Literature DB >> 29796885

Investigating the driving forces of China's carbon intensity based on a dynamic spatial model.

Junbing Huang1.   

Abstract

In extant literature on China's carbon intensity, economic growth is considered an important determinant. However, the corresponding policy implications are slightly weak in subsequent practice because economic growth is an outcome of many economic activities, such as technological progress and capital stock accumulation. Furthermore, spatial spillover effects are ignored when using regional datasets. As a result, this study uses the dynamic spatial model to analyze the driving forces of China's provincial carbon intensity over the period 2000-2014. Results indicate that both technological progress and capital stock accumulation are important measures to carbon intensity reduction. China's current industrialization, urbanization, and special energy structure exert a negative effect on the decline in carbon intensity. In addition, China's provincial carbon intensity also exhibits considerable spatiotemporal distribution characteristics. As such, the corresponding policy measures are presented.

Entities:  

Keywords:  Carbon intensity; Dynamic spatial model; Spatial spillover effects

Mesh:

Substances:

Year:  2018        PMID: 29796885     DOI: 10.1007/s11356-018-2307-5

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model.

Authors:  Xiaopeng Guo; Dongfang Ren; Jiaxing Shi
Journal:  Environ Sci Pollut Res Int       Date:  2016-09-22       Impact factor: 4.223

  1 in total
  6 in total

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Journal:  Environ Sci Pollut Res Int       Date:  2018-12-01       Impact factor: 4.223

2.  How indebted farmers perceive and address financial risk in environmentally degraded areas in Bangladesh.

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Journal:  Environ Sci Pollut Res Int       Date:  2019-12-28       Impact factor: 4.223

3.  Determining the factors driving energy demand in the Sichuan-Chongqing region: an examination based on DEA-Malmquist approach and spatial characteristics.

Authors:  Junbing Huang; Tianchi Yang; Jing Jia
Journal:  Environ Sci Pollut Res Int       Date:  2019-09-04       Impact factor: 4.223

4.  CO2 embodied in trade: trends and fossil fuel drivers.

Authors:  Sylvain Weber; Reyer Gerlagh; Nicole A Mathys; Daniel Moran
Journal:  Environ Sci Pollut Res Int       Date:  2021-01-29       Impact factor: 5.190

5.  Preventing a rebound in carbon intensity post-COVID-19 - lessons learned from the change in carbon intensity before and after the 2008 financial crisis.

Authors:  Qiang Wang; Shasha Wang; Xue-Ting Jiang
Journal:  Sustain Prod Consum       Date:  2021-04-24

6.  Study on the extension of the dynamic benchmark system of per capita carbon emissions in China's county.

Authors:  Fengmei Yang; Longyu Shi; Xiaotong Wang; Lijie Gao
Journal:  Environ Sci Pollut Res Int       Date:  2022-09-07       Impact factor: 5.190

  6 in total

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