Evidence map›Paper›PMID 40594952›Full record

ArticleScientific reports2025

Spatial temporal trends and inequality in agricultural eco-efficiency under carbon constraints in China.

Shuaibang Fan, Hongyu Lin, Na Luo, Herbert Sima, Yanping Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shuaibang FanSchool of Public Administration, Dongbei University of Finance & Economics, Dalian, 116025, China.
Hongyu LinSchool of Public Administration, Dongbei University of Finance & Economics, Dalian, 116025, China.
Na LuoSchool of Public Administration, Dongbei University of Finance & Economics, Dalian, 116025, China. ronaluo@dufe.edu.cn.
Herbert SimaDepartment of Marketing, The University of Auckland, Auckland, 1142, New Zealand, New Zealand.
Yanping LiuDepartment of Management Science and Engineering, Business School, Nankai University, Tianjin, 300071, China.

Funding

The National Social Science Key Project 24AGL006The National Social Science Project 24BTJ038
6 · The paper itself

Abstract

Eco-efficiency in cultivated land use is crucial for ensuring food security and promoting sustainable agriculture amidst the challenges posed by urbanization and climate change. Distinct from much of the existing literature, this paper adopts a super-efficiency EBM model that integrates both radial and non-radial perspectives to estimate the eco-efficiency of cultivated land use and identify sources of inefficiency under carbon emission constraints, focusing on 180 prefecture-level cities in China's major grain-producing regions. Additionally, spatial kernel density estimation is applied to analyze the spatiotemporal dynamics and long-term trends in eco-efficiency, while the Dagum Gini coefficient is used to examine the causes of spatiotemporal disparities. The key findings include (1) The Songhua River Basin shows significantly higher eco-efficiency (mean: 0.718) than the Yellow River (mean: 0.559) and Yangtze River Basins (mean: 0.587), with distinct evolutionary patterns; (2) Long-term evolution reflects a bipolar pattern with spatial agglomeration disparities; (3) Positive spatial spillover effects are evident in regions with efficiency levels between 0.4 and 0.9, with inter-regional differences and super-variable density as significant spatial variation sources. This study reveals a "long-term increase - short-term decline" trend in ecological efficiency, highlighting carbon emissions as the primary limiting factor, although this constraint is gradually diminishing.

Indexed as

ChinaDynamic evolutionEcological efficiencySpatial-temporal patternsSuper-efficiency EBM model

Identifiers

PMID40594952
PMCPMC12217078

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.