scholarly journals Efficiency Measurement of Green Regional Development and Its Influencing Factors: An Improved Data Envelopment Analysis Framework

2020 ◽  
Vol 12 (11) ◽  
pp. 4361
Author(s):  
Yingyu Lu ◽  
Bo Cao ◽  
Yidi Hua ◽  
Lei Ding
Keyword(s):  
Data Envelopment Analysis ◽  
Regional Development ◽  
Environmental Regulation ◽  
Influencing Factors ◽  
Industrial Structure ◽  
Data Envelopment ◽  
Dynamic Efficiency ◽  
Urbanization Level ◽  
Static Efficiency ◽  
Green Development

Reasonably assessing the efficiency of green regional development is a key to improving environmental management and implementing sustainable development strategies. From the perspectives of environmental pollutant emissions, energy consumption, and production factor cost, the non-radial data envelopment analysis model based on the Malmquist index was applied to measure the green development efficiency and regional differences of 11 cities in Zhejiang from 2007 to 2016 from both static and dynamic aspects. This paper further analyzes the inherent influencing factors through the panel data model. The result shows: (1) The average static efficiency of green development in Zhejiang Province is 0.844. There is still 15.6% of improvement space from the frontier of production. Pollution emission management has the greatest improvement potential. Pure technical efficiency is the main factor restricting the static efficiency. (2) The dynamic efficiency of Zhejiang’s green development achieves an average annual rate of 0.26%, with a cumulative growth of 2.33%. The improvement of green development efficiency mainly depends on scale efficiency change. (3) The inherent factors affecting the efficiency of green development in the 11 cities mainly include three factors: the industrial structure, environmental regulation, and the urbanization level. The industrial structure has a positive effect, while environmental regulation and the urbanization level have negative effects. (4) The 11 cities are relatively evenly distributed in the four “static–dynamic efficiency” classification quadrants, and there is no "Matthew effect" of high–high, low–low polarization.

scholarly journals ANALISIS EFISIENSI TEKNIS BANK PEMBANGUNAN DAERAH DI INDONESIA PERIODE TAHUN 2008 – 2009 DENGAN MENGGUNAKAN METODE DEA (DATA ENVELOPMENT ANALYSIS)

Media Ekonomi ◽  
2017 ◽  
Vol 19 (2) ◽  
pp. 52
Author(s):  
Amrina Rosyada
Keyword(s):  
Data Envelopment Analysis ◽  
Regional Development ◽  
Technical Efficiency ◽  
Maximum Efficiency ◽  
Data Envelopment ◽  
Net Income ◽  
Development Bank ◽  
Input Variables ◽  
Regional Development Banks ◽  
Regional Development Bank

The purpose of this research is to acknowledge the level of efficiency from Regional Development Bank (Bank Pembangunan Daerah)/BPD) in Indonesia. This research use non - parametric approach which is DEA (Data Envelopment Analysis), to measure the efficiency of input and output of the Regional Development Banks. The input variables include are interest expense, administration and public expenses and salary expenses and the output variables are interest net income and other operasional income. The research concluded shows that the performance of the technical efficiency of banks BPD is not all reach the level of 100% and showed a fluctuation grow from 2008 – 2009. Pursuant to the technical efficiency level showed that there are 4 banks from 26 existing banks are showing a maximum efficiency. While the remaining 21 BPD banks fluctuating during 2008 to 2009. Keywords : Efficiency, Data Envelopment Analysis (DEA), Regional Development Bank

scholarly journals Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3054 ◽  
Author(s):  
Zhen Li ◽  
Yanbin Li ◽  
Shuangshuang Shao
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Total Population ◽  
Industrial Structure ◽  
Global Climate ◽  
Primary Energy ◽  
Urbanization Level ◽  
Per Capita ◽  
Technology Level

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

scholarly journals Analysis of Main Influencing Factors of Global Electrification Development

2020 ◽  
Vol 165 ◽  
pp. 06025
Author(s):  
Junshu Feng ◽  
Peng Wang
Keyword(s):  
Economic Development ◽  
Influencing Factors ◽  
Development Process ◽  
Industrial Structure ◽  
Urbanization Level ◽  
Resource Endowment ◽  
The World ◽  
Development Paths ◽  
Policy Guidance

Based on the analysis of electrification development process in major countries, this paper systematically studied and judged 5 important influencing factors of electrification development in the world, including resource endowment, economic development, improvement of people’s livelihood, infrastructure and policy guidance, among economic development includes industrial structure and urbanization level. The opinions of this paper can support different regions or countries to choose proper electrification development paths.

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