scholarly journals Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

2021 ◽  
Vol 18 (20) ◽  
pp. 10719
Author(s):  
Bing Xia ◽  
Suocheng Dong ◽  
Yu Li ◽  
Zehong Li ◽  
Dongqi Sun ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Influencing Factors ◽  
Economic Efficiency ◽  
Population Distribution ◽  
Hot Spot ◽  
Spillover Effect ◽  
Green Technology ◽  
Environmental Indicators ◽  
Energy Structure ◽  
Tobit Regression

The sandstorm in 2021 in East Asia demonstrated the ecological issues that culminated for decades in Mongolia. Mongolia is facing challenges to realize green and sustainable development. This article aims to increase the understanding of eco-efficiency and its influencing factors in Mongolia and to provide a reference for similar developing countries and regions to achieve green and sustainable development. This article used the Slacks-Based Measure of Efficiency (SBM) model with advantages of dimension freedom and unit variable to estimate the economic efficiency and eco-efficiency of 22 provinces in Mongolia from 2007 to 2016; energy consumption and undesirable environmental outputs were taken as ecological/environmental indicators in the input and output system of regional eco-efficiency in Mongolia, combining traditional indicators of economic efficiency to build Mongolia’s eco-efficiency input–output framework. This article applied hot spot analysis and gravity center analysis to reveal the temporal and spatial evolution characters of eco-efficiency in Mongolia. Finally, the article applied panel Tobit regression to analyze the influencing factors of eco-efficiency. We were found that Mongolia’s eco-efficiency slightly improved from 0.7379 in 2007 to 0.7673 in 2016, lower than the economic efficiency. The high eco-efficiency provinces appeared in the capital Ulaanbaatar and its surrounding areas, showing an obvious spatial spillover effect. The low eco-efficiency provinces were mainly in the undeveloped western region. The relationship between per capita GDP and eco-efficiency was U-shaped and consistent with environmental Kuznets theory. Accelerating economic growth, optimizing population distribution, and improving energy structure and green technology can improve Mongolia’s eco-efficiency.

scholarly journals Spatial analysis of logistics ecological efficiency and its influencing factors in China: based on Super-SBM-Undesirable and spatial Dubin models

2021 ◽  
Author(s):  
Dongling Bai ◽  
Qianli Dong ◽  
Syed Abdul Rehman Khan ◽  
Yan Chen ◽  
Dongfang Wang ◽  
...  
Keyword(s):  
Environmental Regulation ◽  
Influencing Factors ◽  
Energy Intensity ◽  
Spatial Information ◽  
Spillover Effect ◽  
Positive Influence ◽  
Energy Structure ◽  
Level Energy ◽  
Ecological Efficiency ◽  
Logistics Industry

Abstract Improving the logistics ecological efficiency (LEE) has become a significant part of ensuring a sustainable development and tackling environmental pollution. Previous studies in the logistics industry seldom considered air pollutants and the association of spatial information. Therefore, innovatively considering SO2, NOx, and PM, this study adopted the Super-SBM-Undesirable model to calculate the LEE of 30 provinces in China from 2004 to 2017, and thereafter, developed information-based matrix to explore its influencing factors by using the spatial Dubin model. The results indicated that: (1) The overall LEE during the study period was low, presenting a U-shaped trend of an initial decrease and subsequent rise, and significant regional differences with the decreasing gradient pattern of the “Eastern-Central-Western.” (2) A spatial directionality distributed from the northeast to southwest, and a significant spatial autocorrelation were observed. (3) The industrial structure had the greatest positive influence on the local LEE, followed by the urbanization level, technology innovation level, environmental regulation, while the energy intensity was identified as the main inhibiting factor, followed by the economic level, energy structure and opening level. (4) The LEE had a significant positive spillover effect; the energy intensity and environmental regulation positively affected the LEE in neighboring areas, while the opening level had negative impacts. In addition, policy recommendations for enhancing the LEE were made.

