Spatiotemporal heterogeneity effect of technological progress and agricultural centrality on agricultural carbon emissions in China

Abstract

Reducing agricultural carbon emissions is an important aspect of achieving China’s carbon peak and neutrality goals. Different agricultural centrality result in different agriculture status and role in different regions, affecting agricultural carbon emissions. In this study, agricultural centrality is introduced from the perspective of social network analysis. Spatial autocorrelation analysis, geographically and temporally weighted regression (GTWR) and other methods are used to empirically explore the effect of technological progress and agricultural centrality on the spatiotemporal heterogeneity of agricultural carbon emissions. The moderating effect of agricultural centrality on the relationship between technological progress and agricultural carbon emissions is further explored. The results show that 1) during the research period (2001–2019), the agricultural carbon emissions first increased and then decreased, with remarkable spatial agglomeration characteristics, revealing a significant spatial autocorrelation of carbon emissions among provinces; 2) provinces have distinctly uneven characteristics in the social network of agricultural carbon emissions, while the same province shows relative consistency in terms of location centrality and betweenness centrality. Areas with high centrality are the major grain producing areas, and they invariably play an important role in the spatially linked network of agricultural carbon emissions; 3) technological progress has an inhibitory effect on agricultural carbon emissions, and the regression coefficient decreases from western to eastern regions, demonstrating a spatial gradient distribution. The location centrality has a negative effect on agricultural carbon emissions, with significant spatial heterogeneity. The effect of betweenness centrality on agricultural carbon emissions has increased from positive to negative over time, and the promotion of each province’s intermediary role has inhibited the increase of agricultural carbon emissions; 4) both agricultural location centrality and betweenness centrality have significant positive moderating effects on the relationship between technological progress and agricultural carbon emissions. With the increase of location centrality and betweenness centrality, technological progress has an increasingly strong inhibitory effect on agricultural carbon emissions. We put forward targeted suggestions based on different agricultural centrality in order to reduce agricultural carbon emissions and provide directions for achieving the China’s carbon peak and neutrality goals and the Sustainable Development Goals of the United Nations’ Agenda 2030.

Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China from the Perspective of Sustainable Development

“…A smaller agricultural carbon emissions value represents a higher level of agricultural green development. The carbon emissions from agriculture are examined based on a broad agricultural scope, reflecting the level of agricultural green development [57][58][59][60][61][62][63][64].…”

Spatial Correlation Network Structure of and Factors Influencing Technological Progress in Citrus-Producing Regions in China

“…In the study of the spatial correlation network structure of technological progress, Wang et al concluded that there are obvious spatial correlation and spillover effects in the development of agricultural science and technology innovation in China and presented the shape of the spatial correlation network structure [28]. He et al concluded that agricultural location centrality and intermediary centrality have a significant positive moderating effect on technological progress [29].…”

Regional heterogeneity of agricultural carbon emission reduction potential in China

“…Meanwhile, with the development of the regional economic level, environmental regulations significantly affect total factor productivity in agriculture and show a positive correlation [20,21]. Some academics believe that significant differences exist in economic development, technological progress, population, and other factors across China owing to its vast size [22][23][24][25], which, in turn, also leads to significant spatial heterogeneity in the impact of various factors on carbon emissions [26][27][28].…”