Pathways to Sustainability: Analyzing Green Innovation in Polluting Agricultural Enterprises

Green technological innovation is critical for highly polluting agricultural operations to accelerate green transformation and achieve sustainable growth. A detailed review of these firms’ green technological innovation efficiency, as well as an analysis of the internal and external factors influencing it, can help accelerate their green technological innovation processes. As a result, this study employs firms identified as key polluting units (extremely polluting) by environmental protection authorities from 2018 to 2023 as decision-making entities. After removing ST samples, a final sample of 198 companies was obtained. This study measures and analyses the efficiency of green technological innovation using the Super-efficiency SBM model and the Super-efficiency SBM–Malmquist index model. A random-effects model investigates additional contributing elements. The findings show that the average comprehensive technological efficiency of green innovation in high-pollution agricultural enterprises is 0.29, which is only 75% of the overall efficiency level for all agricultural enterprises, and that this efficiency is distributed regionally as follows: western > central > eastern. External factors, including economic development levels and environmental restrictions, have a considerable detrimental impact on the green technology innovation efficiency of high-pollution agricultural operations. Internally, enterprise scale and equity concentration have a considerable favorable impact. The impact of technological spillover levels, government support, and how well capital is allocated is not significant and needs to be looked at more closely in a more detailed and contextual way.

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Publication Details

Journal
Sustainability
Published
2026-02-27
DOI
https://doi.org/10.3390/su18052315
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
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article

Pathways to Sustainability: Analyzing Green Innovation in Polluting Agricultural Enterprises

Su Wenjun, Ju Qiu
Sustainability
Impact of AI and Big Data on Business and Society
article

Pathways to Sustainability: Analyzing Green Innovation in Polluting Agricultural Enterprises

Su Wenjun, Ju Qiu
article en

Abstract

Green technological innovation is critical for highly polluting agricultural operations to accelerate green transformation and achieve sustainable growth. A detailed review of these firms’ green technological innovation efficiency, as well as an analysis of the internal and external factors influencing it, can help accelerate their green technological innovation processes. As a result, this study employs firms identified as key polluting units (extremely polluting) by environmental protection authorities from 2018 to 2023 as decision-making entities. After removing ST samples, a final sample of 198 companies was obtained. This study measures and analyses the efficiency of green technological innovation using the Super-efficiency SBM model and the Super-efficiency SBM–Malmquist index model. A random-effects model investigates additional contributing elements. The findings show that the average comprehensive technological efficiency of green innovation in high-pollution agricultural enterprises is 0.29, which is only 75% of the overall efficiency level for all agricultural enterprises, and that this efficiency is distributed regionally as follows: western > central > eastern. External factors, including economic development levels and environmental restrictions, have a considerable detrimental impact on the green technology innovation efficiency of high-pollution agricultural operations. Internally, enterprise scale and equity concentration have a considerable favorable impact. The impact of technological spillover levels, government support, and how well capital is allocated is not significant and needs to be looked at more closely in a more detailed and contextual way.

SustainabilityVol. 18(5)
Hunan Agricultural University (CN)
Zero hunger
Openalex Percentile: Top 46%
Impact of AI and Big Data on Business and Society
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