scholarly journals Research on Carbon Productivity and It's Spatial Convergence of Steel Industry in China

2021 ◽  
Author(s):  
Xiping Wang ◽  
Rong Tang
Keyword(s):  
Steel Industry ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Conditional Convergence ◽  
Growth Trend ◽  
Average Value ◽  
Spatial Convergence ◽  
Convergence Trend ◽  
Convergence Model ◽  
Carbon Productivity

Abstract The Global-Malmquist-Luenberger (GML) index was applied to analyse the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2017, and then the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) The overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with EC, TC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

scholarly journals Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

2021 ◽  
Vol 13 (16) ◽  
pp. 9014
Author(s):  
Yongjiao Wu ◽  
Huazhu Zheng ◽  
Yu Li ◽  
Claudio O. Delang ◽  
Jiao Qian
Keyword(s):  
Carbon Emissions ◽  
Industrial Development ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Spatial Lag Model ◽  
Workforce Allocation ◽  
Lag Model ◽  
Using Data ◽  
Shape Curve ◽  
Carbon Productivity

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

scholarly journals The Effect of Manufacturing Agglomeration on Haze Pollution in China

2018 ◽  
Vol 15 (11) ◽  
pp. 2490 ◽  
Author(s):  
Jun Liu ◽  
Yuhui Zhao ◽  
Zhonghua Cheng ◽  
Huiming Zhang
Keyword(s):  
Industrial Structure ◽  
Spillover Effects ◽  
Eastern Region ◽  
Spatial Panel ◽  
Panel Model ◽  
Specific Analysis ◽  
Haze Pollution ◽  
Regional Haze ◽  
Spatial Panel Model ◽  
Development Levels

Based on panel data on 285 Chinese cities from 2003 to 2012, we use a dynamic spatial panel model to empirically analyze the effect of manufacturing agglomeration on haze pollution. The results show that when economic development levels, population, technological levels, industrial structure, transportation, foreign direct investment, and greening levels are stable, manufacturing agglomeration significantly aggravates haze pollution. However, region-specific analysis reveals that the effects of manufacturing agglomeration on inter-regional haze pollution depends on the region: the effect of manufacturing agglomeration on haze pollution is the largest in the Western region, followed by the Central region, and is the least in the Eastern region. Based on the above conclusions, we put forward several specific suggestions, such as giving full play to the technology and knowledge spillover effects of manufacturing agglomeration, guiding manufacturing agglomerations in a scientific and rational way, accelerating the transformation and upgrading of manufacturing industries in agglomeration regions.

scholarly journals THE MEASUREMENT OF HIERARCHICALLY SPATIAL INDUSTRIAL KNOWLEDGE SPILLOVER EFFECTS

2018 ◽  
Vol 6 (6) ◽  
pp. 67-74
Author(s):  
Qianting Ye
Keyword(s):  
Industrial Structure ◽  
Knowledge Spillovers ◽  
Three Dimensional ◽  
Guangdong Province ◽  
Spillover Effects ◽  
Spatial Effect ◽  
The Hierarchical Structure ◽  
Economic Reality ◽  
Spatial Autoregressive ◽  
The Relationship

Based on the “year–region–industry” three - dimensional unbalanced industrial production panel data of Guangdong Province in China from 2005-2013, the relationship between knowledge spillovers and industrial structure is investigated by hierarchically spatial lagged with spatial autoregressive error (HSARAR) model. The empirical results indicate that the impacts of MAR, Jacobs, and Porter spillover on Guangdong's industry economic growth is positive and statistically significant. The industrial HSARAR model considers the hierarchical structure and spatial effect simultaneously, which has a better description on economic reality than the pooled model and SARAR model.

Are Industrial Structure Adjustment and Technical Progress Conducive to Environmental Improvement?

