scholarly journals Influence of Agricultural Mechanization Development on Agricultural Green Transformation in Western China, Based on the ML Index and Spatial Panel Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiaoming Guo ◽  
Sen Huang ◽  
Yu Wang
Keyword(s):  
Positive Impact ◽  
Northwest China ◽  
Western China ◽  
Western Region ◽  
Positive Role ◽  
Spatial Panel ◽  
Panel Model ◽  
Agricultural Mechanization ◽  
Spatial Panel Model ◽  
The Impact

The agricultural mechanization development can promote the high-quality development of agriculture, but the agriculture which faces the huge environmental pressure also needs to transform to the green sustainable development. Based on agricultural panel data of 30 provinces in China from the year 2008 to 2017, this paper measures the level of agricultural green transformation using the Malmquist–Luenberger (ML) index. Furthermore, the spatial panel econometric model is used to empirically investigate the impact of agricultural mechanization development on this green transformation. It concludes that the agricultural green transformation in western China shows an upward trend in fluctuations, with its overall transformation level still lower than the national average level. Besides, the agricultural mechanization operation level in the western region has a positive role in promoting the green transformation of agriculture, while the agricultural mechanization equipment level shows the opposite force. Compared with southwest China, the agricultural mechanization operation level in northwest China has a more positive impact. The improvement of farmers’ income and agricultural technology has positive impacts on agriculture green transformation in the whole western region, while agricultural human capital shows an opposite impact on the northwest and southwest.

scholarly journals Estimating the local employment impacts of immigration: A dynamic spatial panel model

Urban Studies ◽  
2019 ◽  
Vol 57 (13) ◽  
pp. 2646-2662 ◽  
Author(s):  
Bernard Fingleton ◽  
Daniel Olner ◽  
Gwilym Pryce
Keyword(s):  
Positive Impact ◽  
Evidence Base ◽  
Data Set ◽  
Spatial Panel ◽  
Panel Model ◽  
Spatial Panel Model ◽  
Local Impacts ◽  
The Impact ◽  
Employment Impacts

This paper highlights a number of important gaps in the UK evidence base on the employment impacts of immigration, namely: (1) the lack of research on the local impacts of immigration – existing studies only estimate the impact for the country as a whole; (2) the absence of long-term estimates – research has focused on relatively short time spans – there are no estimates of the impact over several decades, for example; (3) the tendency to ignore spatial dependence of employment which can bias the results and distort inference – there are no robust spatial econometric estimates we are aware of. We aim to address these shortcomings by creating a unique data set of linked Census geographies spanning five Censuses since 1971. These yield a large enough sample to estimate the local impacts of immigration using a novel spatial panel model which controls for endogenous selection effects arising from migrants being attracted to high-employment areas. We illustrate our approach with an application to London and find that no migrant group has a statistically significant long-term negative effect on employment. EU migrants, however, are found to have a significant positive impact, which may have important implications for the Brexit debate. Our approach opens up a new avenue of inquiry into subnational variations in the impacts of immigration on employment.

scholarly journals Maternal Health and Green Spaces in China: A Longitudinal Analysis of MMR Based on Spatial Panel Model

Healthcare ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 154 ◽  
Author(s):  
Jin ◽  
Gao ◽  
Liu ◽  
Peng ◽  
Wu
Keyword(s):  
Maternal Health ◽  
Maternal Mortality ◽  
Green Space ◽  
Positive Impact ◽  
Green Spaces ◽  
Valuable Insight ◽  
Spatial Panel ◽  
Panel Model ◽  
Spatial Panel Model ◽  
Insight Into

The positive impact of green spaces on public health has attracted increasing attention, and maternal health has also been shown to be related to green spaces. However, there are different kinds of green space indicators that represent different mechanisms for mitigating maternal health, and few studies have investigated the different relevance amongst them with longitudinal data. This study explores the correlation between various green space indicators and maternal health using spatial panel models with provincial data from China from 2007 to 2016. The results indicate that all kinds of green spaces could decrease maternal mortality, wherein public green spaces may play a key role. In terms of spatial correlation, an increase in green space coverage in adjacent provinces may also result in a slight decline in maternal mortality. This paper provides valuable insight into the correlation between maternal health and green spaces.

