The CO 2  emission Gini index and the environmental efficiency: An analysis for 60 leading world economies

Soares, Thiago Costa; Fernandes, Elaine Aparecida; Toyoshima, Silvia Harumi

doi:10.1016/j.econ.2017.06.001

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…The possibility of further research is to modify and apply the approach proposed by [39] to create a dynamic, large data model that adapts to changing circumstances. It is also possible to test variables such as the Gini Index [1] or the Quality of Life Index, the Human Development Index, the Environmental Performance Index, and similar aggregate indices.…”

Section: Discussionmentioning

confidence: 99%

“…In the previous literature, there are several studies that have addressed the topic of environmental efficiency of selected countries, economic or political communities. Some of these studies addressed global environmental issues [1][2][3][4], some addressed issues of selected political groups, such as the V4 [5], selected geographic groupings [6], or selected countries of the world [7][8][9][10][11], but many studies have dealt with the issue of environmental efficiency and the state of the environment in the EU. The authors of study [12] analyzed the countries of the European Union in 2001-2008 from the point of view of energy and environmental eco-efficiency; the authors found that countries with strong environmental policies are less efficient than countries where the environment is not so important, also pointing to the inefficiency of Eastern European countries.…”

Section: Introductionmentioning

confidence: 99%

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Determinants of Environmental Efficiency of the EU Countries Using Two-Step DEA Approach

Lacko

Hajduová

2018

Sustainability

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The article discusses the environmental efficiency of the countries of the European Union and its main objective is to verify models that could be used to explain changes in efficiency in selected countries and to add to the contributions of other authors. Two-step Data Envelopment Analysis (DEA) has been used, which is a suitable method for verifying the statistical significance of environmental variables. Two DEA models—CCR and BCC—were used and efficiencies have undergone a double-bootstrap procedure. As input variables, three emission per capita indices were computed to single output GDP per capita. Three truncated regression models have been proposed. Results have shown that the model with drivers of climate change and variables connected to socio-economic factors is the most relevant and significant. There are common variables, whose improvement increases the efficiencies in all European Union countries, but there are also indices which have to be considered in individual/subgroup policies and implications, such as environmental taxes, waste management, resources productivity, and transport of goods management.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determinants of Environmental Efficiency of the EU Countries Using Two-Step DEA Approach

Lacko

Hajduová

2018

Sustainability

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“…The Gini coefficient has wide applications in economic analysis of income inequalities. However, the computational principle finds equal use in environmental studies as well, such as inequality in CO 2 emission (Soares, Fernandes, and Toyoshima 2018), carbon taxes (Oladosu and Rose 2007), energy consumption (Jacobson, Milman, and Kammen 2005) and water use patterns in Africa (Cullis and van Koppen 2007) and China (Wang et al 2012).…”

Section: Methodsmentioning

confidence: 99%

Water for All (Har Ghar Jal): Rural Water Supply Services (RWSS) in India (2013–2018), Challenges and Opportunities

Chaudhuri

Roy

McDonald

et al. 2020

International Journal of Rural Management

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Sustainable delivery of drinking water of adequate quantity/quality sits at the core of rural development paradigms worldwide. The overarching goal of this study was to assess operational performance of rural water supply services (RWSS) in India to help authorities understand challenges/shortfalls vis-à-vis opportunities. Data on habitation-level coverage, aggregated by states between 2013 and 2018, were obtained from the National Rural Drinking Water Programme (NRDWP) database, against two water supply norms, namely, 40 lpcd and 55 lpcd (litres per capita per day). Results indicate that certain states are faring better (providing full coverage to over 90% habitations) while others are lagging (e.g., the north-eastern region, and Kerala and Karnataka in the South, for both norms). Several states yet fail to provide 55 lpcd to over half of their rural habitations. Overall, RWSS is marked by high spatial heterogeneity, inequality and recurrent slip-backs (decline in year-to-year habitation coverage) that thwart the basic motto of NRDWP— Har Ghar Jal (Water for All). Ground-level experience reveals a mismatch between theoretical systems’ output (40 lpcd and 55 lpcd) and on-site delivery, and highly intermittent services. Moreover, frequent scheme failure/abandonment adds to systems’ uncertainties and water users’ plight. A multitude of operational/organisational flaws, associated with government waterworks bodies, at different levels of systems’ hierarchy, limit RWSS operational performance. To that end, the concluding section argues for a demand-driven RWSS model (bottom-up systems’ governance) and highlights the core tenets of the same that call for integration of environmental, social, cultural, ethical and political perspectives in RWSS systems’ thinking/design.

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“…First proposed by Corrado Gini in 1912 [1], the Gini coefficient or Gini index has been widely used in describing inequalities in various fields, such as income/wealth [2], meteorology [3], ecology [4], hydrology [5], water resources [6], and the environment [7]. However, accurate estimation of the Gini coefficient has been a continuing topic of investigation, especially for grouped data [2,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Estimating Gini Coefficient from Grouped Data Based on Shape-Preserving Cubic Hermite Interpolation of Lorenz Curve

Shang

2021

Mathematics

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The Lorenz curve and Gini coefficient are widely used to describe inequalities in many fields, but accurate estimation of the Gini coefficient is still difficult for grouped data with fewer groups. We proposed a shape-preserving cubic Hermite interpolation method to approximate the Lorenz curve by maximizing or minimizing the strain energy or curvature variation energy of the interpolation curve, and a method to estimate the Gini coefficient directly from the coefficients of the interpolation curve. This interpolation method can preserve the essential requirements of the Lorenz curve, i.e., non-negativity, monotonicity, and convexity, and can estimate the derivatives at intermediate points and endpoints at the same time. These methods were tested with 16 grouped quintiles or unequally spaced datasets, and the results were compared with the true Gini coefficients calculated with all census data and results estimated with other methods. Results indicate that the maximum strain energy interpolation method generally performs the best among different methods, which is applicable to both equally and unequally spaced grouped datasets with higher precision, especially for grouped data with fewer groups.

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The CO 2 emission Gini index and the environmental efficiency: An analysis for 60 leading world economies

Cited by 21 publications

References 13 publications

Determinants of Environmental Efficiency of the EU Countries Using Two-Step DEA Approach

Determinants of Environmental Efficiency of the EU Countries Using Two-Step DEA Approach

Water for All (Har Ghar Jal): Rural Water Supply Services (RWSS) in India (2013–2018), Challenges and Opportunities

Estimating Gini Coefficient from Grouped Data Based on Shape-Preserving Cubic Hermite Interpolation of Lorenz Curve

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