2022 ◽  
Vol 30 (6) ◽  
pp. 0-0
Keyword(s):  
Environmental Pollution ◽  
Technical Progress ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Pollutant Emissions ◽  
Temporal Dependence ◽  
Environmental Improvement ◽  
Structure Adjustment ◽  
Industrial Structure Adjustment ◽  
Spatio Temporal

China actively broadens its channels for environmental protection and limits pollutant emissions through industrial structure adjustment and technical progress. Based on panel data of 30 provinces in China from 2003 to 2017, this study investigated the effects of industrial structure adjustment and technical progress on environmental pollution using spatial Dubin models. The findings show the following. (1) As the economy develops, the situation of environmental pollution in various regions deteriorates; moreover, spatio-temporal dependence is an aspect of environmental pollution. (2) Industrial structure adjustment and technical progress are beneficial to environmental improvement. Furthermore, there are spillover effects in factors such as industrial structure and technical progress to varying degrees. Thus, this study suggests that the path of coupling between industrial structure and technical progress should be explored to establish a pollution filtering mechanism, thereby improving environmental quality.

scholarly journals Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

2019 ◽  
Vol 16 (4) ◽  
pp. 582 ◽  
Author(s):  
Xueqian Song ◽  
Yongping Wei ◽  
Wei Deng ◽  
Shaoyao Zhang ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Industrial Structure ◽  
Healthcare Expenditure ◽  
Government Expenditure ◽  
Spillover Effects ◽  
Public Healthcare ◽  
Long Term Effects ◽  
Healthcare Resources ◽  
Spatio Temporal ◽  
Main Determinants

In China, upper-level healthcare (ULHC) and lower-level healthcare (LLHC) provide different public medical and health services. Only when these two levels of healthcare resources are distributed equally and synergistically can the public’s demands for healthcare be met fairly. Despite a number of previous studies having analysed the spatial distribution of healthcare and its determinants, few have evaluated the differences in spatial equity between ULHC and LLHC and investigated their institutional, geographical and socioeconomic influences and spillover effects. This study aims to bridge this gap by analysing panel data on the two levels of healthcare resources in 31 Chinese provinces covering the period 2003–2015 using Moran’s I models and dynamic spatial Durbin panel models (DSDMs). The results indicate that, over the study period, although both levels of healthcare resources improved considerably in all regions, spatial disparities were large. The spatio-temporal characteristics of ULHC and LLHC differed, although both levels were relatively low to the north-west of the Hu Huanyong Line. DSDM analysis revealed direct and indirect effects at both short-and long-term scales for both levels of healthcare resources. Meanwhile, the influencing factors had different impacts on the different levels of healthcare resources. In general, long-term effects were greater for ULHC and short-term effects were greater for LLHC. The spillover effects of ULHC were more significant than those of LLHC. More specifically, industrial structure, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of ULHC, while industrial structure, urbanisation, topography, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of LLHC. These findings have important implications for policymakers seeking to optimize the availability of the two levels of healthcare resources.

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.

scholarly journals Research on Coordination and Driving Factors of Sports Industry and Regional Sustainable Development—Empirical Research Based on Panel Data of Provinces and Cities in Eastern China

2020 ◽  
Vol 12 (3) ◽  
pp. 813 ◽  
Author(s):  
Shaoxiong Yang ◽  
Jinfu Xu ◽  
Ruoyu Yang
Keyword(s):  
Sustainable Development ◽  
Index System ◽  
Eastern China ◽  
Spillover Effects ◽  
Driving Factors ◽  
Dynamic Factor ◽  
Growth Trend ◽  
Sports Industry ◽  
Regional Sustainable Development ◽  
Comprehensive Level

The steady and healthy development of the sports industry can promote regional sustainable development. In order to explore the coordination situation and driving factors between the sports industry and regional sustainable development, this article builds a coupling coordination evaluation index system and dynamic factor index system for the sports industry and regional sustainable development. Using the entropy method, coupling coordination model and random effect model, this article analyzes the comprehensive level, coupling coordination relationship and driving factors of the sports industry and regional sustainable development in eleven provinces and cities in eastern China, from 2013 to 2017. The results show that the comprehensive level of the sports industry and regional sustainable development in eastern China is showing a steady growth trend, and the sports industry is growing faster than regional sustainable development. The degree of coupling coordination among provinces and cities has increased significantly, but the spillover effects of coupling coordination in each region are not obvious. The level of overall coupling coordination is primary coordination; regional innovation, industrial structure upgrade, and human capital can promote the improvement of coupling coordination. Therefore, it is necessary to strengthen cross-regional cooperation, build a cross-regional “sports+” industry group, give play to the “sports +” spillover effect, create a coupling platform for the sports industry and regional sustainable development, release the superimposed effect of multiple driving factors, and facilitate the evolution of coupling coordination to higher levels.

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