scholarly journals Fujian’s Industrial Eco-Efficiency: Evaluation Based on SBM and the Empirical Analysis of lnfluencing Factors

2018 ◽  
Vol 10 (9) ◽  
pp. 3333 ◽  
Author(s):  
Xiaoqing Wang ◽  
Qiuming Wu ◽  
Salman Majeed ◽  
Donghao Sun
Keyword(s):  
Sustainable Development ◽  
Influencing Factors ◽  
Business Strategy ◽  
Industrial Development ◽  
Industrial Structure ◽  
Economic Benefits ◽  
Tobit Regression ◽  
Fujian Province ◽  
Systematic Analysis ◽  
Measure Model

The coordinated development of industrialization and its ecological environment are vital antecedents to sustainable development in China. However, along with the accelerating development of industrialization in China, the contradiction between industrial development and environment preservation has turned out to be increasingly evident and inevitable. Eco-efficiency can be seen either as an indicator of environmental performance, or as a business strategy for sustainable development. Hence, industrial eco-efficiency promotion is the key factor for green industrial development. This study selects indicators relevant to resources, economy, and the environment of industrial development, and the indicators can well reflect the characteristics of industrial eco-efficiency. The SBM (Slacks-Based Measure) model overcomes the limitations of a radial model and directly accounts for input and output slacks in the efficiency measurements, with the advantage of capturing the entire aspect of inefficiency. This study evaluates the industrial eco-efficiency of nine cities in Fujian province during the period of 2006–2016, based on undesired output SBM (Slacks-Based Measure) model and also uses a Tobit regression model to analyze the influencing factors. The results show that there is a positive correlation among the economic development level, opening level, research and development (R&D) innovation, and industrial eco-efficiency in Fujian Province. However, a negative correlation was found between the industrial structure and industrial eco-efficiency in Fujian Province. Moreover, environmental regulation in Fujian Province was not found to significantly influence the industrial eco-efficiency. Hence, through the systematic analysis of industrial eco-efficiency and its influencing factors in Fujian, the study gives further insight on how policy-making can help achieve sustainable development, balancing between economic benefits and ecological improvements.

scholarly journals Selection of indicators for the scenario modeling of the progressive countries’ economic development

2020 ◽  
Vol 18 (2) ◽  
pp. 441-452
Author(s):  
Michael Zgurovsky ◽  
Oleg Gavrysh ◽  
Sergiy Solntsev ◽  
Anna Kukharuk ◽  
Natalia Skorobogatova
Keyword(s):  
Sustainable Development ◽  
Population Distribution ◽  
Environmental Indicators ◽  
Preliminary Training ◽  
Affecting Factors ◽  
Data Set ◽  
Economic Changes ◽  
The Sustainable Development ◽  
Scenario Modeling ◽  
The Usa

The study aims to improve methodical approach for formalizing the sustainable development models for progressive countries by suggesting the relevant representative indicators. The study is performed using the statistical approach to determine the suitability of data for further modeling using indicators of variation, taking into account the normality of the population distribution as the main criteria of the data set quality. The study highlights the results of processing measurable quantitative economic, social, and environmental indicators of different countries that may be used for identifying possible changes in the world’s sustainable development. The authors select the indicators for scenario modeling of the sustainable development of Brazil, India, China, Republic of Korea, and the USA, as well as suggest a set of relevant affecting factors. To confirm the meaningful impact of different factors, such as biological balance, conflicts intensity, corruption perception and other, a neural network is developed, and its preliminary training on the test data is conducted. The obtained results can be used to predict economic changes in the world under the influence of specific economic, social, and environmental factors.

scholarly journals How Officials’ Competitive Pressure Affects Sustainable Development Capacity From a Spatial Perspective: Empirical Evidence From China

2021 ◽  
Vol 12 ◽  
Author(s):  
He Xu ◽  
Kun Wang ◽  
Guoping Li ◽  
Yufeng Zhang
Keyword(s):  
Sustainable Development ◽  
Central Government ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Entropy Method ◽  
Environmental Indicators ◽  
Western China ◽  
Competitive Pressure ◽  
Appraisal System ◽  
Development Capacity

