Evidence map›Paper›PMID 33411274›Full record

ArticleEnvironmental science and pollution research international2021

Abatement potential and cost of agricultural greenhouse gases in Australian dryland farming system.

Kai Tang, Mingzhe Wang, Di Zhou

Abstract read
PubMed Publisher
In one paragraph

Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. The dynamics and drivers of environmental performance in Chinese cities: a decomposition analysis.Environmental science and pollution research international · 2021
    Article
  8. The effectiveness and heterogeneity of carbon emissions trading scheme in China.Environmental science and pollution research international · 2021
    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

3 authors.

Kai TangSchool of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou, 510006, China.
Mingzhe WangGraduate School of Chinese Academy of Social Sciences, Beijing, 102488, China.
Di ZhouSchool of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, China. zhoudi880101@126.com.ORCID http://orcid.org/0000-0003-1472-5371

Funding

Guangdong Basic and Applied Basic Research Foundation 2019A1515010884Humanities and Social Sciences Project from Ministry of Education of China 20YJC790191Humanities and Social Sciences Project from Ministry of Education of China 20YJCZH144Pearl River Talents Plan of Guangdong Province 20170133
6 · The paper itself

Abstract

Evaluating the cost-effectiveness of GHG mitigation in the dryland agricultural sector is needed in terms of designing and implementing detailed and efficient mitigation programs, which is currently rarely covered by the literature. In this paper, we use a parametric directional distance approach to explore the farm-level abatement potential and cost (shadow value) of GHG for dryland farms in southwestern Australia. The study indicates that dryland agriculture could abate substantial GHG emissions and save agricultural inputs simultaneously. For the years 2006-2013, the average abatement potential ratios fluctuated between 17 and 33%, with a mean value of 21%. The mean shadow price of dryland agricultural GHG was $17.60 per tonne CO

Indexed as

Greenhouse GasesAgricultureAustraliaFarmsGreenhouse EffectGreenhouse GasesAustraliaClimate change mitigationDirectional distance function frontierDryland agricultureReduction potentialShadow price

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.