The impact of new quality productivity driven on green and low-carbon transformation of agriculture based on explainable machine learning

As an important source of carbon emissions in China, how to promote its green and low-carbon transformation of agriculture is an important starting point for policy formulation, and the emergence of new quality productivity, has brought new opportunities for agriculture to achieve green transformation. In order to deeply explore the influence of new quality productivity on green and low-carbon transformation of agriculture, based on the panel data of 30 provinces in China from 2011 to 2023, this paper uses kernel density estimation, global Moran index, standard deviational ellipse and Bayesian-optimized interpretable XGBoost model for analysis, and draws the following conclusions: First, the green and low-carbon transformation of agriculture in China exhibits a significant growth trend, with regional development showing a polarized pattern. Meanwhile, this transformation demonstrates pronounced spatial clustering characteristics, evolving from “single provincial agglomeration” to “multi-provincial agglomeration”, the spatial difference gradually diminishes. Second, the contribution rate of new quality productivity to green and low-carbon transformation of agriculture is as high as 27.4%. When the development level of new quality productivity is exceeds the threshold value of 0.2207, it will significantly accelerate the green and low-carbon transformation of agriculture. In addition, agricultural new-quality workers, agricultural new quality labor materials, and agricultural new quality labor inputs all significantly contribute to the green and low carbon transformation of agriculture. Third, the impact of new quality productivity on the green and low-carbon transformation of agriculture is moderated by land production efficiency. Finally, the impact of new quality productivity on the green and low-carbon transformation of agriculture exhibits heterogeneity across the functional region of grain production and and economic region. Based on the above conclusions and with corresponding policy recommendations proposed, the aim is to enhance the development level of new quality productivity and promote the sustainable development of China’s agricultural economy.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-09-12
DOI
https://doi.org/10.1038/s41598-026-70420-w
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

The impact of new quality productivity driven on green and low-carbon transformation of agriculture based on explainable machine learning

Sheng Qu, Na Gong, Xiwu Shao, Zhuo He et al.
Scientific Reports
Energy, Environment, Economic Growth
article

The impact of new quality productivity driven on green and low-carbon transformation of agriculture based on explainable machine learning

Sheng Qu, Na Gong, Xiwu Shao, Zhuo He, Xue Zhu, Ran Gong, Shidi Shao
article en

Abstract

As an important source of carbon emissions in China, how to promote its green and low-carbon transformation of agriculture is an important starting point for policy formulation, and the emergence of new quality productivity, has brought new opportunities for agriculture to achieve green transformation. In order to deeply explore the influence of new quality productivity on green and low-carbon transformation of agriculture, based on the panel data of 30 provinces in China from 2011 to 2023, this paper uses kernel density estimation, global Moran index, standard deviational ellipse and Bayesian-optimized interpretable XGBoost model for analysis, and draws the following conclusions: First, the green and low-carbon transformation of agriculture in China exhibits a significant growth trend, with regional development showing a polarized pattern. Meanwhile, this transformation demonstrates pronounced spatial clustering characteristics, evolving from “single provincial agglomeration” to “multi-provincial agglomeration”, the spatial difference gradually diminishes. Second, the contribution rate of new quality productivity to green and low-carbon transformation of agriculture is as high as 27.4%. When the development level of new quality productivity is exceeds the threshold value of 0.2207, it will significantly accelerate the green and low-carbon transformation of agriculture. In addition, agricultural new-quality workers, agricultural new quality labor materials, and agricultural new quality labor inputs all significantly contribute to the green and low carbon transformation of agriculture. Third, the impact of new quality productivity on the green and low-carbon transformation of agriculture is moderated by land production efficiency. Finally, the impact of new quality productivity on the green and low-carbon transformation of agriculture exhibits heterogeneity across the functional region of grain production and and economic region. Based on the above conclusions and with corresponding policy recommendations proposed, the aim is to enhance the development level of new quality productivity and promote the sustainable development of China’s agricultural economy.

Scientific Reports
Dongbei University of Finance and Economics (CN), Chinese Academy of Sciences (CN), Northeast Institute of Geography and Agroecology (CN), Jilin Agricultural University (CN)
Zero hunger
Openalex Percentile: Top 5%
Energy, Environment, Economic Growth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.