The view of political achievements suggests that officials will prefer to implement measures that benefit their own development in order to seek promotions. In the past, GDP weighed heavily in officials’ appraisals, leading them to develop the economy without regard to sustainability. Now that the central government has incorporated environmental indicators into the officials’ appraisal system, will this lead officials to implement sustainable development strategies to the fullest extent? Are there spillover effects and regional heterogeneity in this role? This paper discusses these questions with the help of entropy method and a spatial Durbin model using data of 30 provincial-level regions in China from 2006 to 2016. The conclusions show that, firstly, the officials’ competitive pressure is beneficial to enhance the sustainable development capacity of the province, but this effect is only effective in western China. Secondly, there is no spillover effect of officials’ competitive pressure on sustainable development capacity; thirdly, foreign direct investment, the proportion of state-owned enterprises and environmental regulations have their own unique effects on sustainable development capacity, and there are spillover effects. Based on these findings, this paper proposes corresponding policy recommendations in terms of officials’ appraisal system, talent training, foreign investment introduction, and state-owned enterprise reform.

Challenges of the Crisis: How to Measure Sustainable Development?

2015 ◽  
pp. 147-160 ◽  
Author(s):  
S. Bobylev ◽  
N. Zubarevich ◽  
S. Solovyeva
Keyword(s):  
Sustainable Development ◽  
World Bank ◽  
Environmental Indicators ◽  
Calculation Methods ◽  
Basic Principles ◽  
The Sustainable Development ◽  
Development Index ◽  
Development Indicators ◽  
Sustainable Development Indicators ◽  
Adjusted Net Savings

The article emphasizes the fact that traditional socio-economic indicators do not reflect the challenges of sustainable development adequately, and this is particularly true for the widely-used GDP indicator. In this connection the elaboration of sustainable development indicators is needed, taking into account economic, social and environmental factors. For Russia, adaptation and use of concepts and basic principles of calculation methods for adjusted net savings index (World Bank) and human development index (UNDP) as integral indicators can be promising. The authors have developed the sustainable development index for Russia, which aggregates and allows taking into account balanced economic, social and environmental indicators.

scholarly journals Spatio-Temporal Characteristics and Obstacle Factors of the Urban-Rural Integration of China’s Shrinking Cities in the Context of Sustainable Development

2021 ◽  
Vol 13 (8) ◽  
pp. 4203
Author(s):  
Bin Du ◽  
Ying Wang ◽  
Jiaxin He ◽  
Wai Li ◽  
Xiaohong Chen
Keyword(s):  
Sustainable Development ◽  
Economic Efficiency ◽  
Formation Mechanisms ◽  
Shrinking Cities ◽  
The Sustainable Development ◽  
Factors Affecting ◽  
Temporal Characteristics ◽  
The North ◽  
Urban Rural ◽  
Spatio Temporal

Based on the fundamental concept of sustainable development, this study empirically analyzes the spatio-temporal characteristics, formation mechanisms and obstacle factors of the urban-rural integration of shrinking cities in China, from 2008 to 2018. The conclusions are as follows: the overall level of the urban-rural integration of shrinking cities in China is low; the internal differences of urban-rural integration are also small, and the changes are slow. Next, the space difference is high in the east and low in the west, high in the south and low in the north. Moreover, differences exist among different levels of urban agglomerations. Urban economic efficiency, urban resources and environment, urban social equity and rural economic efficiency are the main factors affecting the urban-rural integration of shrinking cities in China. Urban and rural economic efficiency are the two most prominent shortcomings that restrict the urban-rural integration of shrinking cities. The spatial resistance mode of each city is more than the two-system resistance; the main resistance of shrinking cities with a higher level of urban-rural integration also comes from the non-economic field. This study expands the research scope that up till now has ignored the discussion of urban-rural issues in the research of shrinking cities at home and abroad, and provides practical guidance for the sustainable development of shrinking cities in China.

