Evidence map›Paper›PMID 32740836›Full record

ArticleEnvironmental science and pollution research international2020

Measurement of China's green GDP and its dynamic variation based on industrial perspective.

Feng Wang, Ruiqi Wang, Junyao Wang

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Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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.

Feng WangSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an, 710061, China.
Ruiqi WangSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an, 710061, China. wangruiqi1994@stu.xjtu.edu.cn.ORCID http://orcid.org/0000-0002-2095-160X
Junyao WangXi'an Qujiang Culture Finance Holding Co. Ltd., Xi'an, 710061, China.

Funding

National Natural Science Foundation of China 71673217
6 · The paper itself

Abstract

Over the previous two decades, Chinese economic development presented a rapid growth. However, with continuous industrialization and urbanization, China is confronted with great challenges of energy security and environmental issues. These problems are closely related to the current accounting method of economic growth to a certain extent. In order to meet these challenges, it is imperative to establish a green accounting system of economic growth and measure China's green GDP and its changing trend based on the industrial perspective. Using the System of Environmental Economic Accounting (SEEA) and industry data, this paper estimates China's green GDP and green value added by industry sectors in 2005, 2007, 2010, 2012, 2015, and 2017. The results reveal the following: First, the ratio of green GDP to traditional GDP gradually increases from 89.85 to 95.83% during 2005-2017, which means that the negative externalities of economic growth of the resource and environment are gradually weakened. Second, the difference between traditional GDP and green GDP during 2005-2017 is about 6.96%, with the carbon emissions accounting for 70.71% of environmental impact. Third, due to more than 80% of the environmental impact coming from three sectors: manufacturing (49.99%), electricity industry (22.63%), and other services (11.37%), these three sectors should be key sectors for energy conservation and emission reduction; fourth, the green GDP of the mining, electricity industries, and manufacturing accounts for the lowest proportion of GDP, which means that the development patterns of these three industries in recent years should be adjusted and optimized step by step.

Indexed as

Economic DevelopmentIndustryChinaCommerceUrbanizationDynamic variationsEnvironmental pollution costGreen GDPIndustryResource depletion costSEEA

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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.