Evidence map›Paper›PMID 34729713›Full record

ArticleEnvironmental science and pollution research international2022

Could China's long-term low-carbon energy transformation achieve the double dividend effect for the economy and environment?

Ling He, Bangpei Wang, Wanting Xu, Qi Cui, Hao Chen

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China.International journal of environmental research and public health · 2022
    Article
  5. Article
  6. 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.

Ling HeBeijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, 100875, Beijing, China.
Bangpei WangBeijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, 100875, Beijing, China.
Wanting XuBeijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, 100875, Beijing, China.
Qi CuiBeijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, 100875, Beijing, China.
Hao ChenBeijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, 100875, Beijing, China. hchen@bnu.edu.cn.

Funding

Beijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development 217300010Beijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development BNU.310432101National Natural Science Foundation of China 71903014
6 · The paper itself

Abstract

Exploring the low-carbon energy transformation pathway is vital to coordinate economic growth and environmental improvement for achieving China's carbon peak target. Three energy-target scenarios are developed in this paper, considering the targets of energy structure, electrification rate, and carbon mitigation towards 2030 announced by the Chinese government. A dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine the economic and environmental effects under different pathways of long-term low-carbon transformation. It detects that China's energy structure would substantially transfer to the low-carbon and clean one, whereas CO

Indexed as

CarbonCarbon DioxideChinaEconomic DevelopmentCarbonCarbon DioxideCarbon neutralityCarbon peakChinaEconomic impactEnvironmental impactLow-carbon transformation

Identifiers

What OpenQuestion holds

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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.