scholarly journals Impact of Manufacturing Industry Agglomeration on Carbon Emissions based on New Evidence from China

2019 ◽  
Author(s):  
Li Wei ◽  
Ningning Liu
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Industry Agglomeration ◽  
New Evidence

The End of Multi-Fibre Arrangement and Firm Performance in the Textile Industry: New Evidence

2013 ◽  
Vol 52 (2) ◽  
pp. 97-126 ◽  
Author(s):  
Zara Liaqat
Keyword(s):  
Textile Industry ◽  
Manufacturing Industry ◽  
Firm Heterogeneity ◽  
Production Functions ◽  
Firm Level ◽  
Quality Ladders ◽  
Sectoral Differences ◽  
The Mean ◽  
New Evidence ◽  
Empirical Methodology

Using a sample of 321 textile and clothing companies for the years 1992 to 2010, this paper analyses the effect of quota phase-outs on firm-level efficiency in Pakistan following the end of the Multi-Fibre Arrangement (MFA). It highlights sectoral heterogeneity within the manufacturing industry as a result of MFA expiration. The empirical methodology uses the structural techniques proposed by Olley and Pakes (1996), and Levinsohn and Petrin (2003) in order to take care of endogeneity in the estimation of production functions. The results differ for the two industries: MFA expiration lead to an increase in the average productivity of textile producing firms but a significant reduction in the mean productivity of clothing producers. We offer a number of explanations for this outcome, such as a change in the input and product mix, entry by non-exporters in the clothing sector, and sectoral differences in quality ladders. A number of crucial policy lessons can be drawn from the findings of this study. JEL Classification:F13; F14; D24; C14; O19 Keywords: Multi-Fibre Arrangement, Trade Liberalisation, Productivity, Firm Heterogeneity, Simultaneity and Production Functions, Endogeneity of Protection

scholarly journals Analysis of China’s Manufacturing Industry Carbon Lock-In and Its Influencing Factors

2020 ◽  
Vol 12 (4) ◽  
pp. 1502 ◽  
Author(s):  
Xia Wang ◽  
Lijun Zhang ◽  
Yaochen Qin ◽  
Jingfei Zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Ferrous Metal ◽  
Bohai Bay ◽  
High Carbon ◽  
Intergovernmental Panel ◽  
Resource Endowment ◽  
Regional Factors ◽  
Lock In

There are industry lock-in and regional lock-in phenomena in China’s manufacturing industry carbon emissions. However, the existing researches often focus on global carbon emissions, which is not adverse to finding the main problems of manufacturing industry carbon emissions. The biggest contributions of this study are the identification of the industry lock-in and regional lock-in of China’s manufacturing industry and the finding of the regional factors that affect the carbon lock-in of the manufacturing industry, which points out the direction for the low-carbon transformation of the local manufacturing industry. This paper is based on the IPCC (Intergovernmental Panel on Climate Change) carbon emissions coefficient method and energy consumption data from 2000 to 2016 to count the manufacturing industry carbon emissions of 30 provinces in China (except Hong Kong, Macao, Taiwan and Tibet). On this basis, the paper uses a spatial–temporal geographical weighted regression (GTWR) model to analysis the regional influencing factors of the high-carbon manufacturing industry. Results demonstrate that China’s high-carbon manufacturing industry mainly concentrates on the ferrous metal processing industry, non-metallic mineral manufacturing industry and other sectors. In addition, the carbon emissions of high-carbon manufacturing industries are mainly concentrated in Bohai Bay and the North China Plain. The industrial structure and economic scale are the main reasons for the regional carbon lock-in of the high-carbon manufacturing industry, and the strength of the lock-in has continued to increase. Resource endowment is a stable factor of carbon lock-in in high-carbon regions. Technological progress helps to unlock carbon, while foreign direct investment results in the enhancement of carbon regional lock-in. This study focuses on the regional factors of carbon lock-in in the manufacturing industry, hoping to provide decision support for the green development of China’s manufacturing industry.

Influencing Factors and Decoupling Analysis of Carbon Emissions in China's Manufacturing Industry

2021 ◽  
Author(s):  
baoling jin ◽  
ying Han
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Investment Efficiency ◽  
Fixed Asset ◽  
Carbon Productivity ◽  
Fixed Asset Investment

Abstract The manufacturing industry directly reflects national productivity, and it is also an industry with serious carbon emissions, which has attracted wide attention. This study decomposes the influential factors on carbon emissions in China’s manufacturing industry from 1995 to 2018 into industry value added (IVA), energy consumption (E), fixed asset investment (FAI), carbon productivity (CP), energy structure (EC), energy intensity (EI), investment carbon intensity (ICI) and investment efficiency (IE) by Generalized Divisia Index Model (GDIM). The decoupling analysis is carried out to investigate the decoupling states of the manufacturing industry under the pressure of "low carbon" and "economy.” Considering the technological heterogeneity, we study the influential factors and decoupling status of the light industry and the heavy industry. The results show that: (1) Carbon emissions of the manufacturing industry present an upward trend, and the heavy industry is the main contributor. (2) Fixed asset investment (FAI), industry value added (IVA) are the driving forces of carbon emissions. Investment carbon intensity (ICI), carbon productivity (CP), investment efficiency (IE), and energy intensity (EI) have inhibitory effects. The impact of the energy consumption (E) and energy structure (EC) are fluctuating. (3) The decoupling state of the manufacturing industry has improved. Fixed asset investment (FAI), industry value added (IVA) hinder the decoupling; carbon productivity (CP), investment carbon intensity (ICI), investment efficiency (IE), and energy intensity (EI) promote the decoupling.

scholarly journals The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1428 ◽  
Author(s):  
Na Lu ◽  
Shuyi Feng ◽  
Ziming Liu ◽  
Weidong Wang ◽  
Hualiang Lu ◽  
...  
Keyword(s):  
Construction Industry ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Energy Structure ◽  
Chinese Government ◽  
Low Carbon ◽  
Industry Agglomeration ◽  
Technology Innovations ◽  
Growth Energy

As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

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