Evidence map›Paper›PMID 36148276›Full record

ArticleHeliyon2022

Modelling and discussion on emission reduction transformation path of China's electric power industry under "double carbon" goal.

Shengyuan Liu, Zhenzhi Lin, Yicheng Jiang, Tianhan Zhang, Li Yang, Weitao Tan, Feng Lu

Abstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Aluminum saving and COiScience · 2024
    Article
  2. Article
  3. 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

7 authors.

Shengyuan LiuSchool of Electrical Engineering, Zhejiang University, Hangzhou, China.
Zhenzhi LinSchool of Electrical Engineering, Zhejiang University, Hangzhou, China.
Yicheng JiangState Grid Zhejiang Electric Power Corporation, Hangzhou, China.
Tianhan ZhangSchool of Electrical Engineering, Zhejiang University, Hangzhou, China.
Li YangSchool of Electrical Engineering, Zhejiang University, Hangzhou, China.
Weitao TanSchool of Electrical Engineering, Zhejiang University, Hangzhou, China.
Feng LuState Grid Huzhou Power Supply Company, Huzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China has promised to peak carbon emission before 2030 and to achieve carbon neutrality before 2060 (i.e., "double carbon" goal). Under this background, the emission reduction transformation path of China's electric power industry is studied in this paper. First, several boundary conditions (i.e., assumptions) of electric power structure transformation (i.e., the costs of power generations, the costs of energy storage systems, the developments of carbon sinks, the emission factors, and the quotas of carbon sinks) are given considering the whole society electricity consumption in the future. Second, a transformation path optimization model is established aim to minimize the total cost in the electric power industry. Then, according to the optimization results, the transformation predictions for the power industry under the "30·60 scenario" (i.e., the scenario that can achieve carbon peak and carbon neutrality before 2030 and 2060) are analyzed in detail, and are compared with the ones of "2 °C scenario" and "1.5 °C scenario" defined by IPCC. Furthermore, the influence of different carbon prices on the transformation path is also analyzed. It can be concluded from the simulation results that the "30·60 scenario" is a scenario between "2 °C scenario" and "1.5 °C scenario", and carbon emission can be reduced rapidly under the guidance of high carbon prices.

Indexed as

Carbon dioxide emissionCarbon neutralityChinaElectric power structurePower industryScenarioTransformation path

Identifiers

PMID36148276
PMCPMC9485049

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.