scholarly journals An Analysis of the Impact of Market Segmentation on Energy Efficiency: A Spatial Econometric Model Applied in China

2021 ◽  
Vol 13 (14) ◽  
pp. 7659
Author(s):  
Liangjun Yi ◽  
Wei Zhang ◽  
Yuanxin Liu ◽  
Weilin Zhang
Keyword(s):  
Energy Efficiency ◽  
Environmental Protection ◽  
Market Segmentation ◽  
Geographically Weighted Regression ◽  
Positive Impact ◽  
Weighted Regression ◽  
Eastern Region ◽  
Geographically Weighted Regression Model ◽  
Negative Effect ◽  
The Impact

China’s recent development has been nothing short of remarkable, but energy-saving, and environmental protection is still a serious problem. The improvement of energy efficiency (EE) is an important factor for China to better follow the path of energy conservation, sustainable development, and environmental protection. Meanwhile, market segmentation is a unique phenomenon in the process of China’s economic development. Hence, studying market segmentation on energy efficiency has positive significance for improving energy efficiency. The major objective of this study is to investigate the relationship between EE and market segmentation. This paper measures market segmentation by the Price-Based Approach, calculating EE by super slack-based measure (super-SBM), and integrated spatial Durbin model and geographically weighted regression model. Based on the panel data of 30 provinces in China from 1995 to 2018, this paper finds that: (1) Regional market segmentation has a significant negative effect on EE. Moreover, in terms of spatial effect, market segmentation has a positive spatial spillover on EE estimated by 0-1 matrix suggesting that market segmentation in the surrounding area has a positive impact on local EE. (2) The negative effect of Market segmentation on EE demonstrates the obvious regional difference: Eastern region > central region > western region. In addition, geographically weighted regression results show that the impact of market segmentation on EE shows that in regional spatial distribution, Shanghai, Jiangsu, Zhejiang, and Anhui have the strongest negative effect, second in Fujian, Jiangxi, Shandong, Henan, Hubei, Beijing, Tianjin, and Hebei. (3) This paper confirms that market segmentation can affect EE through local protectionism, technological difference, and scale effect. Finally, through the above research basis, put forward the corresponding policy suggestions.

scholarly journals Examining the driving factors of industrial CO2 emissions in Chinese cities using geographically weighted regression model

2021 ◽  
Author(s):  
Huiping Wang ◽  
Xueying Zhang
Keyword(s):  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
Service Industry ◽  
Industrial Sector ◽  
Weighted Regression ◽  
Impact Factors ◽  
Geographically Weighted Regression Model ◽  
Level Of Economic Development ◽  
Investment In Fixed Assets ◽  
The Impact

Abstract The industrial sector is the sector with the largest CO2 emissions, and to reduce overall CO2 emissions, analysis of the impact factors holds significance. Based on the 2015 industrial CO2 emissions of 282 cities in China combined with economic and social data, and a geographically weighted regression (GWR) model, we analysed the characteristics of the spatial distribution of CO2 emissions and the influencing factors of spatial heterogeneity. The results show that China's urban industrial CO2 emissions present a significant spatial agglomeration state that includes Shandong, Beijing, Tianjin, Shanghai, Zhejiang, and Jiangsu, and the core of the coastal areas form a high-high (H-H) concentration; a low-low aggregation (L-L) is formed in less developed areas such as Guizhou, Yunnan, Sichuan and Guangxi. The influence of various factors on industrial CO2 emissions has significant spatial heterogeneity. The Industrial scale, industry share of GDP, and share of the service industry in GDP are factors that promote industrial CO2 emissions. The technological innovation, population density, and social investment in fixed assets are important factors that inhibit industrial CO2 emissions, but their impact on industrial CO2 emissions shows spatial differences. In contrast, the level of economic development, foreign direct investment, financial development and government intervention have a two-way impact on industrial CO2 emissions.

scholarly journals A Geographically Weighted Regression Model for Health Improvement: Insights from the Extension of Life Expectancy in China

2021 ◽  
Vol 11 (5) ◽  
pp. 2022
Author(s):  
Tao Liu ◽  
Shuimiao Yang ◽  
Rongxi Peng ◽  
Daquan Huang
Keyword(s):  
Economic Development ◽  
Life Expectancy ◽  
Regression Model ◽  
Low Income ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Health Improvement ◽  
Eastern Region ◽  
Geographically Weighted Regression Model ◽  
Development Levels

Health improvement is an important social development goal for every country. By using a geographical weighted regression (GWR) model on the 5th and 6th censuses data, this paper analyzes the spatially varied influencing factors of the change in life expectancy of residents in Chinses cities. The results indicate that: (1) The initial level of life expectancy may have a negative correlation with its increase, indicating that life expectancy in different areas may eventually converge to a higher level; moreover, the degree of convergence of life expectancy in cities with different economic development levels is variant. (2) Results of geographically weighted regression model demonstrate significant spatial heterogeneity in the effects of the level of economic development, medical conditions, demographic structure, and natural environment on health improvement. Natural conditions, such as topography, dictate the change in life expectancy in most cities in the middle eastern region of China. Change of educational level is the leading factor in the vast western region while the change in birth rate is the most critical in Xinjiang. Thus, local-based strategies are critical for solving health problems, especially with a focus on promoting health conditions in middle-income and low-income areas.

