Agriculture is affected by industrial undesirable output: Policy recommendations for two-stage dynamic DEA and productivity changes

Pollution generated during industrial manufacturing has become increasingly severe, posing a threat to food security. In response to the United Nations Sustainable Development Goals (SDG-2 and SDG-7) (the list of abbreviations and symbols can be found in Appendix A), this study employs the two-stage Dynamic Network Directional Distance Function (DN-DDF) and the two-stage Dynamic Total Factor Productivity (TFP) model to evaluate the efficiency and productivity of the industrial and agricultural sectors across 30 administrative regions in China from 2015 to 2019.The results indicate that the average efficiency of the industrial sector was 0.9274, higher than that of the agricultural sector at 0.8716. In the industrial sector, 14 regions achieved the optimal efficiency score of 1 over the 5-year average, while Gansu (0.7637), Liaoning (0.7431), and Jilin (0.5634) recorded the lowest efficiency levels. Similarly, in the agricultural sector, 14 regions reached an efficiency score of 1, whereas Jiangxi (0.4548), Xinjiang (0.4411), and Gansu (0.3113) performed the worst. Regarding overall efficiency, only 10 regions achieved the optimal level, while significant gaps remained across the others. Dynamic productivity analysis further revealed that the industrial sector experienced growth between 2015 and 2016 but stagnated from 2017 onwards. In contrast, the agricultural sector showed steady annual improvements, suggesting greater potential for resource-use efficiency and continuous progress. This study offers two main innovations: first, it breaks through the limitations of traditional single-stage models by more comprehensively revealing cross-sector efficiency interactions; second, it empirically fills the research gap on the relationship between industrial pollution and agricultural production efficiency. The findings not only provide a new academic perspective on the energy–environment–agriculture nexus but also offer empirical evidence to inform China's policies on pollution control, resource allocation, and food security. The results reveal significant efficiency gaps between industry and agriculture, with coal use and pollution dynamically linked across sectors. Policies should promote new energy development, enhance pollution reduction technologies, optimize resource allocation, and foster coordinated industry–agriculture growth for sustainable development.

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Publication Details

Journal
Energy & Environment
Published
2026-01-14
DOI
https://doi.org/10.1177/0958305x251410123
Primary Topic
Efficiency Analysis Using DEA
Type
article
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Agriculture is affected by industrial undesirable output: Policy recommendations for two-stage dynamic DEA and productivity changes

Ying Feng, Chiu-Mi Chen, Ching-Cheng Lu, Chih-Yu Yang
Energy & Environment
Efficiency Analysis Using DEA
article

Agriculture is affected by industrial undesirable output: Policy recommendations for two-stage dynamic DEA and productivity changes

Ying Feng, Chiu-Mi Chen, Ching-Cheng Lu, Chih-Yu Yang
article en

Abstract

Pollution generated during industrial manufacturing has become increasingly severe, posing a threat to food security. In response to the United Nations Sustainable Development Goals (SDG-2 and SDG-7) (the list of abbreviations and symbols can be found in Appendix A), this study employs the two-stage Dynamic Network Directional Distance Function (DN-DDF) and the two-stage Dynamic Total Factor Productivity (TFP) model to evaluate the efficiency and productivity of the industrial and agricultural sectors across 30 administrative regions in China from 2015 to 2019.The results indicate that the average efficiency of the industrial sector was 0.9274, higher than that of the agricultural sector at 0.8716. In the industrial sector, 14 regions achieved the optimal efficiency score of 1 over the 5-year average, while Gansu (0.7637), Liaoning (0.7431), and Jilin (0.5634) recorded the lowest efficiency levels. Similarly, in the agricultural sector, 14 regions reached an efficiency score of 1, whereas Jiangxi (0.4548), Xinjiang (0.4411), and Gansu (0.3113) performed the worst. Regarding overall efficiency, only 10 regions achieved the optimal level, while significant gaps remained across the others. Dynamic productivity analysis further revealed that the industrial sector experienced growth between 2015 and 2016 but stagnated from 2017 onwards. In contrast, the agricultural sector showed steady annual improvements, suggesting greater potential for resource-use efficiency and continuous progress. This study offers two main innovations: first, it breaks through the limitations of traditional single-stage models by more comprehensively revealing cross-sector efficiency interactions; second, it empirically fills the research gap on the relationship between industrial pollution and agricultural production efficiency. The findings not only provide a new academic perspective on the energy–environment–agriculture nexus but also offer empirical evidence to inform China's policies on pollution control, resource allocation, and food security. The results reveal significant efficiency gaps between industry and agriculture, with coal use and pollution dynamically linked across sectors. Policies should promote new energy development, enhance pollution reduction technologies, optimize resource allocation, and foster coordinated industry–agriculture growth for sustainable development.

Energy & Environment
Fo Guang University (TW)
Zero hunger
Openalex Percentile: Top 89%
Efficiency Analysis Using DEA
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