2022
DOI: 10.3389/fenvs.2022.1058612
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Spatial differences, distributional dynamics, and driving factors of green total factor productivity in China

Abstract: Graphical AbstractFlow chart of China GTFP.

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Cited by 7 publications
(3 citation statements)
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“…The aforementioned research results underscore the regional variation in the factors influencing AGTFP. This aligns with the findings of Zhao et al (2022) and Yang et al (2019). To improve AGTFP in a specific region, it is imperative to conduct a thorough analysis of the local context and avoid adopting practices from other regions indiscriminately.…”
Section: Discussionsupporting
confidence: 66%
“…The aforementioned research results underscore the regional variation in the factors influencing AGTFP. This aligns with the findings of Zhao et al (2022) and Yang et al (2019). To improve AGTFP in a specific region, it is imperative to conduct a thorough analysis of the local context and avoid adopting practices from other regions indiscriminately.…”
Section: Discussionsupporting
confidence: 66%
“…However, in reality, because agriculture has the attribute of a public good, there will inevitably be some correlation among provinces, and whether and what kind of impact the influencing factors in the home province will have on the green total factor productivity of maize in neighboring provinces is one of the pressing questions in this paper. Some research (Liu, 2019;Hu J. et al, 2022;Hu Q. et al, 2022;Xiao et al, 2022;Zhao et al, 2022) argues that while spatial correlations and maize GTFP are considered together, the interactions between different regions are neglected, and the traditional regression model analysis fails to reflect the role of each influencing factor well. To better study the trends of GTFP changes in China's major maize-producing regions, this study further examined the spatial characteristics of agricultural GTFP in the major maizeproducing regions using the Moran index, based on which an empirical study was conducted on the factors affecting the green development of maize in China using the spatial Durbin model.…”
Section: Discussionmentioning
confidence: 99%
“…This approach offers a more precise depiction of the correlation between input and output in agricultural production. To establish a measurement system for AETFP, we have considered the practices of other scholars [60,61]. The research has selected several input indicators, including human capital, farmland, fertilizers, agricultural machines, effective irrigation area, and irrigation water resources.…”
Section: Explained Variablementioning
confidence: 99%