scholarly journals Evaluating the Investment Efficiency of China’s Provincial Power Grid Enterprises under New Electricity Market Reform: Empirical Evidence Based on Three-Stage DEA Model

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3524 ◽  
Author(s):  
Jingqi Sun ◽  
Nuermaimaiti Ruze ◽  
Jianjun Zhang ◽  
Haoran Zhao ◽  
Boyang Shen
Keyword(s):  
Electricity Market ◽  
Power Grid ◽  
External Environment ◽  
Electricity Consumption ◽  
Market Reform ◽  
Gdp Per Capita ◽  
Scale Efficiency ◽  
Investment Efficiency ◽  
Environment Variables ◽  
Per Capita

The new round of electricity market reform in 2015 completely changed the profit pattern of power grid enterprises (PGEs) in China, and directly affected their investment plans. Under the new electricity market reform (NEMR), the government regulatory authority made higher requirements for the investment efficiency of PGEs, and the investment effectiveness hence became the core criterion for investment plans. Therefore, the PGEs are now attaching great importance to the investment efficiency. According to their geographical differences, this paper divides the Chinese provincial PGEs into three groups, namely eastern, central and western region enterprises. Based on the NEMR, we developed an evaluation system of investment efficiency for the above-mentioned enterprises. Moreover, this paper selects GDP per capita, electricity consumption in industry, and electrification rate as external environment variables, and conducts an empirical research on the investment efficiency of 31 provincial PGEs in China in 2017. The analysis reveals that three external environment variables have considerable impacts on the investment efficiency. Though the increase of GDP per capita and electricity consumption in industry are not conducive to improving investment efficiency, the advancement of electrification plays a positive role in its improvement. And from the real efficiency results, Tianjin, Liaoning, Jiangsu, and Fujian have relatively higher investment efficiency, while Henan, Shandong, and Shanghai exhibit lower investment efficiency. By comparing the investment efficiency of PGEs in the first and third stage, conclusions can be drawn that in the first stage the investment efficiency of PGEs was overestimated, and the inefficient investments prevailed some provincial PGEs, which caused by low scale efficiency.

scholarly journals Causality and Stationarity with Structural Break in Electricity Consumption and GDP per capita in Mexico

2020 ◽  
Vol 15 (4) ◽  
pp. 725-744
Author(s):  
Vicente German-Soto
Keyword(s):  
Structural Break ◽  
Electricity Consumption ◽  
Gdp Per Capita ◽  
Per Capita

La electricidad es clave en la mayoría de los procesos de producción, por tanto, entender causalidad, cointegración y estacionariedad entre consumo de electricidad y producción es un punto de partida en el debate de sus efectos económicos. Modelos de corrección de errores (VECM) y cointegración, junto a pruebas de estacionariedad, examinan esta relación en México durante 1940-2018. Los resultados apoyan esta hipótesis, pero después de considerar el cambio estructural subrayado por la apertura comercial, ya que causalidad, estacionariedad y cointegración solo pueden demostrarse dividiendo el periodo en 1985, fecha de quiebre estimada para producto per cápita. En la primera etapa, la causalidad corrió de electricidad a producto, mientras que en la segunda fue bidireccional. Se recomienda adaptar los programas de electricidad a cambios en la esfera política. La originalidad de esta contribución descansa en el análisis de largo plazo del sector de energía enfatizando la importancia de quiebres estructurales. A pesar de alguna sensibilidad al ejecutar las regresiones, las conclusiones recomiendan fortalecer el sector de energía como medio factible de recuperar el crecimiento sostenido que México alcanzó en otros tiempos.

scholarly journals Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3442 ◽  
Author(s):  
Habeebur Rahman ◽  
Iniyan Selvarasan ◽  
Jahitha Begum A
Keyword(s):  
Energy Consumption ◽  
Developing Economies ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Black Box ◽  
Gdp Per Capita ◽  
Energy Planning ◽  
Short Term ◽  
Expert Model ◽  
Per Capita

Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy’s short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely multiple linear regression (MLR), simple regression model (SRM) along with correlation, exponential smoothing, Holt’s, Brown’s and expert model with the input variables population, GDP and GDP per capita using the software used are IBM SPSS Statistics 20 and Microsoft Excel 1997–2003 Worksheet. The input factors namely GDP, population and GDP per capita were taken into consideration. Analyses were also carried out to find the important variables influencing the energy consumption pattern. Several models such as Brown’s model, Holt’s model, Expert model and damped trend model were analysed. The TEC for the years 2019, 2024 and 2030 were forecasted to be 1,162,453 MW, 1,442,410 MW and 1,778,358 MW respectively. When compared with Population, GDP per capita, it is concluded that GDP foresees TEC better. The forecasting of total electricity consumption for the year 2030–2031 for India is found to be 1834349 MW. Therefore energy planning of a country relies heavily upon precise proper demand forecasting. Precise forecasting is one of the major challenges to manage in the energy sector of any nation. Moreover forecasts are important for the effective formulation of energy laws and policies in order to conserve the natural resources, protect the ecosystem, promote the nation’s economy and protect the health and safety of the society.