scholarly journals Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

2021 ◽  
Vol 13 (14) ◽  
pp. 8032
Author(s):  
Chengzhuo Wu ◽  
Li Zhuo ◽  
Zhuo Chen ◽  
Haiyan Tao
Keyword(s):  
Influencing Factors ◽  
Information Flow ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Urban Agglomeration ◽  
Coordinated Development ◽  
Spatial Spillover ◽  
Urban Agglomerations ◽  
Search Index ◽  
Spatial Spillover Effect

Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

scholarly journals Cooperative Green Technology Innovation of an E-Commerce Sales Channel in a Two-Stage Supply Chain

2021 ◽  
Vol 13 (13) ◽  
pp. 7499
Author(s):  
Zongyu Mu ◽  
Yuangang Zheng ◽  
Hao Sun
Keyword(s):  
Supply Chain ◽  
Technology Innovation ◽  
Spillover Effect ◽  
Product Market ◽  
Free Riding ◽  
Green Technology ◽  
Market Demand ◽  
Two Stage ◽  
Sales Channel ◽  
Innovation Activities

The potential broad market of green consumption has encouraged an increasing number of enterprises to carry out green technology innovation activities. This paper examines a two-stage supply chain of e-commerce sales channels under different cooperative models. We find that consumers’ green preferences are the main factor that affects green product market demand. The manufacturer and the retailer can raise the levels of green technology innovation and extend green promotional services to expand product market demand in online and offline channels. However, consumers’ e-commerce preferences and online free-riding behaviors affect the manufacturer’s sales channel choice. The retailer can improve the level of green promotional services to hold offline channel market demand, while promotional behaviors have a positive/negative spillover effect on online market demand if the level of free riding falls above/below consumers’ e-commerce preferences. The higher the cooperative level is, the later the manufacturer will open the online channel and close the offline channel to ensure a high level of green promotional service from the cooperative retailer. The results show that the stronger the level of cooperation among all members is, the better the economic, ecological, and social benefits will be. Therefore, we design a revenue-cost sharing contract that can effectively motivate green technology innovation and green promotional services and afford all members win-win profits.

scholarly journals Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

2021 ◽  
Vol 13 (8) ◽  
pp. 4194
Author(s):  
Yanhua Guo ◽  
Lianjun Tong ◽  
Lin Mei
Keyword(s):  
Influencing Factors ◽  
Industrial Pollution ◽  
Production Capacity ◽  
Pollution Index ◽  
Green Technology ◽  
Econometric Analysis ◽  
Ecological Civilization ◽  
Entropy Weight ◽  
Spatial Econometric ◽  
Spatial Econometric Analysis

Winning the battle against pollution and strengthening ecological protection in all respects are vital for promoting green development and building a moderately prosperous ecological civilization in China. Using the entropy weight method, this paper establishes and evaluates a comprehensive industrial pollution index that contains and synthesizes six major industrial pollutants (wastewater, COD, waste gas, SO2, NOx, and solid waste) in the 2006–2015 period. Subsequently, this paper studies the spatiotemporal characteristics and influencing factors of industrial pollution via the Moran index and spatial econometric analysis. The empirical results indicate that (1) the temporal evolution of the industrial pollution index is characterized by an overall trend of first decreasing and then increasing. (2) The industrial pollution index of each county has certain geographical disparities and significant spatially polarized characteristics in 2006, 2009, 2012, and 2015. (3) The Moran test shows that there is a relatively significant spatial autocorrelation of the industrial pollution index among counties and that the geographical distribution of the industrial pollution index tends to show clustering. (4) Spatial regression models that incorporate spatial factors better explain the influencing factors of industrial pollution. The economic development level, technological progress, and industrialization are negatively correlated with industrial pollution, while population density and industrial production capacity are positively correlated. (5) Consequently, as relevant policy recommendations, this paper proposes that environmental cooperation linkage mechanisms, environmental protection credit systems, and green technology innovation systems should be established in different geographical locations to achieve the goals of green county construction and sustainable development.

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