Analyzing the spatial network structure of agricultural greenhouse gases in China

He, Yanqiu; Lan, Xiang; Zhou, Zuoang; Fang, Wang

doi:10.1007/s11356-020-10945-3

Cited by 16 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) Livestock and poultry industry structure (LPS): The research objective of this paper focuses on planting and farming, which have different pollution emission intensity and characteristics. However, the differences in the share of farming and livestock between different regions make the emissions of NPS vary from region to region (He et al 2021).…”

Section: Control Variablementioning

confidence: 99%

“…The current research on industrial agglomeration, agricultural agglomeration and environmental pollution has been widely studied by domestic and foreign scholars from different perspectives.Firstly, studies on industrial agglomeration mainly involve: the definition of the concept of industrial agglomeration by Alfred, M (2013), Alfred, W(1929), Joseph (1983, Edgar (1975), Michael(1990); the reasons for the formation of industrial agglomeration, such as spatial costs (Henry 1992), regional house price differences (Zhou and Zhang 2021), and marketization (Wenfang Anlu 2021); the regional economic effects of industrial agglomeration, such as promoting technological progress (Hou et al 2021), optimizing resource allocation (Zhang and Lin 2022), and reducing pollution emissions (Zhuang et al 2021); and the measurement of industrial agglomeration, such as the Herfindahl index (Qiu and Wu 2010), concentration index (Dong et al 2021), Gini coefficient (Chang and Oxley 2009), and locational entropy index (Sikorski and Brezden 2021).Secondly, studies on agricultural agglomeration mainly concern: the causes of agricultural agglomeration formation, including the improvement of technology and human capital (Griffith et al 2004;Rosenthal and Strange 2004), technology exchange (XIAO and LI 2014), demonstration and learning effects (Xu et al 2022), and artificial intelligence [(WANG 2021); the spatial and temporal evolutionary trends of agricultural agglomeration (Chi et al 2022;Li and Li 2022;FU et al 2021;Ni and Wang 2018); and the economic effects of agricultural agglomeration, including promoting the economic growth of agricultural industries (He et al 2021;Wang and Liu 2012;Hailu and Deaton 2016), facilitating labor productivity (TVETERAS and BATTESE 2006;Xin and Qin 2011), boosting agricultural industrialization , and advancing the income level of farmers (Balezentis and Sapolaite 2020). Finall...…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Spillover Effects of Agricultural Agglomeration on Agricultural Non-Point Source Pollution in the Yangtze River Basin of China

Zhu

Huang

2022

Preprint

View full text Add to dashboard Cite

Studying the impact of agricultural agglomeration (AGG) on agricultural non-point source pollution (NPS) is important for reconciling the resource environment and agricultural economic growth.The paper is based on the panel data of 89 prefecture-level cities in the main agricultural production areas of the Yangtze River basin from 2000 to 2020, measuring the level of AGG and NPS emissions in each area and employing spatial analysis techniques and spatial econometric models to empirically investigate the spatial and temporal distribution characteristics and spatial spillover effects of AGG and NPS.The results show that there are differences in temporal changes, spatial distribution, spatial association and spatial evolutionary trends between AGG and agricultural surface pollution, and the changes in the upper, middle and lower basin show non-equilibrium characteristics;NPS has a positive spatial spillover effect, and AGG has a positive direct impact effect and a negative spatial spillover effect on NPS, and there are a differences in the direct impact effect and spatial spillover effect between upper, middle and lower basin;Agricultural production conditions and the share of livestock and poultry industry have a positive direct effect and a negative spatial spillover effect on NPS, while agricultural population size has a positive direct effect and spatial spillover effect;The urbanization rate exacerbates the emission of NPS, while the industrial structure reduces the emission of NPS, but neither has a spatial spillover effect.The fndings of this paper cannot only provide a theoretical basis and practice reference for NPS and AGG evaluation but also beneft more focused policy-making of the Green and sustainable development of agriculture in Yangtze River Basin.

show abstract

Section: Control Variablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Spillover Effects of Agricultural Agglomeration on Agricultural Non-Point Source Pollution in the Yangtze River Basin of China