Searching for new Sources of growth. Are the Developing Countries Catching up with the Developed ones?

2015 ◽  
pp. 30-53
Author(s):  
V. Popov
Keyword(s):  
Developing Countries ◽  
Western Europe ◽  
Gdp Per Capita ◽  
The West ◽  
Catching Up ◽  
The Us ◽  
The World ◽  
Economic Divergence ◽  
Per Capita ◽  
Relative Standing

This paper examines the trajectory of growth in the Global South. Before the 1500s all countries were roughly at the same level of development, but from the 1500s Western countries started to grow faster than the rest of the world and PPP GDP per capita by 1950 in the US, the richest Western nation, was nearly 5 times higher than the world average and 2 times higher than in Western Europe. Since 1950 this ratio stabilized - not only Western Europe and Japan improved their relative standing in per capita income versus the US, but also East Asia, South Asia and some developing countries in other regions started to bridge the gap with the West. After nearly half of the millennium of growing economic divergence, the world seems to have entered the era of convergence. The factors behind these trends are analyzed; implications for the future and possible scenarios are considered.

Atlas of economic complexity, Russian regional pages

2018 ◽  
pp. 71-91 ◽  
Author(s):  
I. L. Lyubimov ◽  
M. V. Lysyuk ◽  
M. A. Gvozdeva
Keyword(s):  
Value Added ◽  
Growth Strategy ◽  
Gdp Per Capita ◽  
Export Diversification ◽  
Russian Regions ◽  
Economic Complexity ◽  
Diversification Strategies ◽  
Central Regions ◽  
Per Capita ◽  
Regions Of Russia

Well-established results indicate that export diversification might be a better growth strategy for an emerging economy as long as its GDP per capita level is smaller than an empirically defined threshold. As average incomes in Russian regions are likely to be far below the threshold, it might be important to estimate their diversification potential. The paper discusses the Atlas of economic complexity for Russian regions created to visualize regional export baskets, to estimate their complexity and evaluate regional export potential. The paper’s results are consistent with previous findings: the complexity of export is substantially higher and diversification potential is larger in western and central regions of Russia. Their export potential might become larger if western and central regions, first, try to join global value added chains and second, cooperate and develop joint diversification strategies. Northern and eastern regions are by contrast much less complex and their diversification potential is small.

The Reproduction Vector of the Russian Economy: 1999-2007

2008 ◽  
pp. 94-109 ◽  
Author(s):  
D. Sorokin
Keyword(s):  
Economic Policy ◽  
Human Capital ◽  
Growth Rates ◽  
Developed Countries ◽  
Gdp Per Capita ◽  
Real Sector ◽  
Russian Economy ◽  
Per Capita ◽  
The Developed Countries

The problem of the Russian economy’s growth rates is considered in the article in the context of Russia’s backwardness regarding GDP per capita in comparison with the developed countries. The author stresses the urgency of modernization of the real sector of the economy and the recovery of the country’s human capital. For reaching these goals short- or mid-term programs are not sufficient. Economic policy needs a long-term (15-20 years) strategy, otherwise Russia will be condemned to economic inertia and multiplying structural disproportions.

The monetary value of disability-adjusted-life-years lost in Kenya in 2017.pdf

2019 ◽  
Author(s):  
Joses Kirigia ◽  
Rose Nabi Deborah Karimi Muthuri
Keyword(s):  
New York ◽  
International Monetary Fund ◽  
Health Expenditure ◽  
Analytical Framework ◽  
World Health ◽  
Gdp Per Capita ◽  
Current Health ◽  
Monetary Value ◽  
Life Years ◽  
Per Capita

<div>A variant of human capital (or net output) analytical framework was applied to monetarily value DALYs lost from 166 diseases and injuries. The monetary value of each of the 166 diseases (or injuries) was obtained through multiplication of the net 2019 GDP per capita for Kenya by the number of DALYs lost from each specific cause. Where net GDP per capita was calculated by subtracting current health expenditure from the GDP per capita. </div><div> </div><p>The DALYs data for the 166 causes were from IHME (Global Burden of Disease Collaborative Network, 2018), GDP per capita data from the International Monetary Fund world economic outlook database (International Monetary Fund, 2019), and the current health expenditure per person data from the WHO Global Health Expenditure Database (World Health Organization, 2019b). A model consisting of fourteen equations was calculated with Excel Software developed by Microsoft (New York).</p><p> </p>

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