Synergies or Trade‐Offs Between Environmental and Economic Outcomes? An Analysis of Agricultural Production Performance

ABSTRACT Incorporating greenhouse gas (GHG) emissions as an undesirable output of agricultural production in a directional distance function provides a better understanding of agricultural production performance. Based on balanced panel data from 126 prefecture‐level regions in the Yangtze River Basin in China, we employed a quadratic directional output‐oriented distance function to measure agricultural production performance in terms of environmental efficiency, relative shadow price of GHG emissions and Morishima elasticities of substitution between inputs. We found an upward trend, with average environmental efficiency of 0.871 during 2000–2020. This indicates that 12.9% more agricultural added value can be achieved, along with a 12.9% reduction in GHG emissions. Agricultural production in the upstream Yangtze River Basin region showed the best performance in terms of environmental efficiency. The estimated relative shadow price of GHG emissions was 4.606 and showed a negative association with environmental efficiency. Morishima elasticities of substitution indicated a substitution relationship of labour and fertiliser to land, and a strong complementary relationship between fertiliser and labour.

Authors

Institutions

Publication Details

Journal
Australian Journal of Agricultural and Resource Economics
Published
2026-07-25
DOI
https://doi.org/10.1111/1467-8489.70146
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Synergies or Trade‐Offs Between Environmental and Economic Outcomes? An Analysis of Agricultural Production Performance

Wei Huang, Xinli Ke, Wei Zhang
Australian Journal of Agricultural and Resource Economics
Efficiency Analysis Using DEA
article

Synergies or Trade‐Offs Between Environmental and Economic Outcomes? An Analysis of Agricultural Production Performance

Wei Huang, Xinli Ke, Wei Zhang
article en

Abstract

ABSTRACT Incorporating greenhouse gas (GHG) emissions as an undesirable output of agricultural production in a directional distance function provides a better understanding of agricultural production performance. Based on balanced panel data from 126 prefecture‐level regions in the Yangtze River Basin in China, we employed a quadratic directional output‐oriented distance function to measure agricultural production performance in terms of environmental efficiency, relative shadow price of GHG emissions and Morishima elasticities of substitution between inputs. We found an upward trend, with average environmental efficiency of 0.871 during 2000–2020. This indicates that 12.9% more agricultural added value can be achieved, along with a 12.9% reduction in GHG emissions. Agricultural production in the upstream Yangtze River Basin region showed the best performance in terms of environmental efficiency. The estimated relative shadow price of GHG emissions was 4.606 and showed a negative association with environmental efficiency. Morishima elasticities of substitution indicated a substitution relationship of labour and fertiliser to land, and a strong complementary relationship between fertiliser and labour.

Australian Journal of Agricultural and Resource Economics
Huazhong Agricultural University (CN), Swedish University of Agricultural Sciences (SE)
Swedish Foundation for International Cooperation in Research and Higher Education, Svenska Forskningsrådet Formas
Zero hunger
Openalex Percentile: Top 5%
Efficiency Analysis Using DEA
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.