In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided.

A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016

Being newly immersed in the upstream part of the oil business, I just recently had my first work session with data in ARC–GIS®. The project involves subsurface geographical modeling. Obviously I had considerable interest in discovering if the methodology in this book would enhance my modeling capabilities. The book is for social scientists, but I had no difficulty imagining my own important oil exploration application within the framework of geographically weighted regression (GWR). The first chapter nicely explains what is unique in this book. A standard regression model using geographically oriented data (the example is housing prices across all of England) is a global representation of a spatial relationship, an average that does not account for any local differences. In y = f (x), imagine a whole family of f ’s that are indexed by spatial location. That is the focus of this book. It is about one form of local spatial modeling, which is GWR. A more general resource for this topic is the earlier book by Fotheringham and Wegener (2000), which escaped the notice of Technometrics. Imagine a display of model parameters in a geographical information system (GIS) and you will understand the focus for this book. The authors note, “only where there is no significant spatial variation in a measured relationship can global models be accepted” (p. 10). The second chapter develops the basis of GWR. It analyzes the housing sales prices versus the 33 boroughs in London and begins by fitting a conventional multiple regression model versus housing characteristics. The GWR is motivated by differences in the regression models fitted separately by borough. The GWR is a spatial moving-window approach with all data distances weighted versus a specific data point using a weighting function and a bandwidth. A GIS can then be used to evaluate the spatial dependency of the parameters. As in kriging, local standard errors also are calculated. The chapter also provides all the math. Chapter 3 comprises several further considerations: parameters that are globally constant, outliers, and spatial heteroscedasticity. The first issue leads to hypothesis tests for model comparison using an Akaike information criterion (AIC). Local outliers are hard to detect. Studentized (deletion) residuals are recommended. The outliers can be plotted geographically. Robust regression is suggested as a less computationally intensive alternative. Hetereoscedasticity is harder to handle. Chapter 4 adds statistical inference to the capabilities of GWR: both a confidence interval approach using local likelihood and an AIC method. Four additional methodology chapters present various extensions of GWR. Chapter 5 considers the relationship between GWR and spatial autocorrelation, and includes a combined version of GWR and spatial regression using some complex hybrid models. Chapter 6 examines the relationship of scale and zoning problems in spatial analysis to GWR. Chapter 7 introduces the use of initial exploratory data analysis using geographically weighted statistics, which are based on the idea of using a kernel around each data point to create weights. Univariate statistics and correlation coefficients are defined for exploring local patterns in data. A final set of extensions in Chapter 8 discusses regression models with non-Gaussian errors, logistic regression, local principal components analysis, and local probability density estimation. The methods all use some kind of distributional model. The million-dollar question for me is always, “What about software?” The authors have a stand-alone program, GWR 3, available in CD–ROM by contacting the authors. Basically the drill with GWR 3 is to gather your data, use Excel to transform and reformat the data for GWR 3, use GWR 3 to produce a set of coefficients, and feed those coefficients to your favorite GIS to produce your maps. Forty pages of discussion about using the software are provided. A final epilogue chapter also discusses embedding GWR in R or Matlab and includes some references to people who have done that type of work. I probably would not have read this book if I had not happened to have had it in my briefcase on a visit with the exploration technologists. Though inclusive of appropriate mathematical development, this material is readily approachable because of the many illustrations and the pages and pages of GIS displays. The authors unabashedly present much of the material as their developmental work, so GWR offers a lot of opportunity for research and further development through novel applications and extensions.

Geographically Weighted Regression

Developing countries are facing the problem of environmental degradation. Environmental degradation is caused by the use of non-renewable energy consumptions for economic growth but the consequences of environmental degradation cannot be ignored. This primary purpose of this study is to investigate the nexus between energy consumption, economic growth and CO2 emission in Pakistan by using annual time series data from 1965 to 2015. The estimated results of ARDL indicate that energy consumption and economic growth increase the CO2 emissions in Pakistan both in short run and long run. Based on the estimated results it is recommended that policy maker in Pakistan should adopt and promote such renewable energy sources that will help to meet the increased demand for energy by replacing old traditional energy sources such as coal, gas, and oil. Renewable energy sources are reusable that can reduce the CO2 emissions and also ensure sustainable economic development of Pakistan.

/pdf/the-relationship-between-energy-consumption-economic-growth-4spr0iy8pl.pdf

The relationship between energy consumption, economic growth and carbon dioxide emissions in Pakistan

The nexus between urbanization, renewable energy, trade, and ecological footprint in ASEAN countries

The present study explores the relationship between economic growth, electricity consumption, urbanization and environmental degradation in case of United Arab Emirates (UAE). The study covers the quarter frequency data over the period of 1975–2011. We have applied the ARDL bounds testing approach to examine the long run relationship between the variables in the presence of structural breaks. The VECM Granger causality is applied to investigate the direction of causal relationship between the variables. Our empirical exercise reported the existence of cointegration among the series. Further, we found an inverted U-shaped relationship between economic growth and CO 2 emissions i.e. economic growth raises energy emissions initially and declines it after a threshold point of income per capita (EKC exists). Electricity consumption declines CO 2 emissions. The relationship between urbanization and CO 2 emissions is positive. Exports seem to improve the environmental quality by lowering CO 2 emissions. The causality analysis validates the feedback effect between CO 2 emissions and electricity consumption. Economic growth and urbanization Granger cause CO 2 emissions.

Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates

https://www.econstor.eu/bitstream/10419/243593/1/1666085774.pdf

Renewable energy consumption and economic growth nexus: A fresh evidence from West Africa

The main aim of this article is to examine empirically the impact of urbanization on carbon dioxide emissions in Singapore from 1970 to 2015 The autoregressive distributed lags (ARDL) approach is applied within the analysis The main finding reveals a negative and significant impact of urbanization on carbon emissions in Singapore, which means that urban development in Singapore is not a barrier to the improvement of environmental quality Thus, urbanization enhances environmental quality by reducing carbon emissions in the sample country The result also highlighted that economic growth has a positive and significant impact on carbon emissions, which suggests that economic growth reduces environmental quality through its direct effect of increasing carbon emissions in the country Despite the high level of urbanization in Singapore, which shows that 100 % of the populace is living in the urban center, it does not lead to more environmental degradation Hence, urbanization will not be considered an obstacle when initiating policies that will be used to reduce environmental degradation in the country Policy makers should consider the country’s level of economic growth instead of urbanization when formulating policies to reduce environmental degradation, due to its direct impact on increasing carbon dioxide emissions

/pdf/urbanization-and-carbon-dioxide-emissions-in-singapore-2gzn8j14dj.pdf

Urbanization and carbon dioxide emissions in Singapore: evidence from the ARDL approach

Does wood biomass energy use reduce CO2 emissions in European Union member countries? Evidence from 27 members

Theoretically, population growth is believed to increase greenhouse gas emissions, particularly CO2 emissions through the increase in human activities. Accordingly, this study aimed to investigate ...

https://journals.sagepub.com/doi/pdf/10.1177/2158244018765916

Abdul Samad Abdul-Rahim

Papers

Renewable energy consumption and economic growth nexus: A fresh evidence from West Africa

Urbanization and carbon dioxide emissions in Singapore: evidence from the ARDL approach

Does wood biomass energy use reduce CO2 emissions in European Union member countries? Evidence from 27 members

Population Growth and CO2 Emission in Nigeria: A Recursive ARDL Approach:

Wood fuel consumption, institutional quality, and forest degradation in sub-Saharan Africa: Evidence from a dynamic panel framework