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 effects of environmental regulation and technological innovation on green growth: A theoretical analysis

2018 ◽  
Vol 53 ◽  
pp. 04054
Author(s):  
Xuefei Xu ◽  
Lili Wang ◽  
Shang Chen
Keyword(s):  
Environmental Regulation ◽  
Technological Innovation ◽  
Positive Impact ◽  
Causal Chain ◽  
Mediation Model ◽  
Green Growth ◽  
Positive Role ◽  
Long Run ◽  
The Impact ◽  
Causality Chain

As green growth has attracted a great deal of attention due to the growing concern about the degradation of natural resources and environmental pollution in China, the questions of how to achieve it and which factors drive green growth have become hot topics. Environmental regulation and technological innovation are two main fulcrums in the realization of green growth. However, there is lacking a deeper understanding of the impact of environmental regulation and technological innovation on green growth in a methodological framework. Accordingly, this paper attempts to analyze how these factors affect the implementation of green growth in a model. The findings reveal that (1) in the short term, environmental regulation has inhibited green growth, but has a positive impact on green growth in the long run, (2) technological innovation plays a positive role in green growth improvement, and (3) the causality chain among regulation, technological innovation, and green growth is a typical mediation model. Technological innovation plays an important mediation role in the causal chain. This study not only enriches and deepens theories on green growth, but also successfully implements green growth practices and improve their performance.

scholarly journals DEMOGRAPHIC BONUS ANALYSIS AND ECONOMIC GROWTH OF EAST JAVA

2018 ◽  
Vol 2 (1) ◽  
pp. 8-21
Author(s):  
Amirusholihin ◽  
Listiono
Keyword(s):  
Negative Impact ◽  
Positive Impact ◽  
Demographic Variables ◽  
Spatial Inequality ◽  
Solow Model ◽  
Dynamic Panel Model ◽  
Goods And Services ◽  
Panel Model ◽  
Working Age ◽  
The Impact

BKKBN predicts that Indonesia will get demographic bonus in 2020 until 2030. The question is whether the demographic bonus has a positive impact on the economy of East Java or even a negative impact. Based on data from BPS, by 2015 the workingage population in East Java is around 69.4 percent of the total population, while the child and old-age is 30.6 percent. The size of the working-age population is closely related to the amount of labor, which also greatly determines the amount of output on goods and services produced. This paper aims to explain how the impact of demographic bonuses on East Java's regional economy, based on the Solow model extended to include demographic variables. The analysis uses a dynamic panel model by 38 districts in East Java that have demographic bonuses in 2020 with GDP as a reference in determining the growth of economists. From these analyzes it can be seen the impact of demographic bonuses in East Java as an advantage or even create new spatial inequality between regions.

scholarly journals The Determinants of Foreign Direct Investment in China: A Spatial Panel Analysis

2016 ◽  
Author(s):  
Chen-Chen Yong ◽  
Siew-Yong Yew ◽  
Xin Huang ◽  
Mui-Yin Chin
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Eastern Region ◽  
Panel Analysis ◽  
Fdi Inflows ◽  
Spatial Panel ◽  
Panel Model ◽  
Spatial Panel Model ◽  
Investment In China ◽  
Determinants Of Fdi

China is currently the major foreign direct investment (FDI) destination arising from her open door policies since 1978. FDI has become a large impetus to China’s economic growth. However, the geographical distribution of FDI in China is severely biased with 83% concentrated in the eastern region. This is a result of not only differences in locational advantages but also the result of the initiating policies and temporal differences of FDI inflows among the regions. This study aims to examine the determinants of FDI and examine empirically the possible coherent policies for the three regions of China (Eastern, Central and Western) using the spatial panel analysis for the data within the period of 1994 to 2008. The empirical results show that the determinants of FDI vary among the three regions, depending on the motives of the investor and the results of policy bias. The entrepreneurial nature of competition of FDI among the provinces revealed by the spatial FDI factor is a conclusion that cannot be ignored. A more coherent policy on FDI inflows into China is an urgent necessity, though the policies for each region must be, of necessity, different for each of the three regions.   Keywords: Foreign Direct Investment, China, Spatial panel model, spatial variables JEL: F14, C33

scholarly journals Returns to Scale for EU Regions – evidence from the spatial panel model

2017 ◽  
Vol 3 (329) ◽  
Author(s):  
Alicja Anna Olejnik
Keyword(s):  
Fixed Effects ◽  
Regional Growth ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Increasing Returns To Scale ◽  
Spatial Panel ◽  
Panel Model ◽  
Eu Regions ◽  
Spatial Panel Model ◽  
The Eu

Recent findings emphasise the importance of localised returns to scale for the regional growth as well as for the agglomeration processes. However, it is still not well established whether returns to scale are constant or increasing, and to what extent. Therefore, in this study we apply specification which describes the productivity growth with the growth of output through the Verdoorn’s law. This study aims to provide some new estimates of the degree of returns to scale for EU regions. Our findings show that the hypothesis of increasing returns to scale is still valid in today’s EU economy. To test the hypothesis, we have employed the Multidimensional Spatial Panel Durbin Model with Spatial Fixed Effects. The research is conducted for 261 regions of the EU 28. The paper concludes that increasing returns to scale in EU regions are substantial.

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