How tourism industry agglomeration improves tourism economic efficiency?

2021 ◽  
pp. 135481662110091
Author(s):  
Zhoufei Li ◽  
Huiyue Liu
Keyword(s):  
Positive Impact ◽  
Regional Analysis ◽  
Tourism Industry ◽  
Scale Efficiency ◽  
Eastern Region ◽  
Location Quotient ◽  
Industry Agglomeration ◽  
Central Regions ◽  
National Perspective ◽  
The Impact

The agglomeration of the tourism industry has important effects on its efficiency. This article used panel data on the Chinese provincial tourism industry for the 2011–2016 period, applied the location quotient index and three-stage data envelopment analysis method to, respectively, measure the degree of agglomeration and efficiency, and explained the impact of agglomeration on tourism efficiency. The empirical results of this study indicate the following. (1) China’s tourism industry shows a trend towards agglomeration, revealing gradient differences where the highest degree of agglomeration is in the eastern region, followed by the western and central regions. (2) After eliminating random and environmental factors, the adjusted efficiencies are lower than the unadjusted efficiencies. The average overall tourism efficiency is higher in the eastern region than in the central and western regions. (3) From the national perspective, industrial agglomeration can significantly improve the overall efficiency (TE), pure technical efficiency (PTE), and scale efficiency of the tourism industry. (4) Based on regional analysis, the agglomeration of the eastern tourism industry can significantly enhance its TE and PTE. Agglomeration for the western area has a significant positive impact on PTE. There is no significant relationship between agglomeration and efficiency in the central region.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

The Impact of Employee Empathy on Brand Trust in Organizational Complaint Response Emails: A Closer Look at Linguistic Realization

2021 ◽  
pp. 232948842110323
Author(s):  
Rebecca Van Herck ◽  
Sofie Decock ◽  
Bernard De Clerck ◽  
Liselot Hudders
Keyword(s):  
Positive Impact ◽  
Brand Trust ◽  
Mediating Effects ◽  
Customer Complaints ◽  
Moderation Effects ◽  
Complaint Handling ◽  
Negative Effect ◽  
Handling Quality ◽  
The Impact

This study investigates the effect of linguistic realizations of employee empathy (LREE) on brand trust in email responses to customer complaints. We explore possible mediating effects of perceived empathy and perceived complaint handling quality and we look into moderation effects of compensation (Study 1) or customer’s acceptance of blame (Study 2). Our aim is to find out if LREE have a negative or positive impact on the customer in cases of partial refunds, either because LREE are being perceived as insincere or as genuine expressions of concern. The results of two experiments show that LREE positively influence brand trust through higher perceived empathy and perceived complaint handling quality. However, the expected negative effect is not found, as LREE are more effective in a low versus high compensation condition. The effectiveness itself is not influenced by the acceptance of blame when a partial refund is offered.

Every coin has two sides: the impact of time pressure on employees’ knowledge hiding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xianchun Zhang ◽  
Zhu Yao ◽  
Wan Qunchao ◽  
Fu-Sheng Tsai
Keyword(s):  
Time Pressure ◽  
Negative Impact ◽  
Positive Impact ◽  
Job Security ◽  
Statistical Technique ◽  
Content Type ◽  
Individual Knowledge ◽  
Negative Effect ◽  
Knowledge Hiding ◽  
The Impact

Purpose Time pressure is the most common kind of work pressure that employees face in the workplace; the existing research results on the effect of time pressure are highly controversial (positive, negative, inverted U-shaped). Especially in the era of knowledge economy, there remains a research gap in the impact of time pressure on individual knowledge hiding. The purpose of this paper is to explore the impact of different time pressure (challenge and hindrance) on knowledge hiding and to explain why there is controversy about the effect of time pressure in the academics. Design/methodology/approach The authors collected two waves of data and surveyed 341 R&D employees in China. Moreover, they used regression analysis, bootstrapping and Johnson–Neyman statistical technique to verify research hypotheses. Findings The results show that challenge time pressure (CTP) has a significant negative effect on knowledge hiding, whereas hindrance time pressure (HTP) has a significant positive effect on knowledge hiding; job security mediates the relationship between time pressure and knowledge hiding; temporal leadership strengthen the positive impact of CTP on job security; temporal leadership can mitigate the negative impact of HTP on job security. Originality/value The findings not only respond to the academic debate about the effect of time pressure and point out the reasons for the controversy but also enhance the scholars’ attention and understanding of the internal mechanism between time pressure and knowledge hiding.

Government failures and non-performing loans in European countries: a spatial approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ferdinando Ofria ◽  
Massimo Mucciardi
Keyword(s):  
Public Debt ◽  
Geographically Weighted Regression ◽  
European Countries ◽  
Weighted Regression ◽  
Second Step ◽  
Ols Regression ◽  
Content Type ◽  
The Impact ◽  
The Relationship ◽  
The Eu

PurposeThe purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.Design/methodology/approachThe authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).FindingsThe results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.Originality/valueIn a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

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