Zhu

Huang

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This contributes to the construction and visual analysis of spatial correlation networks (Wang et al, 2018). Social network analysis method is widely used in social science, ecological governance, energy and environment (Zhang et al, 2022a), and the latest scholars also apply it to power (Wei et al, 2020), tourism (Wang et al, 2020b), agriculture (He et al, 2020), transportation (Ma et al, 2019), architecture (Huo et al, 2022) and other related carbon emission fields.…”

Section: Introductionmentioning

confidence: 99%

Carbon emission efficiency and spatially linked network structure of China’s logistics industry

Tang

Yang

Yao

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

This paper uses the EBM model to measure the carbon emission efficiency of the logistics industry in 30 provinces in China from 2010 to 2019 and analyzes its spatial and temporal evolution characteristics using ARCGIS visualization. On this basis, the structural characteristics of the spatial correlation network of carbon emission efficiency of the logistics industry in China and its influencing factors are explored and analyzed by using the social network analysis method and the quadratic distribution method (QAP). The study shows that: 1) The national average logistics industry carbon emission efficiency increased from 2010 to 2019, and the spatial logistics industry carbon emission efficiency shows the characteristics of East > Central > Northeast > West, and most of the provinces in China are still in the middle and low logistics industry carbon emission efficiency zone. 2) The carbon emission efficiency of logistics industry in 30 provinces in China has formed a stable spatial correlation network, and there is an obvious spatial spillover relationship. However, the structure of the spatial association network is loose, and there are obvious gaps in the status of each province in the spatial association network. The provinces in the eastern region are at the core of the spatial correlation network, while the provinces in the northeast and west are at the periphery of the spatial correlation network. 3) The reduction of geographical distance between provinces, the greater the differences in logistics development level, logistics energy intensity and logistics environmental protection level, and the higher the similarity of logistics informatization level, the more it can promote the formation of spatial correlation network.

show abstract

“…Some scholars have also used social network analysis to study spatial association networks in areas such as carbon emissions. They found that greenhouse gases are also spatially correlated, and that carbon reduction measures can be targeted through the role played by different regions in the correlation network [35,36]. In the existing literature, studies on spatial association networks formed by air pollution have not been analyzed for the northeastern region of China.…”

Section: Introductionmentioning

confidence: 99%

Spatial Correlation of Air Pollution and Its Causes in Northeast China

Liu

Hao

2021

IJERPH

View full text Add to dashboard Cite

To understand the status of air pollution in northeastern China, we explore the structure of air pollution transmission networks and propose targeted policy recommendations. Using air pollution data from 35 cities in northeastern China for a total of 879 periods from 6 January 2015 to 3 June 2017, this paper used social network analysis (SNA) to construct a spatial association network of air pollution in the region, and analyzed the spatial association of air pollution among cities and its causes in an attempt to reveal the transmission path of air pollution in the region. The results show that inter-city air pollution in northeast China forms a complex and stable correlation network with obvious seasonal differences of “high in winter and low in summer”. Different cities in the region play the roles of “spillover”, “intermediary” and “receiver” of air pollution in the network. Small respirable particulate (PM2.5) pollution constitutes a significant component of air pollution in northeast China, which spreads from Liaoning province to Heilongjiang province via Jilin province. Therefore, regional joint pollution prevention and control measures should be adopted to combat the air pollution problem, and different treatment measures should be developed for different city “roles” in the pollution network, in order to fundamentally solve the air pollution problem in the region.

show abstract

Analyzing the spatial network structure of agricultural greenhouse gases in China

Cited by 16 publications

References 34 publications

Spatial Spillover Effects of Agricultural Agglomeration on Agricultural Non-Point Source Pollution in the Yangtze River Basin of China

Spatial Spillover Effects of Agricultural Agglomeration on Agricultural Non-Point Source Pollution in the Yangtze River Basin of China

Carbon emission efficiency and spatially linked network structure of China’s logistics industry

Spatial Correlation of Air Pollution and Its Causes in Northeast China

Contact Info

Product

Resources

About