Evidence map›Paper›PMID 36429434›Full record

ArticleInternational journal of environmental research and public health2022

Green Total Factor Productivity and Its Saving Effect on the Green Factor in China's Strategic Minerals Industry from 1998-2017.

Yujian Jin, Lihong Yu, Yan Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yujian JinSchool of Business, East China University of Science and Technology, Shanghai 200237, China.ORCID 0000-0003-4020-2873
Lihong YuSchool of Business, East China University of Science and Technology, Shanghai 200237, China.
Yan WangSchool of Business, Linyi University, Linyi 276000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Improving green total factor productivity (GTFP) is a fundamental solution to help the strategic mineral industry to achieve green and sustainable development. This study incorporates the dual negative externalities of resource depletion and environmental pollution into the GTFP measurement to capture the 'green' elements. By employing a truncated third-order (TTO) translog cost function and the feasible generalized least squares (FGLS) approach, we evaluate the GTFP growth performance and its components in China's strategic minerals industry from 1998 to 2017. Moreover, we explore the bias of technological progress toward the resource and environmental factors to grasp the green factor saving effects. The results show that: (1) during the sample period, the average GTFP growth rate of China's strategic minerals industry was 0.46%, but there were variances between mineral sectors. Nevertheless, after 2012, the GTFP of all mineral sectors experienced different degrees of decrease. (2) The main driver of adjustments in GTFP growth shifted from technological progress to changes in scale efficiency, with technological progress contributing less to GTFP growth. This is particularly evident in the metal and energy minerals sectors. (3) Green technological progress is biased toward saving environmental factor input but enhancing resource extraction. Therefore, the current development of China's strategic minerals industry falls into a non-sustainable mode of being environmentally friendly but not resource-saving.

Indexed as

EfficiencyIndustryChinaMineralsTechnologyMineralsbiased technological progressgreen total factor productivityscale efficiency changestrategic mineraltruncated third-order function

Identifiers

PMID36429434
PMCPMC9691160

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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.