Evidence map›Paper›PMID 33058061›Full record

ArticleEnvironmental science and pollution research international2021

Relationship between adaptation to climate change and provincial government expenditure in Pakistan.

Khuda Bakhsh, Aasma Latif, Rafaqet Ali, Muhammad Asim Yasin

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 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

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

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

4 authors.

Khuda BakhshDepartment of Management Sciences, COMSATS University Islamabad, Vehari Campus, Vehari, Pakistan. kbakhsh@ciitvehari.edu.pk.ORCID http://orcid.org/0000-0001-5607-3370
Aasma LatifDepartment of Management Sciences, COMSATS University Islamabad, Vehari Campus, Vehari, Pakistan.
Rafaqet AliDepartment of Management Sciences, COMSATS University Islamabad, Vehari Campus, Vehari, Pakistan.
Muhammad Asim YasinDepartment of Management Sciences, COMSATS University Islamabad, Vehari Campus, Vehari, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public expenditures on adaptation to climate change have direct effects on budgets of governments. Such direct impacts on government budgetary allocation have been analyzed in the developed countries and very little research is available considering highly vulnerable and exposed developing countries which are facing huge threats of climate change. The current study is designed to bridge this information gap by examining the effects of climate change adaptation on public expenditures in Pakistan employing provincial level data over the period of 1990-2016. Adaptation expenditures included both overall government expenditures and expenditures on communal amenities and housing. In addition to a fixed effects estimation, we employed long difference model to analyze the impact in the long run. Results indicated that variations in temperature and rainfall were significantly related with public expenditures. Short run and long run scenarios showed that rising temperature caused an increase in public expenditures. The effect of rainfall on total public expenditure was positive and statistically significant while rainfall and expenditure incurred on communal amenities and housing were negatively related in the long run. The study posits important policy implications.

Indexed as

Climate ChangeHealth ExpendituresAcclimatizationPakistanState GovernmentAdaptationClimate changePakistanPublic expenditureRainfallTemperature

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.