ◽  
Spatial Panel Data ◽  
The Relationship

Purpose The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ. Design/methodology/approach This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ. Findings The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution. Originality/value This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.

scholarly journals A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

2018 ◽  
Vol 10 (8) ◽  
pp. 2800 ◽  
Author(s):  
Rui Jin ◽  
Jianya Gong ◽  
Min Deng ◽  
Yiliang Wan ◽  
Xuexi Yang
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Interaction Model ◽  
Spillover Effects ◽  
Spatiotemporal Pattern ◽  
Regional Economic ◽  
Spatial Panel ◽  
Economic Agglomeration ◽  
Spatial Panel Data

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

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