Evidence map›Paper›PMID 34283353›Full record

ArticleEnvironmental science and pollution research international2021

Does technological innovation improve energy-environmental efficiency? New evidence from China's transportation sector.

Yongfeng Zhu, Zilong Wang, Lingling Zhu

Abstract read
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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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Yongfeng ZhuCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China. zhuyongfeng@nuaa.edu.cn.ORCID http://orcid.org/0000-0003-4684-2935
Zilong WangCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Lingling ZhuCollege of Information Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China.

Funding

Major Program of National Fund of Philosophy and Social Science of China 20ZDA092
6 · The paper itself

Abstract

The rapid modernization of the transportation sector has greatly escalated many problems, especially the high energy consumption and vehicle exhaust pollution. How to reduce pollution in the transportation sector has attracted widespread attention in recent years. Based on a balanced panel dataset of 30 Chinese provinces spanning the period of 2005-2017, this study attempts to investigate the influence of technological innovation on the energy-environmental efficiency of the transportation sector (EETS) using the spatial econometric approach. The empirical results suggest that first, transportation-related technological innovation and EETS exhibited obvious hot spots and cold spots at the provincial level in China. Second, technological innovation could facilitate the energy-environmental efficiency of transportation sector in China. Third, one province developing transportation-related technological innovations might promote EETS in its neighboring provinces. Fourth, the transportation-related technological innovation in eastern China could boost EETS, while the transportation-related technological innovation in central and western China had a rebound effect on EETS. One possible innovation is that this study extends the relationship between technological innovation and energy-environmental efficiency to the transportation sector.

Indexed as

Economic DevelopmentInventionsChinaEfficiencyEnvironmental PollutionChinaDEA modelEnergy-environmental efficiencySpatial econometric modelTechnological innovationTransportation sector

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.